Python-Intro-Numpy-Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python Code Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This Jupyter Notebook Contains extensive examples of \n",
    "- Python Language Elements\n",
    "- Built-in data types\n",
    "- Numpy Library functions for Array Management\n",
    "- Pandas Library Data Structures - Series and DataFrames\n",
    "- String Operations\n",
    "- Date/Time Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.9.5\r\n"
     ]
    }
   ],
   "source": [
    "!python3 --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variables\n",
    "-\tInteger: Integer variables can hold Integers such as 1, 10, 100, 650, etc. They cannot store values such as 12.5. \n",
    "-\tFloat: Floating point variables can hold values such as 12.5, 126.7897, etc. However, they can also store 1, 2, 3 or 78 (Integers).\n",
    "-\tBoolean: Boolean variables can hold a Boolean Value, i.e. True or False\n",
    "-\tString: A string type variable can store value such as “My name is John Doe” or “X”. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "<class 'str'>\n",
      "12.678\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# Creating Variables\n",
    "i = 5\n",
    "print(i)\n",
    "str1 = \"My name is John Doe\"\n",
    "print(str)\n",
    "flt = 12.678\n",
    "print(flt)\n",
    "bln = True\n",
    "print(bln)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Printing the Type of Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'int'>\n",
      "<class 'type'>\n",
      "<class 'float'>\n",
      "<class 'bool'>\n"
     ]
    }
   ],
   "source": [
    "# Print the Type of each of the variables\n",
    "print(type(i))\n",
    "print(type(str))\n",
    "print(type(flt))\n",
    "print(type(bln))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Interestingly, Python allows the programmer to assign a value to a variable different from the initial type of value assigned. Python dynamically changes the type of the variable at run-time and continues. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "Changed to a String\n"
     ]
    }
   ],
   "source": [
    "# A string value is passed without any problem \n",
    "i = 5\n",
    "print(i)\n",
    "i = \"Changed to a String\"\n",
    "print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Python supports a number of types of operators. \n",
    "- Arithmetic Operators\n",
    "- Comparison (Relational) Operators\n",
    "- Assignment Operators\n",
    "- Logical Operators\n",
    "- Bitwise Operators\n",
    "- Membership Operators\n",
    "- Identity Operators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n",
      "John Doe\n"
     ]
    }
   ],
   "source": [
    "i = 5\n",
    "j = 6\n",
    "print(i+j)\n",
    "# \n",
    "str1 = \"John \"\n",
    "str2 = \"Doe\"\n",
    "print(str1 + str2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find the Modulus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "i = 30\n",
    "j = 7\n",
    "print(i%j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditions in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inequal\n"
     ]
    }
   ],
   "source": [
    "# Checking Conditions\n",
    "x = 7\n",
    "y = 9\n",
    "if x == y:\n",
    "    print(\"Equal\")\n",
    "else: \n",
    "    print(\"Inequal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Condition Met – Multiline Block\n",
      "Condition Met\n"
     ]
    }
   ],
   "source": [
    "# Checking Conditions (Multiple Simultaneous Conditions)\n",
    "x = 7\n",
    "y = 9\n",
    "if x == 7 and y < 10:\n",
    "    print(\"Condition Met – Multiline Block\")\n",
    "    print(\"Condition Met\")\n",
    "else:\n",
    "    print(\"Condition not Met – Multiline Block\")\n",
    "    print(\"Condition not Met\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x is smaller than y\n"
     ]
    }
   ],
   "source": [
    "# Checking Conditions (Multiple Conditions)\n",
    "x = 7\n",
    "y = 9\n",
    "if x == y:\n",
    "    print(\"Equal\")\n",
    "elif x > y:\n",
    "    print(\"x is greater than y\")\n",
    "elif x < y:\n",
    "    print(\"x is smaller than y\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Points from the above example:\n",
    "- Note the use of ‘elif’ interpreted as ‘else if’ to check further conditions\n",
    "- ‘else’ is used to perform an action if none of the above conditions are met\n",
    "- ‘and’ is used to check multiple conditions together\n",
    "- ‘or’ is to be used in place of ‘and’ if the condition is ‘if this’ or ‘that’ then perform the subsequent action"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "# Checking for Boolean\n",
    "x = True\n",
    "if x == False:\n",
    "    print(x)\n",
    "else:\n",
    "    print(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Nested Conditions: Nested conditions are used when further conditions are to be checked based on the True/False status of the outer conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nested Condition Met\n"
     ]
    }
   ],
   "source": [
    "# Nested ‘if’\n",
    "x = 7\n",
    "y = 9\n",
    "if x < y:\n",
    "    if x > 5:\n",
    "        print(\"Nested Condition Met\")\n",
    "    else:\n",
    "        print(\"Inner nested False Block\")\n",
    "else:\n",
    "    print(\"Outer nested False Block\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Indentation and Block marking: Note that after each condition line, a ‘:’ sign is used to denote the line as a conditional construct line. \n",
    "- The action following the condition line, ended by ‘:’ is the action block. \n",
    "- Note that unlike C/C++/Java, Python does not use a block denoting character like ‘{}’. \n",
    "- Blocks in Python are to be perfectly indented after the starting line (e.g. ‘if’/’elif’/’else’). \n",
    "- Each line in a block are to be similarly indented, otherwise it results in an error. This indentation need or property of Python is also known as the ‘Off-side Rule’. \n",
    "- Apart from Python, a few other computer languages (such as, Cobra, Coffeescript, Curry, Elixir, etc) follow the Off-side rule.  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### There are various types of Loops or Iterations supported by Python for different types of uses and based on various types of inputs provided to it. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Definite Iteration: When we know the number of times the loop will iterate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration  1\n",
      "Iteration  2\n",
      "Iteration  3\n",
      "Iteration  4\n",
      "Iteration  5\n",
      "Final value of m =  15\n"
     ]
    }
   ],
   "source": [
    "# Example of Definite Iteration\n",
    "l = 5\n",
    "m = 0 \n",
    "i = 0\n",
    "while l > 0:\n",
    "    m += l  # m = m + l\n",
    "    l-=1    # l = l - 1\n",
    "    i+=1    # i = i + 1\n",
    "    print(\"Iteration \", i)\n",
    "print(\"Final value of m = \", m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Indefinite Iteration: In the case of an Indefinite Iteration, one of the variables used as part of the condition is dynamic and is determined at runtime (e.g. in the above example, if the value of variable ‘l’ if not fixed and is determined dynamically by the program at runtime). Thereby, estimating the number of times the loop will iterate will not be possible by looking at the code. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sunday\n",
      "Monday\n",
      "Tuesday\n"
     ]
    }
   ],
   "source": [
    "# Example of 'for' loop for a List\n",
    "WeekdaysList = [\"Sunday\", \"Monday\", \"Tuesday\"]\n",
    "for x in WeekdaysList:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "India\n",
      "US\n",
      "UK\n",
      "Germany\n"
     ]
    }
   ],
   "source": [
    "# Example of 'for' loop for a Tuple\n",
    "CountryTuple = (\"India\", \"US\", \"UK\", \"Germany\")\n",
    "for x in CountryTuple:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neymar\n",
      "Messi\n",
      "Ronaldo\n"
     ]
    }
   ],
   "source": [
    "# Example of 'for' loop for a Set\n",
    "PlayerSet = {\"Ronaldo\", \"Messi\", \"Neymar\"}\n",
    "for x in PlayerSet:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name John Doe\n",
      "Age 30\n",
      "Salary 80000\n"
     ]
    }
   ],
   "source": [
    "# Example of 'for' loop for a Dictionary\n",
    "PersonDict = {\"Name\": \"John Doe\", \"Age\": 30, \"Salary\": 80000}\n",
    "for x in PersonDict:\n",
    "    print(x, PersonDict[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "J\n",
      "o\n",
      "h\n",
      "n\n",
      " \n",
      "D\n",
      "o\n",
      "e\n"
     ]
    }
   ],
   "source": [
    "# Example of 'for' loop for a String\n",
    "strx = \"John Doe\"\n",
    "for x in strx:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Numeric Ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0  is being processed\n",
      "1  is being processed\n",
      "2  is being processed\n",
      "3  is being processed\n",
      "4  is being processed\n"
     ]
    }
   ],
   "source": [
    "# Example of 'for' loop for a Numeric Range\n",
    "for x in range(5):\n",
    "    print(x, \" is being processed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3  is being processed\n",
      "4  is being processed\n",
      "5  is being processed\n",
      "6  is being processed\n",
      "7  is being processed\n"
     ]
    }
   ],
   "source": [
    "# Example of 'for' loop for a Numeric Range with custom start and end values\n",
    "for x in range(3,8):\n",
    "    print(x, \" is being processed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2  is being processed\n",
      "4  is being processed\n",
      "6  is being processed\n",
      "8  is being processed\n",
      "10  is being processed\n",
      "12  is being processed\n"
     ]
    }
   ],
   "source": [
    "# Example of 'for' loop for a Numeric Range with custom start and end values and step amounts\n",
    "for x in range(2,14,2):\n",
    "    print(x, \" is being processed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Custom Iterator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "10\n",
      "15\n",
      "20\n",
      "25\n"
     ]
    }
   ],
   "source": [
    "# Creating a Custom Range\n",
    "def cust_range(start_val, end_val, step_val):\n",
    "    while start_val <= end_val:\n",
    "        yield start_val \n",
    "        start_val += step_val\n",
    "\n",
    "for x in cust_range(5, 25, 5):\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Break, Continue and Pass Statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5  is being processed\n",
      "10  is being processed\n",
      "15  is being processed\n",
      "20  is being processed\n",
      "Breaking away..\n"
     ]
    }
   ],
   "source": [
    "# Example of 'break'ing away from a loop\n",
    "for x in range(5,31,5):\n",
    "    print(x, \" is being processed\")\n",
    "    if x == 20:\n",
    "        print(\"Breaking away..\")\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5  is being processed\n",
      "10  is being processed\n",
      "15  is being processed\n",
      "Continue to the top of the loop\n",
      "25  is being processed\n",
      "30  is being processed\n"
     ]
    }
   ],
   "source": [
    "# Example of 'continuing'into a loop\n",
    "for x in range(5,31,5):\n",
    "    if x == 20:\n",
    "        print(\"Continue to the top of the loop\")\n",
    "        continue\n",
    "    print(x, \" is being processed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5  is being processed\n",
      "10  is being processed\n",
      "15  is being processed\n",
      "Continue to the top of the loop\n",
      "20  is being processed\n",
      "25  is being processed\n",
      "30  is being processed\n"
     ]
    }
   ],
   "source": [
    "# Example of 'pass'in a loop\n",
    "for x in range(5,31,5):\n",
    "    if x == 20:\n",
    "        print(\"Continue to the top of the loop\")\n",
    "        pass\n",
    "    print(x, \" is being processed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collection Objects in Python\n",
    "-  Lists\n",
    "-  Tuples\n",
    "-  Dictionaries\n",
    "-  Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Python Lists\n",
    "A List in Python is a collection object. Lists can hold a sequence of items or elements which can be of same or different data types including other collection objects. Lists elements can be accessed using the Index or relative position numbers starting from the start or end of the List. \n",
    "Lists are mutable, i.e. they can be changed anytime after they are created. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "# Create our first List\n",
    "lst1 = [1,2,3,4]\n",
    "print(lst1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Jimi', 5000, True, 3.8, 2019, 300]\n",
      "Demonstrate Mutability:  ['Jimi', 7000, True, 3.8, 2019, 300]\n"
     ]
    }
   ],
   "source": [
    "# Create a List with Multiple types of Data and Change them after creation\n",
    "lst1 = ['Jimi', 5000, True, 3.8, 2019, 300]\n",
    "print(lst1)\n",
    "lst1[1] = 7000    # Update the 2nd element of the List\n",
    "print(\"Demonstrate Mutability: \", lst1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the 3rd Element of lst1:  True\n",
      "Print the 3rd Element of lst1:  True\n"
     ]
    }
   ],
   "source": [
    "# Access Lists with relative positioning\n",
    "lst1 = ['Jimi', 5000, True, 3.8, 2019, 300]\n",
    "print(\"Print the 3rd Element of lst1: \", lst1[2])   # Relative indexing from the start\n",
    "print(\"Print the 3rd Element of lst1: \", lst1[-4])   # Relative indexing from the end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Slicing Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Gilchrist', 'Waugh', 'Dravid', 'Pollock']\n",
      "['Gayle', 'Gilchrist', 'Waugh', 'Dravid', 'Pollock']\n",
      "['Gilchrist', 'Waugh', 'Dravid', 'Pollock', 'Tendulkar', 'Gavaskar', 'Dhoni']\n"
     ]
    }
   ],
   "source": [
    "# Access cross section of Lists \n",
    "lst1 = ['Gayle', 'Gilchrist', 'Waugh', 'Dravid', 'Pollock', 'Tendulkar', 'Gavaskar', 'Dhoni']\n",
    "print(lst1[1:5])    # Print elements 2 to 5\n",
    "print(lst1[:5])    # Print elements start to 5 - default start 0\n",
    "print(lst1[1:])    # Print all elements starting from 2nd - default end is all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Striding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 4, 6, 8, 10, 12]\n"
     ]
    }
   ],
   "source": [
    "# Access cross section of Lists with stride\n",
    "lst1 = [1,2,3,4,5,6,7,8,9,10,11,12,13,14]\n",
    "print(lst1[1:12:2])    # Print elements 2 to 12 jumping 2 elements at a time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding elements in a List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 0, 1, 2, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "# Adding single elements in Lists\n",
    "lst1 = [1,2,3,4,5,6,7,8,9,10]\n",
    "for x in range(5):\n",
    "    lst1.append(x)   # Append one element each time to the List\n",
    "print(lst1)          # Print the final List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]\n"
     ]
    }
   ],
   "source": [
    "# Adding multiple elements in Lists\n",
    "lst1 = [1,2,3,4,5,6,7,8,9,10]\n",
    "lst1.extend([11,12,13,14])     # Extend the List by several elements\n",
    "print(lst1)                    # Print the final List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]\n"
     ]
    }
   ],
   "source": [
    "# Adding multiple elements in Lists -- another way\n",
    "lst1 = [1,2,3,4,5,6,7,8,9,10]\n",
    "lst2 = [11,12,13,14]\n",
    "lst1 += lst2    # Add lst2 to lst1\n",
    "print(lst1)     # Print the final List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 7, 8, 4, 9, 10]\n"
     ]
    }
   ],
   "source": [
    "# Using Insert to insert an element at a specified position\n",
    "lst1 = [1,2,3,4,5,6,7,8,9,10]\n",
    "lst1.insert(8, 4)\n",
    "print(lst1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing an Element from a List "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After pop:  [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
      "After pop of 4th element:  [1, 2, 3, 5, 6, 7, 8, 9]\n"
     ]
    }
   ],
   "source": [
    "# Removing an element using the pop method\n",
    "lst1 = [1,2,3,4,5,6,7,8,9,10]\n",
    "lst1.pop()    # Removes the last element in-place\n",
    "print(\"After pop: \", lst1)\n",
    "lst1.pop(3)    # Removes the specified element in-place\n",
    "print(\"After pop of 4th element: \", lst1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Gayle', 'Gilchrist', 'Dravid', 'Pollock', 'Tendulkar', 'Gavaskar', 'Dhoni']\n"
     ]
    }
   ],
   "source": [
    "# Removing an element using the remove method\n",
    "lst1 = ['Gayle', 'Gilchrist', 'Waugh', 'Dravid', 'Pollock', 'Tendulkar', 'Gavaskar', 'Dhoni']\n",
    "lst1.remove('Waugh')    # Removes the specified element in-place\n",
    "print(lst1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Gayle', 'Gilchrist', 'Pollock', 'Tendulkar', 'Gavaskar', 'Dhoni']\n"
     ]
    }
   ],
   "source": [
    "# Removing an element using the del keyword\n",
    "lst1 = ['Gayle', 'Gilchrist', 'Waugh', 'Dravid', 'Pollock', 'Tendulkar', 'Gavaskar', 'Dhoni']\n",
    "del lst1[2:4]    # Removes the elements at the specified index range\n",
    "print(lst1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gayle\n",
      "Gilchrist\n",
      "Waugh\n",
      "Dravid\n",
      "Pollock\n",
      "Tendulkar\n",
      "Gavaskar\n",
      "Dhoni\n"
     ]
    }
   ],
   "source": [
    "lst1 = ['Gayle', 'Gilchrist', 'Waugh', 'Dravid', 'Pollock', 'Tendulkar', 'Gavaskar', 'Dhoni']\n",
    "for x in lst1:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Refer to Differences between Tuple and Lists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TUPLES\n",
    "Tuple is an ordered sequence of items same as a list. The only difference is that tuples are immutable. Tuples once created cannot be modified.\n",
    "\n",
    "Tuples are used to write-protect data and are usually faster than lists as they cannot change dynamically."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating Tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contents of an empty Tuple:  ()\n",
      "Contents of a simple Tuple:  (1, 2, 3, 4, 5)\n",
      "Contents of a Nested Tuple:  (1, 2, (5, 6, 7), 4, 5)\n",
      "Contents of a Tuple having a Nested List:  (1, 2, (5, 6, 7), 4, [9, 8, 7])\n",
      "Contents of a Tuple having mixed Data Types:  ('Rohit', 'Karnataka', True, 2000)\n"
     ]
    }
   ],
   "source": [
    "# Creating Tuples in various ways\n",
    "tup1 = ()   # Create an empty Tuple\n",
    "print(\"Contents of an empty Tuple: \", tup1)\n",
    "tup1 = (1,2,3,4,5)\n",
    "print(\"Contents of a simple Tuple: \", tup1)\n",
    "tup1 = (1,2,(5,6,7),4,5)\n",
    "print(\"Contents of a Nested Tuple: \", tup1)\n",
    "tup1 = (1,2,(5,6,7),4,[9,8,7])\n",
    "\n",
    "print(\"Contents of a Tuple having a Nested List: \", tup1)\n",
    "tup1 = ('Rohit', 'Karnataka', True, 2000)\n",
    "print(\"Contents of a Tuple having mixed Data Types: \", tup1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print Tuple Created from a List (1, 2, 3, 4, 5)\n",
      "Print Tuple Created from a Dictionary ('Name', 'Salary')\n",
      "Print Tuple Created from a String ('C', 'h', 'r', 'i', 's', 't', 'i', 'a', 'n', 'o', ' ', 'R', 'o', 'n', 'a', 'l', 'd', 'o')\n"
     ]
    }
   ],
   "source": [
    "# Creating Tuple using the tuple function\n",
    "lst1 = [1,2,3,4,5]       # Create a List\n",
    "tup1 = tuple(lst1)\n",
    "print(\"Print Tuple Created from a List\", tup1)\n",
    "dict1 = {'Name': 'Robert', 'Salary': 2000}     # Dictionary are key:value pairs separated by comma\n",
    "tup2 = tuple(dict1)     # Note that the Tuple is created with the values of the Dictionary Indexes \n",
    "print(\"Print Tuple Created from a Dictionary\", tup2)\n",
    "str1 = \"Christiano Ronaldo\"\n",
    "tup3 = tuple(str1)   # Tuple created from a String\n",
    "print(\"Print Tuple Created from a String\", tup3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accessing Tuple Elements\n",
    "\n",
    "Like Lists, Tuple elements can be accessed using a relative index value that can be counted from the start (using a positive index number) or at the end (using a negative index number). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the 3rd Element of Tup1:  True\n",
      "Print the 3rd Element of Tup1:  True\n"
     ]
    }
   ],
   "source": [
    "# Access Tuples with relative positioning\n",
    "tup1 = ('Jimi', 5000, True, 3.8, 2019, 300)\n",
    "print(\"Print the 3rd Element of Tup1: \", tup1[2])   # Relative indexing from the start\n",
    "print(\"Print the 3rd Element of Tup1: \", tup1[-4])   # Relative indexing from the end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Slicing Tuples\n",
    "\n",
    "We can access a cross section of a Tuple by providing a start and an end position. \n",
    "Syntax: Tuplename[start:end]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Gilchrist', 'Waugh', 'Dravid', 'Pollock')\n",
      "('Gayle', 'Gilchrist', 'Waugh', 'Dravid', 'Pollock')\n",
      "('Gilchrist', 'Waugh', 'Dravid', 'Pollock', 'Tendulkar', 'Gavaskar', 'Dhoni')\n"
     ]
    }
   ],
   "source": [
    "# Access cross section of Tuples \n",
    "tup1 = ('Gayle', 'Gilchrist', 'Waugh', 'Dravid', 'Pollock', 'Tendulkar', 'Gavaskar', 'Dhoni')\n",
    "print(tup1[1:5])    # Print elements 2 to 5\n",
    "print(tup1[:5])    # Print elements start to 5 - default start 0\n",
    "print(tup1[1:])    # Print all elements starting from 2nd - default end is all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 4, 6, 8, 10, 12)\n"
     ]
    }
   ],
   "source": [
    "# Access cross section of Tuples with stride\n",
    "tup1 = (1,2,3,4,5,6,7,8,9,10,11,12,13,14)\n",
    "print(tup1[1:12:2])    # Print elements 2 to 12 jumping 2 elements at a time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "# Index - Returns the zero-based (positive) index of the first element matching the value of the parameter.\n",
    "\n",
    "tup1 = (1, 3, 7, 8, 7, 5)\n",
    "print(tup1.index(8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "# Count - Returns the number of times the a value passed as a parameter appears on the Tuple. \n",
    "\n",
    "tup1 = (1,2,3,4,5,6,7,8,5,10,11,12,13,14)\n",
    "print(tup1.count(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# Any - The any method returns True if any of the elements is True. \n",
    "\n",
    "tup1 = (1,2,3,4,5,6,7,8)\n",
    "tup2 = (0, 0)\n",
    "print(any(tup1))\n",
    "print(any(tup2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# All - Returns True if all the elements in the Tuple are True. \n",
    "\n",
    "tup1 = (1,2,3,4,6,0,7,8)\n",
    "tup2 = (1,1,1,1)\n",
    "print(all(tup1))\n",
    "print(all(tup2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 'Gayle'), (1, 'Gilchrist'), (2, 'Waugh'), (3, 'Dravid'), (4, 'Pollock'), (5, 'Tendulkar'), (6, 'Gavaskar'), (7, 'Dhoni')]\n"
     ]
    }
   ],
   "source": [
    "# Enumerate - The Enumerate method takes in an iterable (Tuple in this case), adds a sequence number to it and returns an Enumerate object. \n",
    "\n",
    "tup1 = ('Gayle', 'Gilchrist', 'Waugh', 'Dravid', 'Pollock', 'Tendulkar', 'Gavaskar', 'Dhoni')\n",
    "print(list(enumerate(tup1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1, 'Gayle'), (2, 'Gilchrist'), (3, 'Waugh'), (4, 'Dravid'), (5, 'Pollock')]\n"
     ]
    }
   ],
   "source": [
    "# Zip is a built in Python function that takes in two sequences and returns a combined pair set. \n",
    "\n",
    "tup1 = (1,2,3,4,5)\n",
    "tup2 = ('Gayle', 'Gilchrist', 'Waugh', 'Dravid', 'Pollock')\n",
    "print(list(zip(tup1, tup2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sorted:  ['Dravid', 'Gayle', 'Gilchrist', 'Pollock', 'Waugh']\n",
      "Reverse Sorted:  ['Waugh', 'Pollock', 'Gilchrist', 'Gayle', 'Dravid']\n"
     ]
    }
   ],
   "source": [
    "# Sorted - Returns a sorted List of the passed Tuple. \n",
    "res = sorted(('Gayle', 'Gilchrist', 'Waugh', 'Dravid', 'Pollock'))\n",
    "print(\"Sorted: \", res)\n",
    "rev = sorted(('Gayle', 'Gilchrist', 'Waugh', 'Dravid', 'Pollock'), reverse=True)\n",
    "print(\"Reverse Sorted: \", rev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reversed:  ['Pollock', 'Dravid', 'Waugh', 'Gilchrist', 'Gayle']\n"
     ]
    }
   ],
   "source": [
    "# Returns a reversed iterator of the sequence that is passed as parameter. \n",
    "\n",
    "tup1 = ('Gayle', 'Gilchrist', 'Waugh', 'Dravid', 'Pollock')\n",
    "res = list(reversed(tup1))\n",
    "print(\"Reversed: \", res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n"
     ]
    }
   ],
   "source": [
    "# Max - Returns the maximum value from the Tuple.\n",
    "\n",
    "tup1 = (1,2,3,4,5,6,7,8,9,10,11,12,13,14)\n",
    "print(max(tup1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "# Returns the maximum value from the Tuple.\n",
    "\n",
    "tup1 = (1,2,3,4,5,6,7,8,9,10,11,12,13,14)\n",
    "print(min(tup1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "105\n"
     ]
    }
   ],
   "source": [
    "# Returns the maximum value from the Tuple.\n",
    "\n",
    "tup1 = (1,2,3,4,5,6,7,8,9,10,11,12,13,14)\n",
    "print(sum(tup1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Copying a Tuple - As Tuples are mutable, there cannot be much of a rationale to create a copy of a Tuple. The assignment operator ‘=’ and ‘deepcopy’ works but they simply create a new reference to the same memory location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copy:  (1, 2, 3, 4, 5)\n",
      "140718893539552\n",
      "140718893539552\n"
     ]
    }
   ],
   "source": [
    "# Copy Using Deepcopy\n",
    "from copy import deepcopy\n",
    "tup1 = (1, 2, 3, 4, 5)\n",
    "tup2 = deepcopy(tup1)\n",
    "print(\"Copy: \", tup2)\n",
    "print(id(tup1))\n",
    "print(id(tup2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copy:  (1, 2, 3, 4, 5)\n",
      "140641022357392\n",
      "140641022357392\n"
     ]
    }
   ],
   "source": [
    "# Copy using assignment\n",
    "tup3 = (1, 2, 3, 4, 5)\n",
    "tup4 = tup3\n",
    "print(\"Copy: \", tup4)\n",
    "print(id(tup3))\n",
    "print(id(tup4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5]\n",
      "[1, 2, 3, 4, 5, 3]\n",
      "[1, 2, 3, 4, 5, 3]\n"
     ]
    }
   ],
   "source": [
    "lstx = [1,2,3,4,5]\n",
    "lsty = lstx\n",
    "print(lsty)\n",
    "lsty.append(3)\n",
    "print(lsty)\n",
    "print(lstx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5]\n",
      "[1, 2, 3, 4, 5, 3]\n",
      "[1, 2, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "lstx = [1,2,3,4,5]\n",
    "lsty = lstx.copy()\n",
    "print(lsty)\n",
    "lsty.append(3)\n",
    "print(lsty)\n",
    "print(lstx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dictionaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Dictionary in Python is a sequence or collection of Key:Value pairs. A dictionary is expressed within curling braces {}. The key is called the Index and the values can be accessed using the Index value from any specific pair. \n",
    "Dictionaries “values” are mutable, i.e. values against a key can be updated after the dictionary is created and can be of any data type. However, the “Key” cannot be updated (immutable). The Key cannot be duplicated also. i.e. there cannot be two keys in the same dictionary with the same name. \n",
    "Dictionary can hold “values” as other collections such as a Numpy Array, List or Set.  \n",
    "It is generally used when we have a huge amount of data. Dictionaries are optimized for retrieving data. We must know the key to retrieve the value.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Tran_id': 'T0089', 'Cust_Id': 'C000876', 'ATM_Location': 'HSR Layout', 'Acc_Type': 'Savings', 'Amount_Drawn': 8000}\n"
     ]
    }
   ],
   "source": [
    "# Create our First Dictionary\n",
    "dict_tran = {'Tran_id' : 'T0089', 'Cust_Id': 'C000876', \n",
    "             'ATM_Location': 'HSR Layout', 'Acc_Type': 'Savings' , 'Amount_Drawn': 8000}\n",
    "print(dict_tran)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Cust_id': 'C0789', 'Cust_Trans': ['T001', 'T003', 'T006']}\n"
     ]
    }
   ],
   "source": [
    "# Create a Dictionary with a List as one of the values\n",
    "dict_tran = {'Cust_id' : 'C0789', 'Cust_Trans': ['T001', 'T003', 'T006']}\n",
    "print(dict_tran)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Cust_id': 'C0789', 'Cust_Trans': 'T001'}\n"
     ]
    }
   ],
   "source": [
    "# Create a Dictionary from a set of Tuples in a List using the Dict Method\n",
    "dict_tran = dict([('Cust_id', 'C0789'), ('Cust_Trans', 'T001')])\n",
    "print(dict_tran)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Cust_id': 'C0789', 'Cust_Trans': 'T001'}\n"
     ]
    }
   ],
   "source": [
    "# Populate from an empty dictionary\n",
    "dict_tran = {}\n",
    "dict_tran['Cust_id'] = 'C0789'\n",
    "dict_tran['Cust_Trans'] = 'T001'\n",
    "print(dict_tran)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accessing Dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing Value of Tran_id:  T0089\n",
      "Printing Value of Cust_Id:  C000876\n"
     ]
    }
   ],
   "source": [
    "# Access Dictionary Elements by using 'Keys' to access the 'Values'\n",
    "dict_tran = {'Tran_id' : 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings' , 'Amount_Drawn': 8000}\n",
    "print(\"Printing Value of Tran_id: \", dict_tran['Tran_id'])\n",
    "print(\"Printing Value of Cust_Id: \", dict_tran['Cust_Id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing Value of Tran_id:  T0089\n",
      "Printing Value of Tran_info:  {'Acc_Type': 'Savings', 'Amount_Drawn': 8000}\n",
      "Printing Value of Amount_Drawn:  8000\n"
     ]
    }
   ],
   "source": [
    "# Access Dictionary Elements by using 'Keys' to access the 'Values' -- Nested Dictionaries\n",
    "dict_tran = {'Tran_id' : 'T0089', 'Cust_Id': 'C000876', \n",
    "             'Tran_info': {'Acc_Type': 'Savings' , 'Amount_Drawn': 8000}}\n",
    "print(\"Printing Value of Tran_id: \", dict_tran['Tran_id'])\n",
    "print(\"Printing Value of Tran_info: \", dict_tran['Tran_info'])\n",
    "print(\"Printing Value of Amount_Drawn: \", dict_tran['Tran_info']['Amount_Drawn'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding Elements in a Dictionary\n",
    "Elements or new key:value pairs can be added into a Dictionary by assigning the value against the new key. The following example demonstrates.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing the updated dictionary:  {'Tran_id': 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings', 'Amount_Drawn': 8000, 'ATM_Location': 'Bengaluru'}\n"
     ]
    }
   ],
   "source": [
    "# Adding an element (key:value) by value assignment against a new key\n",
    "dict_tran = {'Tran_id' : 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings' , 'Amount_Drawn': 8000}\n",
    "dict_tran['ATM_Location'] = 'Bengaluru'\n",
    "print(\"Printing the updated dictionary: \", dict_tran)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing the updated dictionary:  {'Tran_id': 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings', 'Amount_Drawn': 8000, 'ATM_Location': 'Bengaluru', 'Amount_Given': 6000}\n"
     ]
    }
   ],
   "source": [
    "# Adding an element (key:value) by using the update method of dictionary\n",
    "dict_tran = {'Tran_id' : 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings' , 'Amount_Drawn': 8000}\n",
    "dict_tran.update({'ATM_Location': 'Bengaluru', 'Amount_Given': 6000})\n",
    "print(\"Printing the updated dictionary: \", dict_tran)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing an Element from a Dictionary\n",
    "Removing an element from a dictionary requires identifying the element through the ‘key’. The ‘del’ statement removed the element in-place.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing the updated dictionary:  {'Tran_id': 'T0089', 'Cust_Id': 'C000876', 'Amount_Drawn': 8000}\n"
     ]
    }
   ],
   "source": [
    "# Deleting an Element from a Dictionary\n",
    "dict_tran = {'Tran_id' : 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings' , 'Amount_Drawn': 8000}\n",
    "del dict_tran['Acc_Type']\n",
    "print(\"Printing the updated dictionary: \", dict_tran)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing the updated dictionary:  {'Tran_id': 'T0089', 'Cust_Id': 'C000876', 'Amount_Drawn': 8000}\n",
      "What did I pop?  Savings\n"
     ]
    }
   ],
   "source": [
    "# Deleting an Element from a Dictionary using 'pop'\n",
    "dict_tran = {'Tran_id' : 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings' , 'Amount_Drawn': 8000}\n",
    "popped = dict_tran.pop('Acc_Type')\n",
    "print(\"Printing the updated dictionary: \", dict_tran)\n",
    "print(\"What did I pop? \", popped)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trapping KeyError in a Dictionary\n",
    "Most operations on a Python Dictionary requires a ‘key’ to access the value, hence there is probability of specifying a wrong key. A dictionary operation throws a ‘KeyError’ when a wrong key is specified and it is advisable to trap it, especially if the key is provided dynamically in the program. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key Specified is not found in dict_tran\n"
     ]
    }
   ],
   "source": [
    "# Deleting an Element from a Dictionary using 'pop' while trapping 'KeyError'\n",
    "dict_tran = {'Tran_id' : 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings' , 'Amount_Drawn': 8000}\n",
    "try:\n",
    "    popped = dict_tran.pop('Acc_Type1')\n",
    "    print(\"Printing the updated dictionary: \", dict_tran)\n",
    "    print(\"What did I pop? \", popped)\n",
    "except KeyError:\n",
    "    print(\"Key Specified is not found in dict_tran\")      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{}\n"
     ]
    }
   ],
   "source": [
    "# Clear - Deletes all elements in the Dictionary\n",
    "# 'Clear' the Dictionary\n",
    "dict_tran = {'Tran_id' : 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings' , 'Amount_Drawn': 8000}\n",
    "dict_tran.clear()\n",
    "print(dict_tran)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Tran_id': 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings', 'Amount_Drawn': 8000}\n"
     ]
    }
   ],
   "source": [
    "# Copy - Returns a copy of the Dictionary\n",
    "# 'Copy' the Dictionary\n",
    "dict_tran = {'Tran_id' : 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings' , 'Amount_Drawn': 8000}\n",
    "dict_copy = dict_tran.copy()\n",
    "print(dict_copy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Cust_Id': None, 'Acc_Type': None}\n"
     ]
    }
   ],
   "source": [
    "# Creates a dictionary from a specified sequence of keys.\n",
    "# Create a new Dictionary with a specified sequence of key-values\n",
    "dict_tran = {'Tran_id' : 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings' , 'Amount_Drawn': 8000}\n",
    "keys = {'Cust_Id', 'Acc_Type'}\n",
    "dict_copy = dict_tran.fromkeys(keys)\n",
    "print(dict_copy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C000876\n"
     ]
    }
   ],
   "source": [
    "# Get - Returns the value of a key specified.\n",
    "# 'get' the value of a specified key\n",
    "dict_tran = {'Tran_id' : 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings' , 'Amount_Drawn': 8000}\n",
    "dict_get = dict_tran.get('Cust_Id')\n",
    "print(dict_get)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_items([('Tran_id', 'T0089'), ('Cust_Id', 'C000876'), ('Acc_Type', 'Savings'), ('Amount_Drawn', 8000)])\n"
     ]
    }
   ],
   "source": [
    "# Returns all the key:value pairs in the dictionary in the form of a List of Tuples.\n",
    "# Return the key:value pairs from the dictionary as a list of key:value Tuples \n",
    "dict_tran = {'Tran_id' : 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings' , 'Amount_Drawn': 8000}\n",
    "dict_items = dict_tran.items()\n",
    "print(dict_items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['Tran_id', 'Cust_Id', 'Acc_Type', 'Amount_Drawn'])\n"
     ]
    }
   ],
   "source": [
    "# Returns a list containing all the keys of the Dictionary.\n",
    "# Return all the Keys of the dictionary\n",
    "dict_tran = {'Tran_id' : 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings' , 'Amount_Drawn': 8000}\n",
    "dict_keys = dict_tran.keys()\n",
    "print(dict_keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_values(['T0089', 'C000876', 'Savings', 8000])\n"
     ]
    }
   ],
   "source": [
    "# Values - Returns all the values \n",
    "# Return all the Values of the dictionary\n",
    "dict_tran = {'Tran_id' : 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings' , 'Amount_Drawn': 8000}\n",
    "dict_vals = dict_tran.values()\n",
    "print(dict_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing the updated dictionary:  {'Tran_id': 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings'}\n",
      "What did I pop?  ('Amount_Drawn', 8000)\n"
     ]
    }
   ],
   "source": [
    "# Popitem - Removes the last Inserted key:value pair\n",
    "# Deleting the last Element from a Dictionary using 'popitem'\n",
    "dict_tran = {'Tran_id' : 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings' , 'Amount_Drawn': 8000}\n",
    "popped = dict_tran.popitem()\n",
    "print(\"Printing the updated dictionary: \", dict_tran)\n",
    "print(\"What did I pop? \", popped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing the updated dictionary - pass 1:  {'Tran_id': 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings', 'Amount_Drawn': 8000, 'New_field': 6766}\n",
      "Printing the updated dictionary - pass 2:  {'Tran_id': 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings', 'Amount_Drawn': 8000, 'New_field': 6766}\n",
      "Checking the returned value:  8000\n"
     ]
    }
   ],
   "source": [
    "# Setdefault - Insert a specified key:value pair. If the Key exists, returns the value.\n",
    "# Insert a key:value pair. If the pair exists, return the value\n",
    "dict_tran = {'Tran_id' : 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings' , 'Amount_Drawn': 8000}\n",
    "dict_tran.setdefault('New_field', 6766)\n",
    "print(\"Printing the updated dictionary - pass 1: \", dict_tran)\n",
    "ret = dict_tran.setdefault('Amount_Drawn', 6766)\n",
    "print(\"Printing the updated dictionary - pass 2: \", dict_tran)\n",
    "print(\"Checking the returned value: \", ret)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Iterating through a Dictionary\n",
    "Traversing through a dictionary is similar to traversing through other iterators in Python. While traversing through a dictionary in a for loop, python returns the current Key. The value as usual can be extracted. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key:  Tran_id  -- Value:  T0089\n",
      "Key:  Cust_Id  -- Value:  C000876\n",
      "Key:  Acc_Type  -- Value:  Savings\n",
      "Key:  Amount_Drawn  -- Value:  8000\n"
     ]
    }
   ],
   "source": [
    "# Iterating through a Dictionary\n",
    "dict_tran = {'Tran_id' : 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings' , 'Amount_Drawn': 8000}\n",
    "for x in dict_tran:\n",
    "    print(\"Key: \", x, \" -- Value: \", dict_tran[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('Tran_id', 'T0089'), ('Cust_Id', 'C000876'), ('Acc_Type', 'Savings'), ('Amount_Drawn', 8000)])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Iterating through a Dictionary\n",
    "dict_tran = {'Tran_id' : 'T0089', 'Cust_Id': 'C000876', 'Acc_Type': 'Savings' , 'Amount_Drawn': 8000}\n",
    "dict_tran.items()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SETs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sets are unordered collection objects containing unique mutable elements. Set objects are used in Python the purpose of carrying out special set operations such as finding unions, differences, intersections, symmetric intersections, etc between datasets. Sets are also used for membership testing, i.e. testing for whether an element is part of the set or not. Another key property of sets is that elements in a set are unordered. Hence, it is not possible to access elements in a set using indexes, or carrying out other sequence operations like slicing, unlike other collection objects in Python such as Lists, Tuples, Dictionaries and Numpy Arrays. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Gavaskar', 'Gilchrist', 'Pollock', 'Waugh', 'Dhoni', 'Tendulkar', 'Dravid', 'Gayle'}\n"
     ]
    }
   ],
   "source": [
    "# Creating a Set\n",
    "set1 = set(['Gayle', 'Gilchrist', 'Waugh', 'Dravid', 'Pollock', 'Tendulkar', 'Gavaskar', 'Dhoni'])\n",
    "print(set1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding the Intersections of two Sets \n",
    "Intersections return the elements that are common across the two sets used as operands. \n",
    "The following code demonstrates two ways intersections between two sets are found.\n",
    "- Use the ‘&’ operator that returns the elements common between set1 & set2.\n",
    "- Use the Intersection method of set object. \n",
    "At the end, we compare the result sets (int1 and int2) to find if they are the same.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set 1:  {'f', 'a', 'd', 'c', 'e', 'b', 'g', 'h'}\n",
      "Set 2:  {'f', 'a', 'c', 'm', 'e', 'b', 'q', 'h', 't'}\n",
      "Intersection using the & Operator:  {'f', 'a', 'c', 'e', 'b', 'h'}\n",
      "Intersection using the Intersection method:  {'f', 'a', 'c', 'e', 'b', 'h'}\n",
      "The Intersections are same\n"
     ]
    }
   ],
   "source": [
    "# Finding an Intersection\n",
    "set1 = set(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])\n",
    "set2 = set(['a', 'b', 'c', 'e', 'f', 'h', 'm', 't', 'q'])\n",
    "print(\"Set 1: \", set1)\n",
    "print(\"Set 2: \", set2)\n",
    "int1 = set1 & set2     # Use the '&' - intersection operator\n",
    "int2 = set1.intersection(set2)     # Use the intersection method of set object\n",
    "print(\"Intersection using the & Operator: \", int1)\n",
    "print(\"Intersection using the Intersection method: \", int2)\n",
    "if int1 == int2:\n",
    "    print(\"The Intersections are same\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding the Union of two Sets \n",
    "Union return unique occurrences of all elements among the two sets used as operands. \n",
    "The following code demonstrates two ways unions between two sets are found.\n",
    "- Use the ‘|’ operator that returns the union between set1 & set2. Interpreted as ‘return all which are in either set1 or set2 plus the unique of the ones common across both sets’.  \n",
    "- Use the Union method of set object. \n",
    "At the end, we compare the result sets (uni1 and uni2) to find if they are the same.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set 1:  {'f', 'a', 'd', 'c', 'e', 'b', 'g', 'h'}\n",
      "Set 2:  {'f', 'a', 'c', 'm', 'e', 'b', 'q', 'h', 't'}\n",
      "Union using the | Operator:  {'f', 'a', 'd', 'c', 'm', 'e', 'b', 'q', 'g', 'h', 't'}\n",
      "Union using the Union method:  {'f', 'a', 'd', 'c', 'm', 'e', 'b', 'q', 'g', 'h', 't'}\n",
      "The Unions are same\n"
     ]
    }
   ],
   "source": [
    "# Finding an Union\n",
    "set1 = set(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])\n",
    "set2 = set(['a', 'b', 'c', 'e', 'f', 'h', 'm', 't', 'q'])\n",
    "print(\"Set 1: \", set1)\n",
    "print(\"Set 2: \", set2)\n",
    "uni1 = set1 | set2     # Use the '|' - union operator\n",
    "uni2 = set1.union(set2)     # Use the union method of set object\n",
    "print(\"Union using the | Operator: \", uni1)\n",
    "print(\"Union using the Union method: \", uni2)\n",
    "if uni1 == uni2:\n",
    "    print(\"The Unions are same\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding the Difference of two Sets \n",
    "The difference operation on sets, set1 and set2 return all elements that are found in set1 but not there in set2. \n",
    "The following code demonstrates two ways differences between two sets are found.\n",
    "- Use the ‘-’ operator that returns the difference between set1 & set2. Interpreted as ‘return all elements that are found in set1 but not in set2’.  \n",
    "- Use the difference method of set object. \n",
    "At the end, we compare the result sets (dif1 and dif2) to find if they are the same.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set 1:  {'f', 'a', 'd', 'c', 'e', 'b', 'g', 'h'}\n",
      "Set 2:  {'f', 'a', 'c', 'm', 'e', 'b', 'q', 'h', 't'}\n",
      "Difference using the - Operator:  {'g', 'd'}\n",
      "Difference using the Difference method:  {'g', 'd'}\n",
      "The Differences are same\n"
     ]
    }
   ],
   "source": [
    "# Finding a Difference\n",
    "set1 = set(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])\n",
    "set2 = set(['a', 'b', 'c', 'e', 'f', 'h', 'm', 't', 'q'])\n",
    "print(\"Set 1: \", set1)\n",
    "print(\"Set 2: \", set2)\n",
    "dif1 = set1 - set2     # Use the '-' (Difference operator)\n",
    "dif2 = set1.difference(set2)     # Use the Difference method of set object\n",
    "print(\"Difference using the - Operator: \", dif1)\n",
    "print(\"Difference using the Difference method: \", dif2)\n",
    "if dif1 == dif2:\n",
    "    print(\"The Differences are same\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding the Symmetric Difference of two Sets \n",
    "The symmetric difference operation on sets, set1 and set2 return all elements that are found in set1 and set2 but not in both. \n",
    "The following code demonstrates two ways symmetric differences between two sets are found.\n",
    "- Use the ‘^’ operator that returns the symmetric difference between set1 & set2. Interpreted as ‘return all elements that are found in set1 and set2 but not in both’.  \n",
    "- Use the symmetric_difference method of set object. \n",
    "At the end, we compare the result sets (dif1 and dif2) to find if they are the same.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set 1:  {'f', 'a', 'd', 'c', 'e', 'b', 'g', 'h'}\n",
      "Set 2:  {'f', 'a', 'c', 'm', 'e', 'b', 'q', 'h', 't'}\n",
      "Symmetric Difference using the ^ Operator:  {'d', 'm', 'q', 'g', 't'}\n",
      "Symmetric Difference using the symmetric_difference method:  {'d', 'm', 'q', 'g', 't'}\n",
      "The Symmetric Differences are same\n"
     ]
    }
   ],
   "source": [
    "# Finding Symmetric Difference\n",
    "set1 = set(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])\n",
    "set2 = set(['a', 'b', 'c', 'e', 'f', 'h', 'm', 't', 'q'])\n",
    "print(\"Set 1: \", set1)\n",
    "print(\"Set 2: \", set2)\n",
    "dif1 = set1 ^ set2     # Use the '^' (Symmetric Difference operator)\n",
    "dif2 = set1.symmetric_difference(set2)     # Use the symmetric_difference method of set object\n",
    "print(\"Symmetric Difference using the ^ Operator: \", dif1)\n",
    "print(\"Symmetric Difference using the symmetric_difference method: \", dif2)\n",
    "if dif1 == dif2:\n",
    "    print(\"The Symmetric Differences are same\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set 1:  {'f', 'a', 'd', 'c', 'e', 'b', 'g', 'h'}\n",
      "Updated Set1:  {'f', 'a', 'd', 'c', 'e', 'b', 'i', 'g', 'h'}\n"
     ]
    }
   ],
   "source": [
    "# Add an element in a Set\n",
    "set1 = set(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])\n",
    "print(\"Set 1: \", set1)\n",
    "set1.add('i')     # Use the add method of set object to add the new element 'i'\n",
    "print(\"Updated Set1: \", set1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test of Membership\n",
    "We can check if an element is part of a set by using the ‘in’ or ‘not in’ statements. A Boolean is returned based on whether or not the specified element if part of the set in question. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Whether c is present in Set1:  False\n",
      "Whether c is present in Set1...negative way:  True\n"
     ]
    }
   ],
   "source": [
    "# Check if element 'c' is part of set1\n",
    "set1 = set(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])\n",
    "print(\"Whether c is present in Set1: \", '7' in set1)\n",
    "print(\"Whether c is present in Set1...negative way: \", '7' not in set1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set 1:  {'f', 'a', 'd', 'c', 'e', 'b', 'g', 'h'}\n",
      "Set 2:  {'b', 'f', 'h', 'e'}\n",
      "Whether Set2 is a subset of set1:  True\n",
      "Whether Set1 is a superset of set2:  True\n"
     ]
    }
   ],
   "source": [
    "# Check for subset or superset\n",
    "set1 = set(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])\n",
    "set2 = set(['b', 'e', 'f', 'h'])\n",
    "print(\"Set 1: \", set1)\n",
    "print(\"Set 2: \", set2)\n",
    "print(\"Whether Set2 is a subset of set1: \", set2.issubset(set1))\n",
    "print(\"Whether Set1 is a superset of set2: \", set1.issuperset(set2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'f', 'a', 'd', 'x', 'c', 'm', 'e', 'b', 'g', 'h', 'w'}\n"
     ]
    }
   ],
   "source": [
    "# Update - The ‘update’ function updates multiple elements into a set.\n",
    "# Adding Multiple Elements using the 'update' method \n",
    "set1 = set(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])\n",
    "set1.update(['w', 'x', 'm', 'a'])\n",
    "print(set1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    }
   ],
   "source": [
    "# Len - The ‘len function returns the number of elements in a set.\n",
    "# Finding the number of elements in a Set\n",
    "set1 = set(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])\n",
    "print(len(set1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing original set:  {'f', 'a', 'd', 'c', 'e', 'b', 'g', 'h'}\n",
      "Printing the copied set:  {'f', 'a', 'd', 'c', 'e', 'b', 'g', 'h'}\n",
      "Memory Address of Set1:  140718894114848\n",
      "Memory Address of Set2:  140718894113952\n",
      "{'f', 'a', 'd', 'c', 'e', 'b', 'g', 'h', 't'}\n",
      "{'f', 'a', 'd', 'c', 'e', 'b', 'g', 'h'}\n"
     ]
    }
   ],
   "source": [
    "# Copy - The copy function creates a Physically separate copy of the set. \n",
    "# An “=” assignment operator creates only a reference (a different variable name on the same memory location). \n",
    "# To create a physically separate new copy, or a shallow copy, the ‘copy’ method has to be used.  \n",
    "\n",
    "set1 = set(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])\n",
    "set2 = set1.copy()\n",
    "\n",
    "print(\"Printing original set: \", set1)\n",
    "print(\"Printing the copied set: \", set2)\n",
    "print(\"Memory Address of Set1: \", id(set1))\n",
    "print(\"Memory Address of Set2: \", id(set2))\n",
    "set1.add('t')\n",
    "print(set1)\n",
    "print(set2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'f', 'a', 'c', 'e', 'b', 'g', 'h'}\n"
     ]
    }
   ],
   "source": [
    "# Remove - The ‘remove’ method removes an element from the set. ‘remove’ raises a KeyError if the element is not present.\n",
    "# Remove an element from the Set\n",
    "set1 = set(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])\n",
    "set1.remove('d')\n",
    "print(set1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Popped Item:  f\n",
      "New Set:  {'a', 'd', 'c', 'e', 'b', 'g', 'h'}\n"
     ]
    }
   ],
   "source": [
    "# Pop - Removes an arbitrary element from the set and returns it. \n",
    "# Pop an element from the Set\n",
    "set1 = set(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])\n",
    "x = set1.pop()\n",
    "print(\"Popped Item: \", x)\n",
    "print(\"New Set: \", set1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intersection Update:  {'b', 'f', 'h', 'e'}\n"
     ]
    }
   ],
   "source": [
    "# Intersection_update - The Intersection_update method returns set1 keeping only those elements that \n",
    "# are also found in set2. Intersection_update --- Return set1 keeping only those elements also found in set2\n",
    "set1 = set(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])\n",
    "set2 = set(['b', 'e', 'f', 'h', 'u', 'j'])\n",
    "set1.intersection_update(set2)\n",
    "print(\"Intersection Update: \", set1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference Update:  {'a', 'd', 'c', 'g'}\n"
     ]
    }
   ],
   "source": [
    "# Difference_update - The difference_update method returns those elements in set1 after removing those also found in set2.\n",
    "# Difference_update --- Return set1 after removing  those elements also found in set2\n",
    "set1 = set(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])\n",
    "set2 = set(['b', 'e', 'f', 'h', 'u', 'j'])\n",
    "set1.difference_update(set2)\n",
    "print(\"Difference Update: \", set1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Symmetric Difference Update:  {'u', 'a', 'd', 'c', 'g', 'j'}\n"
     ]
    }
   ],
   "source": [
    "# symmetric_difference_update - Updates set1 with elements found in both set1 or set2 but not both.\n",
    "# Symmetric_Difference_update --- Updates set1 keeping those elements found in Set1 and Set2 but not both\n",
    "set1 = set(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])\n",
    "set2 = set(['b', 'e', 'f', 'h', 'u', 'j'])\n",
    "set1.symmetric_difference_update(set2)\n",
    "print(\"Symmetric Difference Update: \", set1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iterating through a Set\n",
    "Traversing through a set is similar to traversing through other iterators in Python. With each iteration, Python returns an element from the set till all elements are exhausted.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f\n",
      "a\n",
      "d\n",
      "c\n",
      "e\n",
      "b\n",
      "g\n",
      "h\n"
     ]
    }
   ],
   "source": [
    "# Traversing through a set\n",
    "set1 = set(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])\n",
    "for x in set1:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NUMPY Library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data that we deal with comes in various shapes and form and originate from varied sources, however at the end all of them can be fundamentally represented as arrays of Numeric digits. \n",
    "\n",
    "Some examples as follows: \n",
    "-\tData that are fundamentally numeric (Financial Transaction amounts, Your Monthly income, Number of times you visited a website in last 7 days)\n",
    "-\tSound: Stored as a one dimensional array of frequency vs time\n",
    "-\tImages: Stored as two Dimensional numeric arrays representing brightness values of each pixel over an area\n",
    "-\tText: Can be represented as Numeric digits as well in various ways\n",
    "\n",
    "Numeric representation of all kinds of data are essential to the heart of Data Analysis / Data Science as creation of effective models of predictions and decisions are dependent on all data being available in Numeric form. Note that Prediction Modelling can take only Numeric input variables. \n",
    "\n",
    "Numpy is the foundational library of Python. NumPy provides for a N-dimensional array storage, powerful set of array handling functions and strong support of mathematical operations on arrays including Linear Algebra, Fourier Transformation and Random Number capabilities. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installation of Numpy\n",
    "If you are using Anaconda, NumPy comes pre-installed. However, just in case, you do not have the NumPy library installed, here is what you can do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# $ pip3 install numpy\n",
    "# or\n",
    "# $ brew install numpy  \n",
    "# Or \n",
    "# $ conda install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You need to import the NumPy library in your Python code before using it: \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numpy Arrays\n",
    "Numpy supports a multidimensional homogenous array. The index of a numpy array is a Tuple of integers starting from 0. The number of dimensions is called the rank of the array. The shape of the array is also represented by the Tuple of integers denoting the length of each dimension. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating Arrays: \n",
    "A common way of creating a numpy array is from Python Tuples. For multiple dimensions, multiple Tuples are used. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3 4 5]\n",
      " [2 3 4 5 6]]\n",
      "(2, 5)\n"
     ]
    }
   ],
   "source": [
    "# Create a numpy Array from Tuple\n",
    "arr = np.array([[1,2,3,4,5], [2,3,4,5,6]])\n",
    "print(arr)\n",
    "print(arr.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. 0.]\n",
      " [0. 0. 0. 0.]\n",
      " [0. 0. 0. 0.]\n",
      " [0. 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "# Create an array of all zeros\n",
    "arr = np.zeros((4,4))   \n",
    "print(arr)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "# Create an array of all ones\n",
    "arr = np.ones((3,4))    \n",
    "print(arr)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9 9 9]\n",
      " [9 9 9]\n",
      " [9 9 9]\n",
      " [9 9 9]]\n"
     ]
    }
   ],
   "source": [
    "# Create an array filling all elements with a specified value\n",
    "arr = np.full((4,3), 9)  \n",
    "print(arr)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 0. 1. 0.]\n",
      " [0. 0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "# Create a 4x4 identity matrix\n",
    "arr = np.eye(4)         \n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.69181518 0.43490717 0.59864197]\n",
      " [0.83924079 0.08251114 0.82617788]\n",
      " [0.59139455 0.77884998 0.35567771]]\n"
     ]
    }
   ],
   "source": [
    "# Create an array filled with random values\n",
    "arr = np.random.random((3,3))  \n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an array with sequential values starting from 0 to a given range limit\n",
    "arr = np.arange(30)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an array with sequential values starting from a given value to a final value and with a step value\n",
    "arr = np.arange(0, 30, 2)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5],\n",
       "       [ 6,  7,  8,  9, 10, 11],\n",
       "       [12, 13, 14, 15, 16, 17],\n",
       "       [18, 19, 20, 21, 22, 23],\n",
       "       [24, 25, 26, 27, 28, 29]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reshape the array to a new specified dimension\n",
    "arr = np.arange(30)\n",
    "arr = arr.reshape(5,6)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5],\n",
       "       [ 6,  7,  8,  9, 10, 11],\n",
       "       [12, 13, 14, 15, 16, 17],\n",
       "       [18, 19, 20, 21, 22, 23],\n",
       "       [24, 25, 26, 27, 28, 29]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reshape the array to a new specified dimension -- a shorter way\n",
    "arr = np.arange(30).reshape(5,6)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['John', 'Robert'],\n",
       "       ['Pranav', 'Tauseef']], dtype='<U7')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a string array \n",
    "arr = np.array([['John', 'Robert'], ['Pranav', 'Tauseef']])\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5],\n",
       "       [ 6,  7,  8,  9, 10, 11],\n",
       "       [12, 13, 14, 15, 16, 17],\n",
       "       [18, 19, 20, 21, 22, 23],\n",
       "       [24, 25, 26, 27, 28, 29]])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an array of dimension 5,6 using the arange function. \n",
    "arr = np.arange(30).reshape(5,6)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3  4  5]\n",
      " [ 6  7  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29]]\n",
      "[[ 0  6 12 18 24]\n",
      " [ 1  7 13 19 25]\n",
      " [ 2  8 14 20 26]\n",
      " [ 3  9 15 21 27]\n",
      " [ 4 10 16 22 28]\n",
      " [ 5 11 17 23 29]]\n",
      "[[  55  145  235  325  415]\n",
      " [ 145  451  757 1063 1369]\n",
      " [ 235  757 1279 1801 2323]\n",
      " [ 325 1063 1801 2539 3277]\n",
      " [ 415 1369 2323 3277 4231]]\n"
     ]
    }
   ],
   "source": [
    "# Return the dot product of two arrays\n",
    "arr1 = np.arange(30).reshape(5,6)\n",
    "print(arr1)\n",
    "arr2 = arr1.T\n",
    "print(arr2)\n",
    "arr_res = arr1.dot(arr2)\n",
    "print(arr_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3  4  5]\n",
      " [ 6  7  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29]]\n",
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
      " 24 25 26 27 28 29]\n"
     ]
    }
   ],
   "source": [
    "# Flatten an Array\n",
    "arr1 = np.arange(30).reshape(5,6)\n",
    "print(arr1)\n",
    "arr = arr1.flatten()\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum:  0\n",
      "Maximum:  29\n",
      "Mean:  14.5\n",
      "Standard Deviation:  [1.70782513 1.70782513 1.70782513 1.70782513 1.70782513]\n",
      "Variance:  [72. 72. 72. 72. 72. 72.]\n"
     ]
    }
   ],
   "source": [
    "# Find the Min, Max, Mean, Standard Deviation and Variance of the elements\n",
    "arr1 = np.arange(30).reshape(5,6)\n",
    "minm = arr1.min()\n",
    "maxm = arr1.max()\n",
    "mean = arr1.mean()\n",
    "std = arr1.std(axis=1)  # Along Columns\n",
    "var = arr1.var(axis=0)  # Along Rows\n",
    "print(\"Minimum: \", minm)\n",
    "print(\"Maximum: \", maxm)\n",
    "print(\"Mean: \", mean)\n",
    "print(\"Standard Deviation: \", std)\n",
    "print(\"Variance: \", var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3  4  5]\n",
      " [ 6  7  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29]]\n"
     ]
    }
   ],
   "source": [
    "# Create a copy\n",
    "arr1 = np.arange(30).reshape(5,6)\n",
    "arrc = arr1.copy()\n",
    "print(arrc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array 1: \n",
      " [[0.19405976 0.71143692 0.76735805]\n",
      " [0.7579119  0.24637614 0.88290936]\n",
      " [0.64673472 0.40167265 0.29264018]]\n",
      "Array 2: \n",
      " [[0.19 0.71 0.77]\n",
      " [0.76 0.25 0.88]\n",
      " [0.65 0.4  0.29]]\n"
     ]
    }
   ],
   "source": [
    "# Rounding elements\n",
    "arr1 = np.random.random((3,3)) \n",
    "print(\"Array 1: \\n\", arr1)\n",
    "arr2 = arr1.round(2)\n",
    "print(\"Array 2: \\n\", arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array 1: \n",
      " [[ 0  1  2  3  4  5]\n",
      " [ 6  7  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29]]\n",
      "Array 2: \n",
      " [['0' '1' '2' '3' '4' '5']\n",
      " ['6' '7' '8' '9' '10' '11']\n",
      " ['12' '13' '14' '15' '16' '17']\n",
      " ['18' '19' '20' '21' '22' '23']\n",
      " ['24' '25' '26' '27' '28' '29']]\n",
      "Array 3: \n",
      " [[ 0  1  2  3  4  5]\n",
      " [ 6  7  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29]]\n"
     ]
    }
   ],
   "source": [
    "# Converting Data Types with astype\n",
    "arr1 = np.arange(30).reshape(5,6)  \n",
    "print(\"Array 1: \\n\", arr1)  \n",
    "arr2 = arr1.astype(str)    # Convert elements from Int to String\n",
    "print(\"Array 2: \\n\", arr2)\n",
    "arr3 = arr2.astype(np.int64)    # Convert String elements to Int again\n",
    "print(\"Array 3: \\n\", arr3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing Array Elements\n",
    "Array elements are accessed through the use of indexes. Array index position numbers start from 0. The 2nd row, 3rd columns will be denoted as [1,2]. For an element to be accessed, it’s position along the dimensions have to be specified within square brackets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array 1: \n",
      " [[ 0  1  2  3  4  5]\n",
      " [ 6  7  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29]]\n",
      "Element at the 2nd row, 3rd column:  8\n"
     ]
    }
   ],
   "source": [
    "# Accessing Array Elements - 2nd row, 3rd column\n",
    "arr1 = np.arange(30).reshape(5,6)  \n",
    "print(\"Array 1: \\n\", arr1)  \n",
    "print(\"Element at the 2nd row, 3rd column: \", arr1[1, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array 1: \n",
      " [[ 0  1  2  3  4  5]\n",
      " [ 6  7  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29]]\n",
      "Element at the 2nd row, 3rd column:  8\n",
      "All Elements on the 2nd row:  [ 6  7  8  9 10 11]\n",
      "Element on the 3rd  column:  [ 2  8 14 20 26]\n",
      "All Elements: \n",
      " [[ 0  1  2  3  4  5]\n",
      " [ 6  7  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29]]\n"
     ]
    }
   ],
   "source": [
    "# Accessing Array Elements \n",
    "arr1 = np.arange(30).reshape(5,6)  \n",
    "print(\"Array 1: \\n\", arr1)  \n",
    "print(\"Element at the 2nd row, 3rd column: \", arr1[1,2])   \n",
    "print(\"All Elements on the 2nd row: \", arr1[1])   # Print the 2nd row\n",
    "print(\"Element on the 3rd  column: \", arr1[:, 2])   # Print the 3rd column\n",
    "print(\"All Elements: \\n\", arr1[:,:])   # Another way of doing this is not mentioning the indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array 1: \n",
      " [[ 0  1  2  3  4  5]\n",
      " [ 6  7  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29]]\n",
      "Elements on the 2nd row, 3rd to 5th positions:  [ 8  9 10]\n",
      "Elements on the 3rd column, 3rd to 5th row positions:  [14 20 26]\n",
      "Elements on the 2nd row, 3rd to 5th positions , Elements on the 3rd column, 3rd to 5th row positions :\n",
      "[[14 15 16]\n",
      " [20 21 22]\n",
      " [26 27 28]]\n"
     ]
    }
   ],
   "source": [
    "# Accessing Array Elements \n",
    "arr1 = np.arange(30).reshape(5,6)  \n",
    "print(\"Array 1: \\n\", arr1)  \n",
    "print(\"Elements on the 2nd row, 3rd to 5th positions: \", arr1[1, 2:5])   \n",
    "print(\"Elements on the 3rd column, 3rd to 5th row positions: \", arr1[2:5, 2])   \n",
    "# Elements on the 2nd row, 3rd to 5th positions, Elements on the 3rd column, 3rd to 5th row positions\n",
    "print(\"Elements on the 2nd row, 3rd to 5th positions , Elements on the 3rd column, 3rd to 5th row positions :\")\n",
    "print(arr1[2:5, 2:5])   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array 1: \n",
      " [[[ 0  1  2  3]\n",
      "  [ 4  5  6  7]\n",
      "  [ 8  9 10 11]\n",
      "  [12 13 14 15]]\n",
      "\n",
      " [[16 17 18 19]\n",
      "  [20 21 22 23]\n",
      "  [24 25 26 27]\n",
      "  [28 29 30 31]]\n",
      "\n",
      " [[32 33 34 35]\n",
      "  [36 37 38 39]\n",
      "  [40 41 42 43]\n",
      "  [44 45 46 47]]\n",
      "\n",
      " [[48 49 50 51]\n",
      "  [52 53 54 55]\n",
      "  [56 57 58 59]\n",
      "  [60 61 62 63]]]\n",
      "Elements at the positions: first dimension: 0, second dimension: 3, third dimension: 2 :\n",
      "14\n",
      "Elements at the positions: first dimension: 0, second dimension: positions 1st to 2nd, third dimension: 2 :\n",
      "[2 6]\n"
     ]
    }
   ],
   "source": [
    "# Accessing Array Elements in a 3D array\n",
    "arr1 = np.arange(64).reshape(4,4,4)  \n",
    "print(\"Array 1: \\n\", arr1) \n",
    "print(\"Elements at the positions: first dimension: 0, second dimension: 3, third dimension: 2 :\")\n",
    "print(arr1[0, 3, 2])   \n",
    "print(\"Elements at the positions: first dimension: 0, second dimension: positions 1st to 2nd, third dimension: 2 :\")\n",
    "print(arr1[0, 0:2, 2])   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iterating through an Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing Through Rows:\n",
      "[0 1 2 3 4 5]\n",
      "[ 6  7  8  9 10 11]\n",
      "[12 13 14 15 16 17]\n",
      "[18 19 20 21 22 23]\n",
      "[24 25 26 27 28 29]\n",
      "Printing Through Columns:\n",
      "[ 0  6 12 18 24]\n",
      "[ 1  7 13 19 25]\n",
      "[ 2  8 14 20 26]\n",
      "[ 3  9 15 21 27]\n",
      "[ 4 10 16 22 28]\n",
      "[ 5 11 17 23 29]\n"
     ]
    }
   ],
   "source": [
    "# Iterating through Rows\n",
    "arr1 = np.arange(30).reshape(5,6)  \n",
    "print(\"Printing Through Rows:\")\n",
    "for row in arr1:      # Returns one row at a time - default\n",
    "    print(row)\n",
    "# Iterating through Rows\n",
    "print(\"Printing Through Columns:\")\n",
    "cols = arr1.shape[1]   # Returns number of columns\n",
    "for n in range(cols):\n",
    "    print(arr1[:,n])  # Prints all elements on n-th column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacking Arrays\n",
    "The hstack and vstack functions of Numpy are used to stack multiple arrays together. The stacking can be horizontal (using hstack function) or vertical (using vertical function)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array 1 :\n",
      " [[ 0  1  2  3  4  5]\n",
      " [ 6  7  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29]]\n",
      "Array 2 :\n",
      " [[100 101 102 103 104 105]\n",
      " [106 107 108 109 110 111]\n",
      " [112 113 114 115 116 117]\n",
      " [118 119 120 121 122 123]\n",
      " [124 125 126 127 128 129]]\n",
      "Stacked Array :\n",
      " [[  0   1   2   3   4   5 100 101 102 103 104 105]\n",
      " [  6   7   8   9  10  11 106 107 108 109 110 111]\n",
      " [ 12  13  14  15  16  17 112 113 114 115 116 117]\n",
      " [ 18  19  20  21  22  23 118 119 120 121 122 123]\n",
      " [ 24  25  26  27  28  29 124 125 126 127 128 129]]\n"
     ]
    }
   ],
   "source": [
    "# Horizontal Stacking of Two Arrays\n",
    "arr1 = np.arange(30).reshape(5,6)\n",
    "print(\"Array 1 :\\n\", arr1)\n",
    "arr2 = np.arange(100, 130).reshape(5,6)\n",
    "print(\"Array 2 :\\n\", arr2)\n",
    "print(\"Stacked Array :\\n\", np.hstack((arr1, arr2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array 1 :\n",
      " [[ 0  1  2  3  4  5]\n",
      " [ 6  7  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29]]\n",
      "Array 2 :\n",
      " [[100 101 102 103 104 105]\n",
      " [106 107 108 109 110 111]\n",
      " [112 113 114 115 116 117]\n",
      " [118 119 120 121 122 123]\n",
      " [124 125 126 127 128 129]]\n",
      "Stacked Array :\n",
      " [[  0   1   2   3   4   5]\n",
      " [  6   7   8   9  10  11]\n",
      " [ 12  13  14  15  16  17]\n",
      " [ 18  19  20  21  22  23]\n",
      " [ 24  25  26  27  28  29]\n",
      " [100 101 102 103 104 105]\n",
      " [106 107 108 109 110 111]\n",
      " [112 113 114 115 116 117]\n",
      " [118 119 120 121 122 123]\n",
      " [124 125 126 127 128 129]]\n"
     ]
    }
   ],
   "source": [
    "# Vertical Stacking of Two Arrays\n",
    "arr1 = np.arange(30).reshape(5,6)\n",
    "print(\"Array 1 :\\n\", arr1)\n",
    "arr2 = np.arange(100, 130).reshape(5,6)\n",
    "print(\"Array 2 :\\n\", arr2)\n",
    "print(\"Stacked Array :\\n\", np.vstack((arr1, arr2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting arrays\n",
    "Numpy also allows for arrays to be split vertically or horizontally using the vsplit or hsplit functions. Vsplit and hsplit functions take in arguments of the array name and the number of equal splits that are to be made. The second argument necessitates that the array has a dimension that can be equally split into the specified number of sections. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3  4  5]\n",
      " [ 6  7  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29]\n",
      " [30 31 32 33 34 35]] \n",
      "\n",
      "[[ 0  1  2  3  4  5]\n",
      " [ 6  7  8  9 10 11]\n",
      " [12 13 14 15 16 17]] \n",
      "\n",
      "[[18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29]\n",
      " [30 31 32 33 34 35]]\n"
     ]
    }
   ],
   "source": [
    "# Vertical Splitting of Two Arrays\n",
    "arr1 = np.arange(36).reshape(6,6)\n",
    "print(arr1, '\\n')\n",
    "splt = np.vsplit(arr1, 2)\n",
    "print(splt[0], '\\n')\n",
    "print(splt[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3  4  5]\n",
      " [ 6  7  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29]\n",
      " [30 31 32 33 34 35]] \n",
      "\n",
      "[[ 0  1  2]\n",
      " [ 6  7  8]\n",
      " [12 13 14]\n",
      " [18 19 20]\n",
      " [24 25 26]\n",
      " [30 31 32]] \n",
      "\n",
      "[[ 3  4  5]\n",
      " [ 9 10 11]\n",
      " [15 16 17]\n",
      " [21 22 23]\n",
      " [27 28 29]\n",
      " [33 34 35]]\n"
     ]
    }
   ],
   "source": [
    "# Horizontal Splitting of Two Arrays\n",
    "arr1 = np.arange(36).reshape(6,6)\n",
    "print(arr1, '\\n')\n",
    "splt = np.hsplit(arr1, 2)\n",
    "print(splt[0], '\\n')\n",
    "print(splt[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Array Assignment vs Copy\n",
    "When an array is assigned into another using the “=” operator, it creates a reference to the original array object and does not create a new copy. Hence, any change in either of the original or copy arrays will reflect in the other. Developers need to be particularly mindful of this to avoid any inadvertent mistake while working with array copies. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array 1: \n",
      " [[ 0  1  2  3  4  5]\n",
      " [ 6 99  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29]\n",
      " [30 31 32 33 34 35]]\n",
      "Array 2: \n",
      " [[ 0  1  2  3  4  5]\n",
      " [ 6 99  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29]\n",
      " [30 31 32 33 34 35]]\n"
     ]
    }
   ],
   "source": [
    "# Demonstrate Array assignment\n",
    "arr1 = np.arange(36).reshape(6,6)\n",
    "arr2 = arr1       # arr2 is a reference to arr1, and not a physically different copy\n",
    "arr1[1,1] = 99    # Change reflected in both arr1 and arr2\n",
    "print(\"Array 1: \\n\", arr1)\n",
    "print(\"Array 2: \\n\", arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array 1: \n",
      " [[ 0  1  2  3  4  5]\n",
      " [ 6 99  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29]\n",
      " [30 31 32 33 34 35]]\n",
      "Array 2: \n",
      " [[ 0  1  2  3  4  5]\n",
      " [ 6  7  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29]\n",
      " [30 31 32 33 34 35]]\n"
     ]
    }
   ],
   "source": [
    "# Demostrate Array Copy\n",
    "arr1 = np.arange(36).reshape(6,6)\n",
    "arr2 = arr1.copy()  # A physically different copy of the input array is created \n",
    "arr1[1,1] = 99      # Change reflected in both arr1 and arr2\n",
    "print(\"Array 1: \\n\", arr1)\n",
    "print(\"Array 2: \\n\", arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PANDAS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas is another library that has become almost all pervading in Data Analysis using Python. Pandas offer two powerful Data Structures named, Series and DataFrame. In this chapter we will look into the features and uses of these two data structures in detail. Before that, some of the key points about the Pandas library:\n",
    "1.\tPandas is open source, BSD-licensed. \n",
    "2.\tPandas is built on top of the Numpy Library. \n",
    "3.\tPandas offer Series, a one-dimensional array of labelled or indexed data. In some effect, that makes the Series object act like a Dictionary. \n",
    "4.\tDataFrames are multidimensional arrays. DataFrames provide row and column labels or indexes that can be used for efficient access of rows, columns or elements. \n",
    "5.\tDataFrames allow for working with missing data. \n",
    "6.\tDataFrames act similarly to DataBase tables or spreadsheets and offer similar features to them such as data grouping, group operations, filtering, efficient condition-based data manipulations, creating pivot tables, etc.  \n",
    "7.\tPandas offer features to manipulate data structures and interoperability between various data structures. \n",
    "8.\tPandas offer efficient date/time operations and handling of time-indexed data.   \n",
    "9.\tIn both Series and DataFrame, indexes can be explicitly defined and manipulated. \n",
    "10.\tPandas offer integration with several data/file formats such as csv, excel, json. Import from or export to these data formats are efficient and easy. \n",
    "11.\tPandas also offer integration with various visualization tools such as matplotlib or seaborn libraries. \n",
    "Pandas data structures and operations are particularly useful when working with tabular data similar to databases or spreadsheets and similar operations are needed. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# $ pip install pandas\n",
    "# or\n",
    "# $ brew install pandas  \n",
    "# Or \n",
    "# $ conda install pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas  \n",
    "# or \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas Series Object "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas Series is a one-dimensional indexed object that is mutable, iterable and can hold elements of various types. Each Series item is indexed – by default, elements assume a serial numeric index starting with 0. Indexes can be specified explicitly as well. A key difference between a Numpy array and Pandas Series is the use of index. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Series Objects\n",
    "A Series object can be created using the pandas.Series method. A Series can be created by specifying explicit values or importing values from other collection or iterable objects. Alongside the values, we may also want to specify corresponding Index values unless we want Pandas to assign them automatically. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    3\n",
      "1    4\n",
      "2    8\n",
      "3    9\n",
      "4    5\n",
      "dtype: int64\n",
      "a    3\n",
      "b    4\n",
      "c    8\n",
      "d    9\n",
      "e    5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Create my first Series Object\n",
    "ser1 = pd.Series([3, 4, 8, 9, 5])\n",
    "print(ser1)\n",
    "ser1 = pd.Series([3, 4, 8, 9, 5], index=['a', 'b', 'c', 'd', 'e'])   # Specify the Index values\n",
    "print(ser1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "3    4\n",
      "4    5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Create a Series Object using a Numpy Array\n",
    "arr = np.array([1,2,3,4,5])\n",
    "ser1 = pd.Series(arr)\n",
    "print(ser1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Import the whole Dictionary: \n",
      " a    1\n",
      "b    2\n",
      "c    3\n",
      "d    4\n",
      "dtype: int64\n",
      "Import Only specified Indexes from teh dictionary: \n",
      " c    3\n",
      "b    2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Create a Series Object using a Dictionary\n",
    "dict1 = {'a':1, 'b':2, 'c':3, 'd':4}\n",
    "ser1 = pd.Series(dict1)\n",
    "print(\"Import the whole Dictionary: \\n\", ser1)\n",
    "ser1 = pd.Series(dict1, index=['c', 'b'])   # Explicitely mention which keys to import\n",
    "print(\"Import Only specified Indexes from teh dictionary: \\n\", ser1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accessing Series Elements\n",
    "Series objects can be accessed by specifying index values of the position of the element to be accessed. Slice of the Series object can also be accessed by specifying the range. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the whole Series:\n",
      " a    3\n",
      "b    4\n",
      "c    8\n",
      "d    9\n",
      "e    5\n",
      "dtype: int64\n",
      "Print the 3rd Element:\n",
      " 8\n",
      "Print the elements between the 2nd to 4th positions:\n",
      " b    4\n",
      "c    8\n",
      "d    9\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Access elements of a Series object\n",
    "ser1 = pd.Series([3, 4, 8, 9, 5], index=['a', 'b', 'c', 'd', 'e'])   \n",
    "print(\"Print the whole Series:\\n\", ser1)\n",
    "print(\"Print the 3rd Element:\\n\", ser1[2])   # Access a single element\n",
    "print(\"Print the elements between the 2nd to 4th positions:\\n\", ser1[1:4])   # Access a slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print a slice using explicite indexes: \n",
      " b    4\n",
      "c    8\n",
      "d    9\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Access elements of a Series object by specifying Index values\n",
    "ser1 = pd.Series([3, 4, 8, 9, 5], index=['a', 'b', 'c', 'd', 'e'])   \n",
    "print(\"Print a slice using explicite indexes: \\n\", ser1['b':'d'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accessing Series objects using ‘loc’ and ‘iloc’ Indexers\n",
    "Python provides a host of Indexers for selection and accessing of elements from Series and DataFrame objects. In the this section, we discuss how we can extract single or range of elements from a Series using the ‘loc’ and ‘iloc’ indexer methods. \n",
    "Python provides the ‘loc’ method which takes explicit or label-based index values or range of indexe values to access corresponding elements. The explicit Index labels are the ones that are mentioned explicitly for the Series object. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print a single element using explicit indexes with 'loc': \n",
      " 4\n",
      "Print a slice of elements using explicit indexes with 'loc': \n",
      " b    4\n",
      "c    8\n",
      "d    9\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Access elements of a Series object by using the 'loc' \n",
    "ser1 = pd.Series([3, 4, 8, 9, 5], index=['a', 'b', 'c', 'd', 'e'])   \n",
    "print(\"Print a single element using explicit indexes with 'loc': \\n\", ser1.loc['b'])\n",
    "print(\"Print a slice of elements using explicit indexes with 'loc': \\n\", ser1.loc['b':'d'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implicit or Integer based Indexing using ‘iloc’\n",
    "The ‘iloc’ indexer takes implicit index or range of indexes to access corresponding elements. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print a single element using implicit indexes with 'iloc': \n",
      " 8\n",
      "Print a slice of elements using implicit indexes with 'iloc': \n",
      " b    4\n",
      "c    8\n",
      "d    9\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Access elements of a Series object by using the 'iloc' \n",
    "ser1 = pd.Series([3, 4, 8, 9, 5], index=['a', 'b', 'c', 'd', 'e'])   \n",
    "print(\"Print a single element using implicit indexes with 'iloc': \\n\", ser1.iloc[2])\n",
    "print(\"Print a slice of elements using implicit indexes with 'iloc': \\n\", ser1.iloc[1:4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implicit or Integer based Indexing using ‘iat’\n",
    "Another indexer provided by Pandas is the ‘iat’ indexer, that is similar to ‘iloc’, but can only extract single elements. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    }
   ],
   "source": [
    "# Access single element of a Series object by using the 'iat' \n",
    "ser1 = pd.Series([3, 4, 8, 9, 5], index=['a', 'b', 'c', 'd', 'e'])   \n",
    "print(ser1.iat[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series Object Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<PandasArray>\n",
      "[3, 4, 8, 9, 5]\n",
      "Length: 5, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "### The ‘array’ attribute returns the array equivalent of the Series object. \n",
    "# Return the Python Array equivalent of the Series\n",
    "arr1 = ser1.array\n",
    "print(arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Index(['a', 'b', 'c', 'd', 'e'], dtype='object')]\n"
     ]
    }
   ],
   "source": [
    "## 'Axes' attritute Returns the Rw Axes Label, i.e. for a Series object, returns the index labels.\n",
    "# Return the Row Axes labels\n",
    "axes1 = ser1.axes\n",
    "print(axes1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dtype of ser1 -  int64\n",
      "Dtype of ser2 -  object\n",
      "Dtype of ser3 -  float64\n",
      "Dtype of ser4 -  object\n"
     ]
    }
   ],
   "source": [
    "## The dtypes attributes return the datatype of the underlying values in the SSeries. In case of heterogeneity of data types in the Series, dtypes return an Object type. \n",
    "# Return the datatype of the underlying data with dtypes attribute\n",
    "ser1 = pd.Series([3, 4, 8, 9, 5])     # All elements are of type int\n",
    "ser2 = pd.Series(['a', 'b', 'c', 'd', 'e'])   # All elements are of type char\n",
    "ser3 = pd.Series([12.3, 34.56, 45.999])    # All elements are of type float\n",
    "ser4 = pd.Series([1, 's', 12.3, True])\n",
    "print(\"Dtype of ser1 - \", ser1.dtypes)\n",
    "print(\"Dtype of ser2 - \", ser2.dtypes)\n",
    "print(\"Dtype of ser3 - \", ser3.dtypes)\n",
    "print(\"Dtype of ser4 - \", ser4.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check whether ser1 has NaN values:  False\n",
      "Print ser1 \n",
      " 0    3.0\n",
      "1    4.0\n",
      "2    NaN\n",
      "3    9.0\n",
      "4    5.0\n",
      "dtype: float64\n",
      "Check whether ser1 has NaN values:  True\n"
     ]
    }
   ],
   "source": [
    "## The hasnans attribute return whether or not there is any NaN value in the Series. NaN in Python data structures denote a missing value. \n",
    "# Return True if the Series has any NaN values, otherwise False\n",
    "ser1 = pd.Series([3, 4, 8, 9, 5])     \n",
    "print(\"Check whether ser1 has NaN values: \", ser1.hasnans)\n",
    "ser1[2] = np.nan     # Asssign a NaN value to an element in ser1\n",
    "print(\"Print ser1 \\n\", ser1)\n",
    "print(\"Check whether ser1 has NaN values: \", ser1.hasnans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['a', 'b', 'c', 'd', 'e'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "## Returns the index values of the Series object.\n",
    "# Print the Index labels of the Series object\n",
    "ser1 = pd.Series([3, 4, 8, 9, 5], index=['a', 'b', 'c', 'd', 'e'])   \n",
    "print(ser1.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check if ser1 is monotonic Increasing  True\n",
      "Check if ser1 is monotonic Increasing after the change  False\n"
     ]
    }
   ],
   "source": [
    "## Returns Boolean result of if the values of the elements in the Series are increasing monotonic.  \n",
    "# Print if the Series values are monotonic increasing\n",
    "ser1 = pd.Series([1, 2, 3, 4, 5, 6])   \n",
    "print(\"Check if ser1 is monotonic Increasing \", ser1.is_monotonic)\n",
    "ser1 = pd.Series([1, 2, 3, 4, 7, 5, 6])   \n",
    "print(\"Check if ser1 is monotonic Increasing after the change \", ser1.is_monotonic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Returns True if the values of the Series elements are unique, otherwise False.\n",
    "# Print if the values of the Series are unique\n",
    "ser1 = pd.Series([1, 2, 2, 4, 4, 6])   \n",
    "ser1.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Returns the total number of memory bytes occupied by the Series. \n",
    "# Print the total storage bytes taken by ser1\n",
    "ser1 = pd.Series([1, 2, 2, 4, 4, 6])   \n",
    "ser1.nbytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Returns the size of the Object in terms of total number of elements. \n",
    "# Print the size of the Series object\n",
    "ser1 = pd.Series([1, 2, 2, 4, 4, 6])   \n",
    "ser1.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 2, 4, 4, 6])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Returns the values of the elements.\n",
    "# Print the values of the Series object\n",
    "ser1 = pd.Series([1, 2, 2, 4, 4, 6])   \n",
    "ser1.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series Object Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    11\n",
      "1    22\n",
      "2    22\n",
      "3    44\n",
      "4    44\n",
      "5    66\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "## The ‘add’ method adds corresponding elements from two Series objects. \n",
    "\n",
    "# Check out the 'add' method to add corresponding elements from two Series objects\n",
    "ser1 = pd.Series([1, 2, 2, 4, 4, 6])   \n",
    "ser2 = pd.Series([10, 20, 20, 40, 40, 60])   \n",
    "print(ser1.add(ser2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     1\n",
      "1     2\n",
      "2     2\n",
      "3     4\n",
      "4     4\n",
      "5     6\n",
      "0    10\n",
      "1    20\n",
      "2    20\n",
      "3    40\n",
      "4    40\n",
      "5    60\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "## The ‘append’ method concatenates one Series object at the end of the other. \n",
    "\n",
    "# Use of the 'append' method to concatenate (add one after the other) \n",
    "ser1 = pd.Series([1, 2, 2, 4, 4, 6])   \n",
    "ser2 = pd.Series([10, 20, 20, 40, 40, 60])   \n",
    "print(ser1.append(ser2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1', '2', '2', '4', '4', '6']\n"
     ]
    }
   ],
   "source": [
    "## The ‘astype’ method is used to cast the elements of a Pandas Series to another Data Type. In the following example, we are converting int elements to a string type. \n",
    "\n",
    "# Use of the 'astype' method to cast the elements to another data type \n",
    "ser1 = pd.Series([1, 2, 2, 4, 4, 6])   \n",
    "print(list(ser1.astype(str)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series: \n",
      " 2020-04-01 00:00:00        Sleep\n",
      "2020-04-01 06:00:00    Breakfast\n",
      "2020-04-01 12:00:00        Lunch\n",
      "2020-04-01 18:00:00       Snacks\n",
      "2020-04-02 00:00:00       Dinner\n",
      "Freq: 6H, dtype: object\n",
      "\n",
      " 2020-04-01 18:00:00    Snacks\n",
      "Freq: 6H, dtype: object\n"
     ]
    }
   ],
   "source": [
    "## Extracts values of an element at a specified date/time in a Date/Time Index Series.\n",
    "\n",
    "# Filter/Extract specific elements with date/time index Series\n",
    "dt_tm = pd.date_range('2020-04-01', periods=5, freq='6H')\n",
    "ser1 = pd.Series(['Sleep', 'Breakfast', 'Lunch', 'Snacks', 'Dinner'], index=dt_tm)\n",
    "print(\"Series: \\n\", ser1)\n",
    "print(\"\\n\", ser1.at_time('18:00'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    False\n",
      "1     True\n",
      "2     True\n",
      "3     True\n",
      "4    False\n",
      "5    False\n",
      "dtype: bool\n"
     ]
    }
   ],
   "source": [
    "## The ‘between’ method returns a Boolean vector corresponding to all the elements in the passed Series as to whether or not each of the elements are between two other passed values. \n",
    "\n",
    "# Use the 'between' method to create a Boolean vector corresponding to all the elements in the passed Series \n",
    "# as to whether or not each of the elements are between two other passed values. \n",
    "ser1 = pd.Series([1, 2, 3, 4, 5, 6])\n",
    "print(ser1.between(2,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ser1: \n",
      " 0    3\n",
      "1    4\n",
      "2    8\n",
      "dtype: int64\n",
      "Ser2 - Deep Copy: \n",
      " 0    3\n",
      "1    4\n",
      "2    5\n",
      "dtype: int64\n",
      "Ser3 - Shallow Copy: \n",
      " 0    3\n",
      "1    4\n",
      "2    8\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Copies one Series to a new one.\n",
    "# Deep copy: Creates a new object with values and indexes of the original Series. Change in any element in the \n",
    "# original Series will not reflect in the Deep copy.\n",
    "# Shallow Copy: Creates a new object with references of values and indexes of the original Series. \n",
    "# Change in any element in the original Series will reflect in the Shallow copy.\n",
    "\n",
    "# Series Deep Copy & Shallow Copy\n",
    "ser1 = pd.Series([3, 4, 5])\n",
    "ser2 = ser1.copy(deep=True)\n",
    "ser1[2] = 8\n",
    "print(\"Ser1: \\n\", ser1)\n",
    "print(\"Ser2 - Deep Copy: \\n\", ser2)\n",
    "ser3 = ser1.copy(deep=False)\n",
    "ser1[2] = 8\n",
    "print(\"Ser3 - Shallow Copy: \\n\", ser3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "## Returns the number of elements in the Series.\n",
    "\n",
    "# Count of Elements\n",
    "ser1 = pd.Series([3, 4, 5])\n",
    "print(ser1.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     6.000000\n",
       "mean     15.166667\n",
       "std       4.167333\n",
       "min      10.000000\n",
       "25%      12.500000\n",
       "50%      14.500000\n",
       "75%      18.000000\n",
       "max      21.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Prints the Descriptive statistics of the Series elements.\n",
    "\n",
    "# Print Discriptive Statistics of the Series elements\n",
    "ser1 = pd.Series([12, 21, 14, 10, 15, 19])   \n",
    "ser1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.50\n",
      "1    0.20\n",
      "2    2.60\n",
      "3    1.60\n",
      "4    1.18\n",
      "5    0.75\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "## Divides corresponding elements from two Series Objects:\n",
    "\n",
    "# 'div' method divides corresponding elements from two Series objects\n",
    "ser1 = pd.Series([5, 4, 52, 64, 47, 45])   \n",
    "ser2 = pd.Series([10, 20, 20, 40, 40, 60])   \n",
    "print(round(ser1.div(ser2),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    3\n",
      "b    4\n",
      "d    9\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "## The ‘drop’ method removes the specified elements from a Series object. You can mention multiple labels/indexes as a List object in the parameter. It returns the updated Series after the drop operation. \n",
    "\n",
    "# Drop elements with specified label(s)\n",
    "ser1 = pd.Series([3, 4, 8, 9, 5], index=['a', 'b', 'c', 'd', 'e'])   \n",
    "ser2 = ser1.drop(labels=['c', 'e'])\n",
    "print(ser2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print ser1 \n",
      " 0    3.0\n",
      "1    4.0\n",
      "2    NaN\n",
      "3    9.0\n",
      "4    5.0\n",
      "dtype: float64\n",
      "Check the Series after dropping NaNs: \n",
      " 0    3.0\n",
      "1    4.0\n",
      "3    9.0\n",
      "4    5.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "## The ‘dropna’ method drops the ‘NaN’ (Null or missing) values from the Series.\n",
    "\n",
    "# Drops the NaN elements in a Series\n",
    "ser1 = pd.Series([3, 4, 8, 9, 5])     \n",
    "ser1[2] = np.nan     # Asssign a NaN value to an element in ser1\n",
    "print(\"Print ser1 \\n\", ser1)\n",
    "print(\"Check the Series after dropping NaNs: \\n\", ser1.dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "## Checks if two Series objects passed to it are equal.\n",
    "\n",
    "# Validates if two Series objects are equal \n",
    "ser1 = pd.Series([3, 4, 8, 9, 5]) \n",
    "ser2 = pd.Series([3, 4, 87, 9, 5]) \n",
    "print(ser1.equals(ser2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print ser1 \n",
      " 0    3.0\n",
      "1    4.0\n",
      "2    NaN\n",
      "3    9.0\n",
      "4    5.0\n",
      "dtype: float64\n",
      "Check the Series after filling NaNs: \n",
      " 0     3.0\n",
      "1     4.0\n",
      "2    12.0\n",
      "3     9.0\n",
      "4     5.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "## Fills the NaN values in the Series object with a specified value.\n",
    "\n",
    "# Fills the NaN elements in a Series with a Specified value\n",
    "ser1 = pd.Series([3, 4, 8, 9, 5])     \n",
    "ser1[2] = np.nan     # Asssign a NaN value to an element in ser1\n",
    "print(\"Print ser1 \\n\", ser1)\n",
    "print(\"Check the Series after filling NaNs: \\n\", ser1.fillna(12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0\n",
      "1    0\n",
      "2    2\n",
      "3    1\n",
      "4    1\n",
      "5    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "## The 'floordiv' method divides corresponding elements from two Series objects and returns the interger of the quotient. \n",
    "\n",
    "# 'floordiv' method divides corresponding elements from two Series objects and returns the interger of the quotient\n",
    "ser1 = pd.Series([5, 4, 52, 64, 47, 45])   \n",
    "ser2 = pd.Series([10, 20, 20, 40, 40, 60])   \n",
    "print(ser1.floordiv(ser2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HR       242\n",
       "SALES    196\n",
       "dtype: int64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## The ‘groupby’ method groups elements of the same label together to enable performing a group operation such as mean, sum, etc. In the following example, we find the ‘mean’ of all the elements with HR and SALES labels respectively. Note, in this example, we also establish that Series elements can have same labels. \n",
    "\n",
    "# Grouping of series elements\n",
    "ser1 = pd.Series([23, 34, 56, 76, 53, 54, 55, 87], index=['HR', 'HR', 'SALES', 'HR', 'SALES', 'HR', 'HR', 'SALES'])\n",
    "ser1.groupby(level=0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5\n",
       "1     4\n",
       "2    52\n",
       "3    64\n",
       "dtype: int64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Print the first 5 elements from a Series. Usually used on Series with large numbers for ease of visual inspection. \n",
    "\n",
    "# Print the first 5 elements \n",
    "ser1 = pd.Series([5, 4, 52, 64, 47, 45, 20, 55, 54, 42, 88]) \n",
    "ser1.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'QLTY'"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## The ‘idxmax’ method returns the label or index of the maximum value in a Series. \n",
    "\n",
    "# Return the label of the maximum value\n",
    "ser1 = pd.Series([23, 34, 56, 76, 53, 54, 55, 87], index=['HR', 'FIN', 'SALES', 'MKTG', 'MANU', 'ADVT', 'OPS', 'QLTY'])\n",
    "ser1.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'HR'"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## The ‘idxmin’ method returns the label or index of the minimum value in a Series. \n",
    "\n",
    "# Return the label of the minimum value\n",
    "ser1 = pd.Series([23, 34, 56, 76, 53, 54, 55, 87], index=['HR', 'FIN', 'SALES', 'MKTG', 'MANU', 'ADVT', 'OPS', 'QLTY'])\n",
    "ser1.idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "6    False\n",
       "7     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Returns a Series/vector of whether a specified value equals to each of the corresponding Series object elements.\n",
    "## In the next example, we check if 87 is part of ser1 and it checks agains each element and returns a Boolean vector. \n",
    "\n",
    "# Check for membership (if it is part of) of a specified value in a Series object\n",
    "ser1 = pd.Series([23, 34, 56, 76, 53, 54, 55, 87])\n",
    "ser1.isin([87])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "4    False\n",
       "5    False\n",
       "6    False\n",
       "7    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checks each element in a Series on whether it is a NaN value and returns a Series/vector of Boolean results. \n",
    "\n",
    "# Check for existance of NaN value against each of the elements\n",
    "ser1 = pd.Series([23, 34, 56, None, 53, 54, 55, 87])\n",
    "ser1.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Maximum Value element:  87\n",
      "The Minimum Value element:  23\n",
      "The Mean of all elements:  50.625\n",
      "The Median value among all elements:  53.5\n"
     ]
    }
   ],
   "source": [
    "## Return the ‘max’, ‘mean’, ‘min’ and ‘median’ of the values from the Series object.\n",
    "\n",
    "# Return 'max', 'min', 'mean', 'median' values of a Series\n",
    "ser1 = pd.Series([23, 34, 56, 43, 53, 54, 55, 87])\n",
    "print(\"The Maximum Value element: \", ser1.max())\n",
    "print(\"The Minimum Value element: \", ser1.min())\n",
    "print(\"The Mean of all elements: \", ser1.mean())\n",
    "print(\"The Median value among all elements: \", ser1.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     10\n",
      "1     40\n",
      "2     40\n",
      "3    160\n",
      "4    160\n",
      "5    360\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "## The ‘multiply method multiplies corresponding elements from two Series objects. \n",
    "\n",
    "#  Use of the 'multiply'' method to multiply corresponding elements from two Series objects\n",
    "ser1 = pd.Series([1, 2, 2, 4, 4, 6])   \n",
    "ser2 = pd.Series([10, 20, 20, 40, 40, 60])   \n",
    "print(ser1.multiply(ser2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing ser2: \n",
      " 0    23\n",
      "1    55\n",
      "2    56\n",
      "3    66\n",
      "4    53\n",
      "5    54\n",
      "6    55\n",
      "7    87\n",
      "dtype: int64\n",
      "Printing ser1 after replacing inplace:\n",
      " 0    23\n",
      "1    55\n",
      "2    56\n",
      "3    66\n",
      "4    53\n",
      "5    54\n",
      "6    55\n",
      "7    87\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "## The ‘replace’ method replaces a specified value in the Series with another. The ‘replace’ method returns a \n",
    "## modified Series and the original series remains unchanged. The ‘inplace’ parameter has to be set to True \n",
    "## if the original Series object is to be updated. \n",
    "\n",
    "# Replace a spcified value in the Series with another\n",
    "ser1 = pd.Series([23, 34, 56, 66, 53, 54, 55, 87])\n",
    "ser2 = ser1.replace({34:55})\n",
    "print(\"Printing ser2: \\n\", ser2)\n",
    "ser1.replace({34:55}, inplace=True)\n",
    "print(\"Printing ser1 after replacing inplace:\\n\", ser1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    23.86\n",
      "1    34.76\n",
      "2    56.53\n",
      "3    43.10\n",
      "4    53.64\n",
      "5     5.88\n",
      "6     5.86\n",
      "7     8.66\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "## The ‘round’ method rounds off the series elements to a specified position. In the following example, it rounds off to 2nd decimal place. \n",
    "\n",
    "# Rounding off Series elements \n",
    "ser1 = pd.Series([23.8567876, 34.76465354, 56.53439, 43.09775454, 53.63654, 5.8755424, 5.858765, 8.656547])\n",
    "print(ser1.round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19.27\n"
     ]
    }
   ],
   "source": [
    "## The ‘std’ method returns the Standard Deviation of all the elements of a Series Object.\n",
    "\n",
    "# Find the standard deviation using the std method\n",
    "ser1 = pd.Series([23, 34, 56, 66, 53, 54, 55, 87])\n",
    "stdv = ser1.std()\n",
    "print(round(stdv, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       HR\n",
      "1      FIN\n",
      "2    SALES\n",
      "3     YKTG\n",
      "4     YANU\n",
      "5     ADVT\n",
      "6      OPS\n",
      "7     QLTY\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "## ‘str’ enables String functions to be applied on Series objects.\n",
    "\n",
    "# Enables operation of string functions on string type elements \n",
    "ser1 = pd.Series(['HR', 'FIN', 'SALES', 'MKTG', 'MANU', 'ADVT', 'OPS', 'QLTY'])\n",
    "ser1_str = ser1.str.replace('M', 'Y')\n",
    "print(ser1_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      4\n",
      "1      1\n",
      "2     17\n",
      "3     31\n",
      "4    -42\n",
      "5    -12\n",
      "6    -26\n",
      "7     33\n",
      "8     20\n",
      "9     10\n",
      "10    44\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "## The ‘sub’ function subtracts numeric elements of one Series element from corresponding elements of the original one.\n",
    "\n",
    "# Subtraction of one Series elements from another\n",
    "ser1 = pd.Series([5, 4, 52, 64, 47, 45, 20, 55, 54, 42, 88]) \n",
    "ser2 = pd.Series([1, 3, 35, 33, 89, 57, 46, 22, 34, 32, 44]) \n",
    "print(ser1.sub(ser2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HR': 23, 'FIN': 34, 'SALES': 56, 'MKTG': 76, 'MANU': 53, 'ADVT': 54, 'OPS': 55, 'QLTY': 87}\n"
     ]
    }
   ],
   "source": [
    "## The ‘to_dict’ method converts a Series object to a Python Dictionary. The Indexes get converted into ‘key’s and the Series element values are copied as ‘values’ into the output dictionary. \n",
    "\n",
    "# Convert a Series object to a Dictionary \n",
    "ser1 = pd.Series([23, 34, 56, 76, 53, 54, 55, 87], index=['HR', 'FIN', 'SALES', 'MKTG', 'MANU', 'ADVT', 'OPS', 'QLTY'])\n",
    "ser1dict = ser1.to_dict()\n",
    "print(ser1dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "## The ‘to_excel’ method converts a Series object to an Excel sheet – external file.\n",
    "\n",
    "# Convert a Series object to an Excel sheet (External file)\n",
    "ser1 = pd.Series([23, 34, 56, 76, 53, 54, 55, 87], index=['HR', 'FIN', 'SALES', 'MKTG', 'MANU', 'ADVT', 'OPS', 'QLTY'])\n",
    "ser1dict = ser1.to_excel('ser1.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[23, 34, 56, 76, 53, 54, 55, 87]\n"
     ]
    }
   ],
   "source": [
    "## The ‘to_list’ method converts a Series object to a List object. Note that in the conversion, \n",
    "## we lose the Series ‘indexes’. Only the values get converted as the List elements. \n",
    "\n",
    "# Convert a Series object to a List object\n",
    "ser1 = pd.Series([23, 34, 56, 76, 53, 54, 55, 87], index=['HR', 'FIN', 'SALES', 'MKTG', 'MANU', 'ADVT', 'OPS', 'QLTY'])\n",
    "ser1lst = ser1.to_list()\n",
    "print(ser1lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3    76\n",
      "4    53\n",
      "5    54\n",
      "6    55\n",
      "7    87\n",
      "dtype: int64\n",
      "0    23\n",
      "1    34\n",
      "2    56\n",
      "3    76\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "## The ‘truncate’ method truncates a Series object based on a ‘before’ or ‘after’ index values. ‘before=3’ \n",
    "## parameter indicates truncation of all elements before index number 3. ‘after=3’ indicates truncation of all \n",
    "## elements after the index value 3. \n",
    "\n",
    "# Truncate a Series Object\n",
    "ser1 = pd.Series([23, 34, 56, 76, 53, 54, 55, 87])\n",
    "sert1 = ser1.truncate(before=3)\n",
    "print(sert1)\n",
    "sert2 = ser1.truncate(after=3)\n",
    "print(sert2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[23 34 76 53 55 87]\n"
     ]
    }
   ],
   "source": [
    "## The ‘unique’ method returns unique elements from the Series object after removing all the duplicates.\n",
    "\n",
    "# Find Unique elements in a Series\n",
    "ser1 = pd.Series([23, 34, 23, 76, 53, 53, 55, 87])\n",
    "ser1unq = ser1.unique()\n",
    "print(ser1unq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "542.86\n"
     ]
    }
   ],
   "source": [
    "## The ‘var’ method finds the Variance of all numeric elements in a Series. \n",
    "\n",
    "# Find Variance of all elements in a Series\n",
    "ser1 = pd.Series([23, 34, 23, 76, 53, 53, 55, 87])\n",
    "variance = ser1.var()\n",
    "print(round(variance, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MULTI-INDEXED SERIES OBJECTS\n",
    "In scenarios when we are required to have multi-level indexes for values/elements, a smart way provided by Pandas is the Multi-indexed Series objects. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tendulkar   Tests    16000\n",
      "            ODIs     18000\n",
      "Sangakkara  Tests    12000\n",
      "            ODIs     14000\n",
      "Ponting     Tests    13000\n",
      "            ODIs     13000\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Create a Multi-level Index Series Object\n",
    "index1 = [('Tendulkar', 'Tests'), ('Tendulkar', 'ODIs'), ('Sangakkara', 'Tests'), \n",
    "         ('Sangakkara', 'ODIs'), ('Ponting', 'Tests'), ('Ponting', 'ODIs')]\n",
    "runs = [16000, 18000, 12000, 14000, 13000, 13000]\n",
    "index = pd.MultiIndex.from_tuples(index)\n",
    "records = pd.Series(runs, index=index1)\n",
    "print(records)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing Multi-Level Series Elements\n",
    "The usual methods of accessing Series elements apply. Here, we are showing examples of ‘loc’, ‘iloc’ and direct index methods for accessing Multi-Indexed Series elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing all of Sangakkara's Records: \n",
      " Tests    12000\n",
      "ODIs     14000\n",
      "dtype: int64\n",
      "Printing Tendulkar's ODI record: \n",
      " 18000\n",
      "Printing Ponting's Test record: \n",
      " 13000\n",
      "Printing Ponting's Test record - direct Index Access: \n",
      " 13000\n"
     ]
    }
   ],
   "source": [
    "print(\"Printing all of Sangakkara's Records: \\n\", records.loc['Sangakkara'])\n",
    "print(\"Printing Tendulkar's ODI record: \\n\", records.loc['Tendulkar', 'ODIs'])\n",
    "print(\"Printing Ponting's Test record: \\n\", records.iloc[4])\n",
    "print(\"Printing Ponting's Test record - direct Index Access: \\n\", records[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with DataFrame Objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Methods of Creating DataFrames\n",
    "# - Reading a .csv file into a DataFrame\n",
    "# - From a Dictionary\n",
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>3.545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>24.40</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.5</td>\n",
       "      <td>67.6</td>\n",
       "      <td>176.6</td>\n",
       "      <td>3.042</td>\n",
       "      <td>15.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>66.498812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>15.245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>27.50</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>185.9</td>\n",
       "      <td>3.208</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.654495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>17.531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>186.2</td>\n",
       "      <td>3.259</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.155978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3.493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.50</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>185.7</td>\n",
       "      <td>3.601</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.623357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>18.969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.00</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>157 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "0                16.919        16.360    Passenger               21.50   \n",
       "1                39.384        19.875    Passenger               28.40   \n",
       "2                14.114        18.225    Passenger                 NaN   \n",
       "3                 8.588        29.725    Passenger               42.00   \n",
       "4                20.397        22.255    Passenger               23.99   \n",
       "..                  ...           ...          ...                 ...   \n",
       "152               3.545           NaN    Passenger               24.40   \n",
       "153              15.245           NaN    Passenger               27.50   \n",
       "154              17.531           NaN    Passenger               28.80   \n",
       "155               3.493           NaN    Passenger               45.50   \n",
       "156              18.969           NaN    Passenger               36.00   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "0            1.8       140.0      101.2   67.3   172.4        2.639   \n",
       "1            3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2            3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "3            3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "4            1.8       150.0      102.6   68.2   178.0        2.998   \n",
       "..           ...         ...        ...    ...     ...          ...   \n",
       "152          1.9       160.0      100.5   67.6   176.6        3.042   \n",
       "153          2.4       168.0      104.9   69.3   185.9        3.208   \n",
       "154          2.4       168.0      104.9   69.3   186.2        3.259   \n",
       "155          2.3       236.0      104.9   71.5   185.7        3.601   \n",
       "156          2.9       201.0      109.9   72.1   189.8        3.600   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "0             13.2             28.0          58.280150  \n",
       "1             17.2             25.0          91.370778  \n",
       "2             17.2             26.0                NaN  \n",
       "3             18.0             22.0          91.389779  \n",
       "4             16.4             27.0          62.777639  \n",
       "..             ...              ...                ...  \n",
       "152           15.8             25.0          66.498812  \n",
       "153           17.9             25.0          70.654495  \n",
       "154           17.9             25.0          71.155978  \n",
       "155           18.5             23.0         101.623357  \n",
       "156           21.1             24.0          85.735655  \n",
       "\n",
       "[157 rows x 13 columns]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing a .csv file into a DataFrame\n",
    "\n",
    "csv_df = pd.read_csv(\"car_sales.csv\")\n",
    "csv_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>3.545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>24.40</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.5</td>\n",
       "      <td>67.6</td>\n",
       "      <td>176.6</td>\n",
       "      <td>3.042</td>\n",
       "      <td>15.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>66.498812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>15.245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>27.50</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>185.9</td>\n",
       "      <td>3.208</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.654495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>17.531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>186.2</td>\n",
       "      <td>3.259</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.155978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3.493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.50</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>185.7</td>\n",
       "      <td>3.601</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.623357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>18.969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.00</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>157 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "0                16.919        16.360    Passenger               21.50   \n",
       "1                39.384        19.875    Passenger               28.40   \n",
       "2                14.114        18.225    Passenger                 NaN   \n",
       "3                 8.588        29.725    Passenger               42.00   \n",
       "4                20.397        22.255    Passenger               23.99   \n",
       "..                  ...           ...          ...                 ...   \n",
       "152               3.545           NaN    Passenger               24.40   \n",
       "153              15.245           NaN    Passenger               27.50   \n",
       "154              17.531           NaN    Passenger               28.80   \n",
       "155               3.493           NaN    Passenger               45.50   \n",
       "156              18.969           NaN    Passenger               36.00   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "0            1.8       140.0      101.2   67.3   172.4        2.639   \n",
       "1            3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2            3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "3            3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "4            1.8       150.0      102.6   68.2   178.0        2.998   \n",
       "..           ...         ...        ...    ...     ...          ...   \n",
       "152          1.9       160.0      100.5   67.6   176.6        3.042   \n",
       "153          2.4       168.0      104.9   69.3   185.9        3.208   \n",
       "154          2.4       168.0      104.9   69.3   186.2        3.259   \n",
       "155          2.3       236.0      104.9   71.5   185.7        3.601   \n",
       "156          2.9       201.0      109.9   72.1   189.8        3.600   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "0             13.2             28.0          58.280150  \n",
       "1             17.2             25.0          91.370778  \n",
       "2             17.2             26.0                NaN  \n",
       "3             18.0             22.0          91.389779  \n",
       "4             16.4             27.0          62.777639  \n",
       "..             ...              ...                ...  \n",
       "152           15.8             25.0          66.498812  \n",
       "153           17.9             25.0          70.654495  \n",
       "154           17.9             25.0          71.155978  \n",
       "155           18.5             23.0         101.623357  \n",
       "156           21.1             24.0          85.735655  \n",
       "\n",
       "[157 rows x 13 columns]"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing a .xls file into a DataFrame\n",
    "\n",
    "xls_df = pd.read_excel(\"car_sales.xls\")\n",
    "xls_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Avg_Yearly_Sale</th>\n",
       "      <th>Best_Selling_Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ford</td>\n",
       "      <td>20000</td>\n",
       "      <td>Ecosport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>22000</td>\n",
       "      <td>Fortuner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Renault</td>\n",
       "      <td>31000</td>\n",
       "      <td>Duster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tata</td>\n",
       "      <td>27000</td>\n",
       "      <td>Nexon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mahindra</td>\n",
       "      <td>19000</td>\n",
       "      <td>Scorpio</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Brand  Avg_Yearly_Sale Best_Selling_Model\n",
       "0      Ford            20000           Ecosport\n",
       "1    Toyota            22000           Fortuner\n",
       "2   Renault            31000             Duster\n",
       "3      Tata            27000              Nexon\n",
       "4  Mahindra            19000            Scorpio"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame from a Dictionary\n",
    "\n",
    "dict = {\n",
    "    'Car_Brand': ['Ford', 'Toyota', 'Renault', 'Tata', 'Mahindra'], \n",
    "    'Avg_Yearly_Sale': [20000, 22000, 31000, 27000, 19000],\n",
    "    'Best_Selling_Model': ['Ecosport', 'Fortuner', 'Duster', 'Nexon', 'Scorpio']}\n",
    "\n",
    "car_df = pd.DataFrame(dict)\n",
    "car_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Avg_Yearly_Sale</th>\n",
       "      <th>Best_Selling_Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ford</td>\n",
       "      <td>20000</td>\n",
       "      <td>Ecosport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>22000</td>\n",
       "      <td>Fortuner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Renault</td>\n",
       "      <td>31000</td>\n",
       "      <td>Duster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tata</td>\n",
       "      <td>27000</td>\n",
       "      <td>Nexon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mahindra</td>\n",
       "      <td>19000</td>\n",
       "      <td>Scorpio</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Brand Avg_Yearly_Sale Best_Selling_Model\n",
       "0      Ford           20000           Ecosport\n",
       "1    Toyota           22000           Fortuner\n",
       "2   Renault           31000             Duster\n",
       "3      Tata           27000              Nexon\n",
       "4  Mahindra           19000            Scorpio"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame from a Numpy Array of Arrays\n",
    "\n",
    "nparr = np.array(\n",
    "    [['Ford', 'Toyota', 'Renault', 'Tata', 'Mahindra'],\n",
    "     [20000, 22000, 31000, 27000, 19000],\n",
    "     ['Ecosport', 'Fortuner', 'Duster', 'Nexon', 'Scorpio']])\n",
    "\n",
    "dictarr = {\n",
    "    'Car_Brand': nparr[0],\n",
    "    'Avg_Yearly_Sale': nparr[1],\n",
    "    'Best_Selling_Model': nparr[2]}\n",
    "\n",
    "dfarr = pd.DataFrame(dictarr)\n",
    "dfarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Avg_Yearly_Sale</th>\n",
       "      <th>Best_Selling_Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ford</td>\n",
       "      <td>20000</td>\n",
       "      <td>Ecosport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>22000</td>\n",
       "      <td>Fortuner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Renault</td>\n",
       "      <td>31000</td>\n",
       "      <td>Duster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tata</td>\n",
       "      <td>27000</td>\n",
       "      <td>Nexon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mahindra</td>\n",
       "      <td>19000</td>\n",
       "      <td>Scorpio</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Brand  Avg_Yearly_Sale Best_Selling_Model\n",
       "0      Ford            20000           Ecosport\n",
       "1    Toyota            22000           Fortuner\n",
       "2   Renault            31000             Duster\n",
       "3      Tata            27000              Nexon\n",
       "4  Mahindra            19000            Scorpio"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame from a List of Lists\n",
    "\n",
    "list1 = [\n",
    "    ['Ford', 20000, 'Ecosport'],\n",
    "    ['Toyota', 22000, 'Fortuner'],\n",
    "    ['Renault', 31000, 'Duster'],\n",
    "    ['Tata', 27000, 'Nexon'],\n",
    "    ['Mahindra', 19000, 'Scorpio']]\n",
    "\n",
    "dflist = pd.DataFrame(list1, columns = ['Car_Brand', 'Avg_Yearly_Sale', 'Best_Selling_Model'])\n",
    "dflist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sale</th>\n",
       "      <th>Best_Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ford</td>\n",
       "      <td>20000</td>\n",
       "      <td>Ecosport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>22000</td>\n",
       "      <td>Fortuner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Renault</td>\n",
       "      <td>31000</td>\n",
       "      <td>Duster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tata</td>\n",
       "      <td>27000</td>\n",
       "      <td>Nexon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mahindra</td>\n",
       "      <td>19000</td>\n",
       "      <td>Scorpio</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Sale Best_Model\n",
       "0      Ford  20000   Ecosport\n",
       "1    Toyota  22000   Fortuner\n",
       "2   Renault  31000     Duster\n",
       "3      Tata  27000      Nexon\n",
       "4  Mahindra  19000    Scorpio"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame from a list of Series Objects (use of Dictionary as intermediate)\n",
    "\n",
    "series1 = pd.Series(['Ford', 'Toyota', 'Renault', 'Tata', 'Mahindra'])\n",
    "series2 = pd.Series([20000, 22000, 31000, 27000, 19000])\n",
    "series3 = pd.Series(['Ecosport', 'Fortuner', 'Duster', 'Nexon', 'Scorpio'])\n",
    "dictionary_of_nparray = {'Name': series1, 'Sale': series2, 'Best_Model':series3}\n",
    "\n",
    "dfser = pd.DataFrame(dictionary_of_nparray)\n",
    "dfser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>0.125812</td>\n",
       "      <td>0.651479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-02</th>\n",
       "      <td>0.045759</td>\n",
       "      <td>0.806235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-03</th>\n",
       "      <td>0.792372</td>\n",
       "      <td>0.362166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
       "      <td>0.356586</td>\n",
       "      <td>0.612546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-05</th>\n",
       "      <td>0.912383</td>\n",
       "      <td>0.705932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-06</th>\n",
       "      <td>0.616840</td>\n",
       "      <td>0.216294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-07</th>\n",
       "      <td>0.447513</td>\n",
       "      <td>0.881766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-08</th>\n",
       "      <td>0.206462</td>\n",
       "      <td>0.377051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-09</th>\n",
       "      <td>0.311913</td>\n",
       "      <td>0.043992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-10</th>\n",
       "      <td>0.370682</td>\n",
       "      <td>0.546783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-11</th>\n",
       "      <td>0.395717</td>\n",
       "      <td>0.995537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-12</th>\n",
       "      <td>0.607568</td>\n",
       "      <td>0.805360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-13</th>\n",
       "      <td>0.497364</td>\n",
       "      <td>0.206313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-14</th>\n",
       "      <td>0.599979</td>\n",
       "      <td>0.063293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-15</th>\n",
       "      <td>0.340440</td>\n",
       "      <td>0.528359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16</th>\n",
       "      <td>0.419183</td>\n",
       "      <td>0.630457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-17</th>\n",
       "      <td>0.383856</td>\n",
       "      <td>0.351503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-18</th>\n",
       "      <td>0.828389</td>\n",
       "      <td>0.391632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-19</th>\n",
       "      <td>0.829840</td>\n",
       "      <td>0.823836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-20</th>\n",
       "      <td>0.344552</td>\n",
       "      <td>0.633980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-21</th>\n",
       "      <td>0.241648</td>\n",
       "      <td>0.897774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-22</th>\n",
       "      <td>0.782985</td>\n",
       "      <td>0.541372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-23</th>\n",
       "      <td>0.170845</td>\n",
       "      <td>0.874057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-24</th>\n",
       "      <td>0.880012</td>\n",
       "      <td>0.508863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-25</th>\n",
       "      <td>0.068121</td>\n",
       "      <td>0.957391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-26</th>\n",
       "      <td>0.148089</td>\n",
       "      <td>0.924813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-27</th>\n",
       "      <td>0.271549</td>\n",
       "      <td>0.552846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-28</th>\n",
       "      <td>0.181634</td>\n",
       "      <td>0.119580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-29</th>\n",
       "      <td>0.777173</td>\n",
       "      <td>0.199660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-30</th>\n",
       "      <td>0.583036</td>\n",
       "      <td>0.894758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-31</th>\n",
       "      <td>0.989172</td>\n",
       "      <td>0.235277</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                col1      col2\n",
       "2021-01-01  0.125812  0.651479\n",
       "2021-01-02  0.045759  0.806235\n",
       "2021-01-03  0.792372  0.362166\n",
       "2021-01-04  0.356586  0.612546\n",
       "2021-01-05  0.912383  0.705932\n",
       "2021-01-06  0.616840  0.216294\n",
       "2021-01-07  0.447513  0.881766\n",
       "2021-01-08  0.206462  0.377051\n",
       "2021-01-09  0.311913  0.043992\n",
       "2021-01-10  0.370682  0.546783\n",
       "2021-01-11  0.395717  0.995537\n",
       "2021-01-12  0.607568  0.805360\n",
       "2021-01-13  0.497364  0.206313\n",
       "2021-01-14  0.599979  0.063293\n",
       "2021-01-15  0.340440  0.528359\n",
       "2021-01-16  0.419183  0.630457\n",
       "2021-01-17  0.383856  0.351503\n",
       "2021-01-18  0.828389  0.391632\n",
       "2021-01-19  0.829840  0.823836\n",
       "2021-01-20  0.344552  0.633980\n",
       "2021-01-21  0.241648  0.897774\n",
       "2021-01-22  0.782985  0.541372\n",
       "2021-01-23  0.170845  0.874057\n",
       "2021-01-24  0.880012  0.508863\n",
       "2021-01-25  0.068121  0.957391\n",
       "2021-01-26  0.148089  0.924813\n",
       "2021-01-27  0.271549  0.552846\n",
       "2021-01-28  0.181634  0.119580\n",
       "2021-01-29  0.777173  0.199660\n",
       "2021-01-30  0.583036  0.894758\n",
       "2021-01-31  0.989172  0.235277"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a DataFrame with dates as Row Indexes\n",
    "\n",
    "# Generate a range of dates between 1st Jan 21 to 31st Jan 21.\n",
    "\n",
    "rng = pd.date_range(\"1/1/2021\", periods=31, freq=\"D\")\n",
    "\n",
    "# Generate two sets of 31 random numbers\n",
    "\n",
    "s1 = np.random.rand(31)\n",
    "s2 = np.random.rand(31)\n",
    "\n",
    "# Create a DataFrame with the dates as Row Indexes\n",
    "\n",
    "df1 = pd.DataFrame({'col1': s1, 'col2': s2})\n",
    "df1.set_index(rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.468628</td>\n",
       "      <td>0.559384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.273973</td>\n",
       "      <td>0.287309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.045759</td>\n",
       "      <td>0.043992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.256599</td>\n",
       "      <td>0.356834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.395717</td>\n",
       "      <td>0.552846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.697006</td>\n",
       "      <td>0.815036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.989172</td>\n",
       "      <td>0.995537</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            col1       col2\n",
       "count  31.000000  31.000000\n",
       "mean    0.468628   0.559384\n",
       "std     0.273973   0.287309\n",
       "min     0.045759   0.043992\n",
       "25%     0.256599   0.356834\n",
       "50%     0.395717   0.552846\n",
       "75%     0.697006   0.815036\n",
       "max     0.989172   0.995537"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtaining Statistical details about the Numeric data in DataFrame\n",
    "\n",
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>col1</th>\n",
       "      <td>0.125812</td>\n",
       "      <td>0.045759</td>\n",
       "      <td>0.792372</td>\n",
       "      <td>0.356586</td>\n",
       "      <td>0.912383</td>\n",
       "      <td>0.616840</td>\n",
       "      <td>0.447513</td>\n",
       "      <td>0.206462</td>\n",
       "      <td>0.311913</td>\n",
       "      <td>0.370682</td>\n",
       "      <td>...</td>\n",
       "      <td>0.782985</td>\n",
       "      <td>0.170845</td>\n",
       "      <td>0.880012</td>\n",
       "      <td>0.068121</td>\n",
       "      <td>0.148089</td>\n",
       "      <td>0.271549</td>\n",
       "      <td>0.181634</td>\n",
       "      <td>0.777173</td>\n",
       "      <td>0.583036</td>\n",
       "      <td>0.989172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>col2</th>\n",
       "      <td>0.651479</td>\n",
       "      <td>0.806235</td>\n",
       "      <td>0.362166</td>\n",
       "      <td>0.612546</td>\n",
       "      <td>0.705932</td>\n",
       "      <td>0.216294</td>\n",
       "      <td>0.881766</td>\n",
       "      <td>0.377051</td>\n",
       "      <td>0.043992</td>\n",
       "      <td>0.546783</td>\n",
       "      <td>...</td>\n",
       "      <td>0.541372</td>\n",
       "      <td>0.874057</td>\n",
       "      <td>0.508863</td>\n",
       "      <td>0.957391</td>\n",
       "      <td>0.924813</td>\n",
       "      <td>0.552846</td>\n",
       "      <td>0.119580</td>\n",
       "      <td>0.199660</td>\n",
       "      <td>0.894758</td>\n",
       "      <td>0.235277</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6   \\\n",
       "col1  0.125812  0.045759  0.792372  0.356586  0.912383  0.616840  0.447513   \n",
       "col2  0.651479  0.806235  0.362166  0.612546  0.705932  0.216294  0.881766   \n",
       "\n",
       "            7         8         9   ...        21        22        23  \\\n",
       "col1  0.206462  0.311913  0.370682  ...  0.782985  0.170845  0.880012   \n",
       "col2  0.377051  0.043992  0.546783  ...  0.541372  0.874057  0.508863   \n",
       "\n",
       "            24        25        26        27        28        29        30  \n",
       "col1  0.068121  0.148089  0.271549  0.181634  0.777173  0.583036  0.989172  \n",
       "col2  0.957391  0.924813  0.552846  0.119580  0.199660  0.894758  0.235277  \n",
       "\n",
       "[2 rows x 31 columns]"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose the DataFrame\n",
    "\n",
    "dfT = df1.transpose()\n",
    "dfT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort your DataFrame on a column\n",
    "\n",
    "dfsort = dflist.sort_values(by='Avg_Yearly_Sale', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Avg_Yearly_Sale</th>\n",
       "      <th>Best_Selling_Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Renault</td>\n",
       "      <td>31000</td>\n",
       "      <td>Duster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tata</td>\n",
       "      <td>27000</td>\n",
       "      <td>Nexon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>22000</td>\n",
       "      <td>Fortuner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ford</td>\n",
       "      <td>20000</td>\n",
       "      <td>Ecosport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mahindra</td>\n",
       "      <td>19000</td>\n",
       "      <td>Scorpio</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Brand  Avg_Yearly_Sale Best_Selling_Model\n",
       "2   Renault            31000             Duster\n",
       "3      Tata            27000              Nexon\n",
       "1    Toyota            22000           Fortuner\n",
       "0      Ford            20000           Ecosport\n",
       "4  Mahindra            19000            Scorpio"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfsort"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting Data from DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>3.545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>24.40</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.5</td>\n",
       "      <td>67.6</td>\n",
       "      <td>176.6</td>\n",
       "      <td>3.042</td>\n",
       "      <td>15.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>66.498812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>15.245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>27.50</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>185.9</td>\n",
       "      <td>3.208</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.654495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>17.531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>186.2</td>\n",
       "      <td>3.259</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.155978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3.493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.50</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>185.7</td>\n",
       "      <td>3.601</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.623357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>18.969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.00</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>157 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "0                16.919        16.360    Passenger               21.50   \n",
       "1                39.384        19.875    Passenger               28.40   \n",
       "2                14.114        18.225    Passenger                 NaN   \n",
       "3                 8.588        29.725    Passenger               42.00   \n",
       "4                20.397        22.255    Passenger               23.99   \n",
       "..                  ...           ...          ...                 ...   \n",
       "152               3.545           NaN    Passenger               24.40   \n",
       "153              15.245           NaN    Passenger               27.50   \n",
       "154              17.531           NaN    Passenger               28.80   \n",
       "155               3.493           NaN    Passenger               45.50   \n",
       "156              18.969           NaN    Passenger               36.00   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "0            1.8       140.0      101.2   67.3   172.4        2.639   \n",
       "1            3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2            3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "3            3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "4            1.8       150.0      102.6   68.2   178.0        2.998   \n",
       "..           ...         ...        ...    ...     ...          ...   \n",
       "152          1.9       160.0      100.5   67.6   176.6        3.042   \n",
       "153          2.4       168.0      104.9   69.3   185.9        3.208   \n",
       "154          2.4       168.0      104.9   69.3   186.2        3.259   \n",
       "155          2.3       236.0      104.9   71.5   185.7        3.601   \n",
       "156          2.9       201.0      109.9   72.1   189.8        3.600   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "0             13.2             28.0          58.280150  \n",
       "1             17.2             25.0          91.370778  \n",
       "2             17.2             26.0                NaN  \n",
       "3             18.0             22.0          91.389779  \n",
       "4             16.4             27.0          62.777639  \n",
       "..             ...              ...                ...  \n",
       "152           15.8             25.0          66.498812  \n",
       "153           17.9             25.0          70.654495  \n",
       "154           17.9             25.0          71.155978  \n",
       "155           18.5             23.0         101.623357  \n",
       "156           21.1             24.0          85.735655  \n",
       "\n",
       "[157 rows x 13 columns]"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_df = pd.read_csv(\"car_sales.csv\")\n",
    "csv_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18.780</td>\n",
       "      <td>23.555</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>33.95</td>\n",
       "      <td>2.8</td>\n",
       "      <td>200.0</td>\n",
       "      <td>108.7</td>\n",
       "      <td>76.1</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.561</td>\n",
       "      <td>18.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>84.565105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.380</td>\n",
       "      <td>39.000</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>62.00</td>\n",
       "      <td>4.2</td>\n",
       "      <td>310.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>198.2</td>\n",
       "      <td>3.902</td>\n",
       "      <td>23.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>134.656858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19.747</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>26.99</td>\n",
       "      <td>2.5</td>\n",
       "      <td>170.0</td>\n",
       "      <td>107.3</td>\n",
       "      <td>68.4</td>\n",
       "      <td>176.0</td>\n",
       "      <td>3.179</td>\n",
       "      <td>16.6</td>\n",
       "      <td>26.0</td>\n",
       "      <td>71.191207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.231</td>\n",
       "      <td>28.675</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>33.40</td>\n",
       "      <td>2.8</td>\n",
       "      <td>193.0</td>\n",
       "      <td>107.3</td>\n",
       "      <td>68.5</td>\n",
       "      <td>176.0</td>\n",
       "      <td>3.197</td>\n",
       "      <td>16.6</td>\n",
       "      <td>24.0</td>\n",
       "      <td>81.877069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17.527</td>\n",
       "      <td>36.125</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>38.90</td>\n",
       "      <td>2.8</td>\n",
       "      <td>193.0</td>\n",
       "      <td>111.4</td>\n",
       "      <td>70.9</td>\n",
       "      <td>188.0</td>\n",
       "      <td>3.472</td>\n",
       "      <td>18.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>83.998724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "5              18.780        23.555    Passenger               33.95   \n",
       "6               1.380        39.000    Passenger               62.00   \n",
       "7              19.747           NaN    Passenger               26.99   \n",
       "8               9.231        28.675    Passenger               33.40   \n",
       "9              17.527        36.125    Passenger               38.90   \n",
       "\n",
       "   Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "5          2.8       200.0      108.7   76.1   192.0        3.561   \n",
       "6          4.2       310.0      113.0   74.0   198.2        3.902   \n",
       "7          2.5       170.0      107.3   68.4   176.0        3.179   \n",
       "8          2.8       193.0      107.3   68.5   176.0        3.197   \n",
       "9          2.8       193.0      111.4   70.9   188.0        3.472   \n",
       "\n",
       "   Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "5           18.5             22.0          84.565105  \n",
       "6           23.7             21.0         134.656858  \n",
       "7           16.6             26.0          71.191207  \n",
       "8           16.6             24.0          81.877069  \n",
       "9           18.5             25.0          83.998724  "
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting a ROW Subset from a DataFrame\n",
    "\n",
    "csv_df[5:10]    # [x, y] -> x = starting index position, y = ending position "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      16.360\n",
       "1      19.875\n",
       "2      18.225\n",
       "3      29.725\n",
       "4      22.255\n",
       "        ...  \n",
       "152       NaN\n",
       "153       NaN\n",
       "154       NaN\n",
       "155       NaN\n",
       "156       NaN\n",
       "Name: Resale_value, Length: 157, dtype: float64"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access a single Column from the DataFrame\n",
    "# Returns a Series Object\n",
    "\n",
    "csv_df['Resale_value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Engine_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.875</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.225</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29.725</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Resale_value  Engine_size\n",
       "1        19.875          3.2\n",
       "2        18.225          3.2\n",
       "3        29.725          3.5"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access a Multiple Columns from the DataFrame\n",
    "\n",
    "csv_df.loc[1:3, ['Resale_value','Engine_size']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sales_in_thousands        8.588\n",
       "Resale_value             29.725\n",
       "Vehicle_type          Passenger\n",
       "Price_in_thousands           42\n",
       "Engine_size                 3.5\n",
       "Horsepower                  210\n",
       "Wheelbase                 114.6\n",
       "Width                      71.4\n",
       "Length                    196.6\n",
       "Curb_weight                3.85\n",
       "Fuel_capacity                18\n",
       "Fuel_efficiency              22\n",
       "Power_perf_factor       91.3898\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using numbered indexes with iloc\n",
    "\n",
    "csv_df.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.875</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Resale_value Vehicle_type  Price_in_thousands\n",
       "1        19.875    Passenger                28.4\n",
       "2        18.225    Passenger                 NaN"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using a row and column subset simultaneously\n",
    "\n",
    "csv_df.iloc[1:3, 1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "1              39.384        19.875    Passenger                28.4   \n",
       "2              14.114        18.225    Passenger                 NaN   \n",
       "\n",
       "   Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "1          3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2          3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "\n",
       "   Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "1           17.2             25.0          91.370778  \n",
       "2           17.2             26.0                NaN  "
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using column defaults\n",
    "\n",
    "csv_df.iloc[1:3, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Passenger</td>\n",
       "      <td>24.40</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Passenger</td>\n",
       "      <td>27.50</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.50</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.00</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>157 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Vehicle_type  Price_in_thousands  Engine_size  Horsepower\n",
       "0      Passenger               21.50          1.8       140.0\n",
       "1      Passenger               28.40          3.2       225.0\n",
       "2      Passenger                 NaN          3.2       225.0\n",
       "3      Passenger               42.00          3.5       210.0\n",
       "4      Passenger               23.99          1.8       150.0\n",
       "..           ...                 ...          ...         ...\n",
       "152    Passenger               24.40          1.9       160.0\n",
       "153    Passenger               27.50          2.4       168.0\n",
       "154    Passenger               28.80          2.4       168.0\n",
       "155    Passenger               45.50          2.3       236.0\n",
       "156    Passenger               36.00          2.9       201.0\n",
       "\n",
       "[157 rows x 4 columns]"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using row defaults\n",
    "\n",
    "csv_df.iloc[:, 2:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.400</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18.780</td>\n",
       "      <td>23.555</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>33.950</td>\n",
       "      <td>2.8</td>\n",
       "      <td>200.0</td>\n",
       "      <td>108.7</td>\n",
       "      <td>76.1</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.561</td>\n",
       "      <td>18.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>84.565105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.380</td>\n",
       "      <td>39.000</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>62.000</td>\n",
       "      <td>4.2</td>\n",
       "      <td>310.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>198.2</td>\n",
       "      <td>3.902</td>\n",
       "      <td>23.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>134.656858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>9.835</td>\n",
       "      <td>34.080</td>\n",
       "      <td>Car</td>\n",
       "      <td>51.728</td>\n",
       "      <td>4.7</td>\n",
       "      <td>230.0</td>\n",
       "      <td>112.2</td>\n",
       "      <td>76.4</td>\n",
       "      <td>192.5</td>\n",
       "      <td>5.115</td>\n",
       "      <td>25.4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>102.528984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>15.245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>27.500</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>185.9</td>\n",
       "      <td>3.208</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.654495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>17.531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.800</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>186.2</td>\n",
       "      <td>3.259</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.155978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3.493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.500</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>185.7</td>\n",
       "      <td>3.601</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.623357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>18.969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.000</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "1                39.384        19.875    Passenger              28.400   \n",
       "2                14.114        18.225    Passenger                 NaN   \n",
       "3                 8.588        29.725    Passenger              42.000   \n",
       "5                18.780        23.555    Passenger              33.950   \n",
       "6                 1.380        39.000    Passenger              62.000   \n",
       "..                  ...           ...          ...                 ...   \n",
       "144               9.835        34.080          Car              51.728   \n",
       "153              15.245           NaN    Passenger              27.500   \n",
       "154              17.531           NaN    Passenger              28.800   \n",
       "155               3.493           NaN    Passenger              45.500   \n",
       "156              18.969           NaN    Passenger              36.000   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "1            3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2            3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "3            3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "5            2.8       200.0      108.7   76.1   192.0        3.561   \n",
       "6            4.2       310.0      113.0   74.0   198.2        3.902   \n",
       "..           ...         ...        ...    ...     ...          ...   \n",
       "144          4.7       230.0      112.2   76.4   192.5        5.115   \n",
       "153          2.4       168.0      104.9   69.3   185.9        3.208   \n",
       "154          2.4       168.0      104.9   69.3   186.2        3.259   \n",
       "155          2.3       236.0      104.9   71.5   185.7        3.601   \n",
       "156          2.9       201.0      109.9   72.1   189.8        3.600   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "1             17.2             25.0          91.370778  \n",
       "2             17.2             26.0                NaN  \n",
       "3             18.0             22.0          91.389779  \n",
       "5             18.5             22.0          84.565105  \n",
       "6             23.7             21.0         134.656858  \n",
       "..             ...              ...                ...  \n",
       "144           25.4             15.0         102.528984  \n",
       "153           17.9             25.0          70.654495  \n",
       "154           17.9             25.0          71.155978  \n",
       "155           18.5             23.0         101.623357  \n",
       "156           21.1             24.0          85.735655  \n",
       "\n",
       "[123 rows x 13 columns]"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using a condition on a column\n",
    "\n",
    "csv_df[csv_df['Engine_size'] > 2.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.400</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.380</td>\n",
       "      <td>39.000</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>62.000</td>\n",
       "      <td>4.2</td>\n",
       "      <td>310.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>198.2</td>\n",
       "      <td>3.902</td>\n",
       "      <td>23.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>134.656858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>39.350</td>\n",
       "      <td>13.740</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>25.300</td>\n",
       "      <td>3.8</td>\n",
       "      <td>240.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>72.7</td>\n",
       "      <td>196.2</td>\n",
       "      <td>3.543</td>\n",
       "      <td>17.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>95.636703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>63.729</td>\n",
       "      <td>22.525</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>39.895</td>\n",
       "      <td>4.6</td>\n",
       "      <td>275.0</td>\n",
       "      <td>115.3</td>\n",
       "      <td>74.5</td>\n",
       "      <td>207.2</td>\n",
       "      <td>3.978</td>\n",
       "      <td>18.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>113.854598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15.943</td>\n",
       "      <td>27.100</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>44.475</td>\n",
       "      <td>4.6</td>\n",
       "      <td>275.0</td>\n",
       "      <td>112.2</td>\n",
       "      <td>75.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>115.621358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6.536</td>\n",
       "      <td>25.725</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>39.665</td>\n",
       "      <td>4.6</td>\n",
       "      <td>275.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>75.5</td>\n",
       "      <td>200.6</td>\n",
       "      <td>3.843</td>\n",
       "      <td>19.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>113.765874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>14.785</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>46.225</td>\n",
       "      <td>5.7</td>\n",
       "      <td>255.0</td>\n",
       "      <td>117.5</td>\n",
       "      <td>77.0</td>\n",
       "      <td>201.2</td>\n",
       "      <td>5.572</td>\n",
       "      <td>30.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>109.509117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>17.947</td>\n",
       "      <td>36.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.705</td>\n",
       "      <td>5.7</td>\n",
       "      <td>345.0</td>\n",
       "      <td>104.5</td>\n",
       "      <td>73.6</td>\n",
       "      <td>179.7</td>\n",
       "      <td>3.210</td>\n",
       "      <td>19.1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>141.141150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>13.462</td>\n",
       "      <td>17.325</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.340</td>\n",
       "      <td>3.5</td>\n",
       "      <td>253.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>74.4</td>\n",
       "      <td>207.7</td>\n",
       "      <td>3.564</td>\n",
       "      <td>17.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.329281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>30.696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>29.185</td>\n",
       "      <td>3.5</td>\n",
       "      <td>253.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>74.4</td>\n",
       "      <td>197.8</td>\n",
       "      <td>3.567</td>\n",
       "      <td>17.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.655244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.916</td>\n",
       "      <td>58.470</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>69.725</td>\n",
       "      <td>8.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>96.2</td>\n",
       "      <td>75.7</td>\n",
       "      <td>176.7</td>\n",
       "      <td>3.375</td>\n",
       "      <td>19.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>188.144323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>227.061</td>\n",
       "      <td>15.060</td>\n",
       "      <td>Car</td>\n",
       "      <td>19.460</td>\n",
       "      <td>5.2</td>\n",
       "      <td>230.0</td>\n",
       "      <td>138.7</td>\n",
       "      <td>79.3</td>\n",
       "      <td>224.2</td>\n",
       "      <td>4.470</td>\n",
       "      <td>26.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>90.211700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>101.323</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>26.310</td>\n",
       "      <td>5.2</td>\n",
       "      <td>230.0</td>\n",
       "      <td>115.7</td>\n",
       "      <td>71.7</td>\n",
       "      <td>193.5</td>\n",
       "      <td>4.394</td>\n",
       "      <td>25.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>92.854125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>125.338</td>\n",
       "      <td>23.575</td>\n",
       "      <td>Car</td>\n",
       "      <td>36.135</td>\n",
       "      <td>4.6</td>\n",
       "      <td>240.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>78.7</td>\n",
       "      <td>204.6</td>\n",
       "      <td>4.808</td>\n",
       "      <td>26.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>100.024802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>23.713</td>\n",
       "      <td>19.690</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>29.465</td>\n",
       "      <td>3.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>108.3</td>\n",
       "      <td>70.2</td>\n",
       "      <td>193.7</td>\n",
       "      <td>3.342</td>\n",
       "      <td>18.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>92.436889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>15.467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.800</td>\n",
       "      <td>3.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>114.5</td>\n",
       "      <td>71.6</td>\n",
       "      <td>191.3</td>\n",
       "      <td>3.650</td>\n",
       "      <td>18.4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>102.178985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>12.698</td>\n",
       "      <td>32.075</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>37.805</td>\n",
       "      <td>3.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>110.2</td>\n",
       "      <td>70.9</td>\n",
       "      <td>189.2</td>\n",
       "      <td>3.638</td>\n",
       "      <td>19.8</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.946698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>3.334</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>46.305</td>\n",
       "      <td>4.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>110.2</td>\n",
       "      <td>70.9</td>\n",
       "      <td>189.2</td>\n",
       "      <td>3.693</td>\n",
       "      <td>19.8</td>\n",
       "      <td>21.0</td>\n",
       "      <td>125.013357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>6.375</td>\n",
       "      <td>40.375</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>54.005</td>\n",
       "      <td>4.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>112.2</td>\n",
       "      <td>72.0</td>\n",
       "      <td>196.7</td>\n",
       "      <td>3.890</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>124.446716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>9.126</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>60.105</td>\n",
       "      <td>4.7</td>\n",
       "      <td>230.0</td>\n",
       "      <td>112.2</td>\n",
       "      <td>76.4</td>\n",
       "      <td>192.5</td>\n",
       "      <td>5.401</td>\n",
       "      <td>25.4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>105.760458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>13.798</td>\n",
       "      <td>20.525</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>39.080</td>\n",
       "      <td>4.6</td>\n",
       "      <td>275.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>73.6</td>\n",
       "      <td>208.5</td>\n",
       "      <td>3.868</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>113.540207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>22.925</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>42.660</td>\n",
       "      <td>5.4</td>\n",
       "      <td>300.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>79.9</td>\n",
       "      <td>204.8</td>\n",
       "      <td>5.393</td>\n",
       "      <td>30.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>123.972047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>27.602</td>\n",
       "      <td>41.450</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>49.900</td>\n",
       "      <td>3.2</td>\n",
       "      <td>221.0</td>\n",
       "      <td>111.5</td>\n",
       "      <td>70.8</td>\n",
       "      <td>189.4</td>\n",
       "      <td>3.823</td>\n",
       "      <td>21.1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>98.249737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>16.774</td>\n",
       "      <td>50.375</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>69.700</td>\n",
       "      <td>4.3</td>\n",
       "      <td>275.0</td>\n",
       "      <td>121.5</td>\n",
       "      <td>73.1</td>\n",
       "      <td>203.1</td>\n",
       "      <td>4.133</td>\n",
       "      <td>23.2</td>\n",
       "      <td>21.0</td>\n",
       "      <td>125.273876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>3.311</td>\n",
       "      <td>58.600</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>82.600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>71.3</td>\n",
       "      <td>177.1</td>\n",
       "      <td>4.125</td>\n",
       "      <td>21.1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>139.982294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.954</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>85.500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>113.6</td>\n",
       "      <td>73.1</td>\n",
       "      <td>196.6</td>\n",
       "      <td>4.115</td>\n",
       "      <td>23.2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>141.100985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>79.853</td>\n",
       "      <td>15.125</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>26.249</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>108.3</td>\n",
       "      <td>70.3</td>\n",
       "      <td>190.5</td>\n",
       "      <td>3.294</td>\n",
       "      <td>18.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>89.427820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>14.690</td>\n",
       "      <td>19.890</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.229</td>\n",
       "      <td>4.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>113.8</td>\n",
       "      <td>74.4</td>\n",
       "      <td>205.4</td>\n",
       "      <td>3.967</td>\n",
       "      <td>18.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>103.441693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>1.872</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>43.000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>253.0</td>\n",
       "      <td>113.3</td>\n",
       "      <td>76.3</td>\n",
       "      <td>165.4</td>\n",
       "      <td>2.850</td>\n",
       "      <td>12.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>106.984456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1.280</td>\n",
       "      <td>60.625</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>71.020</td>\n",
       "      <td>3.4</td>\n",
       "      <td>300.0</td>\n",
       "      <td>92.6</td>\n",
       "      <td>69.5</td>\n",
       "      <td>174.5</td>\n",
       "      <td>3.032</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>134.390975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1.866</td>\n",
       "      <td>67.550</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>74.970</td>\n",
       "      <td>3.4</td>\n",
       "      <td>300.0</td>\n",
       "      <td>92.6</td>\n",
       "      <td>69.5</td>\n",
       "      <td>174.5</td>\n",
       "      <td>3.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>135.914710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>9.835</td>\n",
       "      <td>34.080</td>\n",
       "      <td>Car</td>\n",
       "      <td>51.728</td>\n",
       "      <td>4.7</td>\n",
       "      <td>230.0</td>\n",
       "      <td>112.2</td>\n",
       "      <td>76.4</td>\n",
       "      <td>192.5</td>\n",
       "      <td>5.115</td>\n",
       "      <td>25.4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>102.528984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3.493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.500</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>185.7</td>\n",
       "      <td>3.601</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.623357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "1                39.384        19.875    Passenger              28.400   \n",
       "2                14.114        18.225    Passenger                 NaN   \n",
       "6                 1.380        39.000    Passenger              62.000   \n",
       "11               39.350        13.740    Passenger              25.300   \n",
       "14               63.729        22.525    Passenger              39.895   \n",
       "15               15.943        27.100    Passenger              44.475   \n",
       "16                6.536        25.725    Passenger              39.665   \n",
       "18               14.785           NaN          Car              46.225   \n",
       "24               17.947        36.225    Passenger              45.705   \n",
       "32               13.462        17.325    Passenger              28.340   \n",
       "34               30.696           NaN    Passenger              29.185   \n",
       "39                0.916        58.470    Passenger              69.725   \n",
       "40              227.061        15.060          Car              19.460   \n",
       "44              101.323           NaN          Car              26.310   \n",
       "54              125.338        23.575          Car              36.135   \n",
       "65               23.713        19.690    Passenger              29.465   \n",
       "66               15.467           NaN    Passenger              42.800   \n",
       "71               12.698        32.075    Passenger              37.805   \n",
       "72                3.334           NaN    Passenger              46.305   \n",
       "73                6.375        40.375    Passenger              54.005   \n",
       "74                9.126           NaN          Car              60.105   \n",
       "76               13.798        20.525    Passenger              39.080   \n",
       "78               22.925           NaN          Car              42.660   \n",
       "93               27.602        41.450    Passenger              49.900   \n",
       "94               16.774        50.375    Passenger              69.700   \n",
       "95                3.311        58.600    Passenger              82.600   \n",
       "99                0.954           NaN    Passenger              85.500   \n",
       "103              79.853        15.125    Passenger              26.249   \n",
       "111              14.690        19.890    Passenger              36.229   \n",
       "117               1.872           NaN    Passenger              43.000   \n",
       "125               1.280        60.625    Passenger              71.020   \n",
       "126               1.866        67.550    Passenger              74.970   \n",
       "144               9.835        34.080          Car              51.728   \n",
       "155               3.493           NaN    Passenger              45.500   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "1            3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2            3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "6            4.2       310.0      113.0   74.0   198.2        3.902   \n",
       "11           3.8       240.0      109.0   72.7   196.2        3.543   \n",
       "14           4.6       275.0      115.3   74.5   207.2        3.978   \n",
       "15           4.6       275.0      112.2   75.0   201.0          NaN   \n",
       "16           4.6       275.0      108.0   75.5   200.6        3.843   \n",
       "18           5.7       255.0      117.5   77.0   201.2        5.572   \n",
       "24           5.7       345.0      104.5   73.6   179.7        3.210   \n",
       "32           3.5       253.0      113.0   74.4   207.7        3.564   \n",
       "34           3.5       253.0      113.0   74.4   197.8        3.567   \n",
       "39           8.0       450.0       96.2   75.7   176.7        3.375   \n",
       "40           5.2       230.0      138.7   79.3   224.2        4.470   \n",
       "44           5.2       230.0      115.7   71.7   193.5        4.394   \n",
       "54           4.6       240.0      119.0   78.7   204.6        4.808   \n",
       "65           3.0       227.0      108.3   70.2   193.7        3.342   \n",
       "66           3.0       240.0      114.5   71.6   191.3        3.650   \n",
       "71           3.0       225.0      110.2   70.9   189.2        3.638   \n",
       "72           4.0       300.0      110.2   70.9   189.2        3.693   \n",
       "73           4.0       290.0      112.2   72.0   196.7        3.890   \n",
       "74           4.7       230.0      112.2   76.4   192.5        5.401   \n",
       "76           4.6       275.0      109.0   73.6   208.5        3.868   \n",
       "78           5.4       300.0      119.0   79.9   204.8        5.393   \n",
       "93           3.2       221.0      111.5   70.8   189.4        3.823   \n",
       "94           4.3       275.0      121.5   73.1   203.1        4.133   \n",
       "95           5.0       302.0       99.0   71.3   177.1        4.125   \n",
       "99           5.0       302.0      113.6   73.1   196.6        4.115   \n",
       "103          3.0       222.0      108.3   70.3   190.5        3.294   \n",
       "111          4.0       250.0      113.8   74.4   205.4        3.967   \n",
       "117          3.5       253.0      113.3   76.3   165.4        2.850   \n",
       "125          3.4       300.0       92.6   69.5   174.5        3.032   \n",
       "126          3.4       300.0       92.6   69.5   174.5        3.075   \n",
       "144          4.7       230.0      112.2   76.4   192.5        5.115   \n",
       "155          2.3       236.0      104.9   71.5   185.7        3.601   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "1             17.2             25.0          91.370778  \n",
       "2             17.2             26.0                NaN  \n",
       "6             23.7             21.0         134.656858  \n",
       "11            17.5             23.0          95.636703  \n",
       "14            18.5             22.0         113.854598  \n",
       "15            18.5             22.0         115.621358  \n",
       "16            19.0             22.0         113.765874  \n",
       "18            30.0             15.0         109.509117  \n",
       "24            19.1             22.0         141.141150  \n",
       "32            17.0             23.0         101.329281  \n",
       "34            17.0             23.0         101.655244  \n",
       "39            19.0             16.0         188.144323  \n",
       "40            26.0             17.0          90.211700  \n",
       "44            25.0             17.0          92.854125  \n",
       "54            26.0             16.0         100.024802  \n",
       "65            18.5             25.0          92.436889  \n",
       "66            18.4             21.0         102.178985  \n",
       "71            19.8             23.0          94.946698  \n",
       "72            19.8             21.0         125.013357  \n",
       "73            22.5             22.0         124.446716  \n",
       "74            25.4             15.0         105.760458  \n",
       "76            20.0             22.0         113.540207  \n",
       "78            30.0             15.0         123.972047  \n",
       "93            21.1             25.0          98.249737  \n",
       "94            23.2             21.0         125.273876  \n",
       "95            21.1             20.0         139.982294  \n",
       "99            23.2             20.0         141.100985  \n",
       "103           18.5             25.0          89.427820  \n",
       "111           18.5             22.0         103.441693  \n",
       "117           12.0             21.0         106.984456  \n",
       "125           17.0             21.0         134.390975  \n",
       "126           17.0             23.0         135.914710  \n",
       "144           25.4             15.0         102.528984  \n",
       "155           18.5             23.0         101.623357  "
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using multiple conditions with 'and'\n",
    "\n",
    "csv_df[(csv_df['Engine_size'] > 2.0) & (csv_df['Horsepower'] > 220.0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.400</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18.780</td>\n",
       "      <td>23.555</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>33.950</td>\n",
       "      <td>2.8</td>\n",
       "      <td>200.0</td>\n",
       "      <td>108.7</td>\n",
       "      <td>76.1</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.561</td>\n",
       "      <td>18.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>84.565105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.380</td>\n",
       "      <td>39.000</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>62.000</td>\n",
       "      <td>4.2</td>\n",
       "      <td>310.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>198.2</td>\n",
       "      <td>3.902</td>\n",
       "      <td>23.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>134.656858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>63.849</td>\n",
       "      <td>18.140</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>25.545</td>\n",
       "      <td>3.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>107.1</td>\n",
       "      <td>71.7</td>\n",
       "      <td>191.9</td>\n",
       "      <td>3.417</td>\n",
       "      <td>18.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>84.911898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>65.119</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>22.368</td>\n",
       "      <td>3.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>114.2</td>\n",
       "      <td>73.4</td>\n",
       "      <td>193.5</td>\n",
       "      <td>3.759</td>\n",
       "      <td>20.9</td>\n",
       "      <td>22.0</td>\n",
       "      <td>78.027219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>68.411</td>\n",
       "      <td>19.425</td>\n",
       "      <td>Car</td>\n",
       "      <td>22.288</td>\n",
       "      <td>2.7</td>\n",
       "      <td>150.0</td>\n",
       "      <td>105.3</td>\n",
       "      <td>66.5</td>\n",
       "      <td>183.3</td>\n",
       "      <td>3.440</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>62.355577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>9.835</td>\n",
       "      <td>34.080</td>\n",
       "      <td>Car</td>\n",
       "      <td>51.728</td>\n",
       "      <td>4.7</td>\n",
       "      <td>230.0</td>\n",
       "      <td>112.2</td>\n",
       "      <td>76.4</td>\n",
       "      <td>192.5</td>\n",
       "      <td>5.115</td>\n",
       "      <td>25.4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>102.528984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>18.969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.000</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "1                39.384        19.875    Passenger              28.400   \n",
       "2                14.114        18.225    Passenger                 NaN   \n",
       "3                 8.588        29.725    Passenger              42.000   \n",
       "5                18.780        23.555    Passenger              33.950   \n",
       "6                 1.380        39.000    Passenger              62.000   \n",
       "..                  ...           ...          ...                 ...   \n",
       "138              63.849        18.140    Passenger              25.545   \n",
       "141              65.119           NaN          Car              22.368   \n",
       "143              68.411        19.425          Car              22.288   \n",
       "144               9.835        34.080          Car              51.728   \n",
       "156              18.969           NaN    Passenger              36.000   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "1            3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2            3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "3            3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "5            2.8       200.0      108.7   76.1   192.0        3.561   \n",
       "6            4.2       310.0      113.0   74.0   198.2        3.902   \n",
       "..           ...         ...        ...    ...     ...          ...   \n",
       "138          3.0       210.0      107.1   71.7   191.9        3.417   \n",
       "141          3.0       194.0      114.2   73.4   193.5        3.759   \n",
       "143          2.7       150.0      105.3   66.5   183.3        3.440   \n",
       "144          4.7       230.0      112.2   76.4   192.5        5.115   \n",
       "156          2.9       201.0      109.9   72.1   189.8        3.600   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "1             17.2             25.0          91.370778  \n",
       "2             17.2             26.0                NaN  \n",
       "3             18.0             22.0          91.389779  \n",
       "5             18.5             22.0          84.565105  \n",
       "6             23.7             21.0         134.656858  \n",
       "..             ...              ...                ...  \n",
       "138           18.5             26.0          84.911898  \n",
       "141           20.9             22.0          78.027219  \n",
       "143           18.5             23.0          62.355577  \n",
       "144           25.4             15.0         102.528984  \n",
       "156           21.1             24.0          85.735655  \n",
       "\n",
       "[102 rows x 13 columns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using multiple conditions with 'or'\n",
    "\n",
    "csv_df[(csv_df['Engine_size'] > 2.4) | (csv_df['Horsepower'] > 250.0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting Values into DataFrame Cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sales_in_thousands          9.2\n",
       "Resale_value             32.899\n",
       "Vehicle_type          Passenger\n",
       "Price_in_thousands           45\n",
       "Engine_size                 3.2\n",
       "Horsepower                  220\n",
       "Wheelbase                 115.2\n",
       "Width                      80.8\n",
       "Length                    201.9\n",
       "Curb_weight                4.11\n",
       "Fuel_capacity                14\n",
       "Fuel_efficiency              21\n",
       "Power_perf_factor       90.7788\n",
       "dtype: object"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser = pd.Series([9.200, 32.899, 'Passenger', 45, 3.2, 220, 115.2, 80.8, 201.9, 4.11, 14, 21, 90.7788], index = csv_df.columns)\n",
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>15.245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>27.50</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>185.9</td>\n",
       "      <td>3.208</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.654495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>17.531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>186.2</td>\n",
       "      <td>3.259</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.155978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3.493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.50</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>185.7</td>\n",
       "      <td>3.601</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.623357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>18.969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.00</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>9.200</td>\n",
       "      <td>32.899</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.00</td>\n",
       "      <td>3.2</td>\n",
       "      <td>220.0</td>\n",
       "      <td>115.2</td>\n",
       "      <td>80.8</td>\n",
       "      <td>201.9</td>\n",
       "      <td>4.110</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>90.778800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "0                16.919        16.360    Passenger               21.50   \n",
       "1                39.384        19.875    Passenger               28.40   \n",
       "2                14.114        18.225    Passenger                 NaN   \n",
       "3                 8.588        29.725    Passenger               42.00   \n",
       "4                20.397        22.255    Passenger               23.99   \n",
       "..                  ...           ...          ...                 ...   \n",
       "153              15.245           NaN    Passenger               27.50   \n",
       "154              17.531           NaN    Passenger               28.80   \n",
       "155               3.493           NaN    Passenger               45.50   \n",
       "156              18.969           NaN    Passenger               36.00   \n",
       "157               9.200        32.899    Passenger               45.00   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "0            1.8       140.0      101.2   67.3   172.4        2.639   \n",
       "1            3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2            3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "3            3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "4            1.8       150.0      102.6   68.2   178.0        2.998   \n",
       "..           ...         ...        ...    ...     ...          ...   \n",
       "153          2.4       168.0      104.9   69.3   185.9        3.208   \n",
       "154          2.4       168.0      104.9   69.3   186.2        3.259   \n",
       "155          2.3       236.0      104.9   71.5   185.7        3.601   \n",
       "156          2.9       201.0      109.9   72.1   189.8        3.600   \n",
       "157          3.2       220.0      115.2   80.8   201.9        4.110   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "0             13.2             28.0          58.280150  \n",
       "1             17.2             25.0          91.370778  \n",
       "2             17.2             26.0                NaN  \n",
       "3             18.0             22.0          91.389779  \n",
       "4             16.4             27.0          62.777639  \n",
       "..             ...              ...                ...  \n",
       "153           17.9             25.0          70.654495  \n",
       "154           17.9             25.0          71.155978  \n",
       "155           18.5             23.0         101.623357  \n",
       "156           21.1             24.0          85.735655  \n",
       "157           14.0             21.0          90.778800  \n",
       "\n",
       "[158 rows x 13 columns]"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_df = csv_df.append(ser, ignore_index=True)\n",
    "csv_df     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>21.565</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>15.245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>27.50</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>185.9</td>\n",
       "      <td>3.208</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.654495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>17.531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>186.2</td>\n",
       "      <td>3.259</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.155978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3.493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.50</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>185.7</td>\n",
       "      <td>3.601</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.623357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>18.969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.00</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>9.200</td>\n",
       "      <td>32.899</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.00</td>\n",
       "      <td>3.2</td>\n",
       "      <td>220.0</td>\n",
       "      <td>115.2</td>\n",
       "      <td>80.8</td>\n",
       "      <td>201.9</td>\n",
       "      <td>4.110</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>90.778800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "0                16.919        16.360    Passenger               21.50   \n",
       "1                39.384        21.565    Passenger               28.40   \n",
       "2                14.114        18.225    Passenger                 NaN   \n",
       "3                 8.588        29.725    Passenger               42.00   \n",
       "4                20.397        22.255    Passenger               23.99   \n",
       "..                  ...           ...          ...                 ...   \n",
       "153              15.245           NaN    Passenger               27.50   \n",
       "154              17.531           NaN    Passenger               28.80   \n",
       "155               3.493           NaN    Passenger               45.50   \n",
       "156              18.969           NaN    Passenger               36.00   \n",
       "157               9.200        32.899    Passenger               45.00   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "0            1.8       140.0      101.2   67.3   172.4        2.639   \n",
       "1            3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2            3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "3            3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "4            1.8       150.0      102.6   68.2   178.0        2.998   \n",
       "..           ...         ...        ...    ...     ...          ...   \n",
       "153          2.4       168.0      104.9   69.3   185.9        3.208   \n",
       "154          2.4       168.0      104.9   69.3   186.2        3.259   \n",
       "155          2.3       236.0      104.9   71.5   185.7        3.601   \n",
       "156          2.9       201.0      109.9   72.1   189.8        3.600   \n",
       "157          3.2       220.0      115.2   80.8   201.9        4.110   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "0             13.2             28.0          58.280150  \n",
       "1             17.2             25.0          91.370778  \n",
       "2             17.2             26.0                NaN  \n",
       "3             18.0             22.0          91.389779  \n",
       "4             16.4             27.0          62.777639  \n",
       "..             ...              ...                ...  \n",
       "153           17.9             25.0          70.654495  \n",
       "154           17.9             25.0          71.155978  \n",
       "155           18.5             23.0         101.623357  \n",
       "156           21.1             24.0          85.735655  \n",
       "157           14.0             21.0          90.778800  \n",
       "\n",
       "[158 rows x 13 columns]"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Changing the Value of a specified cell\n",
    "\n",
    "csv_df.at[1, 'Resale_value'] = 21.565\n",
    "csv_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.919</td>\n",
       "      <td>19.6320</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>25.8780</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>21.8700</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.588</td>\n",
       "      <td>35.6700</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.397</td>\n",
       "      <td>26.7060</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>15.245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>27.50</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>185.9</td>\n",
       "      <td>3.208</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.654495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>17.531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>186.2</td>\n",
       "      <td>3.259</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.155978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3.493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.50</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>185.7</td>\n",
       "      <td>3.601</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.623357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>18.969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.00</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>9.200</td>\n",
       "      <td>39.4788</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.00</td>\n",
       "      <td>3.2</td>\n",
       "      <td>220.0</td>\n",
       "      <td>115.2</td>\n",
       "      <td>80.8</td>\n",
       "      <td>201.9</td>\n",
       "      <td>4.110</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>90.778800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "0                16.919       19.6320    Passenger               21.50   \n",
       "1                39.384       25.8780    Passenger               28.40   \n",
       "2                14.114       21.8700    Passenger                 NaN   \n",
       "3                 8.588       35.6700    Passenger               42.00   \n",
       "4                20.397       26.7060    Passenger               23.99   \n",
       "..                  ...           ...          ...                 ...   \n",
       "153              15.245           NaN    Passenger               27.50   \n",
       "154              17.531           NaN    Passenger               28.80   \n",
       "155               3.493           NaN    Passenger               45.50   \n",
       "156              18.969           NaN    Passenger               36.00   \n",
       "157               9.200       39.4788    Passenger               45.00   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "0            1.8       140.0      101.2   67.3   172.4        2.639   \n",
       "1            3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2            3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "3            3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "4            1.8       150.0      102.6   68.2   178.0        2.998   \n",
       "..           ...         ...        ...    ...     ...          ...   \n",
       "153          2.4       168.0      104.9   69.3   185.9        3.208   \n",
       "154          2.4       168.0      104.9   69.3   186.2        3.259   \n",
       "155          2.3       236.0      104.9   71.5   185.7        3.601   \n",
       "156          2.9       201.0      109.9   72.1   189.8        3.600   \n",
       "157          3.2       220.0      115.2   80.8   201.9        4.110   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "0             13.2             28.0          58.280150  \n",
       "1             17.2             25.0          91.370778  \n",
       "2             17.2             26.0                NaN  \n",
       "3             18.0             22.0          91.389779  \n",
       "4             16.4             27.0          62.777639  \n",
       "..             ...              ...                ...  \n",
       "153           17.9             25.0          70.654495  \n",
       "154           17.9             25.0          71.155978  \n",
       "155           18.5             23.0         101.623357  \n",
       "156           21.1             24.0          85.735655  \n",
       "157           14.0             21.0          90.778800  \n",
       "\n",
       "[158 rows x 13 columns]"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bulk update \n",
    "\n",
    "csv_df['Resale_value'] = csv_df['Resale_value'] * 1.2\n",
    "csv_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "      <th>Discounted_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.919</td>\n",
       "      <td>19.6320</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "      <td>15.70560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>25.8780</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "      <td>20.70240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>21.8700</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.49600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.588</td>\n",
       "      <td>35.6700</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "      <td>28.53600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.397</td>\n",
       "      <td>26.7060</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "      <td>21.36480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>15.245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>27.50</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>185.9</td>\n",
       "      <td>3.208</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.654495</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>17.531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>186.2</td>\n",
       "      <td>3.259</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.155978</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3.493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.50</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>185.7</td>\n",
       "      <td>3.601</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.623357</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>18.969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.00</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>9.200</td>\n",
       "      <td>39.4788</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.00</td>\n",
       "      <td>3.2</td>\n",
       "      <td>220.0</td>\n",
       "      <td>115.2</td>\n",
       "      <td>80.8</td>\n",
       "      <td>201.9</td>\n",
       "      <td>4.110</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>90.778800</td>\n",
       "      <td>31.58304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "0                16.919       19.6320    Passenger               21.50   \n",
       "1                39.384       25.8780    Passenger               28.40   \n",
       "2                14.114       21.8700    Passenger                 NaN   \n",
       "3                 8.588       35.6700    Passenger               42.00   \n",
       "4                20.397       26.7060    Passenger               23.99   \n",
       "..                  ...           ...          ...                 ...   \n",
       "153              15.245           NaN    Passenger               27.50   \n",
       "154              17.531           NaN    Passenger               28.80   \n",
       "155               3.493           NaN    Passenger               45.50   \n",
       "156              18.969           NaN    Passenger               36.00   \n",
       "157               9.200       39.4788    Passenger               45.00   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "0            1.8       140.0      101.2   67.3   172.4        2.639   \n",
       "1            3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2            3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "3            3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "4            1.8       150.0      102.6   68.2   178.0        2.998   \n",
       "..           ...         ...        ...    ...     ...          ...   \n",
       "153          2.4       168.0      104.9   69.3   185.9        3.208   \n",
       "154          2.4       168.0      104.9   69.3   186.2        3.259   \n",
       "155          2.3       236.0      104.9   71.5   185.7        3.601   \n",
       "156          2.9       201.0      109.9   72.1   189.8        3.600   \n",
       "157          3.2       220.0      115.2   80.8   201.9        4.110   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  Discounted_value  \n",
       "0             13.2             28.0          58.280150          15.70560  \n",
       "1             17.2             25.0          91.370778          20.70240  \n",
       "2             17.2             26.0                NaN          17.49600  \n",
       "3             18.0             22.0          91.389779          28.53600  \n",
       "4             16.4             27.0          62.777639          21.36480  \n",
       "..             ...              ...                ...               ...  \n",
       "153           17.9             25.0          70.654495               NaN  \n",
       "154           17.9             25.0          71.155978               NaN  \n",
       "155           18.5             23.0         101.623357               NaN  \n",
       "156           21.1             24.0          85.735655               NaN  \n",
       "157           14.0             21.0          90.778800          31.58304  \n",
       "\n",
       "[158 rows x 14 columns]"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new Column\n",
    "\n",
    "csv_df['Discounted_value'] = csv_df['Resale_value'] * 0.8\n",
    "csv_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "      <th>Discounted_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.919</td>\n",
       "      <td>19.6320</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "      <td>15.70560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>25.8780</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "      <td>20.70240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>21.8700</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>33.44</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.49600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.588</td>\n",
       "      <td>35.6700</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "      <td>28.53600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.397</td>\n",
       "      <td>26.7060</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "      <td>21.36480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>15.245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>27.50</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>185.9</td>\n",
       "      <td>3.208</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.654495</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>17.531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>186.2</td>\n",
       "      <td>3.259</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.155978</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3.493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.50</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>185.7</td>\n",
       "      <td>3.601</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.623357</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>18.969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.00</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>9.200</td>\n",
       "      <td>39.4788</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.00</td>\n",
       "      <td>3.2</td>\n",
       "      <td>220.0</td>\n",
       "      <td>115.2</td>\n",
       "      <td>80.8</td>\n",
       "      <td>201.9</td>\n",
       "      <td>4.110</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>90.778800</td>\n",
       "      <td>31.58304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "0                16.919       19.6320    Passenger               21.50   \n",
       "1                39.384       25.8780    Passenger               28.40   \n",
       "2                14.114       21.8700    Passenger               33.44   \n",
       "3                 8.588       35.6700    Passenger               42.00   \n",
       "4                20.397       26.7060    Passenger               23.99   \n",
       "..                  ...           ...          ...                 ...   \n",
       "153              15.245           NaN    Passenger               27.50   \n",
       "154              17.531           NaN    Passenger               28.80   \n",
       "155               3.493           NaN    Passenger               45.50   \n",
       "156              18.969           NaN    Passenger               36.00   \n",
       "157               9.200       39.4788    Passenger               45.00   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "0            1.8       140.0      101.2   67.3   172.4        2.639   \n",
       "1            3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2            3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "3            3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "4            1.8       150.0      102.6   68.2   178.0        2.998   \n",
       "..           ...         ...        ...    ...     ...          ...   \n",
       "153          2.4       168.0      104.9   69.3   185.9        3.208   \n",
       "154          2.4       168.0      104.9   69.3   186.2        3.259   \n",
       "155          2.3       236.0      104.9   71.5   185.7        3.601   \n",
       "156          2.9       201.0      109.9   72.1   189.8        3.600   \n",
       "157          3.2       220.0      115.2   80.8   201.9        4.110   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  Discounted_value  \n",
       "0             13.2             28.0          58.280150          15.70560  \n",
       "1             17.2             25.0          91.370778          20.70240  \n",
       "2             17.2             26.0                NaN          17.49600  \n",
       "3             18.0             22.0          91.389779          28.53600  \n",
       "4             16.4             27.0          62.777639          21.36480  \n",
       "..             ...              ...                ...               ...  \n",
       "153           17.9             25.0          70.654495               NaN  \n",
       "154           17.9             25.0          71.155978               NaN  \n",
       "155           18.5             23.0         101.623357               NaN  \n",
       "156           21.1             24.0          85.735655               NaN  \n",
       "157           14.0             21.0          90.778800          31.58304  \n",
       "\n",
       "[158 rows x 14 columns]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using iat to specify numbered indexes\n",
    "\n",
    "csv_df.iat[2, 3] = 33.44\n",
    "csv_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "      <th>Discounted_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.919</td>\n",
       "      <td>19.632</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "      <td>15.7056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>25.878</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "      <td>20.7024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>21.870</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>33.44</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.4960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.588</td>\n",
       "      <td>35.670</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "      <td>28.5360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.397</td>\n",
       "      <td>26.706</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "      <td>21.3648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "0              16.919        19.632    Passenger               21.50   \n",
       "1              39.384        25.878    Passenger               28.40   \n",
       "2              14.114        21.870    Passenger               33.44   \n",
       "3               8.588        35.670    Passenger               42.00   \n",
       "4              20.397        26.706    Passenger               23.99   \n",
       "\n",
       "   Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "0          1.8       140.0      101.2   67.3   172.4        2.639   \n",
       "1          3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2          3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "3          3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "4          1.8       150.0      102.6   68.2   178.0        2.998   \n",
       "\n",
       "   Fuel_capacity  Fuel_efficiency  Power_perf_factor  Discounted_value  \n",
       "0           13.2             28.0          58.280150           15.7056  \n",
       "1           17.2             25.0          91.370778           20.7024  \n",
       "2           17.2             26.0                NaN           17.4960  \n",
       "3           18.0             22.0          91.389779           28.5360  \n",
       "4           16.4             27.0          62.777639           21.3648  "
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Append two DataFrames (row-wise one after the other)\n",
    "\n",
    "csv_df1 = csv_df[0:5]\n",
    "csv_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "      <th>Discounted_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.919</td>\n",
       "      <td>19.632</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "      <td>15.7056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>25.878</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "      <td>20.7024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>21.870</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>33.44</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.4960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.588</td>\n",
       "      <td>35.670</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "      <td>28.5360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.397</td>\n",
       "      <td>26.706</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "      <td>21.3648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.919</td>\n",
       "      <td>19.632</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "      <td>15.7056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>25.878</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "      <td>20.7024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>21.870</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>33.44</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.4960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.588</td>\n",
       "      <td>35.670</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "      <td>28.5360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.397</td>\n",
       "      <td>26.706</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "      <td>21.3648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>163 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "0               16.919        19.632    Passenger               21.50   \n",
       "1               39.384        25.878    Passenger               28.40   \n",
       "2               14.114        21.870    Passenger               33.44   \n",
       "3                8.588        35.670    Passenger               42.00   \n",
       "4               20.397        26.706    Passenger               23.99   \n",
       "..                 ...           ...          ...                 ...   \n",
       "0               16.919        19.632    Passenger               21.50   \n",
       "1               39.384        25.878    Passenger               28.40   \n",
       "2               14.114        21.870    Passenger               33.44   \n",
       "3                8.588        35.670    Passenger               42.00   \n",
       "4               20.397        26.706    Passenger               23.99   \n",
       "\n",
       "    Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "0           1.8       140.0      101.2   67.3   172.4        2.639   \n",
       "1           3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2           3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "3           3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "4           1.8       150.0      102.6   68.2   178.0        2.998   \n",
       "..          ...         ...        ...    ...     ...          ...   \n",
       "0           1.8       140.0      101.2   67.3   172.4        2.639   \n",
       "1           3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2           3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "3           3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "4           1.8       150.0      102.6   68.2   178.0        2.998   \n",
       "\n",
       "    Fuel_capacity  Fuel_efficiency  Power_perf_factor  Discounted_value  \n",
       "0            13.2             28.0          58.280150           15.7056  \n",
       "1            17.2             25.0          91.370778           20.7024  \n",
       "2            17.2             26.0                NaN           17.4960  \n",
       "3            18.0             22.0          91.389779           28.5360  \n",
       "4            16.4             27.0          62.777639           21.3648  \n",
       "..            ...              ...                ...               ...  \n",
       "0            13.2             28.0          58.280150           15.7056  \n",
       "1            17.2             25.0          91.370778           20.7024  \n",
       "2            17.2             26.0                NaN           17.4960  \n",
       "3            18.0             22.0          91.389779           28.5360  \n",
       "4            16.4             27.0          62.777639           21.3648  \n",
       "\n",
       "[163 rows x 14 columns]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_df = csv_df.append(csv_df1)\n",
    "csv_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concatenating DataFrames - Merging/Joining"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Concatenating DataFrames is a very important feature of Pandas and used in scenarios such as where two sets of data with linkage through common keys are existing\n",
    "#### In these cases, we are able to perform operations such as merge or join of these DataFrames using the keys using the concat() function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>3.545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>24.40</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.5</td>\n",
       "      <td>67.6</td>\n",
       "      <td>176.6</td>\n",
       "      <td>3.042</td>\n",
       "      <td>15.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>66.498812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>15.245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>27.50</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>185.9</td>\n",
       "      <td>3.208</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.654495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>17.531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>186.2</td>\n",
       "      <td>3.259</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.155978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3.493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.50</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>185.7</td>\n",
       "      <td>3.601</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.623357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>18.969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.00</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>157 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "0                16.919        16.360    Passenger               21.50   \n",
       "1                39.384        19.875    Passenger               28.40   \n",
       "2                14.114        18.225    Passenger                 NaN   \n",
       "3                 8.588        29.725    Passenger               42.00   \n",
       "4                20.397        22.255    Passenger               23.99   \n",
       "..                  ...           ...          ...                 ...   \n",
       "152               3.545           NaN    Passenger               24.40   \n",
       "153              15.245           NaN    Passenger               27.50   \n",
       "154              17.531           NaN    Passenger               28.80   \n",
       "155               3.493           NaN    Passenger               45.50   \n",
       "156              18.969           NaN    Passenger               36.00   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "0            1.8       140.0      101.2   67.3   172.4        2.639   \n",
       "1            3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2            3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "3            3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "4            1.8       150.0      102.6   68.2   178.0        2.998   \n",
       "..           ...         ...        ...    ...     ...          ...   \n",
       "152          1.9       160.0      100.5   67.6   176.6        3.042   \n",
       "153          2.4       168.0      104.9   69.3   185.9        3.208   \n",
       "154          2.4       168.0      104.9   69.3   186.2        3.259   \n",
       "155          2.3       236.0      104.9   71.5   185.7        3.601   \n",
       "156          2.9       201.0      109.9   72.1   189.8        3.600   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "0             13.2             28.0          58.280150  \n",
       "1             17.2             25.0          91.370778  \n",
       "2             17.2             26.0                NaN  \n",
       "3             18.0             22.0          91.389779  \n",
       "4             16.4             27.0          62.777639  \n",
       "..             ...              ...                ...  \n",
       "152           15.8             25.0          66.498812  \n",
       "153           17.9             25.0          70.654495  \n",
       "154           17.9             25.0          71.155978  \n",
       "155           18.5             23.0         101.623357  \n",
       "156           21.1             24.0          85.735655  \n",
       "\n",
       "[157 rows x 13 columns]"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the Car Sales Data\n",
    "\n",
    "csv_df = pd.read_csv(\"car_sales.csv\")\n",
    "csv_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>152</td>\n",
       "      <td>3.545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>24.40</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.5</td>\n",
       "      <td>67.6</td>\n",
       "      <td>176.6</td>\n",
       "      <td>3.042</td>\n",
       "      <td>15.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>66.498812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>153</td>\n",
       "      <td>15.245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>27.50</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>185.9</td>\n",
       "      <td>3.208</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.654495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>154</td>\n",
       "      <td>17.531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>186.2</td>\n",
       "      <td>3.259</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.155978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>155</td>\n",
       "      <td>3.493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.50</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>185.7</td>\n",
       "      <td>3.601</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.623357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>156</td>\n",
       "      <td>18.969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.00</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>157 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "0        0              16.919        16.360    Passenger               21.50   \n",
       "1        1              39.384        19.875    Passenger               28.40   \n",
       "2        2              14.114        18.225    Passenger                 NaN   \n",
       "3        3               8.588        29.725    Passenger               42.00   \n",
       "4        4              20.397        22.255    Passenger               23.99   \n",
       "..     ...                 ...           ...          ...                 ...   \n",
       "152    152               3.545           NaN    Passenger               24.40   \n",
       "153    153              15.245           NaN    Passenger               27.50   \n",
       "154    154              17.531           NaN    Passenger               28.80   \n",
       "155    155               3.493           NaN    Passenger               45.50   \n",
       "156    156              18.969           NaN    Passenger               36.00   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "0            1.8       140.0      101.2   67.3   172.4        2.639   \n",
       "1            3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2            3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "3            3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "4            1.8       150.0      102.6   68.2   178.0        2.998   \n",
       "..           ...         ...        ...    ...     ...          ...   \n",
       "152          1.9       160.0      100.5   67.6   176.6        3.042   \n",
       "153          2.4       168.0      104.9   69.3   185.9        3.208   \n",
       "154          2.4       168.0      104.9   69.3   186.2        3.259   \n",
       "155          2.3       236.0      104.9   71.5   185.7        3.601   \n",
       "156          2.9       201.0      109.9   72.1   189.8        3.600   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "0             13.2             28.0          58.280150  \n",
       "1             17.2             25.0          91.370778  \n",
       "2             17.2             26.0                NaN  \n",
       "3             18.0             22.0          91.389779  \n",
       "4             16.4             27.0          62.777639  \n",
       "..             ...              ...                ...  \n",
       "152           15.8             25.0          66.498812  \n",
       "153           17.9             25.0          70.654495  \n",
       "154           17.9             25.0          71.155978  \n",
       "155           18.5             23.0         101.623357  \n",
       "156           21.1             24.0          85.735655  \n",
       "\n",
       "[157 rows x 14 columns]"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the Row Index numbers as a Column to use it as an Unique key\n",
    "\n",
    "csv_df.reset_index(inplace=True)\n",
    "csv_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>152</td>\n",
       "      <td>3.545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>24.40</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.5</td>\n",
       "      <td>67.6</td>\n",
       "      <td>176.6</td>\n",
       "      <td>3.042</td>\n",
       "      <td>15.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>66.498812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>153</td>\n",
       "      <td>15.245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>27.50</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>185.9</td>\n",
       "      <td>3.208</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.654495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>154</td>\n",
       "      <td>17.531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>186.2</td>\n",
       "      <td>3.259</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.155978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>155</td>\n",
       "      <td>3.493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.50</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>185.7</td>\n",
       "      <td>3.601</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.623357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>156</td>\n",
       "      <td>18.969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.00</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>157 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "0        0              16.919        16.360    Passenger               21.50   \n",
       "1        1              39.384        19.875    Passenger               28.40   \n",
       "2        2              14.114        18.225    Passenger                 NaN   \n",
       "3        3               8.588        29.725    Passenger               42.00   \n",
       "4        4              20.397        22.255    Passenger               23.99   \n",
       "..     ...                 ...           ...          ...                 ...   \n",
       "152    152               3.545           NaN    Passenger               24.40   \n",
       "153    153              15.245           NaN    Passenger               27.50   \n",
       "154    154              17.531           NaN    Passenger               28.80   \n",
       "155    155               3.493           NaN    Passenger               45.50   \n",
       "156    156              18.969           NaN    Passenger               36.00   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "0            1.8       140.0      101.2   67.3   172.4        2.639   \n",
       "1            3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2            3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "3            3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "4            1.8       150.0      102.6   68.2   178.0        2.998   \n",
       "..           ...         ...        ...    ...     ...          ...   \n",
       "152          1.9       160.0      100.5   67.6   176.6        3.042   \n",
       "153          2.4       168.0      104.9   69.3   185.9        3.208   \n",
       "154          2.4       168.0      104.9   69.3   186.2        3.259   \n",
       "155          2.3       236.0      104.9   71.5   185.7        3.601   \n",
       "156          2.9       201.0      109.9   72.1   189.8        3.600   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "0             13.2             28.0          58.280150  \n",
       "1             17.2             25.0          91.370778  \n",
       "2             17.2             26.0                NaN  \n",
       "3             18.0             22.0          91.389779  \n",
       "4             16.4             27.0          62.777639  \n",
       "..             ...              ...                ...  \n",
       "152           15.8             25.0          66.498812  \n",
       "153           17.9             25.0          70.654495  \n",
       "154           17.9             25.0          71.155978  \n",
       "155           18.5             23.0         101.623357  \n",
       "156           21.1             24.0          85.735655  \n",
       "\n",
       "[157 rows x 14 columns]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a separate physical copy of the DataFrame (Also called 'deep' copy)\n",
    "# dataframe2 = dataframe1 kind of assignment creates a memory reference and not a physically separate copy\n",
    "\n",
    "csv_df1 = csv_df.copy(deep=True)\n",
    "csv_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>...</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>...</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>...</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>...</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>152</td>\n",
       "      <td>3.545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>24.40</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.5</td>\n",
       "      <td>67.6</td>\n",
       "      <td>176.6</td>\n",
       "      <td>...</td>\n",
       "      <td>24.40</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.5</td>\n",
       "      <td>67.6</td>\n",
       "      <td>176.6</td>\n",
       "      <td>3.042</td>\n",
       "      <td>15.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>66.498812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>153</td>\n",
       "      <td>15.245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>27.50</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>185.9</td>\n",
       "      <td>...</td>\n",
       "      <td>27.50</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>185.9</td>\n",
       "      <td>3.208</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.654495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>154</td>\n",
       "      <td>17.531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>186.2</td>\n",
       "      <td>...</td>\n",
       "      <td>28.80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>186.2</td>\n",
       "      <td>3.259</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.155978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>155</td>\n",
       "      <td>3.493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.50</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>185.7</td>\n",
       "      <td>...</td>\n",
       "      <td>45.50</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>185.7</td>\n",
       "      <td>3.601</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.623357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>156</td>\n",
       "      <td>18.969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.00</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>...</td>\n",
       "      <td>36.00</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>157 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "0        0              16.919        16.360    Passenger               21.50   \n",
       "1        1              39.384        19.875    Passenger               28.40   \n",
       "2        2              14.114        18.225    Passenger                 NaN   \n",
       "3        3               8.588        29.725    Passenger               42.00   \n",
       "4        4              20.397        22.255    Passenger               23.99   \n",
       "..     ...                 ...           ...          ...                 ...   \n",
       "152    152               3.545           NaN    Passenger               24.40   \n",
       "153    153              15.245           NaN    Passenger               27.50   \n",
       "154    154              17.531           NaN    Passenger               28.80   \n",
       "155    155               3.493           NaN    Passenger               45.50   \n",
       "156    156              18.969           NaN    Passenger               36.00   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  ...  \\\n",
       "0            1.8       140.0      101.2   67.3   172.4  ...   \n",
       "1            3.2       225.0      108.1   70.3   192.9  ...   \n",
       "2            3.2       225.0      106.9   70.6   192.0  ...   \n",
       "3            3.5       210.0      114.6   71.4   196.6  ...   \n",
       "4            1.8       150.0      102.6   68.2   178.0  ...   \n",
       "..           ...         ...        ...    ...     ...  ...   \n",
       "152          1.9       160.0      100.5   67.6   176.6  ...   \n",
       "153          2.4       168.0      104.9   69.3   185.9  ...   \n",
       "154          2.4       168.0      104.9   69.3   186.2  ...   \n",
       "155          2.3       236.0      104.9   71.5   185.7  ...   \n",
       "156          2.9       201.0      109.9   72.1   189.8  ...   \n",
       "\n",
       "     Price_in_thousands  Engine_size  Horsepower  Wheelbase  Width  Length  \\\n",
       "0                 21.50          1.8       140.0      101.2   67.3   172.4   \n",
       "1                 28.40          3.2       225.0      108.1   70.3   192.9   \n",
       "2                   NaN          3.2       225.0      106.9   70.6   192.0   \n",
       "3                 42.00          3.5       210.0      114.6   71.4   196.6   \n",
       "4                 23.99          1.8       150.0      102.6   68.2   178.0   \n",
       "..                  ...          ...         ...        ...    ...     ...   \n",
       "152               24.40          1.9       160.0      100.5   67.6   176.6   \n",
       "153               27.50          2.4       168.0      104.9   69.3   185.9   \n",
       "154               28.80          2.4       168.0      104.9   69.3   186.2   \n",
       "155               45.50          2.3       236.0      104.9   71.5   185.7   \n",
       "156               36.00          2.9       201.0      109.9   72.1   189.8   \n",
       "\n",
       "     Curb_weight Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "0          2.639          13.2             28.0          58.280150  \n",
       "1          3.517          17.2             25.0          91.370778  \n",
       "2          3.470          17.2             26.0                NaN  \n",
       "3          3.850          18.0             22.0          91.389779  \n",
       "4          2.998          16.4             27.0          62.777639  \n",
       "..           ...           ...              ...                ...  \n",
       "152        3.042          15.8             25.0          66.498812  \n",
       "153        3.208          17.9             25.0          70.654495  \n",
       "154        3.259          17.9             25.0          71.155978  \n",
       "155        3.601          18.5             23.0         101.623357  \n",
       "156        3.600          21.1             24.0          85.735655  \n",
       "\n",
       "[157 rows x 28 columns]"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now Concatenate these two DataFrames horizontally \n",
    "# The output will be a DataFrame that combines the two input DataFranes Horizontally.\n",
    "# The Columns will be duplicated. \n",
    "\n",
    "df_concat = pd.concat([csv_df, csv_df1], axis=1)\n",
    "df_concat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'Sales_in_thousands', 'Resale_value', 'Vehicle_type',\n",
       "       'Price_in_thousands', 'Engine_size', 'Horsepower', 'Wheelbase', 'Width',\n",
       "       'Length', 'Curb_weight', 'Fuel_capacity', 'Fuel_efficiency',\n",
       "       'Power_perf_factor', 'index', 'Sales_in_thousands', 'Resale_value',\n",
       "       'Vehicle_type', 'Price_in_thousands', 'Engine_size', 'Horsepower',\n",
       "       'Wheelbase', 'Width', 'Length', 'Curb_weight', 'Fuel_capacity',\n",
       "       'Fuel_efficiency', 'Power_perf_factor'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_concat.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>152</td>\n",
       "      <td>3.545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>24.40</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.5</td>\n",
       "      <td>67.6</td>\n",
       "      <td>176.6</td>\n",
       "      <td>3.042</td>\n",
       "      <td>15.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>66.498812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>153</td>\n",
       "      <td>15.245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>27.50</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>185.9</td>\n",
       "      <td>3.208</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.654495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>154</td>\n",
       "      <td>17.531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>186.2</td>\n",
       "      <td>3.259</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.155978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>155</td>\n",
       "      <td>3.493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.50</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>185.7</td>\n",
       "      <td>3.601</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.623357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>156</td>\n",
       "      <td>18.969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.00</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>314 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "0        0              16.919        16.360    Passenger               21.50   \n",
       "1        1              39.384        19.875    Passenger               28.40   \n",
       "2        2              14.114        18.225    Passenger                 NaN   \n",
       "3        3               8.588        29.725    Passenger               42.00   \n",
       "4        4              20.397        22.255    Passenger               23.99   \n",
       "..     ...                 ...           ...          ...                 ...   \n",
       "152    152               3.545           NaN    Passenger               24.40   \n",
       "153    153              15.245           NaN    Passenger               27.50   \n",
       "154    154              17.531           NaN    Passenger               28.80   \n",
       "155    155               3.493           NaN    Passenger               45.50   \n",
       "156    156              18.969           NaN    Passenger               36.00   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "0            1.8       140.0      101.2   67.3   172.4        2.639   \n",
       "1            3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2            3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "3            3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "4            1.8       150.0      102.6   68.2   178.0        2.998   \n",
       "..           ...         ...        ...    ...     ...          ...   \n",
       "152          1.9       160.0      100.5   67.6   176.6        3.042   \n",
       "153          2.4       168.0      104.9   69.3   185.9        3.208   \n",
       "154          2.4       168.0      104.9   69.3   186.2        3.259   \n",
       "155          2.3       236.0      104.9   71.5   185.7        3.601   \n",
       "156          2.9       201.0      109.9   72.1   189.8        3.600   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "0             13.2             28.0          58.280150  \n",
       "1             17.2             25.0          91.370778  \n",
       "2             17.2             26.0                NaN  \n",
       "3             18.0             22.0          91.389779  \n",
       "4             16.4             27.0          62.777639  \n",
       "..             ...              ...                ...  \n",
       "152           15.8             25.0          66.498812  \n",
       "153           17.9             25.0          70.654495  \n",
       "154           17.9             25.0          71.155978  \n",
       "155           18.5             23.0         101.623357  \n",
       "156           21.1             24.0          85.735655  \n",
       "\n",
       "[314 rows x 14 columns]"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate the DataFrames Vertically in a stacked manner\n",
    "\n",
    "df_concat1 = pd.concat([csv_df, csv_df1], axis=0)\n",
    "df_concat1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>150</td>\n",
       "      <td>49.463</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>15.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>98.9</td>\n",
       "      <td>67.9</td>\n",
       "      <td>161.1</td>\n",
       "      <td>2.769</td>\n",
       "      <td>14.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>47.329632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>151</td>\n",
       "      <td>16.957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.5</td>\n",
       "      <td>67.6</td>\n",
       "      <td>176.6</td>\n",
       "      <td>2.998</td>\n",
       "      <td>15.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>66.113057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>152</td>\n",
       "      <td>3.545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>24.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.5</td>\n",
       "      <td>67.6</td>\n",
       "      <td>176.6</td>\n",
       "      <td>3.042</td>\n",
       "      <td>15.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>66.498812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>153</td>\n",
       "      <td>15.245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>27.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>185.9</td>\n",
       "      <td>3.208</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.654495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>154</td>\n",
       "      <td>17.531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.8</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>186.2</td>\n",
       "      <td>3.259</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.155978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>155</td>\n",
       "      <td>3.493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>185.7</td>\n",
       "      <td>3.601</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.623357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>156</td>\n",
       "      <td>18.969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "150    150              49.463           NaN    Passenger                15.9   \n",
       "151    151              16.957           NaN    Passenger                23.4   \n",
       "152    152               3.545           NaN    Passenger                24.4   \n",
       "153    153              15.245           NaN    Passenger                27.5   \n",
       "154    154              17.531           NaN    Passenger                28.8   \n",
       "155    155               3.493           NaN    Passenger                45.5   \n",
       "156    156              18.969           NaN    Passenger                36.0   \n",
       "0        0              16.919        16.360    Passenger                21.5   \n",
       "1        1              39.384        19.875    Passenger                28.4   \n",
       "2        2              14.114        18.225    Passenger                 NaN   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "150          2.0       115.0       98.9   67.9   161.1        2.769   \n",
       "151          1.9       160.0      100.5   67.6   176.6        2.998   \n",
       "152          1.9       160.0      100.5   67.6   176.6        3.042   \n",
       "153          2.4       168.0      104.9   69.3   185.9        3.208   \n",
       "154          2.4       168.0      104.9   69.3   186.2        3.259   \n",
       "155          2.3       236.0      104.9   71.5   185.7        3.601   \n",
       "156          2.9       201.0      109.9   72.1   189.8        3.600   \n",
       "0            1.8       140.0      101.2   67.3   172.4        2.639   \n",
       "1            3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2            3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "150           14.5             26.0          47.329632  \n",
       "151           15.8             25.0          66.113057  \n",
       "152           15.8             25.0          66.498812  \n",
       "153           17.9             25.0          70.654495  \n",
       "154           17.9             25.0          71.155978  \n",
       "155           18.5             23.0         101.623357  \n",
       "156           21.1             24.0          85.735655  \n",
       "0             13.2             28.0          58.280150  \n",
       "1             17.2             25.0          91.370778  \n",
       "2             17.2             26.0                NaN  "
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_concat1[150:160]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenation Based on 'Keys'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year         pop continent  lifeExp   gdpPercap\n",
       "0     Afghanistan  1952   8425333.0      Asia   28.801  779.445314\n",
       "1     Afghanistan  1957   9240934.0      Asia   30.332  820.853030\n",
       "2     Afghanistan  1962  10267083.0      Asia   31.997  853.100710\n",
       "3     Afghanistan  1967  11537966.0      Asia   34.020  836.197138\n",
       "4     Afghanistan  1972  13079460.0      Asia   36.088  739.981106\n",
       "...           ...   ...         ...       ...      ...         ...\n",
       "1699     Zimbabwe  1987   9216418.0    Africa   62.351  706.157306\n",
       "1700     Zimbabwe  1992  10704340.0    Africa   60.377  693.420786\n",
       "1701     Zimbabwe  1997  11404948.0    Africa   46.809  792.449960\n",
       "1702     Zimbabwe  2002  11926563.0    Africa   39.989  672.038623\n",
       "1703     Zimbabwe  2007  12311143.0    Africa   43.487  469.709298\n",
       "\n",
       "[1704 rows x 6 columns]"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame from the GapMinder Data\n",
    "\n",
    "gm_df = pd.read_csv('gapminderData.csv')\n",
    "gm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2007</td>\n",
       "      <td>31889923.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>43.828</td>\n",
       "      <td>974.580338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2007</td>\n",
       "      <td>3600523.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>76.423</td>\n",
       "      <td>5937.029526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2007</td>\n",
       "      <td>33333216.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>72.301</td>\n",
       "      <td>6223.367465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2007</td>\n",
       "      <td>12420476.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>42.731</td>\n",
       "      <td>4797.231267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2007</td>\n",
       "      <td>40301927.0</td>\n",
       "      <td>Americas</td>\n",
       "      <td>75.320</td>\n",
       "      <td>12779.379640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2007</td>\n",
       "      <td>85262356.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>74.249</td>\n",
       "      <td>2441.576404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1667</th>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>2007</td>\n",
       "      <td>4018332.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>73.422</td>\n",
       "      <td>3025.349798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>2007</td>\n",
       "      <td>22211743.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>62.698</td>\n",
       "      <td>2280.769906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1691</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2007</td>\n",
       "      <td>11746035.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>42.384</td>\n",
       "      <td>1271.211593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 country  year         pop continent  lifeExp     gdpPercap\n",
       "11           Afghanistan  2007  31889923.0      Asia   43.828    974.580338\n",
       "23               Albania  2007   3600523.0    Europe   76.423   5937.029526\n",
       "35               Algeria  2007  33333216.0    Africa   72.301   6223.367465\n",
       "47                Angola  2007  12420476.0    Africa   42.731   4797.231267\n",
       "59             Argentina  2007  40301927.0  Americas   75.320  12779.379640\n",
       "...                  ...   ...         ...       ...      ...           ...\n",
       "1655             Vietnam  2007  85262356.0      Asia   74.249   2441.576404\n",
       "1667  West Bank and Gaza  2007   4018332.0      Asia   73.422   3025.349798\n",
       "1679         Yemen, Rep.  2007  22211743.0      Asia   62.698   2280.769906\n",
       "1691              Zambia  2007  11746035.0    Africa   42.384   1271.211593\n",
       "1703            Zimbabwe  2007  12311143.0    Africa   43.487    469.709298\n",
       "\n",
       "[142 rows x 6 columns]"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select all the rows with 'year' = '2007'\n",
    "# This will make each row unique for a country\n",
    "\n",
    "gm_df2007 = gm_df[gm_df['year'] == 2007]\n",
    "gm_df2007"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country_code</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2007</td>\n",
       "      <td>31889923.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>43.828</td>\n",
       "      <td>974.580338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2007</td>\n",
       "      <td>3600523.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>76.423</td>\n",
       "      <td>5937.029526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2007</td>\n",
       "      <td>33333216.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>72.301</td>\n",
       "      <td>6223.367465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>Angola</td>\n",
       "      <td>2007</td>\n",
       "      <td>12420476.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>42.731</td>\n",
       "      <td>4797.231267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>2007</td>\n",
       "      <td>40301927.0</td>\n",
       "      <td>Americas</td>\n",
       "      <td>75.320</td>\n",
       "      <td>12779.379640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>1655</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2007</td>\n",
       "      <td>85262356.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>74.249</td>\n",
       "      <td>2441.576404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>1667</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>2007</td>\n",
       "      <td>4018332.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>73.422</td>\n",
       "      <td>3025.349798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1679</td>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>2007</td>\n",
       "      <td>22211743.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>62.698</td>\n",
       "      <td>2280.769906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1691</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2007</td>\n",
       "      <td>11746035.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>42.384</td>\n",
       "      <td>1271.211593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1703</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country_code             country  year         pop continent  lifeExp  \\\n",
       "0              11         Afghanistan  2007  31889923.0      Asia   43.828   \n",
       "1              23             Albania  2007   3600523.0    Europe   76.423   \n",
       "2              35             Algeria  2007  33333216.0    Africa   72.301   \n",
       "3              47              Angola  2007  12420476.0    Africa   42.731   \n",
       "4              59           Argentina  2007  40301927.0  Americas   75.320   \n",
       "..            ...                 ...   ...         ...       ...      ...   \n",
       "137          1655             Vietnam  2007  85262356.0      Asia   74.249   \n",
       "138          1667  West Bank and Gaza  2007   4018332.0      Asia   73.422   \n",
       "139          1679         Yemen, Rep.  2007  22211743.0      Asia   62.698   \n",
       "140          1691              Zambia  2007  11746035.0    Africa   42.384   \n",
       "141          1703            Zimbabwe  2007  12311143.0    Africa   43.487   \n",
       "\n",
       "        gdpPercap  \n",
       "0      974.580338  \n",
       "1     5937.029526  \n",
       "2     6223.367465  \n",
       "3     4797.231267  \n",
       "4    12779.379640  \n",
       "..            ...  \n",
       "137   2441.576404  \n",
       "138   3025.349798  \n",
       "139   2280.769906  \n",
       "140   1271.211593  \n",
       "141    469.709298  \n",
       "\n",
       "[142 rows x 7 columns]"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a Country Code field \n",
    "\n",
    "gm_df2007.reset_index(inplace=True)\n",
    "gm_df2007.rename(columns={'index':'Country_code'}, inplace=True)\n",
    "gm_df2007"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country_code</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>Algeria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>Angola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>Argentina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>1655</td>\n",
       "      <td>Vietnam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>1667</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1679</td>\n",
       "      <td>Yemen, Rep.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1691</td>\n",
       "      <td>Zambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1703</td>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country_code             country\n",
       "0              11         Afghanistan\n",
       "1              23             Albania\n",
       "2              35             Algeria\n",
       "3              47              Angola\n",
       "4              59           Argentina\n",
       "..            ...                 ...\n",
       "137          1655             Vietnam\n",
       "138          1667  West Bank and Gaza\n",
       "139          1679         Yemen, Rep.\n",
       "140          1691              Zambia\n",
       "141          1703            Zimbabwe\n",
       "\n",
       "[142 rows x 2 columns]"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the other columns \n",
    "\n",
    "gm_df2007.drop(columns=['year', 'pop', 'continent', 'lifeExp', 'gdpPercap'], inplace=True)\n",
    "gm_df2007"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "      <th>Country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "      <td>1703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "      <td>1703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "      <td>1703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "      <td>1703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "      <td>1703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year         pop continent  lifeExp   gdpPercap  \\\n",
       "0     Afghanistan  1952   8425333.0      Asia   28.801  779.445314   \n",
       "1     Afghanistan  1957   9240934.0      Asia   30.332  820.853030   \n",
       "2     Afghanistan  1962  10267083.0      Asia   31.997  853.100710   \n",
       "3     Afghanistan  1967  11537966.0      Asia   34.020  836.197138   \n",
       "4     Afghanistan  1972  13079460.0      Asia   36.088  739.981106   \n",
       "...           ...   ...         ...       ...      ...         ...   \n",
       "1699     Zimbabwe  1987   9216418.0    Africa   62.351  706.157306   \n",
       "1700     Zimbabwe  1992  10704340.0    Africa   60.377  693.420786   \n",
       "1701     Zimbabwe  1997  11404948.0    Africa   46.809  792.449960   \n",
       "1702     Zimbabwe  2002  11926563.0    Africa   39.989  672.038623   \n",
       "1703     Zimbabwe  2007  12311143.0    Africa   43.487  469.709298   \n",
       "\n",
       "      Country_code  \n",
       "0               11  \n",
       "1               11  \n",
       "2               11  \n",
       "3               11  \n",
       "4               11  \n",
       "...            ...  \n",
       "1699          1703  \n",
       "1700          1703  \n",
       "1701          1703  \n",
       "1702          1703  \n",
       "1703          1703  \n",
       "\n",
       "[1704 rows x 7 columns]"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert the Country code in the Original DataFrame\n",
    "\n",
    "df_merged = pd.merge(gm_df, gm_df2007, how='left', on='country')\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html\n",
    "# --- Teach from this section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we will see some examples of Left, Right, Inner and Outer joins with merge()\n",
    "# For that we will drop a few keys ('country') from both df_gm and df_gm2007"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Afghanistan', 'Albania', 'Algeria', 'Angola', 'Argentina',\n",
       "       'Australia', 'Austria', 'Bahrain', 'Bangladesh', 'Belgium',\n",
       "       'Benin', 'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil',\n",
       "       'Bulgaria', 'Burkina Faso', 'Burundi', 'Cambodia', 'Cameroon',\n",
       "       'Canada', 'Central African Republic', 'Chad', 'Chile', 'China',\n",
       "       'Colombia', 'Comoros', 'Congo, Dem. Rep.', 'Congo, Rep.',\n",
       "       'Costa Rica', \"Cote d'Ivoire\", 'Croatia', 'Cuba', 'Czech Republic',\n",
       "       'Denmark', 'Djibouti', 'Dominican Republic', 'Ecuador', 'Egypt',\n",
       "       'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Ethiopia',\n",
       "       'Finland', 'France', 'Gabon', 'Gambia', 'Germany', 'Ghana',\n",
       "       'Greece', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Haiti',\n",
       "       'Honduras', 'Hong Kong, China', 'Hungary', 'Iceland', 'India',\n",
       "       'Indonesia', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy',\n",
       "       'Jamaica', 'Japan', 'Jordan', 'Kenya', 'Korea, Dem. Rep.',\n",
       "       'Korea, Rep.', 'Kuwait', 'Lebanon', 'Lesotho', 'Liberia', 'Libya',\n",
       "       'Madagascar', 'Malawi', 'Malaysia', 'Mali', 'Mauritania',\n",
       "       'Mauritius', 'Mexico', 'Mongolia', 'Montenegro', 'Morocco',\n",
       "       'Mozambique', 'Myanmar', 'Namibia', 'Nepal', 'Netherlands',\n",
       "       'New Zealand', 'Nicaragua', 'Niger', 'Nigeria', 'Norway', 'Oman',\n",
       "       'Pakistan', 'Panama', 'Paraguay', 'Peru', 'Philippines', 'Poland',\n",
       "       'Portugal', 'Puerto Rico', 'Reunion', 'Romania', 'Rwanda',\n",
       "       'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia',\n",
       "       'Sierra Leone', 'Singapore', 'Slovak Republic', 'Slovenia',\n",
       "       'Somalia', 'South Africa', 'Spain', 'Sri Lanka', 'Sudan',\n",
       "       'Swaziland', 'Sweden', 'Switzerland', 'Syria', 'Taiwan',\n",
       "       'Tanzania', 'Thailand', 'Togo', 'Trinidad and Tobago', 'Tunisia',\n",
       "       'Turkey', 'Uganda', 'United Kingdom', 'United States', 'Uruguay',\n",
       "       'Venezuela', 'Vietnam', 'West Bank and Gaza', 'Yemen, Rep.',\n",
       "       'Zambia', 'Zimbabwe'], dtype=object)"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gm_df.country.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1668 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year         pop continent  lifeExp   gdpPercap\n",
       "0     Afghanistan  1952   8425333.0      Asia   28.801  779.445314\n",
       "1     Afghanistan  1957   9240934.0      Asia   30.332  820.853030\n",
       "2     Afghanistan  1962  10267083.0      Asia   31.997  853.100710\n",
       "3     Afghanistan  1967  11537966.0      Asia   34.020  836.197138\n",
       "4     Afghanistan  1972  13079460.0      Asia   36.088  739.981106\n",
       "...           ...   ...         ...       ...      ...         ...\n",
       "1699     Zimbabwe  1987   9216418.0    Africa   62.351  706.157306\n",
       "1700     Zimbabwe  1992  10704340.0    Africa   60.377  693.420786\n",
       "1701     Zimbabwe  1997  11404948.0    Africa   46.809  792.449960\n",
       "1702     Zimbabwe  2002  11926563.0    Africa   39.989  672.038623\n",
       "1703     Zimbabwe  2007  12311143.0    Africa   43.487  469.709298\n",
       "\n",
       "[1668 rows x 6 columns]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gm_df1 = gm_df.loc[(gm_df['country'] != 'Switzerland') & (gm_df['country'] != 'Serbia') & (gm_df['country'] != 'Malaysia')]\n",
    "gm_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country_code</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>Algeria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>Angola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>Argentina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>1655</td>\n",
       "      <td>Vietnam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>1667</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1679</td>\n",
       "      <td>Yemen, Rep.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1691</td>\n",
       "      <td>Zambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1703</td>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>139 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country_code             country\n",
       "0              11         Afghanistan\n",
       "1              23             Albania\n",
       "2              35             Algeria\n",
       "3              47              Angola\n",
       "4              59           Argentina\n",
       "..            ...                 ...\n",
       "137          1655             Vietnam\n",
       "138          1667  West Bank and Gaza\n",
       "139          1679         Yemen, Rep.\n",
       "140          1691              Zambia\n",
       "141          1703            Zimbabwe\n",
       "\n",
       "[139 rows x 2 columns]"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gm_df2 = gm_df2007.loc[(gm_df2007['country'] != 'Spain') & (gm_df2007['country'] != 'Japan') & (gm_df2007['country'] != 'France')]\n",
    "gm_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "      <th>Country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1663</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "      <td>1703.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1664</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "      <td>1703.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1665</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "      <td>1703.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1666</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "      <td>1703.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1667</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "      <td>1703.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1668 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year         pop continent  lifeExp   gdpPercap  \\\n",
       "0     Afghanistan  1952   8425333.0      Asia   28.801  779.445314   \n",
       "1     Afghanistan  1957   9240934.0      Asia   30.332  820.853030   \n",
       "2     Afghanistan  1962  10267083.0      Asia   31.997  853.100710   \n",
       "3     Afghanistan  1967  11537966.0      Asia   34.020  836.197138   \n",
       "4     Afghanistan  1972  13079460.0      Asia   36.088  739.981106   \n",
       "...           ...   ...         ...       ...      ...         ...   \n",
       "1663     Zimbabwe  1987   9216418.0    Africa   62.351  706.157306   \n",
       "1664     Zimbabwe  1992  10704340.0    Africa   60.377  693.420786   \n",
       "1665     Zimbabwe  1997  11404948.0    Africa   46.809  792.449960   \n",
       "1666     Zimbabwe  2002  11926563.0    Africa   39.989  672.038623   \n",
       "1667     Zimbabwe  2007  12311143.0    Africa   43.487  469.709298   \n",
       "\n",
       "      Country_code  \n",
       "0             11.0  \n",
       "1             11.0  \n",
       "2             11.0  \n",
       "3             11.0  \n",
       "4             11.0  \n",
       "...            ...  \n",
       "1663        1703.0  \n",
       "1664        1703.0  \n",
       "1665        1703.0  \n",
       "1666        1703.0  \n",
       "1667        1703.0  \n",
       "\n",
       "[1668 rows x 7 columns]"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform a 'Left' Join\n",
    "\n",
    "df_left = pd.merge(gm_df1, gm_df2, how='left', on='country')\n",
    "df_left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Afghanistan', 'Albania', 'Algeria', 'Angola', 'Argentina',\n",
       "       'Australia', 'Austria', 'Bahrain', 'Bangladesh', 'Belgium',\n",
       "       'Benin', 'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil',\n",
       "       'Bulgaria', 'Burkina Faso', 'Burundi', 'Cambodia', 'Cameroon',\n",
       "       'Canada', 'Central African Republic', 'Chad', 'Chile', 'China',\n",
       "       'Colombia', 'Comoros', 'Congo, Dem. Rep.', 'Congo, Rep.',\n",
       "       'Costa Rica', \"Cote d'Ivoire\", 'Croatia', 'Cuba', 'Czech Republic',\n",
       "       'Denmark', 'Djibouti', 'Dominican Republic', 'Ecuador', 'Egypt',\n",
       "       'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Ethiopia',\n",
       "       'Finland', 'France', 'Gabon', 'Gambia', 'Germany', 'Ghana',\n",
       "       'Greece', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Haiti',\n",
       "       'Honduras', 'Hong Kong, China', 'Hungary', 'Iceland', 'India',\n",
       "       'Indonesia', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy',\n",
       "       'Jamaica', 'Japan', 'Jordan', 'Kenya', 'Korea, Dem. Rep.',\n",
       "       'Korea, Rep.', 'Kuwait', 'Lebanon', 'Lesotho', 'Liberia', 'Libya',\n",
       "       'Madagascar', 'Malawi', 'Mali', 'Mauritania', 'Mauritius',\n",
       "       'Mexico', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique',\n",
       "       'Myanmar', 'Namibia', 'Nepal', 'Netherlands', 'New Zealand',\n",
       "       'Nicaragua', 'Niger', 'Nigeria', 'Norway', 'Oman', 'Pakistan',\n",
       "       'Panama', 'Paraguay', 'Peru', 'Philippines', 'Poland', 'Portugal',\n",
       "       'Puerto Rico', 'Reunion', 'Romania', 'Rwanda',\n",
       "       'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Sierra Leone',\n",
       "       'Singapore', 'Slovak Republic', 'Slovenia', 'Somalia',\n",
       "       'South Africa', 'Spain', 'Sri Lanka', 'Sudan', 'Swaziland',\n",
       "       'Sweden', 'Syria', 'Taiwan', 'Tanzania', 'Thailand', 'Togo',\n",
       "       'Trinidad and Tobago', 'Tunisia', 'Turkey', 'Uganda',\n",
       "       'United Kingdom', 'United States', 'Uruguay', 'Venezuela',\n",
       "       'Vietnam', 'West Bank and Gaza', 'Yemen, Rep.', 'Zambia',\n",
       "       'Zimbabwe'], dtype=object)"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_left.country.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "      <th>Country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1392</th>\n",
       "      <td>Spain</td>\n",
       "      <td>1952</td>\n",
       "      <td>28549870.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>64.940</td>\n",
       "      <td>3834.034742</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1393</th>\n",
       "      <td>Spain</td>\n",
       "      <td>1957</td>\n",
       "      <td>29841614.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>66.660</td>\n",
       "      <td>4564.802410</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1394</th>\n",
       "      <td>Spain</td>\n",
       "      <td>1962</td>\n",
       "      <td>31158061.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>69.690</td>\n",
       "      <td>5693.843879</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1395</th>\n",
       "      <td>Spain</td>\n",
       "      <td>1967</td>\n",
       "      <td>32850275.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>71.440</td>\n",
       "      <td>7993.512294</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1396</th>\n",
       "      <td>Spain</td>\n",
       "      <td>1972</td>\n",
       "      <td>34513161.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>73.060</td>\n",
       "      <td>10638.751310</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1397</th>\n",
       "      <td>Spain</td>\n",
       "      <td>1977</td>\n",
       "      <td>36439000.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>74.390</td>\n",
       "      <td>13236.921170</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1398</th>\n",
       "      <td>Spain</td>\n",
       "      <td>1982</td>\n",
       "      <td>37983310.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>76.300</td>\n",
       "      <td>13926.169970</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1399</th>\n",
       "      <td>Spain</td>\n",
       "      <td>1987</td>\n",
       "      <td>38880702.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>76.900</td>\n",
       "      <td>15764.983130</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1400</th>\n",
       "      <td>Spain</td>\n",
       "      <td>1992</td>\n",
       "      <td>39549438.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>77.570</td>\n",
       "      <td>18603.064520</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1401</th>\n",
       "      <td>Spain</td>\n",
       "      <td>1997</td>\n",
       "      <td>39855442.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>78.770</td>\n",
       "      <td>20445.298960</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1402</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2002</td>\n",
       "      <td>40152517.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>79.780</td>\n",
       "      <td>24835.471660</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1403</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2007</td>\n",
       "      <td>40448191.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>80.941</td>\n",
       "      <td>28821.063700</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country  year         pop continent  lifeExp     gdpPercap  Country_code\n",
       "1392   Spain  1952  28549870.0    Europe   64.940   3834.034742           NaN\n",
       "1393   Spain  1957  29841614.0    Europe   66.660   4564.802410           NaN\n",
       "1394   Spain  1962  31158061.0    Europe   69.690   5693.843879           NaN\n",
       "1395   Spain  1967  32850275.0    Europe   71.440   7993.512294           NaN\n",
       "1396   Spain  1972  34513161.0    Europe   73.060  10638.751310           NaN\n",
       "1397   Spain  1977  36439000.0    Europe   74.390  13236.921170           NaN\n",
       "1398   Spain  1982  37983310.0    Europe   76.300  13926.169970           NaN\n",
       "1399   Spain  1987  38880702.0    Europe   76.900  15764.983130           NaN\n",
       "1400   Spain  1992  39549438.0    Europe   77.570  18603.064520           NaN\n",
       "1401   Spain  1997  39855442.0    Europe   78.770  20445.298960           NaN\n",
       "1402   Spain  2002  40152517.0    Europe   79.780  24835.471660           NaN\n",
       "1403   Spain  2007  40448191.0    Europe   80.941  28821.063700           NaN"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_left[df_left['country'] == 'Spain']\n",
    "\n",
    "# Because it's a 'left' join, all the keys from the left DataFrame will remain. \n",
    "# 'Spain' absent from the right DataFrame will mean that in the output DataFrame, Coutry_code will be NaN\n",
    "# In the similar way a 'right' join can be performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "      <th>Country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1627</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "      <td>1703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1628</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "      <td>1703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1629</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "      <td>1703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1630</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "      <td>1703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1631</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "      <td>1703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1632 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year         pop continent  lifeExp   gdpPercap  \\\n",
       "0     Afghanistan  1952   8425333.0      Asia   28.801  779.445314   \n",
       "1     Afghanistan  1957   9240934.0      Asia   30.332  820.853030   \n",
       "2     Afghanistan  1962  10267083.0      Asia   31.997  853.100710   \n",
       "3     Afghanistan  1967  11537966.0      Asia   34.020  836.197138   \n",
       "4     Afghanistan  1972  13079460.0      Asia   36.088  739.981106   \n",
       "...           ...   ...         ...       ...      ...         ...   \n",
       "1627     Zimbabwe  1987   9216418.0    Africa   62.351  706.157306   \n",
       "1628     Zimbabwe  1992  10704340.0    Africa   60.377  693.420786   \n",
       "1629     Zimbabwe  1997  11404948.0    Africa   46.809  792.449960   \n",
       "1630     Zimbabwe  2002  11926563.0    Africa   39.989  672.038623   \n",
       "1631     Zimbabwe  2007  12311143.0    Africa   43.487  469.709298   \n",
       "\n",
       "      Country_code  \n",
       "0               11  \n",
       "1               11  \n",
       "2               11  \n",
       "3               11  \n",
       "4               11  \n",
       "...            ...  \n",
       "1627          1703  \n",
       "1628          1703  \n",
       "1629          1703  \n",
       "1630          1703  \n",
       "1631          1703  \n",
       "\n",
       "[1632 rows x 7 columns]"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform a 'Inner' Join (Intersection of Keys present in both input DataFrames)\n",
    "\n",
    "df_inner = pd.merge(gm_df1, gm_df2, how='inner', on='country')\n",
    "df_inner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "      <th>Country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [country, year, pop, continent, lifeExp, gdpPercap, Country_code]\n",
       "Index: []"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_inner[(df_inner['country'] == 'Spain') | (df_inner['country'] == 'Switzerland')]\n",
    "\n",
    "# Switzerland being absent from gm_df1 and Spain is absent from gm_df2, both will not appear in the merge output.\n",
    "# Similarly an 'Outer' join can be performed (Union of 'key' values from both sides)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping Rows "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1982</td>\n",
       "      <td>12881816.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>39.854</td>\n",
       "      <td>978.011439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1698</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1982</td>\n",
       "      <td>7636524.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.363</td>\n",
       "      <td>788.855041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1701 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year         pop continent  lifeExp   gdpPercap\n",
       "1     Afghanistan  1957   9240934.0      Asia   30.332  820.853030\n",
       "2     Afghanistan  1962  10267083.0      Asia   31.997  853.100710\n",
       "3     Afghanistan  1967  11537966.0      Asia   34.020  836.197138\n",
       "4     Afghanistan  1972  13079460.0      Asia   36.088  739.981106\n",
       "6     Afghanistan  1982  12881816.0      Asia   39.854  978.011439\n",
       "...           ...   ...         ...       ...      ...         ...\n",
       "1698     Zimbabwe  1982   7636524.0    Africa   60.363  788.855041\n",
       "1699     Zimbabwe  1987   9216418.0    Africa   62.351  706.157306\n",
       "1700     Zimbabwe  1992  10704340.0    Africa   60.377  693.420786\n",
       "1702     Zimbabwe  2002  11926563.0    Africa   39.989  672.038623\n",
       "1703     Zimbabwe  2007  12311143.0    Africa   43.487  469.709298\n",
       "\n",
       "[1701 rows x 6 columns]"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping rows based on Index Numbers\n",
    "\n",
    "dfdel = gm_df.drop(gm_df.index[[0, 5, 1701]])\n",
    "dfdel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1977</td>\n",
       "      <td>14880372.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>38.438</td>\n",
       "      <td>786.113360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1982</td>\n",
       "      <td>12881816.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>39.854</td>\n",
       "      <td>978.011439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1987</td>\n",
       "      <td>13867957.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>40.822</td>\n",
       "      <td>852.395945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1992</td>\n",
       "      <td>16317921.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>41.674</td>\n",
       "      <td>649.341395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1997</td>\n",
       "      <td>22227415.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>41.763</td>\n",
       "      <td>635.341351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2002</td>\n",
       "      <td>25268405.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>42.129</td>\n",
       "      <td>726.734055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  year         pop continent  lifeExp   gdpPercap\n",
       "0   Afghanistan  1952   8425333.0      Asia   28.801  779.445314\n",
       "1   Afghanistan  1957   9240934.0      Asia   30.332  820.853030\n",
       "2   Afghanistan  1962  10267083.0      Asia   31.997  853.100710\n",
       "3   Afghanistan  1967  11537966.0      Asia   34.020  836.197138\n",
       "4   Afghanistan  1972  13079460.0      Asia   36.088  739.981106\n",
       "5   Afghanistan  1977  14880372.0      Asia   38.438  786.113360\n",
       "6   Afghanistan  1982  12881816.0      Asia   39.854  978.011439\n",
       "7   Afghanistan  1987  13867957.0      Asia   40.822  852.395945\n",
       "8   Afghanistan  1992  16317921.0      Asia   41.674  649.341395\n",
       "9   Afghanistan  1997  22227415.0      Asia   41.763  635.341351\n",
       "10  Afghanistan  2002  25268405.0      Asia   42.129  726.734055"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping rows from a DataFrame by taking a row subset/range\n",
    "\n",
    "dftrunk = gm_df[0:11]\n",
    "dftrunk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making a conditional selection is another way of deleting rows from a DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping Rows with Null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>3.545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>24.40</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.5</td>\n",
       "      <td>67.6</td>\n",
       "      <td>176.6</td>\n",
       "      <td>3.042</td>\n",
       "      <td>15.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>66.498812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>15.245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>27.50</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>185.9</td>\n",
       "      <td>3.208</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.654495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>17.531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>186.2</td>\n",
       "      <td>3.259</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.155978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3.493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.50</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>185.7</td>\n",
       "      <td>3.601</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.623357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>18.969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.00</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>157 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "0                16.919        16.360    Passenger               21.50   \n",
       "1                39.384        19.875    Passenger               28.40   \n",
       "2                14.114        18.225    Passenger                 NaN   \n",
       "3                 8.588        29.725    Passenger               42.00   \n",
       "4                20.397        22.255    Passenger               23.99   \n",
       "..                  ...           ...          ...                 ...   \n",
       "152               3.545           NaN    Passenger               24.40   \n",
       "153              15.245           NaN    Passenger               27.50   \n",
       "154              17.531           NaN    Passenger               28.80   \n",
       "155               3.493           NaN    Passenger               45.50   \n",
       "156              18.969           NaN    Passenger               36.00   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "0            1.8       140.0      101.2   67.3   172.4        2.639   \n",
       "1            3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2            3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "3            3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "4            1.8       150.0      102.6   68.2   178.0        2.998   \n",
       "..           ...         ...        ...    ...     ...          ...   \n",
       "152          1.9       160.0      100.5   67.6   176.6        3.042   \n",
       "153          2.4       168.0      104.9   69.3   185.9        3.208   \n",
       "154          2.4       168.0      104.9   69.3   186.2        3.259   \n",
       "155          2.3       236.0      104.9   71.5   185.7        3.601   \n",
       "156          2.9       201.0      109.9   72.1   189.8        3.600   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "0             13.2             28.0          58.280150  \n",
       "1             17.2             25.0          91.370778  \n",
       "2             17.2             26.0                NaN  \n",
       "3             18.0             22.0          91.389779  \n",
       "4             16.4             27.0          62.777639  \n",
       "..             ...              ...                ...  \n",
       "152           15.8             25.0          66.498812  \n",
       "153           17.9             25.0          70.654495  \n",
       "154           17.9             25.0          71.155978  \n",
       "155           18.5             23.0         101.623357  \n",
       "156           21.1             24.0          85.735655  \n",
       "\n",
       "[157 rows x 13 columns]"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame from the GapMinder Data\n",
    "\n",
    "car_df = pd.read_csv('Car_sales.csv')\n",
    "car_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19.747</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>26.990</td>\n",
       "      <td>2.5</td>\n",
       "      <td>170.0</td>\n",
       "      <td>107.3</td>\n",
       "      <td>68.4</td>\n",
       "      <td>176.0</td>\n",
       "      <td>3.179</td>\n",
       "      <td>16.6</td>\n",
       "      <td>26.0</td>\n",
       "      <td>71.191207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>14.785</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>46.225</td>\n",
       "      <td>5.7</td>\n",
       "      <td>255.0</td>\n",
       "      <td>117.5</td>\n",
       "      <td>77.0</td>\n",
       "      <td>201.2</td>\n",
       "      <td>5.572</td>\n",
       "      <td>30.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>109.509117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>107.995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>18.890</td>\n",
       "      <td>3.4</td>\n",
       "      <td>180.0</td>\n",
       "      <td>110.5</td>\n",
       "      <td>73.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>3.389</td>\n",
       "      <td>17.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>71.838039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>30.696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>29.185</td>\n",
       "      <td>3.5</td>\n",
       "      <td>253.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>74.4</td>\n",
       "      <td>197.8</td>\n",
       "      <td>3.567</td>\n",
       "      <td>17.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.655244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>101.323</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>26.310</td>\n",
       "      <td>5.2</td>\n",
       "      <td>230.0</td>\n",
       "      <td>115.7</td>\n",
       "      <td>71.7</td>\n",
       "      <td>193.5</td>\n",
       "      <td>4.394</td>\n",
       "      <td>25.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>92.854125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>175.670</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>12.315</td>\n",
       "      <td>2.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>66.9</td>\n",
       "      <td>174.8</td>\n",
       "      <td>2.564</td>\n",
       "      <td>13.2</td>\n",
       "      <td>30.0</td>\n",
       "      <td>43.117132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>15.467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.800</td>\n",
       "      <td>3.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>114.5</td>\n",
       "      <td>71.6</td>\n",
       "      <td>191.3</td>\n",
       "      <td>3.650</td>\n",
       "      <td>18.4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>102.178985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>3.334</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>46.305</td>\n",
       "      <td>4.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>110.2</td>\n",
       "      <td>70.9</td>\n",
       "      <td>189.2</td>\n",
       "      <td>3.693</td>\n",
       "      <td>19.8</td>\n",
       "      <td>21.0</td>\n",
       "      <td>125.013357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>9.126</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>60.105</td>\n",
       "      <td>4.7</td>\n",
       "      <td>230.0</td>\n",
       "      <td>112.2</td>\n",
       "      <td>76.4</td>\n",
       "      <td>192.5</td>\n",
       "      <td>5.401</td>\n",
       "      <td>25.4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>105.760458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>51.238</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>34.605</td>\n",
       "      <td>3.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>71.5</td>\n",
       "      <td>180.1</td>\n",
       "      <td>3.900</td>\n",
       "      <td>17.2</td>\n",
       "      <td>21.0</td>\n",
       "      <td>91.943802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>22.925</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>42.660</td>\n",
       "      <td>5.4</td>\n",
       "      <td>300.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>79.9</td>\n",
       "      <td>204.8</td>\n",
       "      <td>5.393</td>\n",
       "      <td>30.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>123.972047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>7.998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>38.900</td>\n",
       "      <td>2.3</td>\n",
       "      <td>190.0</td>\n",
       "      <td>94.5</td>\n",
       "      <td>67.5</td>\n",
       "      <td>157.9</td>\n",
       "      <td>3.055</td>\n",
       "      <td>15.9</td>\n",
       "      <td>26.0</td>\n",
       "      <td>82.807362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1.526</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>41.000</td>\n",
       "      <td>2.3</td>\n",
       "      <td>185.0</td>\n",
       "      <td>94.5</td>\n",
       "      <td>67.5</td>\n",
       "      <td>157.3</td>\n",
       "      <td>2.975</td>\n",
       "      <td>14.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>81.848969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>11.592</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>41.600</td>\n",
       "      <td>3.2</td>\n",
       "      <td>215.0</td>\n",
       "      <td>105.9</td>\n",
       "      <td>67.8</td>\n",
       "      <td>180.3</td>\n",
       "      <td>3.213</td>\n",
       "      <td>16.4</td>\n",
       "      <td>26.0</td>\n",
       "      <td>92.925792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.954</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>85.500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>113.6</td>\n",
       "      <td>73.1</td>\n",
       "      <td>196.6</td>\n",
       "      <td>4.115</td>\n",
       "      <td>23.2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>141.100985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>28.976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>35.300</td>\n",
       "      <td>3.2</td>\n",
       "      <td>215.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>72.2</td>\n",
       "      <td>180.6</td>\n",
       "      <td>4.387</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>90.495532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>54.158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>22.799</td>\n",
       "      <td>3.3</td>\n",
       "      <td>170.0</td>\n",
       "      <td>104.3</td>\n",
       "      <td>70.4</td>\n",
       "      <td>178.0</td>\n",
       "      <td>3.821</td>\n",
       "      <td>19.4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>69.782944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>65.005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>17.890</td>\n",
       "      <td>3.3</td>\n",
       "      <td>170.0</td>\n",
       "      <td>116.1</td>\n",
       "      <td>66.5</td>\n",
       "      <td>196.1</td>\n",
       "      <td>3.217</td>\n",
       "      <td>19.4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>67.889271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>38.554</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>24.150</td>\n",
       "      <td>3.5</td>\n",
       "      <td>215.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>73.6</td>\n",
       "      <td>195.9</td>\n",
       "      <td>3.455</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.272523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>80.255</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>18.270</td>\n",
       "      <td>2.4</td>\n",
       "      <td>150.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>70.1</td>\n",
       "      <td>186.7</td>\n",
       "      <td>2.958</td>\n",
       "      <td>15.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>60.727447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>1.872</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>43.000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>253.0</td>\n",
       "      <td>113.3</td>\n",
       "      <td>76.3</td>\n",
       "      <td>165.4</td>\n",
       "      <td>2.850</td>\n",
       "      <td>12.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>106.984456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>39.572</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>25.635</td>\n",
       "      <td>3.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>72.7</td>\n",
       "      <td>201.3</td>\n",
       "      <td>3.942</td>\n",
       "      <td>25.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>76.208440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>9.191</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>33.120</td>\n",
       "      <td>2.3</td>\n",
       "      <td>170.0</td>\n",
       "      <td>106.4</td>\n",
       "      <td>70.6</td>\n",
       "      <td>189.2</td>\n",
       "      <td>3.280</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>73.503778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>12.115</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>26.100</td>\n",
       "      <td>2.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>67.4</td>\n",
       "      <td>182.2</td>\n",
       "      <td>2.990</td>\n",
       "      <td>16.9</td>\n",
       "      <td>23.0</td>\n",
       "      <td>76.023048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>8.472</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>18.835</td>\n",
       "      <td>2.2</td>\n",
       "      <td>137.0</td>\n",
       "      <td>106.5</td>\n",
       "      <td>69.0</td>\n",
       "      <td>190.4</td>\n",
       "      <td>3.075</td>\n",
       "      <td>13.1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>56.295243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>49.989</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>15.010</td>\n",
       "      <td>2.2</td>\n",
       "      <td>137.0</td>\n",
       "      <td>106.5</td>\n",
       "      <td>69.0</td>\n",
       "      <td>190.4</td>\n",
       "      <td>2.910</td>\n",
       "      <td>13.1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>54.819728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>47.107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>22.695</td>\n",
       "      <td>2.5</td>\n",
       "      <td>165.0</td>\n",
       "      <td>103.5</td>\n",
       "      <td>67.5</td>\n",
       "      <td>185.8</td>\n",
       "      <td>3.415</td>\n",
       "      <td>16.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.765908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>33.028</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>20.095</td>\n",
       "      <td>2.5</td>\n",
       "      <td>165.0</td>\n",
       "      <td>99.4</td>\n",
       "      <td>68.3</td>\n",
       "      <td>175.2</td>\n",
       "      <td>3.125</td>\n",
       "      <td>15.9</td>\n",
       "      <td>24.0</td>\n",
       "      <td>66.762943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>65.119</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>22.368</td>\n",
       "      <td>3.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>114.2</td>\n",
       "      <td>73.4</td>\n",
       "      <td>193.5</td>\n",
       "      <td>3.759</td>\n",
       "      <td>20.9</td>\n",
       "      <td>22.0</td>\n",
       "      <td>78.027219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>49.463</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>15.900</td>\n",
       "      <td>2.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>98.9</td>\n",
       "      <td>67.9</td>\n",
       "      <td>161.1</td>\n",
       "      <td>2.769</td>\n",
       "      <td>14.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>47.329632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>16.957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.400</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.5</td>\n",
       "      <td>67.6</td>\n",
       "      <td>176.6</td>\n",
       "      <td>2.998</td>\n",
       "      <td>15.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>66.113057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>3.545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>24.400</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.5</td>\n",
       "      <td>67.6</td>\n",
       "      <td>176.6</td>\n",
       "      <td>3.042</td>\n",
       "      <td>15.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>66.498812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>15.245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>27.500</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>185.9</td>\n",
       "      <td>3.208</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.654495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>17.531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.800</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>186.2</td>\n",
       "      <td>3.259</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.155978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3.493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.500</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>185.7</td>\n",
       "      <td>3.601</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.623357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>18.969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.000</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "7                19.747           NaN    Passenger              26.990   \n",
       "18               14.785           NaN          Car              46.225   \n",
       "27              107.995           NaN    Passenger              18.890   \n",
       "34               30.696           NaN    Passenger              29.185   \n",
       "44              101.323           NaN          Car              26.310   \n",
       "50              175.670           NaN    Passenger              12.315   \n",
       "66               15.467           NaN    Passenger              42.800   \n",
       "72                3.334           NaN    Passenger              46.305   \n",
       "74                9.126           NaN          Car              60.105   \n",
       "75               51.238           NaN          Car              34.605   \n",
       "78               22.925           NaN          Car              42.660   \n",
       "96                7.998           NaN    Passenger              38.900   \n",
       "97                1.526           NaN    Passenger              41.000   \n",
       "98               11.592           NaN    Passenger              41.600   \n",
       "99                0.954           NaN    Passenger              85.500   \n",
       "100              28.976           NaN          Car              35.300   \n",
       "106              54.158           NaN          Car              22.799   \n",
       "107              65.005           NaN          Car              17.890   \n",
       "109              38.554           NaN    Passenger              24.150   \n",
       "110              80.255           NaN    Passenger              18.270   \n",
       "117               1.872           NaN    Passenger              43.000   \n",
       "123              39.572           NaN          Car              25.635   \n",
       "127               9.191           NaN    Passenger              33.120   \n",
       "128              12.115           NaN    Passenger              26.100   \n",
       "132               8.472           NaN    Passenger              18.835   \n",
       "133              49.989           NaN    Passenger              15.010   \n",
       "134              47.107           NaN    Passenger              22.695   \n",
       "135              33.028           NaN          Car              20.095   \n",
       "141              65.119           NaN          Car              22.368   \n",
       "150              49.463           NaN    Passenger              15.900   \n",
       "151              16.957           NaN    Passenger              23.400   \n",
       "152               3.545           NaN    Passenger              24.400   \n",
       "153              15.245           NaN    Passenger              27.500   \n",
       "154              17.531           NaN    Passenger              28.800   \n",
       "155               3.493           NaN    Passenger              45.500   \n",
       "156              18.969           NaN    Passenger              36.000   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "7            2.5       170.0      107.3   68.4   176.0        3.179   \n",
       "18           5.7       255.0      117.5   77.0   201.2        5.572   \n",
       "27           3.4       180.0      110.5   73.0   200.0        3.389   \n",
       "34           3.5       253.0      113.0   74.4   197.8        3.567   \n",
       "44           5.2       230.0      115.7   71.7   193.5        4.394   \n",
       "50           2.0       107.0      103.0   66.9   174.8        2.564   \n",
       "66           3.0       240.0      114.5   71.6   191.3        3.650   \n",
       "72           4.0       300.0      110.2   70.9   189.2        3.693   \n",
       "74           4.7       230.0      112.2   76.4   192.5        5.401   \n",
       "75           3.0       220.0      103.0   71.5   180.1        3.900   \n",
       "78           5.4       300.0      119.0   79.9   204.8        5.393   \n",
       "96           2.3       190.0       94.5   67.5   157.9        3.055   \n",
       "97           2.3       185.0       94.5   67.5   157.3        2.975   \n",
       "98           3.2       215.0      105.9   67.8   180.3        3.213   \n",
       "99           5.0       302.0      113.6   73.1   196.6        4.115   \n",
       "100          3.2       215.0      111.0   72.2   180.6        4.387   \n",
       "106          3.3       170.0      104.3   70.4   178.0        3.821   \n",
       "107          3.3       170.0      116.1   66.5   196.1        3.217   \n",
       "109          3.5       215.0      109.0   73.6   195.9        3.455   \n",
       "110          2.4       150.0      107.0   70.1   186.7        2.958   \n",
       "117          3.5       253.0      113.3   76.3   165.4        2.850   \n",
       "123          3.4       185.0      120.0   72.7   201.3        3.942   \n",
       "127          2.3       170.0      106.4   70.6   189.2        3.280   \n",
       "128          2.0       185.0      102.6   67.4   182.2        2.990   \n",
       "132          2.2       137.0      106.5   69.0   190.4        3.075   \n",
       "133          2.2       137.0      106.5   69.0   190.4        2.910   \n",
       "134          2.5       165.0      103.5   67.5   185.8        3.415   \n",
       "135          2.5       165.0       99.4   68.3   175.2        3.125   \n",
       "141          3.0       194.0      114.2   73.4   193.5        3.759   \n",
       "150          2.0       115.0       98.9   67.9   161.1        2.769   \n",
       "151          1.9       160.0      100.5   67.6   176.6        2.998   \n",
       "152          1.9       160.0      100.5   67.6   176.6        3.042   \n",
       "153          2.4       168.0      104.9   69.3   185.9        3.208   \n",
       "154          2.4       168.0      104.9   69.3   186.2        3.259   \n",
       "155          2.3       236.0      104.9   71.5   185.7        3.601   \n",
       "156          2.9       201.0      109.9   72.1   189.8        3.600   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "7             16.6             26.0          71.191207  \n",
       "18            30.0             15.0         109.509117  \n",
       "27            17.0             27.0          71.838039  \n",
       "34            17.0             23.0         101.655244  \n",
       "44            25.0             17.0          92.854125  \n",
       "50            13.2             30.0          43.117132  \n",
       "66            18.4             21.0         102.178985  \n",
       "72            19.8             21.0         125.013357  \n",
       "74            25.4             15.0         105.760458  \n",
       "75            17.2             21.0          91.943802  \n",
       "78            30.0             15.0         123.972047  \n",
       "96            15.9             26.0          82.807362  \n",
       "97            14.0             27.0          81.848969  \n",
       "98            16.4             26.0          92.925792  \n",
       "99            23.2             20.0         141.100985  \n",
       "100           19.0             20.0          90.495532  \n",
       "106           19.4             18.0          69.782944  \n",
       "107           19.4             18.0          67.889271  \n",
       "109           18.0              NaN          86.272523  \n",
       "110           15.0             27.0          60.727447  \n",
       "117           12.0             21.0         106.984456  \n",
       "123           25.0             23.0          76.208440  \n",
       "127           18.5             23.0          73.503778  \n",
       "128           16.9             23.0          76.023048  \n",
       "132           13.1             27.0          56.295243  \n",
       "133           13.1             28.0          54.819728  \n",
       "134           16.9             25.0          67.765908  \n",
       "135           15.9             24.0          66.762943  \n",
       "141           20.9             22.0          78.027219  \n",
       "150           14.5             26.0          47.329632  \n",
       "151           15.8             25.0          66.113057  \n",
       "152           15.8             25.0          66.498812  \n",
       "153           17.9             25.0          70.654495  \n",
       "154           17.9             25.0          71.155978  \n",
       "155           18.5             23.0         101.623357  \n",
       "156           21.1             24.0          85.735655  "
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding Rows with Nissing Values \n",
    "car_dfn = car_df[car_df['Resale_value'].isna()]\n",
    "car_dfn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>9.761</td>\n",
       "      <td>11.425</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>14.90</td>\n",
       "      <td>2.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>98.9</td>\n",
       "      <td>68.3</td>\n",
       "      <td>163.3</td>\n",
       "      <td>2.767</td>\n",
       "      <td>14.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>46.943877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>83.721</td>\n",
       "      <td>13.240</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>16.70</td>\n",
       "      <td>2.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>98.9</td>\n",
       "      <td>68.3</td>\n",
       "      <td>172.3</td>\n",
       "      <td>2.853</td>\n",
       "      <td>14.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>47.638237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>51.102</td>\n",
       "      <td>16.725</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.20</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>106.4</td>\n",
       "      <td>68.5</td>\n",
       "      <td>184.1</td>\n",
       "      <td>3.043</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>61.701381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>9.569</td>\n",
       "      <td>16.575</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>19.99</td>\n",
       "      <td>2.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>97.4</td>\n",
       "      <td>66.7</td>\n",
       "      <td>160.4</td>\n",
       "      <td>3.079</td>\n",
       "      <td>13.7</td>\n",
       "      <td>26.0</td>\n",
       "      <td>48.907372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.596</td>\n",
       "      <td>13.760</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>17.50</td>\n",
       "      <td>2.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>98.9</td>\n",
       "      <td>68.3</td>\n",
       "      <td>163.3</td>\n",
       "      <td>2.762</td>\n",
       "      <td>14.6</td>\n",
       "      <td>26.0</td>\n",
       "      <td>47.946841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "0                16.919        16.360    Passenger               21.50   \n",
       "1                39.384        19.875    Passenger               28.40   \n",
       "2                14.114        18.225    Passenger                 NaN   \n",
       "3                 8.588        29.725    Passenger               42.00   \n",
       "4                20.397        22.255    Passenger               23.99   \n",
       "..                  ...           ...          ...                 ...   \n",
       "145               9.761        11.425    Passenger               14.90   \n",
       "146              83.721        13.240    Passenger               16.70   \n",
       "147              51.102        16.725    Passenger               21.20   \n",
       "148               9.569        16.575    Passenger               19.99   \n",
       "149               5.596        13.760    Passenger               17.50   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "0            1.8       140.0      101.2   67.3   172.4        2.639   \n",
       "1            3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2            3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "3            3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "4            1.8       150.0      102.6   68.2   178.0        2.998   \n",
       "..           ...         ...        ...    ...     ...          ...   \n",
       "145          2.0       115.0       98.9   68.3   163.3        2.767   \n",
       "146          2.0       115.0       98.9   68.3   172.3        2.853   \n",
       "147          1.8       150.0      106.4   68.5   184.1        3.043   \n",
       "148          2.0       115.0       97.4   66.7   160.4        3.079   \n",
       "149          2.0       115.0       98.9   68.3   163.3        2.762   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "0             13.2             28.0          58.280150  \n",
       "1             17.2             25.0          91.370778  \n",
       "2             17.2             26.0                NaN  \n",
       "3             18.0             22.0          91.389779  \n",
       "4             16.4             27.0          62.777639  \n",
       "..             ...              ...                ...  \n",
       "145           14.5             26.0          46.943877  \n",
       "146           14.5             26.0          47.638237  \n",
       "147           16.4             27.0          61.701381  \n",
       "148           13.7             26.0          48.907372  \n",
       "149           14.6             26.0          47.946841  \n",
       "\n",
       "[121 rows x 13 columns]"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Return all rows where the Value of 'Resale_value' is not null\n",
    "\n",
    "cardfn1 = car_df[car_df['Resale_value'].notnull()]\n",
    "cardfn1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18.780</td>\n",
       "      <td>23.555</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>33.95</td>\n",
       "      <td>2.8</td>\n",
       "      <td>200.0</td>\n",
       "      <td>108.7</td>\n",
       "      <td>76.1</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.561</td>\n",
       "      <td>18.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>84.565105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>9.761</td>\n",
       "      <td>11.425</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>14.90</td>\n",
       "      <td>2.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>98.9</td>\n",
       "      <td>68.3</td>\n",
       "      <td>163.3</td>\n",
       "      <td>2.767</td>\n",
       "      <td>14.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>46.943877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>83.721</td>\n",
       "      <td>13.240</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>16.70</td>\n",
       "      <td>2.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>98.9</td>\n",
       "      <td>68.3</td>\n",
       "      <td>172.3</td>\n",
       "      <td>2.853</td>\n",
       "      <td>14.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>47.638237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>51.102</td>\n",
       "      <td>16.725</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.20</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>106.4</td>\n",
       "      <td>68.5</td>\n",
       "      <td>184.1</td>\n",
       "      <td>3.043</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>61.701381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>9.569</td>\n",
       "      <td>16.575</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>19.99</td>\n",
       "      <td>2.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>97.4</td>\n",
       "      <td>66.7</td>\n",
       "      <td>160.4</td>\n",
       "      <td>3.079</td>\n",
       "      <td>13.7</td>\n",
       "      <td>26.0</td>\n",
       "      <td>48.907372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.596</td>\n",
       "      <td>13.760</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>17.50</td>\n",
       "      <td>2.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>98.9</td>\n",
       "      <td>68.3</td>\n",
       "      <td>163.3</td>\n",
       "      <td>2.762</td>\n",
       "      <td>14.6</td>\n",
       "      <td>26.0</td>\n",
       "      <td>47.946841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "0                16.919        16.360    Passenger               21.50   \n",
       "1                39.384        19.875    Passenger               28.40   \n",
       "3                 8.588        29.725    Passenger               42.00   \n",
       "4                20.397        22.255    Passenger               23.99   \n",
       "5                18.780        23.555    Passenger               33.95   \n",
       "..                  ...           ...          ...                 ...   \n",
       "145               9.761        11.425    Passenger               14.90   \n",
       "146              83.721        13.240    Passenger               16.70   \n",
       "147              51.102        16.725    Passenger               21.20   \n",
       "148               9.569        16.575    Passenger               19.99   \n",
       "149               5.596        13.760    Passenger               17.50   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "0            1.8       140.0      101.2   67.3   172.4        2.639   \n",
       "1            3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "3            3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "4            1.8       150.0      102.6   68.2   178.0        2.998   \n",
       "5            2.8       200.0      108.7   76.1   192.0        3.561   \n",
       "..           ...         ...        ...    ...     ...          ...   \n",
       "145          2.0       115.0       98.9   68.3   163.3        2.767   \n",
       "146          2.0       115.0       98.9   68.3   172.3        2.853   \n",
       "147          1.8       150.0      106.4   68.5   184.1        3.043   \n",
       "148          2.0       115.0       97.4   66.7   160.4        3.079   \n",
       "149          2.0       115.0       98.9   68.3   163.3        2.762   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "0             13.2             28.0          58.280150  \n",
       "1             17.2             25.0          91.370778  \n",
       "3             18.0             22.0          91.389779  \n",
       "4             16.4             27.0          62.777639  \n",
       "5             18.5             22.0          84.565105  \n",
       "..             ...              ...                ...  \n",
       "145           14.5             26.0          46.943877  \n",
       "146           14.5             26.0          47.638237  \n",
       "147           16.4             27.0          61.701381  \n",
       "148           13.7             26.0          48.907372  \n",
       "149           14.6             26.0          47.946841  \n",
       "\n",
       "[117 rows x 13 columns]"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping rows with Null value\n",
    "\n",
    "cardfn2 = car_df.dropna()\n",
    "cardfn2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Width</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>67.3</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>70.3</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>70.6</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>71.4</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>68.2</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>3.545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>24.40</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160.0</td>\n",
       "      <td>67.6</td>\n",
       "      <td>3.042</td>\n",
       "      <td>15.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>66.498812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>15.245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>27.50</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>69.3</td>\n",
       "      <td>3.208</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.654495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>17.531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>69.3</td>\n",
       "      <td>3.259</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.155978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3.493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.50</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>71.5</td>\n",
       "      <td>3.601</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.623357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>18.969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.00</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>72.1</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>157 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "0                16.919        16.360    Passenger               21.50   \n",
       "1                39.384        19.875    Passenger               28.40   \n",
       "2                14.114        18.225    Passenger                 NaN   \n",
       "3                 8.588        29.725    Passenger               42.00   \n",
       "4                20.397        22.255    Passenger               23.99   \n",
       "..                  ...           ...          ...                 ...   \n",
       "152               3.545           NaN    Passenger               24.40   \n",
       "153              15.245           NaN    Passenger               27.50   \n",
       "154              17.531           NaN    Passenger               28.80   \n",
       "155               3.493           NaN    Passenger               45.50   \n",
       "156              18.969           NaN    Passenger               36.00   \n",
       "\n",
       "     Engine_size  Horsepower  Width  Curb_weight  Fuel_capacity  \\\n",
       "0            1.8       140.0   67.3        2.639           13.2   \n",
       "1            3.2       225.0   70.3        3.517           17.2   \n",
       "2            3.2       225.0   70.6        3.470           17.2   \n",
       "3            3.5       210.0   71.4        3.850           18.0   \n",
       "4            1.8       150.0   68.2        2.998           16.4   \n",
       "..           ...         ...    ...          ...            ...   \n",
       "152          1.9       160.0   67.6        3.042           15.8   \n",
       "153          2.4       168.0   69.3        3.208           17.9   \n",
       "154          2.4       168.0   69.3        3.259           17.9   \n",
       "155          2.3       236.0   71.5        3.601           18.5   \n",
       "156          2.9       201.0   72.1        3.600           21.1   \n",
       "\n",
       "     Fuel_efficiency  Power_perf_factor  \n",
       "0               28.0          58.280150  \n",
       "1               25.0          91.370778  \n",
       "2               26.0                NaN  \n",
       "3               22.0          91.389779  \n",
       "4               27.0          62.777639  \n",
       "..               ...                ...  \n",
       "152             25.0          66.498812  \n",
       "153             25.0          70.654495  \n",
       "154             25.0          71.155978  \n",
       "155             23.0         101.623357  \n",
       "156             24.0          85.735655  \n",
       "\n",
       "[157 rows x 11 columns]"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping Specific Columns\n",
    "\n",
    "df_drp1 = car_df.drop(['Wheelbase','Length'], axis=1)\n",
    "df_drp1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.380</td>\n",
       "      <td>39.000</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>62.000</td>\n",
       "      <td>4.2</td>\n",
       "      <td>310.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>198.2</td>\n",
       "      <td>3.902</td>\n",
       "      <td>23.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>134.656858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>14.785</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>46.225</td>\n",
       "      <td>5.7</td>\n",
       "      <td>255.0</td>\n",
       "      <td>117.5</td>\n",
       "      <td>77.0</td>\n",
       "      <td>201.2</td>\n",
       "      <td>5.572</td>\n",
       "      <td>30.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>109.509117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>53.480</td>\n",
       "      <td>19.540</td>\n",
       "      <td>Car</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>227.061</td>\n",
       "      <td>15.060</td>\n",
       "      <td>Car</td>\n",
       "      <td>19.460</td>\n",
       "      <td>5.2</td>\n",
       "      <td>230.0</td>\n",
       "      <td>138.7</td>\n",
       "      <td>79.3</td>\n",
       "      <td>224.2</td>\n",
       "      <td>4.470</td>\n",
       "      <td>26.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>90.211700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>16.767</td>\n",
       "      <td>15.510</td>\n",
       "      <td>Car</td>\n",
       "      <td>21.315</td>\n",
       "      <td>3.9</td>\n",
       "      <td>175.0</td>\n",
       "      <td>109.6</td>\n",
       "      <td>78.8</td>\n",
       "      <td>192.6</td>\n",
       "      <td>4.245</td>\n",
       "      <td>32.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>71.135292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>31.038</td>\n",
       "      <td>13.425</td>\n",
       "      <td>Car</td>\n",
       "      <td>18.575</td>\n",
       "      <td>3.9</td>\n",
       "      <td>175.0</td>\n",
       "      <td>127.2</td>\n",
       "      <td>78.8</td>\n",
       "      <td>208.5</td>\n",
       "      <td>4.298</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>70.078322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>111.313</td>\n",
       "      <td>11.260</td>\n",
       "      <td>Car</td>\n",
       "      <td>16.980</td>\n",
       "      <td>2.5</td>\n",
       "      <td>120.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>71.5</td>\n",
       "      <td>215.0</td>\n",
       "      <td>3.557</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>49.645002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>101.323</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>26.310</td>\n",
       "      <td>5.2</td>\n",
       "      <td>230.0</td>\n",
       "      <td>115.7</td>\n",
       "      <td>71.7</td>\n",
       "      <td>193.5</td>\n",
       "      <td>4.394</td>\n",
       "      <td>25.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>92.854125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>181.749</td>\n",
       "      <td>12.025</td>\n",
       "      <td>Car</td>\n",
       "      <td>19.565</td>\n",
       "      <td>2.4</td>\n",
       "      <td>150.0</td>\n",
       "      <td>113.3</td>\n",
       "      <td>76.8</td>\n",
       "      <td>186.3</td>\n",
       "      <td>3.533</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>61.227000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>276.747</td>\n",
       "      <td>16.640</td>\n",
       "      <td>Car</td>\n",
       "      <td>31.930</td>\n",
       "      <td>4.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>111.6</td>\n",
       "      <td>70.2</td>\n",
       "      <td>190.7</td>\n",
       "      <td>3.876</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>87.635496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>155.787</td>\n",
       "      <td>13.175</td>\n",
       "      <td>Car</td>\n",
       "      <td>21.410</td>\n",
       "      <td>3.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>120.7</td>\n",
       "      <td>76.6</td>\n",
       "      <td>200.9</td>\n",
       "      <td>3.761</td>\n",
       "      <td>26.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>62.095048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>125.338</td>\n",
       "      <td>23.575</td>\n",
       "      <td>Car</td>\n",
       "      <td>36.135</td>\n",
       "      <td>4.6</td>\n",
       "      <td>240.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>78.7</td>\n",
       "      <td>204.6</td>\n",
       "      <td>4.808</td>\n",
       "      <td>26.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>100.024802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>220.650</td>\n",
       "      <td>7.850</td>\n",
       "      <td>Car</td>\n",
       "      <td>12.050</td>\n",
       "      <td>2.5</td>\n",
       "      <td>119.0</td>\n",
       "      <td>117.5</td>\n",
       "      <td>69.4</td>\n",
       "      <td>200.7</td>\n",
       "      <td>3.086</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>47.389531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>540.561</td>\n",
       "      <td>15.075</td>\n",
       "      <td>Car</td>\n",
       "      <td>26.935</td>\n",
       "      <td>4.6</td>\n",
       "      <td>220.0</td>\n",
       "      <td>138.5</td>\n",
       "      <td>79.1</td>\n",
       "      <td>224.5</td>\n",
       "      <td>4.241</td>\n",
       "      <td>25.1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>89.401935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>12.855</td>\n",
       "      <td>17.525</td>\n",
       "      <td>Car</td>\n",
       "      <td>26.600</td>\n",
       "      <td>3.2</td>\n",
       "      <td>205.0</td>\n",
       "      <td>106.4</td>\n",
       "      <td>70.4</td>\n",
       "      <td>178.2</td>\n",
       "      <td>3.857</td>\n",
       "      <td>21.1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>83.602501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>76.029</td>\n",
       "      <td>19.490</td>\n",
       "      <td>Car</td>\n",
       "      <td>26.000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>118.1</td>\n",
       "      <td>75.6</td>\n",
       "      <td>201.2</td>\n",
       "      <td>4.288</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>85.217691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>80.556</td>\n",
       "      <td>13.775</td>\n",
       "      <td>Car</td>\n",
       "      <td>21.620</td>\n",
       "      <td>4.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>101.4</td>\n",
       "      <td>69.4</td>\n",
       "      <td>167.5</td>\n",
       "      <td>3.194</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>76.584440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>157.040</td>\n",
       "      <td>18.810</td>\n",
       "      <td>Car</td>\n",
       "      <td>26.895</td>\n",
       "      <td>4.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>105.9</td>\n",
       "      <td>72.3</td>\n",
       "      <td>181.5</td>\n",
       "      <td>3.880</td>\n",
       "      <td>20.5</td>\n",
       "      <td>19.0</td>\n",
       "      <td>80.387779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>6.375</td>\n",
       "      <td>40.375</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>54.005</td>\n",
       "      <td>4.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>112.2</td>\n",
       "      <td>72.0</td>\n",
       "      <td>196.7</td>\n",
       "      <td>3.890</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>124.446716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>9.126</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>60.105</td>\n",
       "      <td>4.7</td>\n",
       "      <td>230.0</td>\n",
       "      <td>112.2</td>\n",
       "      <td>76.4</td>\n",
       "      <td>192.5</td>\n",
       "      <td>5.401</td>\n",
       "      <td>25.4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>105.760458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>13.798</td>\n",
       "      <td>20.525</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>39.080</td>\n",
       "      <td>4.6</td>\n",
       "      <td>275.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>73.6</td>\n",
       "      <td>208.5</td>\n",
       "      <td>3.868</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>113.540207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>22.925</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>42.660</td>\n",
       "      <td>5.4</td>\n",
       "      <td>300.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>79.9</td>\n",
       "      <td>204.8</td>\n",
       "      <td>5.393</td>\n",
       "      <td>30.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>123.972047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>11.337</td>\n",
       "      <td>19.125</td>\n",
       "      <td>Car</td>\n",
       "      <td>31.807</td>\n",
       "      <td>3.5</td>\n",
       "      <td>200.0</td>\n",
       "      <td>107.3</td>\n",
       "      <td>69.9</td>\n",
       "      <td>186.6</td>\n",
       "      <td>4.520</td>\n",
       "      <td>24.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>83.920815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>27.609</td>\n",
       "      <td>20.430</td>\n",
       "      <td>Car</td>\n",
       "      <td>27.560</td>\n",
       "      <td>4.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>111.6</td>\n",
       "      <td>70.2</td>\n",
       "      <td>190.1</td>\n",
       "      <td>3.876</td>\n",
       "      <td>21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>85.949744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>20.380</td>\n",
       "      <td>14.795</td>\n",
       "      <td>Car</td>\n",
       "      <td>22.510</td>\n",
       "      <td>3.3</td>\n",
       "      <td>170.0</td>\n",
       "      <td>112.2</td>\n",
       "      <td>74.9</td>\n",
       "      <td>194.7</td>\n",
       "      <td>3.944</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>69.671461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>27.602</td>\n",
       "      <td>41.450</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>49.900</td>\n",
       "      <td>3.2</td>\n",
       "      <td>221.0</td>\n",
       "      <td>111.5</td>\n",
       "      <td>70.8</td>\n",
       "      <td>189.4</td>\n",
       "      <td>3.823</td>\n",
       "      <td>21.1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>98.249737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>16.774</td>\n",
       "      <td>50.375</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>69.700</td>\n",
       "      <td>4.3</td>\n",
       "      <td>275.0</td>\n",
       "      <td>121.5</td>\n",
       "      <td>73.1</td>\n",
       "      <td>203.1</td>\n",
       "      <td>4.133</td>\n",
       "      <td>23.2</td>\n",
       "      <td>21.0</td>\n",
       "      <td>125.273876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>3.311</td>\n",
       "      <td>58.600</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>82.600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>71.3</td>\n",
       "      <td>177.1</td>\n",
       "      <td>4.125</td>\n",
       "      <td>21.1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>139.982294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.954</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>85.500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>113.6</td>\n",
       "      <td>73.1</td>\n",
       "      <td>196.6</td>\n",
       "      <td>4.115</td>\n",
       "      <td>23.2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>141.100985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>27.308</td>\n",
       "      <td>15.380</td>\n",
       "      <td>Car</td>\n",
       "      <td>26.399</td>\n",
       "      <td>3.3</td>\n",
       "      <td>170.0</td>\n",
       "      <td>112.2</td>\n",
       "      <td>74.9</td>\n",
       "      <td>194.8</td>\n",
       "      <td>3.991</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>71.171664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>42.574</td>\n",
       "      <td>17.810</td>\n",
       "      <td>Car</td>\n",
       "      <td>29.299</td>\n",
       "      <td>3.3</td>\n",
       "      <td>170.0</td>\n",
       "      <td>106.3</td>\n",
       "      <td>71.7</td>\n",
       "      <td>182.6</td>\n",
       "      <td>3.947</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>72.290355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>24.361</td>\n",
       "      <td>15.240</td>\n",
       "      <td>Car</td>\n",
       "      <td>25.345</td>\n",
       "      <td>3.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>72.2</td>\n",
       "      <td>201.4</td>\n",
       "      <td>3.948</td>\n",
       "      <td>25.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>76.096570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>24.155</td>\n",
       "      <td>12.025</td>\n",
       "      <td>Car</td>\n",
       "      <td>18.850</td>\n",
       "      <td>2.4</td>\n",
       "      <td>150.0</td>\n",
       "      <td>113.3</td>\n",
       "      <td>76.8</td>\n",
       "      <td>186.3</td>\n",
       "      <td>3.528</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>60.951185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>39.572</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>25.635</td>\n",
       "      <td>3.4</td>\n",
       "      <td>185.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>72.7</td>\n",
       "      <td>201.3</td>\n",
       "      <td>3.942</td>\n",
       "      <td>25.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>76.208440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>65.119</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car</td>\n",
       "      <td>22.368</td>\n",
       "      <td>3.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>114.2</td>\n",
       "      <td>73.4</td>\n",
       "      <td>193.5</td>\n",
       "      <td>3.759</td>\n",
       "      <td>20.9</td>\n",
       "      <td>22.0</td>\n",
       "      <td>78.027219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>9.835</td>\n",
       "      <td>34.080</td>\n",
       "      <td>Car</td>\n",
       "      <td>51.728</td>\n",
       "      <td>4.7</td>\n",
       "      <td>230.0</td>\n",
       "      <td>112.2</td>\n",
       "      <td>76.4</td>\n",
       "      <td>192.5</td>\n",
       "      <td>5.115</td>\n",
       "      <td>25.4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>102.528984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>18.969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.000</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "6                 1.380        39.000    Passenger              62.000   \n",
       "18               14.785           NaN          Car              46.225   \n",
       "33               53.480        19.540          Car                 NaN   \n",
       "40              227.061        15.060          Car              19.460   \n",
       "41               16.767        15.510          Car              21.315   \n",
       "42               31.038        13.425          Car              18.575   \n",
       "43              111.313        11.260          Car              16.980   \n",
       "44              101.323           NaN          Car              26.310   \n",
       "45              181.749        12.025          Car              19.565   \n",
       "52              276.747        16.640          Car              31.930   \n",
       "53              155.787        13.175          Car              21.410   \n",
       "54              125.338        23.575          Car              36.135   \n",
       "55              220.650         7.850          Car              12.050   \n",
       "56              540.561        15.075          Car              26.935   \n",
       "60               12.855        17.525          Car              26.600   \n",
       "61               76.029        19.490          Car              26.000   \n",
       "68               80.556        13.775          Car              21.620   \n",
       "69              157.040        18.810          Car              26.895   \n",
       "73                6.375        40.375    Passenger              54.005   \n",
       "74                9.126           NaN          Car              60.105   \n",
       "76               13.798        20.525    Passenger              39.080   \n",
       "78               22.925           NaN          Car              42.660   \n",
       "84               11.337        19.125          Car              31.807   \n",
       "90               27.609        20.430          Car              27.560   \n",
       "91               20.380        14.795          Car              22.510   \n",
       "93               27.602        41.450    Passenger              49.900   \n",
       "94               16.774        50.375    Passenger              69.700   \n",
       "95                3.311        58.600    Passenger              82.600   \n",
       "99                0.954           NaN    Passenger              85.500   \n",
       "104              27.308        15.380          Car              26.399   \n",
       "105              42.574        17.810          Car              29.299   \n",
       "113              24.361        15.240          Car              25.345   \n",
       "116              24.155        12.025          Car              18.850   \n",
       "123              39.572           NaN          Car              25.635   \n",
       "141              65.119           NaN          Car              22.368   \n",
       "144               9.835        34.080          Car              51.728   \n",
       "156              18.969           NaN    Passenger              36.000   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "6            4.2       310.0      113.0   74.0   198.2        3.902   \n",
       "18           5.7       255.0      117.5   77.0   201.2        5.572   \n",
       "33           NaN         NaN        NaN    NaN     NaN          NaN   \n",
       "40           5.2       230.0      138.7   79.3   224.2        4.470   \n",
       "41           3.9       175.0      109.6   78.8   192.6        4.245   \n",
       "42           3.9       175.0      127.2   78.8   208.5        4.298   \n",
       "43           2.5       120.0      131.0   71.5   215.0        3.557   \n",
       "44           5.2       230.0      115.7   71.7   193.5        4.394   \n",
       "45           2.4       150.0      113.3   76.8   186.3        3.533   \n",
       "52           4.0       210.0      111.6   70.2   190.7        3.876   \n",
       "53           3.0       150.0      120.7   76.6   200.9        3.761   \n",
       "54           4.6       240.0      119.0   78.7   204.6        4.808   \n",
       "55           2.5       119.0      117.5   69.4   200.7        3.086   \n",
       "56           4.6       220.0      138.5   79.1   224.5        4.241   \n",
       "60           3.2       205.0      106.4   70.4   178.2        3.857   \n",
       "61           3.5       210.0      118.1   75.6   201.2        4.288   \n",
       "68           4.0       190.0      101.4   69.4   167.5        3.194   \n",
       "69           4.0       195.0      105.9   72.3   181.5        3.880   \n",
       "73           4.0       290.0      112.2   72.0   196.7        3.890   \n",
       "74           4.7       230.0      112.2   76.4   192.5        5.401   \n",
       "76           4.6       275.0      109.0   73.6   208.5        3.868   \n",
       "78           5.4       300.0      119.0   79.9   204.8        5.393   \n",
       "84           3.5       200.0      107.3   69.9   186.6        4.520   \n",
       "90           4.0       210.0      111.6   70.2   190.1        3.876   \n",
       "91           3.3       170.0      112.2   74.9   194.7        3.944   \n",
       "93           3.2       221.0      111.5   70.8   189.4        3.823   \n",
       "94           4.3       275.0      121.5   73.1   203.1        4.133   \n",
       "95           5.0       302.0       99.0   71.3   177.1        4.125   \n",
       "99           5.0       302.0      113.6   73.1   196.6        4.115   \n",
       "104          3.3       170.0      112.2   74.9   194.8        3.991   \n",
       "105          3.3       170.0      106.3   71.7   182.6        3.947   \n",
       "113          3.4       185.0      120.0   72.2   201.4        3.948   \n",
       "116          2.4       150.0      113.3   76.8   186.3        3.528   \n",
       "123          3.4       185.0      120.0   72.7   201.3        3.942   \n",
       "141          3.0       194.0      114.2   73.4   193.5        3.759   \n",
       "144          4.7       230.0      112.2   76.4   192.5        5.115   \n",
       "156          2.9       201.0      109.9   72.1   189.8        3.600   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "6             23.7             21.0         134.656858  \n",
       "18            30.0             15.0         109.509117  \n",
       "33             NaN              NaN                NaN  \n",
       "40            26.0             17.0          90.211700  \n",
       "41            32.0             15.0          71.135292  \n",
       "42            32.0             16.0          70.078322  \n",
       "43            22.0             19.0          49.645002  \n",
       "44            25.0             17.0          92.854125  \n",
       "45            20.0             24.0          61.227000  \n",
       "52            21.0             19.0          87.635496  \n",
       "53            26.0             21.0          62.095048  \n",
       "54            26.0             16.0         100.024802  \n",
       "55            20.0             23.0          47.389531  \n",
       "56            25.1             18.0          89.401935  \n",
       "60            21.1             19.0          83.602501  \n",
       "61            20.0             23.0          85.217691  \n",
       "68            20.0             20.0          76.584440  \n",
       "69            20.5             19.0          80.387779  \n",
       "73            22.5             22.0         124.446716  \n",
       "74            25.4             15.0         105.760458  \n",
       "76            20.0             22.0         113.540207  \n",
       "78            30.0             15.0         123.972047  \n",
       "84            24.3             18.0          83.920815  \n",
       "90            21.0             18.0          85.949744  \n",
       "91            20.0             21.0          69.671461  \n",
       "93            21.1             25.0          98.249737  \n",
       "94            23.2             21.0         125.273876  \n",
       "95            21.1             20.0         139.982294  \n",
       "99            23.2             20.0         141.100985  \n",
       "104           20.0             21.0          71.171664  \n",
       "105           21.0             19.0          72.290355  \n",
       "113           25.0             22.0          76.096570  \n",
       "116           20.0             24.0          60.951185  \n",
       "123           25.0             23.0          76.208440  \n",
       "141           20.9             22.0          78.027219  \n",
       "144           25.4             15.0         102.528984  \n",
       "156           21.1             24.0          85.735655  "
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drp2 = car_df.drop(car_df[car_df['Fuel_capacity'] < 20].index)\n",
    "df_drp2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting values in place of Null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>3.545</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>24.40</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.5</td>\n",
       "      <td>67.6</td>\n",
       "      <td>176.6</td>\n",
       "      <td>3.042</td>\n",
       "      <td>15.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>66.498812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>15.245</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>27.50</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>185.9</td>\n",
       "      <td>3.208</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.654495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>17.531</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>186.2</td>\n",
       "      <td>3.259</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.155978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3.493</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.50</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>185.7</td>\n",
       "      <td>3.601</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.623357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>18.969</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.00</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>157 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "0                16.919        16.360    Passenger               21.50   \n",
       "1                39.384        19.875    Passenger               28.40   \n",
       "2                14.114        18.225    Passenger                0.00   \n",
       "3                 8.588        29.725    Passenger               42.00   \n",
       "4                20.397        22.255    Passenger               23.99   \n",
       "..                  ...           ...          ...                 ...   \n",
       "152               3.545         0.000    Passenger               24.40   \n",
       "153              15.245         0.000    Passenger               27.50   \n",
       "154              17.531         0.000    Passenger               28.80   \n",
       "155               3.493         0.000    Passenger               45.50   \n",
       "156              18.969         0.000    Passenger               36.00   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "0            1.8       140.0      101.2   67.3   172.4        2.639   \n",
       "1            3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2            3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "3            3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "4            1.8       150.0      102.6   68.2   178.0        2.998   \n",
       "..           ...         ...        ...    ...     ...          ...   \n",
       "152          1.9       160.0      100.5   67.6   176.6        3.042   \n",
       "153          2.4       168.0      104.9   69.3   185.9        3.208   \n",
       "154          2.4       168.0      104.9   69.3   186.2        3.259   \n",
       "155          2.3       236.0      104.9   71.5   185.7        3.601   \n",
       "156          2.9       201.0      109.9   72.1   189.8        3.600   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "0             13.2             28.0          58.280150  \n",
       "1             17.2             25.0          91.370778  \n",
       "2             17.2             26.0           0.000000  \n",
       "3             18.0             22.0          91.389779  \n",
       "4             16.4             27.0          62.777639  \n",
       "..             ...              ...                ...  \n",
       "152           15.8             25.0          66.498812  \n",
       "153           17.9             25.0          70.654495  \n",
       "154           17.9             25.0          71.155978  \n",
       "155           18.5             23.0         101.623357  \n",
       "156           21.1             24.0          85.735655  \n",
       "\n",
       "[157 rows x 13 columns]"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill the null values with zeroes\n",
    "\n",
    "filldf = car_df.fillna(0)\n",
    "filldf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360000</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.280150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875000</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.370778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225000</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725000</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>91.389779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255000</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.777639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>3.545</td>\n",
       "      <td>18.072975</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>24.40</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160.0</td>\n",
       "      <td>100.5</td>\n",
       "      <td>67.6</td>\n",
       "      <td>176.6</td>\n",
       "      <td>3.042</td>\n",
       "      <td>15.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>66.498812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>15.245</td>\n",
       "      <td>18.072975</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>27.50</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>185.9</td>\n",
       "      <td>3.208</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.654495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>17.531</td>\n",
       "      <td>18.072975</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>28.80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>186.2</td>\n",
       "      <td>3.259</td>\n",
       "      <td>17.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.155978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3.493</td>\n",
       "      <td>18.072975</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>45.50</td>\n",
       "      <td>2.3</td>\n",
       "      <td>236.0</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>185.7</td>\n",
       "      <td>3.601</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.623357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>18.969</td>\n",
       "      <td>18.072975</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>36.00</td>\n",
       "      <td>2.9</td>\n",
       "      <td>201.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>189.8</td>\n",
       "      <td>3.600</td>\n",
       "      <td>21.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>85.735655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>157 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "0                16.919     16.360000    Passenger               21.50   \n",
       "1                39.384     19.875000    Passenger               28.40   \n",
       "2                14.114     18.225000    Passenger                1.00   \n",
       "3                 8.588     29.725000    Passenger               42.00   \n",
       "4                20.397     22.255000    Passenger               23.99   \n",
       "..                  ...           ...          ...                 ...   \n",
       "152               3.545     18.072975    Passenger               24.40   \n",
       "153              15.245     18.072975    Passenger               27.50   \n",
       "154              17.531     18.072975    Passenger               28.80   \n",
       "155               3.493     18.072975    Passenger               45.50   \n",
       "156              18.969     18.072975    Passenger               36.00   \n",
       "\n",
       "     Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "0            1.8       140.0      101.2   67.3   172.4        2.639   \n",
       "1            3.2       225.0      108.1   70.3   192.9        3.517   \n",
       "2            3.2       225.0      106.9   70.6   192.0        3.470   \n",
       "3            3.5       210.0      114.6   71.4   196.6        3.850   \n",
       "4            1.8       150.0      102.6   68.2   178.0        2.998   \n",
       "..           ...         ...        ...    ...     ...          ...   \n",
       "152          1.9       160.0      100.5   67.6   176.6        3.042   \n",
       "153          2.4       168.0      104.9   69.3   185.9        3.208   \n",
       "154          2.4       168.0      104.9   69.3   186.2        3.259   \n",
       "155          2.3       236.0      104.9   71.5   185.7        3.601   \n",
       "156          2.9       201.0      109.9   72.1   189.8        3.600   \n",
       "\n",
       "     Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "0             13.2             28.0          58.280150  \n",
       "1             17.2             25.0          91.370778  \n",
       "2             17.2             26.0                NaN  \n",
       "3             18.0             22.0          91.389779  \n",
       "4             16.4             27.0          62.777639  \n",
       "..             ...              ...                ...  \n",
       "152           15.8             25.0          66.498812  \n",
       "153           17.9             25.0          70.654495  \n",
       "154           17.9             25.0          71.155978  \n",
       "155           18.5             23.0         101.623357  \n",
       "156           21.1             24.0          85.735655  \n",
       "\n",
       "[157 rows x 13 columns]"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mention fill values for each column\n",
    "\n",
    "values = {\"Resale_value\": car_df['Resale_value'].mean(), \"Price_in_thousands\": 1}\n",
    "filldf = car_df.fillna(value=values)\n",
    "filldf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>106.9</td>\n",
       "      <td>70.6</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.47</td>\n",
       "      <td>17.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>53.480</td>\n",
       "      <td>19.540</td>\n",
       "      <td>Car</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sales_in_thousands  Resale_value Vehicle_type  Price_in_thousands  \\\n",
       "2               14.114        18.225    Passenger                 NaN   \n",
       "33              53.480        19.540          Car                 NaN   \n",
       "\n",
       "    Engine_size  Horsepower  Wheelbase  Width  Length  Curb_weight  \\\n",
       "2           3.2       225.0      106.9   70.6   192.0         3.47   \n",
       "33          NaN         NaN        NaN    NaN     NaN          NaN   \n",
       "\n",
       "    Fuel_capacity  Fuel_efficiency  Power_perf_factor  \n",
       "2            17.2             26.0                NaN  \n",
       "33            NaN              NaN                NaN  "
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_df[car_df['Price_in_thousands'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales_in_thousands</th>\n",
       "      <th>Resale_value</th>\n",
       "      <th>Vehicle_type</th>\n",
       "      <th>Price_in_thousands</th>\n",
       "      <th>Engine_size</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Wheelbase</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length</th>\n",
       "      <th>Curb_weight</th>\n",
       "      <th>Fuel_capacity</th>\n",
       "      <th>Fuel_efficiency</th>\n",
       "      <th>Power_perf_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Sales_in_thousands, Resale_value, Vehicle_type, Price_in_thousands, Engine_size, Horsepower, Wheelbase, Width, Length, Curb_weight, Fuel_capacity, Fuel_efficiency, Power_perf_factor]\n",
       "Index: []"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filldf[filldf['Price_in_thousands'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agrregate on Columns using Groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year         pop continent  lifeExp   gdpPercap\n",
       "0     Afghanistan  1952   8425333.0      Asia   28.801  779.445314\n",
       "1     Afghanistan  1957   9240934.0      Asia   30.332  820.853030\n",
       "2     Afghanistan  1962  10267083.0      Asia   31.997  853.100710\n",
       "3     Afghanistan  1967  11537966.0      Asia   34.020  836.197138\n",
       "4     Afghanistan  1972  13079460.0      Asia   36.088  739.981106\n",
       "...           ...   ...         ...       ...      ...         ...\n",
       "1699     Zimbabwe  1987   9216418.0    Africa   62.351  706.157306\n",
       "1700     Zimbabwe  1992  10704340.0    Africa   60.377  693.420786\n",
       "1701     Zimbabwe  1997  11404948.0    Africa   46.809  792.449960\n",
       "1702     Zimbabwe  2002  11926563.0    Africa   39.989  672.038623\n",
       "1703     Zimbabwe  2007  12311143.0    Africa   43.487  469.709298\n",
       "\n",
       "[1704 rows x 6 columns]"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame from the GapMinder Data\n",
    "\n",
    "gm_df = pd.read_csv('gapminderData.csv')\n",
    "gm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>1979.5</td>\n",
       "      <td>9.916003e+06</td>\n",
       "      <td>48.865330</td>\n",
       "      <td>2193.754578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>1979.5</td>\n",
       "      <td>2.450479e+07</td>\n",
       "      <td>64.658737</td>\n",
       "      <td>7136.110356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>1979.5</td>\n",
       "      <td>7.703872e+07</td>\n",
       "      <td>60.064903</td>\n",
       "      <td>7902.150428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>1979.5</td>\n",
       "      <td>1.716976e+07</td>\n",
       "      <td>71.903686</td>\n",
       "      <td>14469.475533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>1979.5</td>\n",
       "      <td>8.874672e+06</td>\n",
       "      <td>74.326208</td>\n",
       "      <td>18621.609223</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             year           pop    lifeExp     gdpPercap\n",
       "continent                                               \n",
       "Africa     1979.5  9.916003e+06  48.865330   2193.754578\n",
       "Americas   1979.5  2.450479e+07  64.658737   7136.110356\n",
       "Asia       1979.5  7.703872e+07  60.064903   7902.150428\n",
       "Europe     1979.5  1.716976e+07  71.903686  14469.475533\n",
       "Oceania    1979.5  8.874672e+06  74.326208  18621.609223"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Grouped sums of numeric fields\n",
    "\n",
    "grpsums = gm_df.groupby('continent').mean()\n",
    "grpsums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "Africa      624\n",
       "Americas    300\n",
       "Asia        396\n",
       "Europe      360\n",
       "Oceania      24\n",
       "dtype: int64"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grpsize = gm_df.groupby('continent').size()\n",
    "grpsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Other group operations are max, min, sum, first, last..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>count</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>1.350312e+08</td>\n",
       "      <td>60011.0</td>\n",
       "      <td>624</td>\n",
       "      <td>4579311.0</td>\n",
       "      <td>9.916003e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>3.011399e+08</td>\n",
       "      <td>662850.0</td>\n",
       "      <td>300</td>\n",
       "      <td>6227510.0</td>\n",
       "      <td>2.450479e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>1.318683e+09</td>\n",
       "      <td>120447.0</td>\n",
       "      <td>396</td>\n",
       "      <td>14530830.5</td>\n",
       "      <td>7.703872e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>8.240100e+07</td>\n",
       "      <td>147962.0</td>\n",
       "      <td>360</td>\n",
       "      <td>8551125.0</td>\n",
       "      <td>1.716976e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>2.043418e+07</td>\n",
       "      <td>1994794.0</td>\n",
       "      <td>24</td>\n",
       "      <td>6403491.5</td>\n",
       "      <td>8.874672e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    max        min  count      median          mean\n",
       "continent                                                          \n",
       "Africa     1.350312e+08    60011.0    624   4579311.0  9.916003e+06\n",
       "Americas   3.011399e+08   662850.0    300   6227510.0  2.450479e+07\n",
       "Asia       1.318683e+09   120447.0    396  14530830.5  7.703872e+07\n",
       "Europe     8.240100e+07   147962.0    360   8551125.0  1.716976e+07\n",
       "Oceania    2.043418e+07  1994794.0     24   6403491.5  8.874672e+06"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the agg() (aggregate method)\n",
    "\n",
    "gm_df.groupby('continent').pop.agg(['max', 'min', 'count', 'median', 'mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1952</td>\n",
       "      <td>9279525.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.077</td>\n",
       "      <td>2449.008185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1957</td>\n",
       "      <td>10270856.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>45.685</td>\n",
       "      <td>3013.976023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1962</td>\n",
       "      <td>11000948.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>48.303</td>\n",
       "      <td>2550.816880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1967</td>\n",
       "      <td>12760499.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>51.407</td>\n",
       "      <td>3246.991771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1972</td>\n",
       "      <td>14760787.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>54.518</td>\n",
       "      <td>4182.663766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year         pop continent  lifeExp    gdpPercap\n",
       "24     Algeria  1952   9279525.0    Africa   43.077  2449.008185\n",
       "25     Algeria  1957  10270856.0    Africa   45.685  3013.976023\n",
       "26     Algeria  1962  11000948.0    Africa   48.303  2550.816880\n",
       "27     Algeria  1967  12760499.0    Africa   51.407  3246.991771\n",
       "28     Algeria  1972  14760787.0    Africa   54.518  4182.663766\n",
       "...        ...   ...         ...       ...      ...          ...\n",
       "1699  Zimbabwe  1987   9216418.0    Africa   62.351   706.157306\n",
       "1700  Zimbabwe  1992  10704340.0    Africa   60.377   693.420786\n",
       "1701  Zimbabwe  1997  11404948.0    Africa   46.809   792.449960\n",
       "1702  Zimbabwe  2002  11926563.0    Africa   39.989   672.038623\n",
       "1703  Zimbabwe  2007  12311143.0    Africa   43.487   469.709298\n",
       "\n",
       "[624 rows x 6 columns]"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract records where group len > 500\n",
    "\n",
    "gm_df.groupby('continent').filter(lambda x: len(x) >= 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Pivot Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Africa</th>\n",
       "      <th>Algeria</th>\n",
       "      <td>4426.025973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>3607.100529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benin</th>\n",
       "      <td>1155.395107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>5031.503557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burkina Faso</th>\n",
       "      <td>843.990665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Europe</th>\n",
       "      <th>Switzerland</th>\n",
       "      <td>27074.334405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>4469.453380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>19380.472986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Oceania</th>\n",
       "      <th>Australia</th>\n",
       "      <td>19980.595634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>17262.622813</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             gdpPercap\n",
       "continent country                     \n",
       "Africa    Algeria          4426.025973\n",
       "          Angola           3607.100529\n",
       "          Benin            1155.395107\n",
       "          Botswana         5031.503557\n",
       "          Burkina Faso      843.990665\n",
       "...                                ...\n",
       "Europe    Switzerland     27074.334405\n",
       "          Turkey           4469.453380\n",
       "          United Kingdom  19380.472986\n",
       "Oceania   Australia       19980.595634\n",
       "          New Zealand     17262.622813\n",
       "\n",
       "[142 rows x 1 columns]"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a Pivot table of sum of all contry GDP per capita under each continent\n",
    "\n",
    "pivot = gm_df.pivot_table(values='gdpPercap', index=['continent','country'], aggfunc=np.mean)\n",
    "pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating User-Defined-Functions in Python\n",
    "- Contains a block of logically grouped statements to perform a function\n",
    "- Can take in one or more input Arguments\n",
    "- Can return Values\n",
    "- Functions are named and hence can be used repeatedly "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "def testfunc(x, y):\n",
    "    \"\"\"\n",
    "    This is a test function for learning purposes.\n",
    "    And we will also learn the use of Docstrings. \n",
    "    \"\"\"\n",
    "    \n",
    "    print(f\"First Argument is {x}\")\n",
    "    print(f\"Second Argument is {y}\")\n",
    "    return x*y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First Argument is 3\n",
      "Second Argument is 5\n",
      "15\n"
     ]
    }
   ],
   "source": [
    "print(testfunc(3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First Argument is 6\n",
      "Second Argument is 7\n",
      "42\n"
     ]
    }
   ],
   "source": [
    "ret = testfunc(6,7)\n",
    "print(ret)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function Docstrings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    This is a test function for learning purposes.\n",
      "    And we will also learn the use of Docstrings. \n",
      "    \n"
     ]
    }
   ],
   "source": [
    "print(testfunc.__doc__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple Returns from Python Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "def testfunc(x, y):\n",
    "    \"\"\"\n",
    "    This is a test function for learning purposes.\n",
    "    And we will also learn the use of Docstrings. \n",
    "    \"\"\"\n",
    "    \n",
    "    print(f\"First Argument is {x}\")\n",
    "    print(f\"Second Argument is {y}\")\n",
    "    return x*y, x+y, x/y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First Argument is 4\n",
      "Second Argument is 8\n",
      "(32, 12, 0.5)\n"
     ]
    }
   ],
   "source": [
    "print(testfunc(4,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First Argument is 8\n",
      "Second Argument is 9\n",
      "72\n",
      "17\n",
      "0.89\n"
     ]
    }
   ],
   "source": [
    "ret1, ret2, ret3 = testfunc(8,9)\n",
    "print(ret1)\n",
    "print(ret2)\n",
    "print(round(ret3, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variable Scopes w.r.t. Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "def testfunc(x, y):\n",
    "    testvar_l = 7     # Available only within the Function\n",
    "    print(\"testvar_g:\", testvar_g)\n",
    "    print(f\"First Argument is {x}\")\n",
    "    print(f\"Second Argument is {y}\")\n",
    "    return x*y+testvar_g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testvar_g: 5\n",
      "First Argument is 4\n",
      "Second Argument is 5\n",
      "25\n"
     ]
    }
   ],
   "source": [
    "testvar_g = 5\n",
    "print(testfunc(4,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(testvar_l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Map, Reduce, Filter and Lambda Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lambda functions are inline anonymous functions. It contains only one expression has no explicit return statement. Lambda functions are faster in execution than traditional functions.\n",
    "\n",
    "Lambda is a mechanisam for building functions (or more precisely, function objects). \n",
    "- Lambda functions can contain only single expressions\n",
    "- Can be nameless\n",
    "- Inline\n",
    "- Cleaner to use in case of single use, simple expressions\n",
    "- They return a Function object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cube of 9 is 729\n",
      "Time taken by traditional method : 285 ms\n",
      "\n",
      "Cube of 9 is 729\n",
      "Time taken by named lambda function: 122 ms\n",
      "\n",
      "Cube of 9 is 729\n",
      "Time taken by inline lambda function: 176 ms\n"
     ]
    }
   ],
   "source": [
    "# In the folowing code, we will perform an operation to find the cube of a given number.\n",
    "# To do that, we will use three different methods:\n",
    "# - Use a traditional Python function created using 'def'\n",
    "# - Create a named Lambda function\n",
    "# - Use an Inline Lambda function\n",
    "\n",
    "from time import time_ns\n",
    "\n",
    "def cubed(x):\n",
    "        return x**3\n",
    "\n",
    "lambda_cubed = lambda x:x**3\n",
    "\n",
    "start = time_ns()   # Returns time in nanoseconds since epoch (Jan 1, 1970) \n",
    "\n",
    "print(f\"Cube of 9 is {cubed(9)}\")\n",
    "print(f\"Time taken by traditional method : {(time_ns()-start)//1000} ms\\n\")\n",
    "\n",
    "start = time_ns()\n",
    "\n",
    "print(f\"Cube of 9 is {lambda_cubed(9)}\")\n",
    "print(f\"Time taken by named lambda function: {(time_ns()-start)//1000} ms\\n\")\n",
    "\n",
    "start = time_ns()\n",
    "print(f\"Cube of 9 is {(lambda x:x**3)(9)}\")\n",
    "print(f\"Time taken by inline lambda function: {(time_ns()-start)//1000} ms\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In the following example, we will see how Lambda functions can be applied on a series of Values\n",
    "### Using map() function\n",
    "#### Syntax of the map() function: \n",
    "#### map(lambda item: item[] expression, iterable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13, 30, 16, 34, 53, 96]\n"
     ]
    }
   ],
   "source": [
    "numbers = [4, 5, 7, 9, 12, 23]\n",
    "mapped_numbers = list(map(lambda x: x^3 + x^2 + 5*x - 8, numbers))\n",
    "print(mapped_numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "      <th>continent</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year         pop continent  lifeExp   gdpPercap\n",
       "0     Afghanistan  1952   8425333.0      Asia   28.801  779.445314\n",
       "1     Afghanistan  1957   9240934.0      Asia   30.332  820.853030\n",
       "2     Afghanistan  1962  10267083.0      Asia   31.997  853.100710\n",
       "3     Afghanistan  1967  11537966.0      Asia   34.020  836.197138\n",
       "4     Afghanistan  1972  13079460.0      Asia   36.088  739.981106\n",
       "...           ...   ...         ...       ...      ...         ...\n",
       "1699     Zimbabwe  1987   9216418.0    Africa   62.351  706.157306\n",
       "1700     Zimbabwe  1992  10704340.0    Africa   60.377  693.420786\n",
       "1701     Zimbabwe  1997  11404948.0    Africa   46.809  792.449960\n",
       "1702     Zimbabwe  2002  11926563.0    Africa   39.989  672.038623\n",
       "1703     Zimbabwe  2007  12311143.0    Africa   43.487  469.709298\n",
       "\n",
       "[1704 rows x 6 columns]"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame from the GapMinder Data\n",
    "\n",
    "gm_df = pd.read_csv('gapminderData.csv')\n",
    "gm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'gm_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-2751492cfdf7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Following is an example when you apply the map() function along with a Lambda function on a DataFrame Column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mgm_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'LifeExp_Der'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m1.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgm_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lifeExp'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mgm_df\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'gm_df' is not defined"
     ]
    }
   ],
   "source": [
    "# Following is an example when you apply the map() function along with a Lambda function on a DataFrame Column\n",
    "\n",
    "gm_df['LifeExp_Der'] = list(map(lambda x: x*1.2, gm_df['lifeExp']))\n",
    "gm_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### reduce() function\n",
    "#### Python’s reduce() implements a mathematical technique commonly known as folding or reduction. You’re doing a fold or reduction when you reduce a list of items to a single cumulative value. \n",
    "##### functools.reduce(function, iterable[, initializer])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define an aggregation function\n",
    "\n",
    "def cumul(a, b):\n",
    "    result = a * b\n",
    "    print(f'{a} * {b} = {result}')\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 * 5 = 20\n",
      "20\n"
     ]
    }
   ],
   "source": [
    "print(cumul(4,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "numbers = [4, 5, 6, 3, 8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 * 5 = 20\n",
      "20 * 6 = 120\n",
      "120 * 3 = 360\n",
      "360 * 8 = 2880\n",
      "2880\n"
     ]
    }
   ],
   "source": [
    "from functools import reduce\n",
    "out = reduce(cumul, numbers)\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now using Reduce with a Lambda Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "cumul_l = lambda x, y: x*y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2880\n"
     ]
    }
   ],
   "source": [
    "out1 = reduce(cumul_l, numbers)\n",
    "print(out1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### filter() function \n",
    "#### The Python filter() function implements a filter condtion. The filter is applied against an input interable and returns only those elements from the iterable that return a True value against the filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "# E.g. We are to return all number greater than 5 from a list of numbers\n",
    "\n",
    "numbers = [4, 3, 6, 7, 3, 9, 11]\n",
    "\n",
    "def filt_gt5(num):\n",
    "    return num < 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 3, 3]"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filt = filter(filt_gt5, numbers)\n",
    "list(filt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filt_gt5(numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6, 7, 9, 11]"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can also be inline filter using a Lambda function\n",
    "\n",
    "filt = filter(lambda x: x > 5, numbers)\n",
    "list(filt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[64.82,\n",
       " 66.22,\n",
       " 67.69,\n",
       " 68.93,\n",
       " 70.42,\n",
       " 72.0,\n",
       " 71.581,\n",
       " 72.95,\n",
       " 75.65100000000001,\n",
       " 76.423,\n",
       " 61.368,\n",
       " 65.79899999999999,\n",
       " 67.744,\n",
       " 69.152,\n",
       " 70.994,\n",
       " 72.301,\n",
       " 62.485,\n",
       " 64.399,\n",
       " 65.142,\n",
       " 65.634,\n",
       " 67.065,\n",
       " 68.48100000000001,\n",
       " 69.942,\n",
       " 70.774,\n",
       " 71.868,\n",
       " 73.275,\n",
       " 74.34,\n",
       " 75.32,\n",
       " 69.12,\n",
       " 70.33,\n",
       " 70.93,\n",
       " 71.1,\n",
       " 71.93,\n",
       " 73.49,\n",
       " 74.74,\n",
       " 76.32,\n",
       " 77.56,\n",
       " 78.83,\n",
       " 80.37,\n",
       " 81.235,\n",
       " 66.8,\n",
       " 67.48,\n",
       " 69.54,\n",
       " 70.14,\n",
       " 70.63,\n",
       " 72.17,\n",
       " 73.18,\n",
       " 74.94,\n",
       " 76.04,\n",
       " 77.51,\n",
       " 78.98,\n",
       " 79.829,\n",
       " 63.3,\n",
       " 65.593,\n",
       " 69.05199999999999,\n",
       " 70.75,\n",
       " 72.601,\n",
       " 73.925,\n",
       " 74.795,\n",
       " 75.635,\n",
       " 62.013000000000005,\n",
       " 64.062,\n",
       " 68.0,\n",
       " 69.24,\n",
       " 70.25,\n",
       " 70.94,\n",
       " 71.44,\n",
       " 72.8,\n",
       " 73.93,\n",
       " 75.35,\n",
       " 76.46,\n",
       " 77.53,\n",
       " 78.32,\n",
       " 79.441,\n",
       " 62.05,\n",
       " 63.883,\n",
       " 65.554,\n",
       " 61.93,\n",
       " 64.79,\n",
       " 67.45,\n",
       " 69.86,\n",
       " 70.69,\n",
       " 71.14,\n",
       " 72.178,\n",
       " 73.244,\n",
       " 74.09,\n",
       " 74.852,\n",
       " 61.483999999999995,\n",
       " 63.622,\n",
       " 62.745,\n",
       " 61.489,\n",
       " 63.336000000000006,\n",
       " 65.205,\n",
       " 67.057,\n",
       " 69.388,\n",
       " 71.006,\n",
       " 72.39,\n",
       " 66.61,\n",
       " 69.51,\n",
       " 70.42,\n",
       " 70.9,\n",
       " 70.81,\n",
       " 71.08,\n",
       " 71.34,\n",
       " 71.19,\n",
       " 70.32,\n",
       " 72.14,\n",
       " 73.005,\n",
       " 68.75,\n",
       " 69.96,\n",
       " 71.3,\n",
       " 72.13,\n",
       " 72.88,\n",
       " 74.21,\n",
       " 75.76,\n",
       " 76.86,\n",
       " 77.95,\n",
       " 78.61,\n",
       " 79.77,\n",
       " 80.653,\n",
       " 60.523,\n",
       " 63.441,\n",
       " 67.05199999999999,\n",
       " 70.565,\n",
       " 72.492,\n",
       " 74.126,\n",
       " 75.816,\n",
       " 77.86,\n",
       " 78.553,\n",
       " 63.118880000000004,\n",
       " 63.96736,\n",
       " 65.525,\n",
       " 67.274,\n",
       " 68.69,\n",
       " 70.426,\n",
       " 72.028,\n",
       " 72.961,\n",
       " 61.623000000000005,\n",
       " 63.836999999999996,\n",
       " 66.653,\n",
       " 67.768,\n",
       " 68.421,\n",
       " 70.313,\n",
       " 71.682,\n",
       " 72.889,\n",
       " 60.66,\n",
       " 62.974,\n",
       " 65.152,\n",
       " 60.026,\n",
       " 62.842,\n",
       " 65.42399999999999,\n",
       " 67.84899999999999,\n",
       " 70.75,\n",
       " 73.45,\n",
       " 74.752,\n",
       " 75.71300000000001,\n",
       " 77.26,\n",
       " 78.123,\n",
       " 78.782,\n",
       " 61.21,\n",
       " 64.77,\n",
       " 67.13,\n",
       " 68.5,\n",
       " 69.61,\n",
       " 70.64,\n",
       " 70.46,\n",
       " 71.52,\n",
       " 72.527,\n",
       " 73.68,\n",
       " 74.876,\n",
       " 75.748,\n",
       " 62.325,\n",
       " 65.24600000000001,\n",
       " 68.29,\n",
       " 70.723,\n",
       " 72.649,\n",
       " 73.717,\n",
       " 74.17399999999999,\n",
       " 74.414,\n",
       " 76.15100000000001,\n",
       " 77.158,\n",
       " 78.273,\n",
       " 66.87,\n",
       " 69.03,\n",
       " 69.9,\n",
       " 70.38,\n",
       " 70.29,\n",
       " 70.71,\n",
       " 70.96,\n",
       " 71.58,\n",
       " 72.4,\n",
       " 74.01,\n",
       " 75.51,\n",
       " 76.486,\n",
       " 70.78,\n",
       " 71.81,\n",
       " 72.35,\n",
       " 72.96,\n",
       " 73.47,\n",
       " 74.69,\n",
       " 74.63,\n",
       " 74.8,\n",
       " 75.33,\n",
       " 76.11,\n",
       " 77.18,\n",
       " 78.332,\n",
       " 61.788000000000004,\n",
       " 63.727,\n",
       " 66.046,\n",
       " 68.457,\n",
       " 69.957,\n",
       " 70.847,\n",
       " 72.235,\n",
       " 61.31,\n",
       " 64.342,\n",
       " 67.23100000000001,\n",
       " 69.613,\n",
       " 72.312,\n",
       " 74.173,\n",
       " 74.994,\n",
       " 63.674,\n",
       " 67.217,\n",
       " 69.806,\n",
       " 71.33800000000001,\n",
       " 63.153999999999996,\n",
       " 66.798,\n",
       " 69.535,\n",
       " 70.734,\n",
       " 71.878,\n",
       " 66.55,\n",
       " 67.49,\n",
       " 68.75,\n",
       " 69.83,\n",
       " 70.87,\n",
       " 72.52,\n",
       " 74.55,\n",
       " 74.83,\n",
       " 75.7,\n",
       " 77.13,\n",
       " 78.37,\n",
       " 79.313,\n",
       " 67.41,\n",
       " 68.93,\n",
       " 70.51,\n",
       " 71.55,\n",
       " 72.38,\n",
       " 73.83,\n",
       " 74.89,\n",
       " 76.34,\n",
       " 77.46,\n",
       " 78.64,\n",
       " 79.59,\n",
       " 80.657,\n",
       " 60.19,\n",
       " 61.36600000000001,\n",
       " 60.461000000000006,\n",
       " 67.5,\n",
       " 69.1,\n",
       " 70.3,\n",
       " 70.8,\n",
       " 71.0,\n",
       " 72.5,\n",
       " 73.8,\n",
       " 74.847,\n",
       " 76.07,\n",
       " 77.34,\n",
       " 78.67,\n",
       " 79.406,\n",
       " 60.022,\n",
       " 65.86,\n",
       " 67.86,\n",
       " 69.51,\n",
       " 71.0,\n",
       " 72.34,\n",
       " 73.68,\n",
       " 75.24,\n",
       " 76.67,\n",
       " 77.03,\n",
       " 77.869,\n",
       " 78.256,\n",
       " 79.483,\n",
       " 60.782,\n",
       " 63.373000000000005,\n",
       " 66.322,\n",
       " 68.97800000000001,\n",
       " 70.259,\n",
       " 60.916000000000004,\n",
       " 60.909,\n",
       " 64.492,\n",
       " 66.399,\n",
       " 67.65899999999999,\n",
       " 68.565,\n",
       " 70.19800000000001,\n",
       " 60.96,\n",
       " 64.75,\n",
       " 67.65,\n",
       " 70.0,\n",
       " 72.0,\n",
       " 73.6,\n",
       " 75.45,\n",
       " 76.2,\n",
       " 77.601,\n",
       " 80.0,\n",
       " 81.495,\n",
       " 82.208,\n",
       " 64.03,\n",
       " 66.41,\n",
       " 67.96,\n",
       " 69.5,\n",
       " 69.76,\n",
       " 69.95,\n",
       " 69.39,\n",
       " 69.58,\n",
       " 69.17,\n",
       " 71.04,\n",
       " 72.59,\n",
       " 73.33800000000001,\n",
       " 72.49,\n",
       " 73.47,\n",
       " 73.68,\n",
       " 73.73,\n",
       " 74.46,\n",
       " 76.11,\n",
       " 76.99,\n",
       " 77.23,\n",
       " 78.77,\n",
       " 78.95,\n",
       " 80.5,\n",
       " 81.757,\n",
       " 60.223,\n",
       " 61.765,\n",
       " 62.879,\n",
       " 64.69800000000001,\n",
       " 60.137,\n",
       " 62.681000000000004,\n",
       " 66.041,\n",
       " 68.58800000000001,\n",
       " 70.65,\n",
       " 63.04,\n",
       " 65.742,\n",
       " 68.042,\n",
       " 69.45100000000001,\n",
       " 70.964,\n",
       " 60.413000000000004,\n",
       " 62.038000000000004,\n",
       " 65.044,\n",
       " 66.91,\n",
       " 68.9,\n",
       " 70.29,\n",
       " 71.08,\n",
       " 71.28,\n",
       " 72.03,\n",
       " 73.1,\n",
       " 74.36,\n",
       " 75.467,\n",
       " 76.122,\n",
       " 77.783,\n",
       " 78.885,\n",
       " 65.39,\n",
       " 67.84,\n",
       " 69.39,\n",
       " 70.75,\n",
       " 71.63,\n",
       " 73.06,\n",
       " 74.45,\n",
       " 75.6,\n",
       " 76.93,\n",
       " 78.26899999999999,\n",
       " 79.696,\n",
       " 80.745,\n",
       " 65.94,\n",
       " 67.81,\n",
       " 69.24,\n",
       " 71.06,\n",
       " 72.19,\n",
       " 73.48,\n",
       " 74.98,\n",
       " 76.42,\n",
       " 77.44,\n",
       " 78.82,\n",
       " 80.24,\n",
       " 80.546,\n",
       " 62.61,\n",
       " 65.61,\n",
       " 67.51,\n",
       " 69.0,\n",
       " 70.11,\n",
       " 71.21,\n",
       " 71.77,\n",
       " 71.766,\n",
       " 72.262,\n",
       " 72.047,\n",
       " 72.567,\n",
       " 63.03,\n",
       " 65.5,\n",
       " 68.73,\n",
       " 71.43,\n",
       " 73.42,\n",
       " 75.38,\n",
       " 77.11,\n",
       " 78.67,\n",
       " 79.36,\n",
       " 80.69,\n",
       " 82.0,\n",
       " 82.603,\n",
       " 61.13399999999999,\n",
       " 63.739,\n",
       " 65.869,\n",
       " 68.015,\n",
       " 69.77199999999999,\n",
       " 71.263,\n",
       " 72.535,\n",
       " 63.983000000000004,\n",
       " 67.15899999999999,\n",
       " 69.1,\n",
       " 70.64699999999999,\n",
       " 69.97800000000001,\n",
       " 67.727,\n",
       " 66.66199999999999,\n",
       " 67.297,\n",
       " 62.611999999999995,\n",
       " 64.766,\n",
       " 67.123,\n",
       " 69.81,\n",
       " 72.244,\n",
       " 74.64699999999999,\n",
       " 77.045,\n",
       " 78.623,\n",
       " 60.47,\n",
       " 64.624,\n",
       " 67.712,\n",
       " 69.343,\n",
       " 71.309,\n",
       " 74.17399999999999,\n",
       " 75.19,\n",
       " 76.156,\n",
       " 76.904,\n",
       " 77.58800000000001,\n",
       " 62.093999999999994,\n",
       " 63.87,\n",
       " 65.421,\n",
       " 66.09899999999999,\n",
       " 66.983,\n",
       " 67.926,\n",
       " 69.292,\n",
       " 70.265,\n",
       " 71.028,\n",
       " 71.993,\n",
       " 62.155,\n",
       " 66.234,\n",
       " 68.755,\n",
       " 71.555,\n",
       " 72.737,\n",
       " 73.952,\n",
       " 63.01,\n",
       " 65.256,\n",
       " 68.0,\n",
       " 69.5,\n",
       " 70.693,\n",
       " 71.938,\n",
       " 73.044,\n",
       " 74.241,\n",
       " 60.43,\n",
       " 62.247,\n",
       " 64.164,\n",
       " 60.246,\n",
       " 61.556999999999995,\n",
       " 62.943999999999996,\n",
       " 64.93,\n",
       " 66.711,\n",
       " 68.74,\n",
       " 69.745,\n",
       " 70.736,\n",
       " 71.954,\n",
       " 72.801,\n",
       " 60.11,\n",
       " 62.361000000000004,\n",
       " 65.032,\n",
       " 67.405,\n",
       " 69.498,\n",
       " 71.455,\n",
       " 73.67,\n",
       " 74.902,\n",
       " 76.195,\n",
       " 60.222,\n",
       " 61.271,\n",
       " 63.625,\n",
       " 65.033,\n",
       " 66.803,\n",
       " 61.448,\n",
       " 63.728,\n",
       " 67.178,\n",
       " 70.63600000000001,\n",
       " 73.066,\n",
       " 74.101,\n",
       " 74.865,\n",
       " 75.435,\n",
       " 75.445,\n",
       " 73.98100000000001,\n",
       " 74.543,\n",
       " 62.677,\n",
       " 65.393,\n",
       " 67.66,\n",
       " 69.615,\n",
       " 71.164,\n",
       " 60.328,\n",
       " 62.068999999999996,\n",
       " 60.835,\n",
       " 61.998999999999995,\n",
       " 61.34,\n",
       " 63.785,\n",
       " 72.13,\n",
       " 72.99,\n",
       " 73.23,\n",
       " 73.82,\n",
       " 73.75,\n",
       " 75.24,\n",
       " 76.05,\n",
       " 76.83,\n",
       " 77.42,\n",
       " 78.03,\n",
       " 78.53,\n",
       " 79.762,\n",
       " 69.39,\n",
       " 70.26,\n",
       " 71.24,\n",
       " 71.52,\n",
       " 71.89,\n",
       " 72.22,\n",
       " 73.84,\n",
       " 74.32,\n",
       " 76.33,\n",
       " 77.55,\n",
       " 79.11,\n",
       " 80.204,\n",
       " 62.008,\n",
       " 65.843,\n",
       " 68.426,\n",
       " 70.836,\n",
       " 72.899,\n",
       " 72.67,\n",
       " 73.44,\n",
       " 73.47,\n",
       " 74.08,\n",
       " 74.34,\n",
       " 75.37,\n",
       " 75.97,\n",
       " 75.89,\n",
       " 77.32,\n",
       " 78.32,\n",
       " 79.05,\n",
       " 80.196,\n",
       " 62.728,\n",
       " 67.734,\n",
       " 71.197,\n",
       " 72.499,\n",
       " 74.193,\n",
       " 75.64,\n",
       " 60.838,\n",
       " 61.818000000000005,\n",
       " 63.61,\n",
       " 65.483,\n",
       " 61.817,\n",
       " 64.071,\n",
       " 66.21600000000001,\n",
       " 68.681,\n",
       " 70.472,\n",
       " 71.523,\n",
       " 72.462,\n",
       " 73.738,\n",
       " 74.712,\n",
       " 75.53699999999999,\n",
       " 62.648999999999994,\n",
       " 63.196000000000005,\n",
       " 64.361,\n",
       " 64.95100000000001,\n",
       " 65.815,\n",
       " 66.35300000000001,\n",
       " 66.874,\n",
       " 67.378,\n",
       " 68.225,\n",
       " 69.4,\n",
       " 70.755,\n",
       " 71.752,\n",
       " 61.406000000000006,\n",
       " 64.134,\n",
       " 66.458,\n",
       " 68.38600000000001,\n",
       " 69.906,\n",
       " 71.421,\n",
       " 60.06,\n",
       " 62.082,\n",
       " 64.15100000000001,\n",
       " 66.458,\n",
       " 68.564,\n",
       " 70.303,\n",
       " 71.688,\n",
       " 61.31,\n",
       " 65.77,\n",
       " 67.64,\n",
       " 69.61,\n",
       " 70.85,\n",
       " 70.67,\n",
       " 71.32,\n",
       " 70.98,\n",
       " 70.99,\n",
       " 72.75,\n",
       " 74.67,\n",
       " 75.563,\n",
       " 61.51,\n",
       " 64.39,\n",
       " 66.6,\n",
       " 69.26,\n",
       " 70.41,\n",
       " 72.77,\n",
       " 74.06,\n",
       " 74.86,\n",
       " 75.97,\n",
       " 77.29,\n",
       " 78.098,\n",
       " 64.28,\n",
       " 68.54,\n",
       " 69.62,\n",
       " 71.1,\n",
       " 72.16,\n",
       " 73.44,\n",
       " 73.75,\n",
       " 74.63,\n",
       " 73.911,\n",
       " 74.917,\n",
       " 77.778,\n",
       " 78.74600000000001,\n",
       " 60.542,\n",
       " 64.274,\n",
       " 67.064,\n",
       " 69.885,\n",
       " 71.913,\n",
       " 73.615,\n",
       " 74.77199999999999,\n",
       " 75.744,\n",
       " 76.442,\n",
       " 61.05,\n",
       " 64.1,\n",
       " 66.8,\n",
       " 66.8,\n",
       " 69.21,\n",
       " 69.46,\n",
       " 69.66,\n",
       " 69.53,\n",
       " 69.36,\n",
       " 69.72,\n",
       " 71.322,\n",
       " 72.476,\n",
       " 60.351000000000006,\n",
       " 61.728,\n",
       " 62.742,\n",
       " 63.306000000000004,\n",
       " 64.337,\n",
       " 65.528,\n",
       " 63.012,\n",
       " 66.295,\n",
       " 68.768,\n",
       " 70.533,\n",
       " 71.626,\n",
       " 72.777,\n",
       " 60.187,\n",
       " 61.6,\n",
       " 63.062,\n",
       " 61.685,\n",
       " 64.531,\n",
       " 66.914,\n",
       " 68.7,\n",
       " 70.3,\n",
       " 70.16199999999999,\n",
       " 71.218,\n",
       " 71.65899999999999,\n",
       " 72.232,\n",
       " 73.21300000000001,\n",
       " 74.002,\n",
       " 60.396,\n",
       " 63.178999999999995,\n",
       " 65.798,\n",
       " 67.946,\n",
       " 69.521,\n",
       " 70.795,\n",
       " 71.76,\n",
       " 73.56,\n",
       " 75.788,\n",
       " 77.158,\n",
       " 78.77,\n",
       " 79.972,\n",
       " 64.36,\n",
       " 67.45,\n",
       " 70.33,\n",
       " 70.98,\n",
       " 70.35,\n",
       " 70.45,\n",
       " 70.8,\n",
       " 71.08,\n",
       " 71.38,\n",
       " 72.71,\n",
       " 73.8,\n",
       " 74.663,\n",
       " 65.57,\n",
       " 67.85,\n",
       " 69.15,\n",
       " 69.18,\n",
       " 69.82,\n",
       " 70.97,\n",
       " 71.063,\n",
       " 72.25,\n",
       " 73.64,\n",
       " 75.13,\n",
       " 76.66,\n",
       " 77.926,\n",
       " 60.833999999999996,\n",
       " 61.888000000000005,\n",
       " 60.236000000000004,\n",
       " 64.94,\n",
       " 66.66,\n",
       " 69.69,\n",
       " 71.44,\n",
       " 73.06,\n",
       " 74.39,\n",
       " 76.3,\n",
       " 76.9,\n",
       " 77.57,\n",
       " 78.77,\n",
       " 79.78,\n",
       " 80.941,\n",
       " 61.456,\n",
       " 62.192,\n",
       " 64.266,\n",
       " 65.042,\n",
       " 65.949,\n",
       " 68.757,\n",
       " 69.01100000000001,\n",
       " 70.37899999999999,\n",
       " 70.457,\n",
       " 70.815,\n",
       " 72.396,\n",
       " 71.86,\n",
       " 72.49,\n",
       " 73.37,\n",
       " 74.16,\n",
       " 74.72,\n",
       " 75.44,\n",
       " 76.42,\n",
       " 77.19,\n",
       " 78.16,\n",
       " 79.39,\n",
       " 80.04,\n",
       " 80.884,\n",
       " 69.62,\n",
       " 70.56,\n",
       " 71.32,\n",
       " 72.77,\n",
       " 73.78,\n",
       " 75.39,\n",
       " 76.21,\n",
       " 77.41,\n",
       " 78.03,\n",
       " 79.37,\n",
       " 80.62,\n",
       " 81.70100000000001,\n",
       " 61.195,\n",
       " 64.59,\n",
       " 66.97399999999999,\n",
       " 69.249,\n",
       " 71.527,\n",
       " 73.053,\n",
       " 74.143,\n",
       " 62.4,\n",
       " 65.2,\n",
       " 67.5,\n",
       " 69.39,\n",
       " 70.59,\n",
       " 72.16,\n",
       " 73.4,\n",
       " 74.26,\n",
       " 75.25,\n",
       " 76.99,\n",
       " 78.4,\n",
       " 60.405,\n",
       " 62.494,\n",
       " 64.597,\n",
       " 66.084,\n",
       " 67.298,\n",
       " 67.521,\n",
       " 68.564,\n",
       " 70.616,\n",
       " 61.8,\n",
       " 64.9,\n",
       " 65.4,\n",
       " 65.9,\n",
       " 68.3,\n",
       " 68.832,\n",
       " 69.582,\n",
       " 69.862,\n",
       " 69.465,\n",
       " 68.976,\n",
       " 69.819,\n",
       " 64.048,\n",
       " 66.89399999999999,\n",
       " 70.001,\n",
       " 71.973,\n",
       " 73.042,\n",
       " 73.923,\n",
       " 61.036,\n",
       " 63.108000000000004,\n",
       " 66.146,\n",
       " 68.835,\n",
       " 70.845,\n",
       " 71.777,\n",
       " 69.18,\n",
       " 70.42,\n",
       " 70.76,\n",
       " 71.36,\n",
       " 72.01,\n",
       " 72.76,\n",
       " 74.04,\n",
       " 75.007,\n",
       " 76.42,\n",
       " 77.218,\n",
       " 78.471,\n",
       " 79.425,\n",
       " 68.44,\n",
       " 69.49,\n",
       " 70.21,\n",
       " 70.76,\n",
       " 71.34,\n",
       " 73.38,\n",
       " 74.65,\n",
       " 75.02,\n",
       " 76.09,\n",
       " 76.81,\n",
       " 77.31,\n",
       " 78.242,\n",
       " 66.071,\n",
       " 67.044,\n",
       " 68.253,\n",
       " 68.468,\n",
       " 68.673,\n",
       " 69.48100000000001,\n",
       " 70.805,\n",
       " 71.918,\n",
       " 72.752,\n",
       " 74.223,\n",
       " 75.307,\n",
       " 76.384,\n",
       " 60.77,\n",
       " 63.479,\n",
       " 65.712,\n",
       " 67.456,\n",
       " 68.557,\n",
       " 70.19,\n",
       " 71.15,\n",
       " 72.146,\n",
       " 72.766,\n",
       " 73.747,\n",
       " 62.82,\n",
       " 67.66199999999999,\n",
       " 70.672,\n",
       " 73.017,\n",
       " 74.249,\n",
       " 60.765,\n",
       " 64.406,\n",
       " 67.046,\n",
       " 69.718,\n",
       " 71.096,\n",
       " 72.37,\n",
       " 73.422,\n",
       " 60.308,\n",
       " 62.698,\n",
       " 60.363,\n",
       " 62.351000000000006,\n",
       " 60.376999999999995]"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying a DataFrame column as an Input\n",
    "\n",
    "gm_filt = filter(lambda x: x > 60, gm_df['lifeExp'])\n",
    "list(gm_filt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Date/Time Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "from datetime import date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find the difference between two dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference in Days:  242 days, 11:00:00\n",
      "Difference in Days:  242\n"
     ]
    }
   ],
   "source": [
    "date1 = datetime(2021, 1, 1, 12, 0, 0)\n",
    "date2 = datetime(2021, 8, 31, 23, 0, 0)\n",
    "\n",
    "# Difference between two dates\n",
    "delta = date2 - date1\n",
    "print(\"Difference in Days: \", delta)\n",
    "print(\"Difference in Days: \", delta.days)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert a String to a Date Object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2021, 1, 1, 0, 0)"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert a Date String to a Date Object\n",
    "\n",
    "datestr = '1 January 2021'\n",
    "dateobj = datetime.strptime(datestr, \"%d %B %Y\")\n",
    "dateobj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "01/01/2021, 12:34:22\n"
     ]
    }
   ],
   "source": [
    "# Convert a Datetime String to a Date Object\n",
    "\n",
    "datestr = '1 January 2021, 12:34:22'\n",
    "dateobj = datetime.strptime(datestr, \"%d %B %Y, %H:%M:%S\")\n",
    "\n",
    "# Format the Date objet and print\n",
    "print(dateobj.strftime(\"%m/%d/%Y, %H:%M:%S\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2021, 9, 19, 18, 3, 55, 760264)"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# current date and time\n",
    "now = datetime.now()\n",
    "now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date and Time: 09/19/2021, 18:03:55\n"
     ]
    }
   ],
   "source": [
    "# Format the current datetime and print\n",
    "\n",
    "date_time = now.strftime(\"%m/%d/%Y, %H:%M:%S\")\n",
    "print(\"Date and Time:\",date_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with Pandas Datetime object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-01-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "date = pd.to_datetime(\"1st January, 2021\")\n",
    "print(date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-01 01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-01 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-01 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-01 04:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>2021-01-30 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>2021-01-30 21:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>2021-01-30 22:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>2021-01-30 23:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>2021-01-31 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>721 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   date\n",
       "0   2021-01-01 00:00:00\n",
       "1   2021-01-01 01:00:00\n",
       "2   2021-01-01 02:00:00\n",
       "3   2021-01-01 03:00:00\n",
       "4   2021-01-01 04:00:00\n",
       "..                  ...\n",
       "716 2021-01-30 20:00:00\n",
       "717 2021-01-30 21:00:00\n",
       "718 2021-01-30 22:00:00\n",
       "719 2021-01-30 23:00:00\n",
       "720 2021-01-31 00:00:00\n",
       "\n",
       "[721 rows x 1 columns]"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_rng1 = pd.date_range(start='1/1/2021', end='31/01/2021', freq='H')\n",
    "dfd = pd.DataFrame(date_rng1, columns=['date'])\n",
    "dfd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract components of the Datetime\n",
    "# Pandas series.dt returns an array of Python datetime.date objects\n",
    "\n",
    "dfd['year'] = dfd['date'].dt.year\n",
    "dfd['month'] = dfd['date'].dt.month\n",
    "dfd['day'] = dfd['date'].dt.day\n",
    "dfd['hour'] = dfd['date'].dt.hour\n",
    "dfd['minute'] = dfd['date'].dt.minute\n",
    "dfd['weekday'] = dfd['date'].dt.weekday\n",
    "dfd['dayofyear'] = dfd['date'].dt.dayofyear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>weekday</th>\n",
       "      <th>dayofyear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-01 00:00:00</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-01 01:00:00</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-01 02:00:00</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-01 03:00:00</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-01 04:00:00</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>2021-01-30 20:00:00</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>2021-01-30 21:00:00</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>2021-01-30 22:00:00</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>2021-01-30 23:00:00</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>2021-01-31 00:00:00</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>721 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   date  year  month  day  hour  minute  weekday  dayofyear\n",
       "0   2021-01-01 00:00:00  2021      1    1     0       0        4          1\n",
       "1   2021-01-01 01:00:00  2021      1    1     1       0        4          1\n",
       "2   2021-01-01 02:00:00  2021      1    1     2       0        4          1\n",
       "3   2021-01-01 03:00:00  2021      1    1     3       0        4          1\n",
       "4   2021-01-01 04:00:00  2021      1    1     4       0        4          1\n",
       "..                  ...   ...    ...  ...   ...     ...      ...        ...\n",
       "716 2021-01-30 20:00:00  2021      1   30    20       0        5         30\n",
       "717 2021-01-30 21:00:00  2021      1   30    21       0        5         30\n",
       "718 2021-01-30 22:00:00  2021      1   30    22       0        5         30\n",
       "719 2021-01-30 23:00:00  2021      1   30    23       0        5         30\n",
       "720 2021-01-31 00:00:00  2021      1   31     0       0        6         31\n",
       "\n",
       "[721 rows x 8 columns]"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### String Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello\n",
      "Hello, welcome to\n",
      "the world of Python\n",
      "\n",
      "Hello's...how are you?\n"
     ]
    }
   ],
   "source": [
    "# Creating strings in Python\n",
    "\n",
    "my_string = 'Hello'\n",
    "print(my_string)\n",
    "\n",
    "# Use Triple quotes to split into multiple lines\n",
    "\n",
    "my_string = \"\"\"Hello, welcome to\n",
    "the world of Python\\n\"\"\"\n",
    "print(my_string)\n",
    "\n",
    "# Using a quote sign within the string (alternately use a '\\' sign before the quote sign)\n",
    "\n",
    "my_string = \"Hello's...how are you?\"\n",
    "print(my_string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accessing sections of the string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First character:  T\n",
      "Last character g\n",
      "5-9th characters:  quick\n",
      "5th to 5th last characters: quick brown fox jumps over the lazy\n"
     ]
    }
   ],
   "source": [
    "#Accessing string characters in Python\n",
    "\n",
    "my_string = \"The quick brown fox jumps over the lazy dog\"\n",
    "\n",
    "# Get the first character\n",
    "print('First character: ', my_string[0])\n",
    "\n",
    "# Get the last character\n",
    "print('Last character', my_string[-1])\n",
    "\n",
    "# Access the 5th to 9th character\n",
    "print(\"5-9th characters: \", my_string[4:9])\n",
    "\n",
    "# Access 5th to 5th last characters\n",
    "print('5th to 5th last characters:', my_string[4:-4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'quick brown fox jumps over the lazy'"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging Strings\n",
    "\n",
    "str1 = \"quick brown fox\"\n",
    "str2 = \"jumps over the lazy\"\n",
    "str = str1 + \" \" + str2\n",
    "str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "q\n",
      "u\n",
      "i\n",
      "c\n",
      "k\n",
      " \n",
      "b\n",
      "r\n",
      "o\n",
      "w\n",
      "n\n",
      " \n",
      "f\n",
      "o\n",
      "x\n",
      " \n",
      "j\n",
      "u\n",
      "m\n",
      "p\n",
      "s\n",
      " \n",
      "o\n",
      "v\n",
      "e\n",
      "r\n",
      " \n",
      "t\n",
      "h\n",
      "e\n",
      " \n",
      "l\n",
      "a\n",
      "z\n",
      "y\n"
     ]
    }
   ],
   "source": [
    "# Iterate through a string one character at a time\n",
    "\n",
    "for c in str:\n",
    "    print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test of Membership\n",
    "\n",
    "my_string = \"The quick brown fox jumps over the lazy dog\"\n",
    "\n",
    "'quick' in my_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'quick' not in my_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The quick brown fox jumps over the lazy dog\n"
     ]
    }
   ],
   "source": [
    "# Using expressions\n",
    "\n",
    "str = \"jumps over the lazy dog\"\n",
    "print(f\"The quick brown fox {str}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Formatting with the format() method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The quick brown fox jumps over the laxy dog'"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using Positonal Arguments\n",
    "\n",
    "str = \"The {1} brown fox {2} over the {0} dog\".format('laxy','quick','jumps')\n",
    "str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The quick brown fox jumps over the lazy dog'"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using keyword arguments\n",
    "\n",
    "str = \"The quick {str1} {str2} jumps over the {str3} dog\".format(str1 = \"brown\", str2 = \"fox\", str3 = \"lazy\")\n",
    "str"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split a string into a List of words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['The', 'quick', 'brown', 'fox', 'jumps', 'over', 'the', 'lazy', 'dog']"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words = str.split()\n",
    "words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Join back"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The quick brown fox jumps over the lazy dog'"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined = ' '.join(words)    # ' ' - space is used as separator\n",
    "joined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The slow brown fox jumps over the lazy dog'"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str = str.replace('quick', 'slow')\n",
    "str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'THE SLOW BROWN FOX JUMPS OVER THE LAZY DOG'"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'the slow brown fox jumps over the lazy dog'"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy Tricks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the 'argsort' method to receive index values in the sorted order of array values\n",
    "For an array arr, np.argmax(arr), np.argmin(arr), and np.argwhere(condition(arr)) return the indices of maximum values, minimum values, and values that satisfy a user-defined condition respectively. While these arg functions are widely used, we often overlook the function np.argsort() that returns the indices that would sort an array.\n",
    "\n",
    "We can use np.argsort to sort values of arrays according to another array. Here is an example of sorting student names using their exam scores. The sorted name array can also be transformed back to its original order using np.argsort(np.argsort(score))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 5 2 1 0 6 4]\n",
      "['Danny' 'Fanny' 'Charlie' 'Ben' 'Ada' 'George' 'Eden']\n",
      "['Ada' 'Ben' 'Charlie' 'Danny' 'Eden' 'Fanny' 'George']\n",
      "2.37 µs ± 65.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "3.98 µs ± 144 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "score = np.array([70, 60, 50, 10, 90, 40, 80])\n",
    "name = np.array(['Ada', 'Ben', 'Charlie', 'Danny', 'Eden', 'Fanny', 'George'])\n",
    "print(np.argsort(score))\n",
    "sorted_name = name[np.argsort(score)] # an array of names in ascending order of their scores\n",
    "print(sorted_name)   # ['Danny' 'Fanny' 'Charlie' 'Ben' 'Ada' 'George' 'Eden']\n",
    "\n",
    "original_name = sorted_name[np.argsort(np.argsort(score))]\n",
    "print(original_name) # ['Ada' 'Ben' 'Charlie' 'Danny' 'Eden' 'Fanny' 'George']\n",
    "\n",
    "\n",
    "%timeit name[np.argsort(score)] \n",
    "# 1.83 µs ± 182 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
    "%timeit sorted(zip(score, name))\n",
    "# 3.2 µs ± 76.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Masked Arrays\n",
    "Numpy has a submodule numpy.ma that supports data arrays with masks. A masked array contains an ordinary numpy array and a mask that indicates the position of invalid entries.\n",
    "Invalid entries in an array are sometimes marked using negative values or strings. If we know the masked value, say -999, we can also create a masked array using np.ma.masked_values(arr, value=-999). Any numpy operation taking a masked array as an argument will automatically ignore those invalid entries as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 3 -- 5 -- 7 -- -- -- 11 -- 13 -- -- -- 17 -- 19 -- -- -- 23 -- -- -- --\n",
      " -- 29 -- 31 -- -- -- -- -- 37 -- -- -- 41 -- 43 -- -- -- 47 -- -- -- --\n",
      " -- 53 -- -- -- -- -- 59 -- 61 -- -- -- -- -- 67 -- -- -- 71 -- 73 -- --\n",
      " -- -- -- 79 -- -- -- 83 -- -- -- -- -- 89 -- -- -- -- -- -- -- 97 -- --]\n",
      "55\n",
      "45\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "def is_prime(n):\n",
    "    assert n > 1, 'Input must be larger than 1'\n",
    "    if n % 2 == 0 and n > 2: \n",
    "        return False\n",
    "    return all(n % i for i in range(3, int(math.sqrt(n)) + 1, 2))\n",
    "\n",
    "arr = np.array(range(2,100))\n",
    "non_prime_mask = [not is_prime(n) for n in arr]\n",
    "prime_arr = np.ma.MaskedArray(data=arr, mask=non_prime_mask)\n",
    "print(prime_arr)\n",
    "\n",
    "arr = np.array(range(11))\n",
    "print(arr.sum())    \n",
    "\n",
    "arr[-1] = -999 # indicates missing value\n",
    "masked_arr = np.ma.masked_values(arr, -999)\n",
    "print(masked_arr.sum()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using argmax and argmin to find Index positions of Max values in an Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "[1 1 0]\n",
      "[1 1]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[10, 12, 11],[13, 14, 10]])\n",
    "print(np.argmax(a))\n",
    "# since if the array is flattened, 14 is at the 4th index\n",
    "\n",
    "print(np.argmax(a, axis=0))   # index of max in each column\n",
    "print(np.argmax(a, axis=1))   # index of max in each row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}