Feb 22 Assignment_Pandas 1.ipynb

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   "source": [
    "### Q1 Create a Pandas Series that contains the following data: 4, 8, 15, 16, 23, and 42. Then, print the series."
   ]
  },
  {
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   "execution_count": 1,
   "id": "affda2f8",
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    {
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     "text": [
      "0     4\n",
      "1     8\n",
      "2    15\n",
      "3    16\n",
      "4    23\n",
      "5    42\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Creating a Pandas Series\n",
    "data = [4, 8, 15, 16, 23, 42]\n",
    "my_series = pd.Series(data)\n",
    "\n",
    "# Print the Series\n",
    "print(my_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b27667d",
   "metadata": {},
   "outputs": [],
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  {
   "cell_type": "markdown",
   "id": "28212c2c",
   "metadata": {},
   "source": [
    "### Q2 Create a variable of list type containing 10 elements in it, and apply pandas.Series function on the variable print it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fba86f7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     1\n",
      "1     2\n",
      "2     3\n",
      "3     4\n",
      "4     5\n",
      "5     6\n",
      "6     7\n",
      "7     8\n",
      "8     9\n",
      "9    10\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Create a list with 10 elements\n",
    "my_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "\n",
    "# Convert the list to a Pandas Series\n",
    "my_series = pd.Series(my_list)\n",
    "\n",
    "# Print the Series\n",
    "print(my_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02fb7e75",
   "metadata": {},
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   "cell_type": "markdown",
   "id": "3b3440fc",
   "metadata": {},
   "source": [
    "### Q3 Create a Pandas DataFrame that contains the following data:"
   ]
  },
  {
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   "execution_count": 3,
   "id": "7c7bb721",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Claire</td>\n",
       "      <td>27</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age  Gender\n",
       "0   Alice   25  Female\n",
       "1     Bob   30    Male\n",
       "2  Claire   27  Female"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Data\n",
    "data = {\n",
    "    'Name': ['Alice', 'Bob', 'Claire'],\n",
    "    'Age': [25, 30, 27],\n",
    "    'Gender': ['Female', 'Male', 'Female']\n",
    "}\n",
    "\n",
    "# Create DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Print the DataFrame\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbae5c3c",
   "metadata": {},
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  {
   "cell_type": "markdown",
   "id": "980987e7",
   "metadata": {},
   "source": [
    "### Q4 What is ‘DataFrame’ in pandas and how is it different from pandas.series? Explain with an example."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f9d2401",
   "metadata": {},
   "source": [
    "In Pandas, a DataFrame is a two-dimensional, tabular data structure that is widely used for data manipulation and analysis. It consists of rows and columns, where each column can have a different data type. Essentially, a DataFrame is like a spreadsheet or SQL table. You can think of it as a container for multiple Pandas Series that share the same index.\n",
    "\n",
    "On the other hand, a Pandas Series is a one-dimensional labeled array, similar to a column in a DataFrame. It can be created from a list, array, or dictionary, and it has an associated index. A DataFrame is essentially a collection of Series, where each column is a Series.\n",
    "\n",
    "* A Pandas Series is a one-dimensional array with an index, and it represents a single column of data.\n",
    "* A Pandas DataFrame is a two-dimensional tabular data structure, which can be thought of as a collection of Series. Each column in a DataFrame is a Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c2835434",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series:\n",
      "\n",
      "Type of series_data: <class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    20\n",
       "2    30\n",
       "3    40\n",
       "Name: Numbers, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a Series\n",
    "series_data = pd.Series([10, 20, 30, 40], name='Numbers')\n",
    "print(\"Series:\")\n",
    "print(\"\\nType of series_data:\", type(series_data))\n",
    "series_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2cbbfc4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DataFrame:\n",
      "\n",
      "Type of df: <class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
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       "    }\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>Age</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Claire</td>\n",
       "      <td>27</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age  Gender\n",
       "0   Alice   25  Female\n",
       "1     Bob   30    Male\n",
       "2  Claire   27  Female"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a DataFrame\n",
    "data = {'Name': ['Alice', 'Bob', 'Claire'],\n",
    "        'Age': [25, 30, 27],\n",
    "        'Gender': ['Female', 'Male', 'Female']}\n",
    "df = pd.DataFrame(data)\n",
    "print(\"\\nDataFrame:\")\n",
    "print(\"\\nType of df:\", type(df))\n",
    "df"
   ]
  },
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   "source": [
    "### Q5 What are some common functions you can use to manipulate data in a Pandas DataFrame? Can you give an example of when you might use one of these functions?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "189b9e46",
   "metadata": {},
   "source": [
    "Pandas provides a wide range of functions to manipulate and analyze data in a DataFrame. Here are some common functions and methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cfc21ef7",
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   "outputs": [
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       "    }\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>Age</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Age  Gender\n",
       "0  Alice   25  Female\n",
       "1    Bob   30    Male"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)  # Display the first 3 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1dbfea6e",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Claire</td>\n",
       "      <td>27</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age  Gender\n",
       "1     Bob   30    Male\n",
       "2  Claire   27  Female"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)  # Display the first 3 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "875ad787",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3 entries, 0 to 2\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   Name    3 non-null      object\n",
      " 1   Age     3 non-null      int64 \n",
      " 2   Gender  3 non-null      object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 200.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "#Display a concise summary of the DataFrame, including data types and missing values.\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2f27b4a2",
   "metadata": {},
   "outputs": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>27.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.516611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>28.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Age\n",
       "count   3.000000\n",
       "mean   27.333333\n",
       "std     2.516611\n",
       "min    25.000000\n",
       "25%    26.000000\n",
       "50%    27.000000\n",
       "75%    28.500000\n",
       "max    30.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describe(): Generate descriptive statistics of the DataFrame's numerical columns.\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "940547b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shape: Get the dimensions of the DataFrame (number of rows, number of columns).\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "684afb65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Age', 'Gender'], dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#columns: Get the column names of the DataFrame.\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "525321c1",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Claire</td>\n",
       "      <td>27</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age  Gender\n",
       "1     Bob   30    Male\n",
       "2  Claire   27  Female\n",
       "0   Alice   25  Female"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sort_values(): Sort the DataFrame by one or more columns.\n",
    "df.sort_values(by='Age', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2bd973dc",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Age\n",
       "Gender      \n",
       "Female  26.0\n",
       "Male    30.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#groupby(): Group DataFrame by a column or a combination of columns.\n",
    "df.groupby('Gender').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31cad4e8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "dafc3224",
   "metadata": {},
   "source": [
    "### Q6 Which of the following is mutable in nature Series, DataFrame, Panel?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc570e57",
   "metadata": {},
   "source": [
    "In Pandas, the mutable nature refers to whether the data structure can be modified in place. Among Series, DataFrame, and Panel, only DataFrame is mutable.\n",
    "\n",
    "Series: Immutable. Once a Series is created, you cannot change its size or modify its elements in place. You can, however, create a new Series with modifications.\n",
    "\n",
    "DataFrame: Mutable. You can modify the contents of a DataFrame, add or remove columns, and perform various operations in place.\n",
    "\n",
    "Panel: Deprecated. Panels were three-dimensional data structures in Pandas, but they have been deprecated as of version 0.25.0. You should use MultiIndex DataFrames or xarray for three-dimensional data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "228c6180",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "aab8cadc",
   "metadata": {},
   "source": [
    "### Q7 Create a DataFrame using multiple Series. Explain with an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "49eed88e",
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Claire</td>\n",
       "      <td>27</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age  Gender\n",
       "0   Alice   25  Female\n",
       "1     Bob   30    Male\n",
       "2  Claire   27  Female"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Creating multiple Pandas Series\n",
    "name_series = pd.Series(['Alice', 'Bob', 'Claire'], name='Name')\n",
    "age_series = pd.Series([25, 30, 27], name='Age')\n",
    "gender_series = pd.Series(['Female', 'Male', 'Female'], name='Gender')\n",
    "\n",
    "# Combining Series into a DataFrame\n",
    "df = pd.DataFrame({'Name': name_series, 'Age': age_series, 'Gender': gender_series})\n",
    "\n",
    "# Print the resulting DataFrame\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b8d03ce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3cb3954",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a992973",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85047cce",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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