MIDS_QUESTIONS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUESTION_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45678\n"
     ]
    }
   ],
   "source": [
    "\n",
    "a=int(input(\"Enter the 5 digit Number\"))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUESTION_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You have bought  30  products\n",
      "Remaining Products are :  20\n",
      "You have bought  20  products\n",
      "Remaining Products are :  0\n"
     ]
    }
   ],
   "source": [
    "no_of_products=50\n",
    "while(no_of_products!=0):\n",
    "    if no_of_products<=150 and no_of_products>0:\n",
    "        product_need=int(input(\"How many products you want ? \"))\n",
    "        print(\"You have bought \",product_need,\" products\")\n",
    "        no_of_products-=product_need\n",
    "        if no_of_products>=0:\n",
    "            print(\"Remaining Products are : \",no_of_products)\n",
    "        else:\n",
    "            print(\"Out of Stock !!, !!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUESTION_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Sagar', 'Munir', 'Karan')\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "def students(*kargs):\n",
    "    print(kargs)\n",
    "a=students('Sagar','Munir','Karan')\n",
    "print(a)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUESTION 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sagar', 'Karan', 'Munir', 'Imran', 'Jatoi', 'Ali', 'Sahil']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name=['Sagar','Karan','Munir','Ali','Sahil']\n",
    "name.insert(3,'Imran')\n",
    "name.insert(4,'Jatoi')\n",
    "\n",
    "name\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUESTION 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "your grade is A\n"
     ]
    }
   ],
   "source": [
    "total_marks=300\n",
    "sub1=eval(input(\"Enter Marks in Subject 1 : \"))\n",
    "sub2=eval(input(\"Enter Marks in Subject 2 : \"))\n",
    "sub3=eval(input(\"Enter Marks in Subject 3 :\"))\n",
    "sum=sub1+sub2+sub3\n",
    "percent=(sum/total_marks)*100\n",
    "if percent>=91 and percent<=100:\n",
    "    print(\"your grade is A+\")\n",
    "elif percent>82 and percent<=90:\n",
    "    print(\"your grade is A\")\n",
    "elif percent>=75 and percent<=82:\n",
    "    print(\"your grade is B+\")\n",
    "elif percent>=65 and percent<=74:\n",
    "    print(\"your grade is B\")\n",
    "elif percent>=60 and percent<=64:\n",
    "    print(\"your grade is C+\")\n",
    "elif percent>=50 and percent<=59:\n",
    "    print(\"your grade is C\")\n",
    "else:\n",
    "    print(\"You are Fail , Sorry!!\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUESTION 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 1  2  3]\n",
      "  [ 7  8  9]\n",
      "  [10 13 15]]]\n",
      "Shape of Array :  (1, 3, 3)\n",
      "Size of Array :  9\n",
      "MInimum : 1\n",
      "Median :  8.0\n",
      "Data Types : int32\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a=np.array([[[1,2,3],[7,8,9],[10,13,15]]])\n",
    "print(a)\n",
    "print(\"Shape of Array : \",a.shape)\n",
    "print(\"Size of Array : \",a.size)\n",
    "print(\"MInimum :\",a.min())\n",
    "print(\"Median : \", np.median(a))\n",
    "print(\"Data Types :\",a.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUESTION 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>action_type</th>\n",
       "      <th>combined_shot_type</th>\n",
       "      <th>game_event_id</th>\n",
       "      <th>game_id</th>\n",
       "      <th>lat</th>\n",
       "      <th>loc_x</th>\n",
       "      <th>loc_y</th>\n",
       "      <th>lon</th>\n",
       "      <th>minutes_remaining</th>\n",
       "      <th>period</th>\n",
       "      <th>...</th>\n",
       "      <th>shot_type</th>\n",
       "      <th>shot_zone_area</th>\n",
       "      <th>shot_zone_basic</th>\n",
       "      <th>shot_zone_range</th>\n",
       "      <th>team_id</th>\n",
       "      <th>team_name</th>\n",
       "      <th>game_date</th>\n",
       "      <th>matchup</th>\n",
       "      <th>opponent</th>\n",
       "      <th>shot_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>10</td>\n",
       "      <td>20000012</td>\n",
       "      <td>33.9723</td>\n",
       "      <td>167</td>\n",
       "      <td>72</td>\n",
       "      <td>-118.1028</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2PT Field Goal</td>\n",
       "      <td>Right Side(R)</td>\n",
       "      <td>Mid-Range</td>\n",
       "      <td>16-24 ft.</td>\n",
       "      <td>1610612747</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>2000-10-31</td>\n",
       "      <td>LAL @ POR</td>\n",
       "      <td>POR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>12</td>\n",
       "      <td>20000012</td>\n",
       "      <td>34.0443</td>\n",
       "      <td>-157</td>\n",
       "      <td>0</td>\n",
       "      <td>-118.4268</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2PT Field Goal</td>\n",
       "      <td>Left Side(L)</td>\n",
       "      <td>Mid-Range</td>\n",
       "      <td>8-16 ft.</td>\n",
       "      <td>1610612747</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>2000-10-31</td>\n",
       "      <td>LAL @ POR</td>\n",
       "      <td>POR</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>35</td>\n",
       "      <td>20000012</td>\n",
       "      <td>33.9093</td>\n",
       "      <td>-101</td>\n",
       "      <td>135</td>\n",
       "      <td>-118.3708</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2PT Field Goal</td>\n",
       "      <td>Left Side Center(LC)</td>\n",
       "      <td>Mid-Range</td>\n",
       "      <td>16-24 ft.</td>\n",
       "      <td>1610612747</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>2000-10-31</td>\n",
       "      <td>LAL @ POR</td>\n",
       "      <td>POR</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>43</td>\n",
       "      <td>20000012</td>\n",
       "      <td>33.8693</td>\n",
       "      <td>138</td>\n",
       "      <td>175</td>\n",
       "      <td>-118.1318</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2PT Field Goal</td>\n",
       "      <td>Right Side Center(RC)</td>\n",
       "      <td>Mid-Range</td>\n",
       "      <td>16-24 ft.</td>\n",
       "      <td>1610612747</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>2000-10-31</td>\n",
       "      <td>LAL @ POR</td>\n",
       "      <td>POR</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Driving Dunk Shot</td>\n",
       "      <td>Dunk</td>\n",
       "      <td>155</td>\n",
       "      <td>20000012</td>\n",
       "      <td>34.0443</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-118.2698</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2PT Field Goal</td>\n",
       "      <td>Center(C)</td>\n",
       "      <td>Restricted Area</td>\n",
       "      <td>Less Than 8 ft.</td>\n",
       "      <td>1610612747</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>2000-10-31</td>\n",
       "      <td>LAL @ POR</td>\n",
       "      <td>POR</td>\n",
       "      <td>5</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>30692</th>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>397</td>\n",
       "      <td>49900088</td>\n",
       "      <td>33.9963</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>-118.2688</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2PT Field Goal</td>\n",
       "      <td>Center(C)</td>\n",
       "      <td>In The Paint (Non-RA)</td>\n",
       "      <td>Less Than 8 ft.</td>\n",
       "      <td>1610612747</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>2000-06-19</td>\n",
       "      <td>LAL vs. IND</td>\n",
       "      <td>IND</td>\n",
       "      <td>30693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30693</th>\n",
       "      <td>Tip Shot</td>\n",
       "      <td>Tip Shot</td>\n",
       "      <td>398</td>\n",
       "      <td>49900088</td>\n",
       "      <td>34.0443</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-118.2698</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2PT Field Goal</td>\n",
       "      <td>Center(C)</td>\n",
       "      <td>Restricted Area</td>\n",
       "      <td>Less Than 8 ft.</td>\n",
       "      <td>1610612747</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>2000-06-19</td>\n",
       "      <td>LAL vs. IND</td>\n",
       "      <td>IND</td>\n",
       "      <td>30694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30694</th>\n",
       "      <td>Running Jump Shot</td>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>426</td>\n",
       "      <td>49900088</td>\n",
       "      <td>33.8783</td>\n",
       "      <td>-134</td>\n",
       "      <td>166</td>\n",
       "      <td>-118.4038</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2PT Field Goal</td>\n",
       "      <td>Left Side Center(LC)</td>\n",
       "      <td>Mid-Range</td>\n",
       "      <td>16-24 ft.</td>\n",
       "      <td>1610612747</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>2000-06-19</td>\n",
       "      <td>LAL vs. IND</td>\n",
       "      <td>IND</td>\n",
       "      <td>30695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30695</th>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>448</td>\n",
       "      <td>49900088</td>\n",
       "      <td>33.7773</td>\n",
       "      <td>31</td>\n",
       "      <td>267</td>\n",
       "      <td>-118.2388</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3PT Field Goal</td>\n",
       "      <td>Center(C)</td>\n",
       "      <td>Above the Break 3</td>\n",
       "      <td>24+ ft.</td>\n",
       "      <td>1610612747</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>2000-06-19</td>\n",
       "      <td>LAL vs. IND</td>\n",
       "      <td>IND</td>\n",
       "      <td>30696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30696</th>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>471</td>\n",
       "      <td>49900088</td>\n",
       "      <td>33.9723</td>\n",
       "      <td>1</td>\n",
       "      <td>72</td>\n",
       "      <td>-118.2688</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2PT Field Goal</td>\n",
       "      <td>Center(C)</td>\n",
       "      <td>In The Paint (Non-RA)</td>\n",
       "      <td>Less Than 8 ft.</td>\n",
       "      <td>1610612747</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>2000-06-19</td>\n",
       "      <td>LAL vs. IND</td>\n",
       "      <td>IND</td>\n",
       "      <td>30697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30697 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             action_type combined_shot_type  game_event_id   game_id      lat  \\\n",
       "0              Jump Shot          Jump Shot             10  20000012  33.9723   \n",
       "1              Jump Shot          Jump Shot             12  20000012  34.0443   \n",
       "2              Jump Shot          Jump Shot             35  20000012  33.9093   \n",
       "3              Jump Shot          Jump Shot             43  20000012  33.8693   \n",
       "4      Driving Dunk Shot               Dunk            155  20000012  34.0443   \n",
       "...                  ...                ...            ...       ...      ...   \n",
       "30692          Jump Shot          Jump Shot            397  49900088  33.9963   \n",
       "30693           Tip Shot           Tip Shot            398  49900088  34.0443   \n",
       "30694  Running Jump Shot          Jump Shot            426  49900088  33.8783   \n",
       "30695          Jump Shot          Jump Shot            448  49900088  33.7773   \n",
       "30696          Jump Shot          Jump Shot            471  49900088  33.9723   \n",
       "\n",
       "       loc_x  loc_y       lon  minutes_remaining  period  ...       shot_type  \\\n",
       "0        167     72 -118.1028                 10       1  ...  2PT Field Goal   \n",
       "1       -157      0 -118.4268                 10       1  ...  2PT Field Goal   \n",
       "2       -101    135 -118.3708                  7       1  ...  2PT Field Goal   \n",
       "3        138    175 -118.1318                  6       1  ...  2PT Field Goal   \n",
       "4          0      0 -118.2698                  6       2  ...  2PT Field Goal   \n",
       "...      ...    ...       ...                ...     ...  ...             ...   \n",
       "30692      1     48 -118.2688                  6       4  ...  2PT Field Goal   \n",
       "30693      0      0 -118.2698                  6       4  ...  2PT Field Goal   \n",
       "30694   -134    166 -118.4038                  3       4  ...  2PT Field Goal   \n",
       "30695     31    267 -118.2388                  2       4  ...  3PT Field Goal   \n",
       "30696      1     72 -118.2688                  0       4  ...  2PT Field Goal   \n",
       "\n",
       "              shot_zone_area        shot_zone_basic  shot_zone_range  \\\n",
       "0              Right Side(R)              Mid-Range        16-24 ft.   \n",
       "1               Left Side(L)              Mid-Range         8-16 ft.   \n",
       "2       Left Side Center(LC)              Mid-Range        16-24 ft.   \n",
       "3      Right Side Center(RC)              Mid-Range        16-24 ft.   \n",
       "4                  Center(C)        Restricted Area  Less Than 8 ft.   \n",
       "...                      ...                    ...              ...   \n",
       "30692              Center(C)  In The Paint (Non-RA)  Less Than 8 ft.   \n",
       "30693              Center(C)        Restricted Area  Less Than 8 ft.   \n",
       "30694   Left Side Center(LC)              Mid-Range        16-24 ft.   \n",
       "30695              Center(C)      Above the Break 3          24+ ft.   \n",
       "30696              Center(C)  In The Paint (Non-RA)  Less Than 8 ft.   \n",
       "\n",
       "          team_id           team_name   game_date      matchup opponent  \\\n",
       "0      1610612747  Los Angeles Lakers  2000-10-31    LAL @ POR      POR   \n",
       "1      1610612747  Los Angeles Lakers  2000-10-31    LAL @ POR      POR   \n",
       "2      1610612747  Los Angeles Lakers  2000-10-31    LAL @ POR      POR   \n",
       "3      1610612747  Los Angeles Lakers  2000-10-31    LAL @ POR      POR   \n",
       "4      1610612747  Los Angeles Lakers  2000-10-31    LAL @ POR      POR   \n",
       "...           ...                 ...         ...          ...      ...   \n",
       "30692  1610612747  Los Angeles Lakers  2000-06-19  LAL vs. IND      IND   \n",
       "30693  1610612747  Los Angeles Lakers  2000-06-19  LAL vs. IND      IND   \n",
       "30694  1610612747  Los Angeles Lakers  2000-06-19  LAL vs. IND      IND   \n",
       "30695  1610612747  Los Angeles Lakers  2000-06-19  LAL vs. IND      IND   \n",
       "30696  1610612747  Los Angeles Lakers  2000-06-19  LAL vs. IND      IND   \n",
       "\n",
       "       shot_id  \n",
       "0            1  \n",
       "1            2  \n",
       "2            3  \n",
       "3            4  \n",
       "4            5  \n",
       "...        ...  \n",
       "30692    30693  \n",
       "30693    30694  \n",
       "30694    30695  \n",
       "30695    30696  \n",
       "30696    30697  \n",
       "\n",
       "[30697 rows x 25 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30697, 25)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>action_type</th>\n",
       "      <th>combined_shot_type</th>\n",
       "      <th>game_event_id</th>\n",
       "      <th>game_id</th>\n",
       "      <th>lat</th>\n",
       "      <th>loc_x</th>\n",
       "      <th>loc_y</th>\n",
       "      <th>lon</th>\n",
       "      <th>minutes_remaining</th>\n",
       "      <th>period</th>\n",
       "      <th>...</th>\n",
       "      <th>shot_type</th>\n",
       "      <th>shot_zone_area</th>\n",
       "      <th>shot_zone_basic</th>\n",
       "      <th>shot_zone_range</th>\n",
       "      <th>team_id</th>\n",
       "      <th>team_name</th>\n",
       "      <th>game_date</th>\n",
       "      <th>matchup</th>\n",
       "      <th>opponent</th>\n",
       "      <th>shot_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>10</td>\n",
       "      <td>20000012</td>\n",
       "      <td>33.9723</td>\n",
       "      <td>167</td>\n",
       "      <td>72</td>\n",
       "      <td>-118.1028</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2PT Field Goal</td>\n",
       "      <td>Right Side(R)</td>\n",
       "      <td>Mid-Range</td>\n",
       "      <td>16-24 ft.</td>\n",
       "      <td>1610612747</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>2000-10-31</td>\n",
       "      <td>LAL @ POR</td>\n",
       "      <td>POR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>12</td>\n",
       "      <td>20000012</td>\n",
       "      <td>34.0443</td>\n",
       "      <td>-157</td>\n",
       "      <td>0</td>\n",
       "      <td>-118.4268</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2PT Field Goal</td>\n",
       "      <td>Left Side(L)</td>\n",
       "      <td>Mid-Range</td>\n",
       "      <td>8-16 ft.</td>\n",
       "      <td>1610612747</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>2000-10-31</td>\n",
       "      <td>LAL @ POR</td>\n",
       "      <td>POR</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>35</td>\n",
       "      <td>20000012</td>\n",
       "      <td>33.9093</td>\n",
       "      <td>-101</td>\n",
       "      <td>135</td>\n",
       "      <td>-118.3708</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2PT Field Goal</td>\n",
       "      <td>Left Side Center(LC)</td>\n",
       "      <td>Mid-Range</td>\n",
       "      <td>16-24 ft.</td>\n",
       "      <td>1610612747</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>2000-10-31</td>\n",
       "      <td>LAL @ POR</td>\n",
       "      <td>POR</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>Jump Shot</td>\n",
       "      <td>43</td>\n",
       "      <td>20000012</td>\n",
       "      <td>33.8693</td>\n",
       "      <td>138</td>\n",
       "      <td>175</td>\n",
       "      <td>-118.1318</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2PT Field Goal</td>\n",
       "      <td>Right Side Center(RC)</td>\n",
       "      <td>Mid-Range</td>\n",
       "      <td>16-24 ft.</td>\n",
       "      <td>1610612747</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>2000-10-31</td>\n",
       "      <td>LAL @ POR</td>\n",
       "      <td>POR</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Driving Dunk Shot</td>\n",
       "      <td>Dunk</td>\n",
       "      <td>155</td>\n",
       "      <td>20000012</td>\n",
       "      <td>34.0443</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-118.2698</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2PT Field Goal</td>\n",
       "      <td>Center(C)</td>\n",
       "      <td>Restricted Area</td>\n",
       "      <td>Less Than 8 ft.</td>\n",
       "      <td>1610612747</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>2000-10-31</td>\n",
       "      <td>LAL @ POR</td>\n",
       "      <td>POR</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         action_type combined_shot_type  game_event_id   game_id      lat  \\\n",
       "0          Jump Shot          Jump Shot             10  20000012  33.9723   \n",
       "1          Jump Shot          Jump Shot             12  20000012  34.0443   \n",
       "2          Jump Shot          Jump Shot             35  20000012  33.9093   \n",
       "3          Jump Shot          Jump Shot             43  20000012  33.8693   \n",
       "4  Driving Dunk Shot               Dunk            155  20000012  34.0443   \n",
       "\n",
       "   loc_x  loc_y       lon  minutes_remaining  period  ...       shot_type  \\\n",
       "0    167     72 -118.1028                 10       1  ...  2PT Field Goal   \n",
       "1   -157      0 -118.4268                 10       1  ...  2PT Field Goal   \n",
       "2   -101    135 -118.3708                  7       1  ...  2PT Field Goal   \n",
       "3    138    175 -118.1318                  6       1  ...  2PT Field Goal   \n",
       "4      0      0 -118.2698                  6       2  ...  2PT Field Goal   \n",
       "\n",
       "          shot_zone_area  shot_zone_basic  shot_zone_range     team_id  \\\n",
       "0          Right Side(R)        Mid-Range        16-24 ft.  1610612747   \n",
       "1           Left Side(L)        Mid-Range         8-16 ft.  1610612747   \n",
       "2   Left Side Center(LC)        Mid-Range        16-24 ft.  1610612747   \n",
       "3  Right Side Center(RC)        Mid-Range        16-24 ft.  1610612747   \n",
       "4              Center(C)  Restricted Area  Less Than 8 ft.  1610612747   \n",
       "\n",
       "            team_name   game_date    matchup opponent  shot_id  \n",
       "0  Los Angeles Lakers  2000-10-31  LAL @ POR      POR        1  \n",
       "1  Los Angeles Lakers  2000-10-31  LAL @ POR      POR        2  \n",
       "2  Los Angeles Lakers  2000-10-31  LAL @ POR      POR        3  \n",
       "3  Los Angeles Lakers  2000-10-31  LAL @ POR      POR        4  \n",
       "4  Los Angeles Lakers  2000-10-31  LAL @ POR      POR        5  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30697 entries, 0 to 30696\n",
      "Data columns (total 25 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   action_type         30697 non-null  object \n",
      " 1   combined_shot_type  30697 non-null  object \n",
      " 2   game_event_id       30697 non-null  int64  \n",
      " 3   game_id             30697 non-null  int64  \n",
      " 4   lat                 30697 non-null  float64\n",
      " 5   loc_x               30697 non-null  int64  \n",
      " 6   loc_y               30697 non-null  int64  \n",
      " 7   lon                 30697 non-null  float64\n",
      " 8   minutes_remaining   30697 non-null  int64  \n",
      " 9   period              30697 non-null  int64  \n",
      " 10  playoffs            30697 non-null  int64  \n",
      " 11  season              30697 non-null  object \n",
      " 12  seconds_remaining   30697 non-null  int64  \n",
      " 13  shot_distance       30697 non-null  int64  \n",
      " 14  shot_made_flag      25697 non-null  float64\n",
      " 15  shot_type           30697 non-null  object \n",
      " 16  shot_zone_area      30697 non-null  object \n",
      " 17  shot_zone_basic     30697 non-null  object \n",
      " 18  shot_zone_range     30697 non-null  object \n",
      " 19  team_id             30697 non-null  int64  \n",
      " 20  team_name           30697 non-null  object \n",
      " 21  game_date           30697 non-null  object \n",
      " 22  matchup             30697 non-null  object \n",
      " 23  opponent            30697 non-null  object \n",
      " 24  shot_id             30697 non-null  int64  \n",
      "dtypes: float64(3), int64(11), object(11)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "action_type              0\n",
       "combined_shot_type       0\n",
       "game_event_id            0\n",
       "game_id                  0\n",
       "lat                      0\n",
       "loc_x                    0\n",
       "loc_y                    0\n",
       "lon                      0\n",
       "minutes_remaining        0\n",
       "period                   0\n",
       "playoffs                 0\n",
       "season                   0\n",
       "seconds_remaining        0\n",
       "shot_distance            0\n",
       "shot_made_flag        5000\n",
       "shot_type                0\n",
       "shot_zone_area           0\n",
       "shot_zone_basic          0\n",
       "shot_zone_range          0\n",
       "team_id                  0\n",
       "team_name                0\n",
       "game_date                0\n",
       "matchup                  0\n",
       "opponent                 0\n",
       "shot_id                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='period', ylabel='Count'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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90451BXA9cCJwW/tIkkZkaGFRVX8JvHpA/XvAxUdoswXYMqA+BRxtvkOSNET+gluS1MmwkCR1MiwkSZ0MC0lSJ8NCktTJsJAkdTIsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVInw0KS1MmwkCR1MiwkSZ0MC0lSp2G+VvWMJF9Jcn+SPUne0+qnJNmZ5KG2XN7X5uoke5M8mOSSvvoFSXa3bde016tKkkZkmFcWTwP/sapeCVwIXJnkHOAqYFdVrQF2te+0bRuAc4F1wLXtlawA1wGb6b2Xe03bLkkakaGFRVXtr6p72vqTwP3ASmA9sK3ttg24tK2vB26qqqeq6mFgL7A2yQpgWVXdUVUF3NDXRpI0AiOZs0iymt77uO8ETq+q/dALFOC0tttK4NG+ZtOttrKtH1of9Hc2J5lKMnXw4ME5PQdJOp4NPSySvAT4HPDeqnriaLsOqNVR6ocXq7ZW1WRVTU5MTDz/zkqSBhpqWCR5Ib2g+HRV3dzKj7WhJdryQKtPA2f0NV8F7Gv1VQPqkqQRGebdUAE+DtxfVR/u27QD2NTWNwG39tU3JDkhyVn0JrLvakNVTya5sB1zY18bSdIILB3isS8C3gnsTnJvq70f+CCwPcnlwCPAZQBVtSfJduA+endSXVlVz7R2VwDXAycCt7WPJGlEhhYWVfVnDJ5vALj4CG22AFsG1KeA8+aud5Kk58NfcEuSOhkWkqROhoUkqZNhIUnqZFhIkjoZFpKkToaFJKmTYSFJ6mRYSJI6GRaSpE6GhSSpk2EhSepkWEiSOhkWkqROhoUkqZNhIUnqNMzXqn4iyYEk3+yrnZJkZ5KH2nJ537ark+xN8mCSS/rqFyTZ3bZd016tKkkaoWFeWVwPrDukdhWwq6rWALvad5KcA2wAzm1trk2ypLW5DthM753cawYcU5I0ZEMLi6r6U+BvDimvB7a19W3ApX31m6rqqap6GNgLrE2yAlhWVXdUVQE39LWRJI3IqOcsTq+q/QBteVqrrwQe7dtvutVWtvVD6wMl2ZxkKsnUwYMH57TjknQ8my8T3IPmIeoo9YGqamtVTVbV5MTExJx1TpKOd6MOi8fa0BJteaDVp4Ez+vZbBexr9VUD6pKkERp1WOwANrX1TcCtffUNSU5Icha9iey72lDVk0kubHdBbexrI0kakaXDOnCSG4E3AKcmmQY+AHwQ2J7kcuAR4DKAqtqTZDtwH/A0cGVVPdMOdQW9O6tOBG5rH0nSCA0tLKrq7UfYdPER9t8CbBlQnwLOm8OuSZKep/kywS1JmscMC0lSJ8NCktTJsJAkdTIsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVInw0KS1Gloz4aStHi97R3vYt/j3z+s/vJTl7P9U58cQ480bIaFpOdt3+PfZ9WvvO+w+vTNvzuG3mgUHIaSJHUyLCRJnQwLSVKnBRMWSdYleTDJ3iRXjbs/knQ8WRBhkWQJ8F+BNwHnAG9Pcs54eyVJx4+FcjfUWmBvVX0bIMlNwHp67+yWpFk70m3A4K3A/VJV4+5DpyRvBdZV1b9p398J/KOq+vVD9tsMbG5fzwYenOWfPBV4fJZt55vFci6L5TzAc5mvFsu5HOt5/P2qmji0uFCuLDKgdljKVdVWYOsx/7Fkqqomj/U488FiOZfFch7gucxXi+VchnUeC2LOApgGzuj7vgrYN6a+SNJxZ6GExdeBNUnOSvIiYAOwY8x9kqTjxoIYhqqqp5P8OvBFYAnwiaraM8Q/ecxDWfPIYjmXxXIe4LnMV4vlXIZyHgtigluSNF4LZRhKkjRGhoUkqZNh0SfJJ5IcSPLNcfflWCQ5I8lXktyfZE+S94y7T7OV5MVJ7kryF+1cfmfcfToWSZYk+UaSz4+7L8ciyXeS7E5yb5KpcffnWCR5WZLPJnmg/X/mtePu02wkObv97/Hs54kk752z4ztn8Zwkrwd+CNxQVeeNuz+zlWQFsKKq7knyUuBu4NKqWnC/eE8S4OSq+mGSFwJ/Brynqr425q7NSpLfBCaBZVX15nH3Z7aSfAeYrKoF/yO2JNuA/1lVH2t3W55UVT8Yc7eOSXtE0l/R+/Hyd+fimF5Z9KmqPwX+Ztz9OFZVtb+q7mnrTwL3AyvH26vZqZ4ftq8vbJ8F+S+cJKuAXwY+Nu6+qCfJMuD1wMcBquonCz0omouBb81VUIBhseglWQ28GrhzzF2ZtTZ0cy9wANhZVQv1XD4C/Dbwf8fcj7lQwJeS3N0es7NQvQI4CHyyDQ9+LMnJ4+7UHNgA3DiXBzQsFrEkLwE+B7y3qp4Yd39mq6qeqarz6f1yf22SBTdEmOTNwIGqunvcfZkjF1XVa+g9CfrKNoS7EC0FXgNcV1WvBn4ELOhXILShtLcAn5nL4xoWi1Qb3/8c8Omqunnc/ZkLbXjgdmDdeHsyKxcBb2lj/TcBv5DkU+Pt0uxV1b62PADcQu/J0AvRNDDdd7X6WXrhsZC9Cbinqh6by4MaFotQmxT+OHB/VX143P05FkkmkrysrZ8IvBF4YKydmoWqurqqVlXVanpDBF+uqneMuVuzkuTkduMEbcjml4AFeQdhVf018GiSs1vpYhb+qw/ezhwPQcECedzHqCS5EXgDcGqSaeADVfXx8fZqVi4C3gnsbmP9AO+vqj8ZX5dmbQWwrd3d8QJge1Ut6NtOF4HTgVt6/yZhKfBHVfWF8XbpmLwb+HQbvvk28K4x92fWkpwE/CLwa3N+bG+dlSR1cRhKktTJsJAkdTIsJEmdDAtJUifDQpLUybCQxiDJv0uy8Xnsv3qhPw1ZC5u/s5BGLMnSqvrouPshPR+GhTQL7QGNX6D3gMZXA/8b2Ai8Evgw8BLgceBfVdX+JLcD/4veDyZ3tF9A/7CqPpTkfOCjwEnAt4B/XVXfT3IB8Angx/QezS6NjcNQ0uydDWytqp8FngCuBH4feGtVPfsf+i19+7+sqv5pVf2XQ45zA/C+dpzdwAda/ZPAb1TVgnwZjxYXryyk2Xu0qv68rX8KeD9wHrCzPQpjCbC/b///fugBkvwMvRD5aittAz4zoP6H9B4QJ42FYSHN3qHPynkS2HOUK4EfPY9jZ8DxpbFxGEqavTP73tf8duBrwMSztSQvTHLu0Q5QVX8LfD/JP2mldwJfbY9j/9sk/7jVf3XOey89D15ZSLN3P7ApyR8AD9Gbr/gicE0bRlpK7+14ezqOswn4aHtiaP9TT98FfCLJj9txpbHxqbPSLLS7oT5fVQvurX3SbDgMJUnq5JWFJKmTVxaSpE6GhSSpk2EhSepkWEiSOhkWkqRO/w+mTRRcyyA/aAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['period'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>game_event_id</th>\n",
       "      <th>game_id</th>\n",
       "      <th>lat</th>\n",
       "      <th>loc_x</th>\n",
       "      <th>loc_y</th>\n",
       "      <th>lon</th>\n",
       "      <th>minutes_remaining</th>\n",
       "      <th>period</th>\n",
       "      <th>playoffs</th>\n",
       "      <th>seconds_remaining</th>\n",
       "      <th>shot_distance</th>\n",
       "      <th>shot_made_flag</th>\n",
       "      <th>team_id</th>\n",
       "      <th>shot_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>game_event_id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002553</td>\n",
       "      <td>-0.060091</td>\n",
       "      <td>-0.028404</td>\n",
       "      <td>0.060091</td>\n",
       "      <td>-0.028404</td>\n",
       "      <td>-0.270736</td>\n",
       "      <td>0.956042</td>\n",
       "      <td>-0.004507</td>\n",
       "      <td>-0.021293</td>\n",
       "      <td>0.062077</td>\n",
       "      <td>-0.037232</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.028478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game_id</th>\n",
       "      <td>-0.002553</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013021</td>\n",
       "      <td>-0.019553</td>\n",
       "      <td>-0.013021</td>\n",
       "      <td>-0.019553</td>\n",
       "      <td>0.014481</td>\n",
       "      <td>0.009879</td>\n",
       "      <td>0.917235</td>\n",
       "      <td>-0.007107</td>\n",
       "      <td>-0.030327</td>\n",
       "      <td>-0.001612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.761656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>-0.060091</td>\n",
       "      <td>0.013021</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.016217</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.016217</td>\n",
       "      <td>0.071462</td>\n",
       "      <td>-0.041248</td>\n",
       "      <td>-0.001143</td>\n",
       "      <td>0.052714</td>\n",
       "      <td>-0.818517</td>\n",
       "      <td>0.148070</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.030790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_x</th>\n",
       "      <td>-0.028404</td>\n",
       "      <td>-0.019553</td>\n",
       "      <td>0.016217</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.016217</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006898</td>\n",
       "      <td>-0.028165</td>\n",
       "      <td>-0.012790</td>\n",
       "      <td>0.001083</td>\n",
       "      <td>0.022654</td>\n",
       "      <td>-0.000848</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.019917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc_y</th>\n",
       "      <td>0.060091</td>\n",
       "      <td>-0.013021</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.016217</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.016217</td>\n",
       "      <td>-0.071462</td>\n",
       "      <td>0.041248</td>\n",
       "      <td>0.001143</td>\n",
       "      <td>-0.052714</td>\n",
       "      <td>0.818517</td>\n",
       "      <td>-0.148070</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.030790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lon</th>\n",
       "      <td>-0.028404</td>\n",
       "      <td>-0.019553</td>\n",
       "      <td>0.016217</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.016217</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006898</td>\n",
       "      <td>-0.028165</td>\n",
       "      <td>-0.012790</td>\n",
       "      <td>0.001083</td>\n",
       "      <td>0.022654</td>\n",
       "      <td>-0.000848</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.019917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>minutes_remaining</th>\n",
       "      <td>-0.270736</td>\n",
       "      <td>0.014481</td>\n",
       "      <td>0.071462</td>\n",
       "      <td>0.006898</td>\n",
       "      <td>-0.071462</td>\n",
       "      <td>0.006898</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.043425</td>\n",
       "      <td>0.015101</td>\n",
       "      <td>0.025558</td>\n",
       "      <td>-0.059767</td>\n",
       "      <td>0.028342</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <td>0.956042</td>\n",
       "      <td>0.009879</td>\n",
       "      <td>-0.041248</td>\n",
       "      <td>-0.028165</td>\n",
       "      <td>0.041248</td>\n",
       "      <td>-0.028165</td>\n",
       "      <td>-0.043425</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.008789</td>\n",
       "      <td>0.005832</td>\n",
       "      <td>0.046993</td>\n",
       "      <td>-0.032152</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>playoffs</th>\n",
       "      <td>-0.004507</td>\n",
       "      <td>0.917235</td>\n",
       "      <td>-0.001143</td>\n",
       "      <td>-0.012790</td>\n",
       "      <td>0.001143</td>\n",
       "      <td>-0.012790</td>\n",
       "      <td>0.015101</td>\n",
       "      <td>0.008789</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002490</td>\n",
       "      <td>-0.008667</td>\n",
       "      <td>-0.001257</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.612571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seconds_remaining</th>\n",
       "      <td>-0.021293</td>\n",
       "      <td>-0.007107</td>\n",
       "      <td>0.052714</td>\n",
       "      <td>0.001083</td>\n",
       "      <td>-0.052714</td>\n",
       "      <td>0.001083</td>\n",
       "      <td>0.025558</td>\n",
       "      <td>0.005832</td>\n",
       "      <td>-0.002490</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.051482</td>\n",
       "      <td>0.030804</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.004868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shot_distance</th>\n",
       "      <td>0.062077</td>\n",
       "      <td>-0.030327</td>\n",
       "      <td>-0.818517</td>\n",
       "      <td>0.022654</td>\n",
       "      <td>0.818517</td>\n",
       "      <td>0.022654</td>\n",
       "      <td>-0.059767</td>\n",
       "      <td>0.046993</td>\n",
       "      <td>-0.008667</td>\n",
       "      <td>-0.051482</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.198242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.015695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shot_made_flag</th>\n",
       "      <td>-0.037232</td>\n",
       "      <td>-0.001612</td>\n",
       "      <td>0.148070</td>\n",
       "      <td>-0.000848</td>\n",
       "      <td>-0.148070</td>\n",
       "      <td>-0.000848</td>\n",
       "      <td>0.028342</td>\n",
       "      <td>-0.032152</td>\n",
       "      <td>-0.001257</td>\n",
       "      <td>0.030804</td>\n",
       "      <td>-0.198242</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.013016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>team_id</th>\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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shot_id</th>\n",
       "      <td>0.028478</td>\n",
       "      <td>0.761656</td>\n",
       "      <td>-0.030790</td>\n",
       "      <td>-0.019917</td>\n",
       "      <td>0.030790</td>\n",
       "      <td>-0.019917</td>\n",
       "      <td>-0.005731</td>\n",
       "      <td>0.000364</td>\n",
       "      <td>0.612571</td>\n",
       "      <td>-0.004868</td>\n",
       "      <td>0.015695</td>\n",
       "      <td>-0.013016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   game_event_id   game_id       lat     loc_x     loc_y  \\\n",
       "game_event_id           1.000000 -0.002553 -0.060091 -0.028404  0.060091   \n",
       "game_id                -0.002553  1.000000  0.013021 -0.019553 -0.013021   \n",
       "lat                    -0.060091  0.013021  1.000000  0.016217 -1.000000   \n",
       "loc_x                  -0.028404 -0.019553  0.016217  1.000000 -0.016217   \n",
       "loc_y                   0.060091 -0.013021 -1.000000 -0.016217  1.000000   \n",
       "lon                    -0.028404 -0.019553  0.016217  1.000000 -0.016217   \n",
       "minutes_remaining      -0.270736  0.014481  0.071462  0.006898 -0.071462   \n",
       "period                  0.956042  0.009879 -0.041248 -0.028165  0.041248   \n",
       "playoffs               -0.004507  0.917235 -0.001143 -0.012790  0.001143   \n",
       "seconds_remaining      -0.021293 -0.007107  0.052714  0.001083 -0.052714   \n",
       "shot_distance           0.062077 -0.030327 -0.818517  0.022654  0.818517   \n",
       "shot_made_flag         -0.037232 -0.001612  0.148070 -0.000848 -0.148070   \n",
       "team_id                      NaN       NaN       NaN       NaN       NaN   \n",
       "shot_id                 0.028478  0.761656 -0.030790 -0.019917  0.030790   \n",
       "\n",
       "                        lon  minutes_remaining    period  playoffs  \\\n",
       "game_event_id     -0.028404          -0.270736  0.956042 -0.004507   \n",
       "game_id           -0.019553           0.014481  0.009879  0.917235   \n",
       "lat                0.016217           0.071462 -0.041248 -0.001143   \n",
       "loc_x              1.000000           0.006898 -0.028165 -0.012790   \n",
       "loc_y             -0.016217          -0.071462  0.041248  0.001143   \n",
       "lon                1.000000           0.006898 -0.028165 -0.012790   \n",
       "minutes_remaining  0.006898           1.000000 -0.043425  0.015101   \n",
       "period            -0.028165          -0.043425  1.000000  0.008789   \n",
       "playoffs          -0.012790           0.015101  0.008789  1.000000   \n",
       "seconds_remaining  0.001083           0.025558  0.005832 -0.002490   \n",
       "shot_distance      0.022654          -0.059767  0.046993 -0.008667   \n",
       "shot_made_flag    -0.000848           0.028342 -0.032152 -0.001257   \n",
       "team_id                 NaN                NaN       NaN       NaN   \n",
       "shot_id           -0.019917          -0.005731  0.000364  0.612571   \n",
       "\n",
       "                   seconds_remaining  shot_distance  shot_made_flag  team_id  \\\n",
       "game_event_id              -0.021293       0.062077       -0.037232      NaN   \n",
       "game_id                    -0.007107      -0.030327       -0.001612      NaN   \n",
       "lat                         0.052714      -0.818517        0.148070      NaN   \n",
       "loc_x                       0.001083       0.022654       -0.000848      NaN   \n",
       "loc_y                      -0.052714       0.818517       -0.148070      NaN   \n",
       "lon                         0.001083       0.022654       -0.000848      NaN   \n",
       "minutes_remaining           0.025558      -0.059767        0.028342      NaN   \n",
       "period                      0.005832       0.046993       -0.032152      NaN   \n",
       "playoffs                   -0.002490      -0.008667       -0.001257      NaN   \n",
       "seconds_remaining           1.000000      -0.051482        0.030804      NaN   \n",
       "shot_distance              -0.051482       1.000000       -0.198242      NaN   \n",
       "shot_made_flag              0.030804      -0.198242        1.000000      NaN   \n",
       "team_id                          NaN            NaN             NaN      NaN   \n",
       "shot_id                    -0.004868       0.015695       -0.013016      NaN   \n",
       "\n",
       "                    shot_id  \n",
       "game_event_id      0.028478  \n",
       "game_id            0.761656  \n",
       "lat               -0.030790  \n",
       "loc_x             -0.019917  \n",
       "loc_y              0.030790  \n",
       "lon               -0.019917  \n",
       "minutes_remaining -0.005731  \n",
       "period             0.000364  \n",
       "playoffs           0.612571  \n",
       "seconds_remaining -0.004868  \n",
       "shot_distance      0.015695  \n",
       "shot_made_flag    -0.013016  \n",
       "team_id                 NaN  \n",
       "shot_id            1.000000  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = df.corr(method=\"pearson\")  \n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUESTION 11\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of Numbers :  55\n"
     ]
    }
   ],
   "source": [
    "sum=0\n",
    "for i in range(1,11):\n",
    "    sum+=i\n",
    "print(\"Sum of Numbers : \",sum)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUESTION 12 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count is  1\n",
      "Count is  2\n",
      "Count is  3\n",
      "Count is  4\n",
      "Count is  5\n",
      "Count is  6\n",
      "Count is  7\n",
      "Count is  8\n",
      "Count is  9\n",
      "Count is  10\n",
      "Total Count is  10\n"
     ]
    }
   ],
   "source": [
    "count=0\n",
    "for i in range(1,11):\n",
    "    print('Count is ',i)\n",
    "    count+=1\n",
    "print(\"Total Count is \",count)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUESTIOn 13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dog\n",
      "Can Eat\n",
      "Can Walk\n",
      "Cat \n",
      "Can Eat\n",
      "Can Walk\n"
     ]
    }
   ],
   "source": [
    "class animal:\n",
    "    def eat(self):\n",
    "        print(\"Can Eat\")\n",
    "    def walk(self):\n",
    "        print(\"Can Walk\")\n",
    "dog=animal()\n",
    "print(\"Dog\")\n",
    "dog.eat()\n",
    "dog.walk()\n",
    "\n",
    "cat=animal()\n",
    "print(\"Cat \")\n",
    "cat.eat() \n",
    "cat.walk()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUESTION 14 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "df=sns.load_dataset('tips')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sex', ylabel='total_bill'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mc89U+TuMnn2wCljfPcxE2uvY85fGexqjJ5o9CHwfeA2wE/iLbvx/CfDnSb4PvBo4sap2JLmG0WyTFw5UtzQnx/wlqUEO+0hSgwx/SWqQ4S9JDTL8JalBhr8kNcjwl6QGGf6S1KD/B7OaN67IKIi1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=sns.barplot(x='sex',y='total_bill',data=df)\n",
    "fig\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr=df.corr(method='pearson')\n",
    "sns.heatmap(corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3=plt.boxplot(data=df,x='total_bill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4=plt.plot('total_bill',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUESTION 15 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Ali', 'Imran', 'Munir', 'Musdd', 'Sagar']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LIST METHODS \n",
    "name=['Sagar','Karan','Munir','Ali','Sahil']\n",
    "name.append('Musdd')\n",
    "name.insert(3,'Imran')\n",
    "name.sort()\n",
    "name.pop()\n",
    "name.remove('Karan')\n",
    "\n",
    "name\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TUPLES \n",
    "name=('Sagar','Karan','Munir','Ali','Sahil')\n",
    "name.index('Sagar')\n",
    "name.count('Karan')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DISTIONARY \n",
    "students={'Name':'Sagar','Age':21,'Dept':'CS'}\n",
    "students.keys()\n",
    "students.items()\n",
    "students.clear()\n",
    "students.get('Name')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUESTION 16\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  4  5  6  7  8  9 10 11 12]\n",
      "[ 1  2  3  4  5  6  7  8  9 10 11 12]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1,2,3,4,5,6,7,8,9,10])\n",
    "newArray = np.append (arr, [11, 12])\n",
    "print(newArray)\n",
    "np.delete(newArray, 11)\n",
    "print(newArray)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUESTION 17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>cosmetics</th>\n",
       "      <th>price</th>\n",
       "      <th>in_stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Faiza_Beauty_Cream</td>\n",
       "      <td>300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acne_Body_wash</td>\n",
       "      <td>700</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ponds_Facewash</td>\n",
       "      <td>350</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lux_Beauty_Soap</td>\n",
       "      <td>100</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            cosmetics  price  in_stock\n",
       "0  Faiza_Beauty_Cream    300      True\n",
       "1      Acne_Body_wash    700     False\n",
       "2      Ponds_Facewash    350      True\n",
       "3     Lux_Beauty_Soap    100      True"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "products={'cosmetics':['Faiza_Beauty_Cream','Acne_Body_wash','Ponds_Facewash','Lux_Beauty_Soap'],'price':[300,700,350,100],'in_stock':[True,False,True,True]}\n",
    "product_list=pd.DataFrame(products)\n",
    "product_list\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Roll_Number</th>\n",
       "      <th>Full_Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Domicile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19CS54</td>\n",
       "      <td>Sagar_Kumar</td>\n",
       "      <td>20</td>\n",
       "      <td>Khaipur_Mirs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19CS55</td>\n",
       "      <td>Karan_Kumar</td>\n",
       "      <td>21</td>\n",
       "      <td>Khaipur_Mirs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19CS49</td>\n",
       "      <td>Gul_Munir_Hassan</td>\n",
       "      <td>21</td>\n",
       "      <td>Thatta</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Roll_Number         Full_Name  Age      Domicile\n",
       "0      19CS54       Sagar_Kumar   20  Khaipur_Mirs\n",
       "1      19CS55       Karan_Kumar   21  Khaipur_Mirs\n",
       "2      19CS49  Gul_Munir_Hassan   21        Thatta"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_record=[['19CS54','Sagar_Kumar',20,'Khaipur_Mirs'],['19CS55','Karan_Kumar',21,'Khaipur_Mirs'],['19CS49','Gul_Munir_Hassan',21,'Thatta']]\n",
    "st_record=pd.DataFrame(student_record,columns=['Roll_Number','Full_Name','Age','Domicile'])\n",
    "st_record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cosmetics</th>\n",
       "      <th>price</th>\n",
       "      <th>in_stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Faiza_Beauty_Cream</td>\n",
       "      <td>300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acne_Body_wash</td>\n",
       "      <td>700</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ponds_Facewash</td>\n",
       "      <td>350</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lux_Beauty_Soap</td>\n",
       "      <td>100</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            cosmetics  price  in_stock\n",
       "0  Faiza_Beauty_Cream    300      True\n",
       "1      Acne_Body_wash    700     False\n",
       "2      Ponds_Facewash    350      True\n",
       "3     Lux_Beauty_Soap    100      True"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products={'cosmetics':('Faiza_Beauty_Cream','Acne_Body_wash','Ponds_Facewash','Lux_Beauty_Soap'),'price':(300,700,350,100),'in_stock':(True,False,True,True)}\n",
    "product_list=pd.DataFrame(products)\n",
    "product_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUESTION 19\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anagrams',\n",
       " 'anscombe',\n",
       " 'attention',\n",
       " 'brain_networks',\n",
       " 'car_crashes',\n",
       " 'diamonds',\n",
       " 'dots',\n",
       " 'exercise',\n",
       " 'flights',\n",
       " 'fmri',\n",
       " 'gammas',\n",
       " 'geyser',\n",
       " 'iris',\n",
       " 'mpg',\n",
       " 'penguins',\n",
       " 'planets',\n",
       " 'taxis',\n",
       " 'tips',\n",
       " 'titanic']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.get_dataset_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUESTION 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>action_type</th>\n",
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       "      <th>minutes_remaining</th>\n",
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       "      <th>...</th>\n",
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       "      <th>team_id</th>\n",
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       "      <th>game_date</th>\n",
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       "      <th>30694</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>30696</th>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30697 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       action_type  combined_shot_type  game_event_id  game_id   lat  loc_x  \\\n",
       "0             True                True           True     True  True   True   \n",
       "1             True                True           True     True  True   True   \n",
       "2             True                True           True     True  True   True   \n",
       "3             True                True           True     True  True   True   \n",
       "4             True                True           True     True  True   True   \n",
       "...            ...                 ...            ...      ...   ...    ...   \n",
       "30692         True                True           True     True  True   True   \n",
       "30693         True                True           True     True  True   True   \n",
       "30694         True                True           True     True  True   True   \n",
       "30695         True                True           True     True  True   True   \n",
       "30696         True                True           True     True  True   True   \n",
       "\n",
       "       loc_y   lon  minutes_remaining  period  ...  shot_type  shot_zone_area  \\\n",
       "0       True  True               True    True  ...       True            True   \n",
       "1       True  True               True    True  ...       True            True   \n",
       "2       True  True               True    True  ...       True            True   \n",
       "3       True  True               True    True  ...       True            True   \n",
       "4       True  True               True    True  ...       True            True   \n",
       "...      ...   ...                ...     ...  ...        ...             ...   \n",
       "30692   True  True               True    True  ...       True            True   \n",
       "30693   True  True               True    True  ...       True            True   \n",
       "30694   True  True               True    True  ...       True            True   \n",
       "30695   True  True               True    True  ...       True            True   \n",
       "30696   True  True               True    True  ...       True            True   \n",
       "\n",
       "       shot_zone_basic  shot_zone_range  team_id  team_name  game_date  \\\n",
       "0                 True             True     True       True       True   \n",
       "1                 True             True     True       True       True   \n",
       "2                 True             True     True       True       True   \n",
       "3                 True             True     True       True       True   \n",
       "4                 True             True     True       True       True   \n",
       "...                ...              ...      ...        ...        ...   \n",
       "30692             True             True     True       True       True   \n",
       "30693             True             True     True       True       True   \n",
       "30694             True             True     True       True       True   \n",
       "30695             True             True     True       True       True   \n",
       "30696             True             True     True       True       True   \n",
       "\n",
       "       matchup  opponent  shot_id  \n",
       "0         True      True     True  \n",
       "1         True      True     True  \n",
       "2         True      True     True  \n",
       "3         True      True     True  \n",
       "4         True      True     True  \n",
       "...        ...       ...      ...  \n",
       "30692     True      True     True  \n",
       "30693     True      True     True  \n",
       "30694     True      True     True  \n",
       "30695     True      True     True  \n",
       "30696     True      True     True  \n",
       "\n",
       "[30697 rows x 25 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.notnull()"
   ]
  }
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