[3]Groupby.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Pandas groupby is used for grouping the data according to the categories and apply a function to the categories. similar to 'GROUP BY' in database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Series_Title</th>\n",
       "      <th>Released_Year</th>\n",
       "      <th>Runtime</th>\n",
       "      <th>Genre</th>\n",
       "      <th>IMDB_Rating</th>\n",
       "      <th>Director</th>\n",
       "      <th>Star1</th>\n",
       "      <th>No_of_Votes</th>\n",
       "      <th>Gross</th>\n",
       "      <th>Metascore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>142</td>\n",
       "      <td>Drama</td>\n",
       "      <td>9.3</td>\n",
       "      <td>Frank Darabont</td>\n",
       "      <td>Tim Robbins</td>\n",
       "      <td>2343110</td>\n",
       "      <td>28341469.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>1972</td>\n",
       "      <td>175</td>\n",
       "      <td>Crime</td>\n",
       "      <td>9.2</td>\n",
       "      <td>Francis Ford Coppola</td>\n",
       "      <td>Marlon Brando</td>\n",
       "      <td>1620367</td>\n",
       "      <td>134966411.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>152</td>\n",
       "      <td>Action</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>Christian Bale</td>\n",
       "      <td>2303232</td>\n",
       "      <td>534858444.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>1974</td>\n",
       "      <td>202</td>\n",
       "      <td>Crime</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Francis Ford Coppola</td>\n",
       "      <td>Al Pacino</td>\n",
       "      <td>1129952</td>\n",
       "      <td>57300000.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>1957</td>\n",
       "      <td>96</td>\n",
       "      <td>Crime</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Sidney Lumet</td>\n",
       "      <td>Henry Fonda</td>\n",
       "      <td>689845</td>\n",
       "      <td>4360000.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "               Series_Title Released_Year  Runtime   Genre  IMDB_Rating  \\\n",
       "0  The Shawshank Redemption          1994      142   Drama          9.3   \n",
       "1             The Godfather          1972      175   Crime          9.2   \n",
       "2           The Dark Knight          2008      152  Action          9.0   \n",
       "3    The Godfather: Part II          1974      202   Crime          9.0   \n",
       "4              12 Angry Men          1957       96   Crime          9.0   \n",
       "\n",
       "               Director           Star1  No_of_Votes        Gross  Metascore  \n",
       "0        Frank Darabont     Tim Robbins      2343110   28341469.0       80.0  \n",
       "1  Francis Ford Coppola   Marlon Brando      1620367  134966411.0      100.0  \n",
       "2     Christopher Nolan  Christian Bale      2303232  534858444.0       84.0  \n",
       "3  Francis Ford Coppola       Al Pacino      1129952   57300000.0       90.0  \n",
       "4          Sidney Lumet     Henry Fonda       689845    4360000.0       96.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = pd.read_csv('imdb-top-1000.csv')\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 10)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#groupby is performed in categorical columns\n",
    "movies.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000002599C9A24D0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genres = movies.groupby('Genre')\n",
    "genres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying builtin aggregation fuctions on groupby objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_9976\\3849270399.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  genres.sum() #sum of all numerical columns\n"
     ]
    },
    {
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       "      <th>Runtime</th>\n",
       "      <th>IMDB_Rating</th>\n",
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       "      <th>Genre</th>\n",
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       "      <th>Action</th>\n",
       "      <td>22196</td>\n",
       "      <td>1367.3</td>\n",
       "      <td>72282412</td>\n",
       "      <td>3.263226e+10</td>\n",
       "      <td>10499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>9656</td>\n",
       "      <td>571.5</td>\n",
       "      <td>22576163</td>\n",
       "      <td>9.496922e+09</td>\n",
       "      <td>5020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animation</th>\n",
       "      <td>8166</td>\n",
       "      <td>650.3</td>\n",
       "      <td>21978630</td>\n",
       "      <td>1.463147e+10</td>\n",
       "      <td>6082.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biography</th>\n",
       "      <td>11970</td>\n",
       "      <td>698.6</td>\n",
       "      <td>24006844</td>\n",
       "      <td>8.276358e+09</td>\n",
       "      <td>6023.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>17380</td>\n",
       "      <td>1224.7</td>\n",
       "      <td>27620327</td>\n",
       "      <td>1.566387e+10</td>\n",
       "      <td>9840.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>13524</td>\n",
       "      <td>857.8</td>\n",
       "      <td>33533615</td>\n",
       "      <td>8.452632e+09</td>\n",
       "      <td>6706.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>36049</td>\n",
       "      <td>2299.7</td>\n",
       "      <td>61367304</td>\n",
       "      <td>3.540997e+10</td>\n",
       "      <td>19208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>215</td>\n",
       "      <td>15.6</td>\n",
       "      <td>551221</td>\n",
       "      <td>4.391106e+08</td>\n",
       "      <td>158.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <td>170</td>\n",
       "      <td>16.0</td>\n",
       "      <td>146222</td>\n",
       "      <td>7.827267e+08</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film-Noir</th>\n",
       "      <td>312</td>\n",
       "      <td>23.9</td>\n",
       "      <td>367215</td>\n",
       "      <td>1.259105e+08</td>\n",
       "      <td>287.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horror</th>\n",
       "      <td>1123</td>\n",
       "      <td>87.0</td>\n",
       "      <td>3742556</td>\n",
       "      <td>1.034649e+09</td>\n",
       "      <td>880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mystery</th>\n",
       "      <td>1429</td>\n",
       "      <td>95.7</td>\n",
       "      <td>4203004</td>\n",
       "      <td>1.256417e+09</td>\n",
       "      <td>633.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>108</td>\n",
       "      <td>7.8</td>\n",
       "      <td>27733</td>\n",
       "      <td>1.755074e+07</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western</th>\n",
       "      <td>593</td>\n",
       "      <td>33.4</td>\n",
       "      <td>1289665</td>\n",
       "      <td>5.822151e+07</td>\n",
       "      <td>313.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "           Runtime  IMDB_Rating  No_of_Votes         Gross  Metascore\n",
       "Genre                                                                \n",
       "Action       22196       1367.3     72282412  3.263226e+10    10499.0\n",
       "Adventure     9656        571.5     22576163  9.496922e+09     5020.0\n",
       "Animation     8166        650.3     21978630  1.463147e+10     6082.0\n",
       "Biography    11970        698.6     24006844  8.276358e+09     6023.0\n",
       "Comedy       17380       1224.7     27620327  1.566387e+10     9840.0\n",
       "Crime        13524        857.8     33533615  8.452632e+09     6706.0\n",
       "Drama        36049       2299.7     61367304  3.540997e+10    19208.0\n",
       "Family         215         15.6       551221  4.391106e+08      158.0\n",
       "Fantasy        170         16.0       146222  7.827267e+08        0.0\n",
       "Film-Noir      312         23.9       367215  1.259105e+08      287.0\n",
       "Horror        1123         87.0      3742556  1.034649e+09      880.0\n",
       "Mystery       1429         95.7      4203004  1.256417e+09      633.0\n",
       "Thriller       108          7.8        27733  1.755074e+07       81.0\n",
       "Western        593         33.4      1289665  5.822151e+07      313.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genres.sum() #sum of all numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>Genre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <td>172</td>\n",
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       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biography</th>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film-Noir</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horror</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mystery</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Series_Title  Released_Year  Runtime  IMDB_Rating  Director  Star1  \\\n",
       "Genre                                                                           \n",
       "Action              172            172      172          172       172    172   \n",
       "Adventure            72             72       72           72        72     72   \n",
       "Animation            82             82       82           82        82     82   \n",
       "Biography            88             88       88           88        88     88   \n",
       "Comedy              155            155      155          155       155    155   \n",
       "Crime               107            107      107          107       107    107   \n",
       "Drama               289            289      289          289       289    289   \n",
       "Family                2              2        2            2         2      2   \n",
       "Fantasy               2              2        2            2         2      2   \n",
       "Film-Noir             3              3        3            3         3      3   \n",
       "Horror               11             11       11           11        11     11   \n",
       "Mystery              12             12       12           12        12     12   \n",
       "Thriller              1              1        1            1         1      1   \n",
       "Western               4              4        4            4         4      4   \n",
       "\n",
       "           No_of_Votes  Gross  Metascore  \n",
       "Genre                                     \n",
       "Action             172    172        143  \n",
       "Adventure           72     72         64  \n",
       "Animation           82     82         75  \n",
       "Biography           88     88         79  \n",
       "Comedy             155    155        125  \n",
       "Crime              107    107         87  \n",
       "Drama              289    289        241  \n",
       "Family               2      2          2  \n",
       "Fantasy              2      2          0  \n",
       "Film-Noir            3      3          3  \n",
       "Horror              11     11         11  \n",
       "Mystery             12     12          8  \n",
       "Thriller             1      1          1  \n",
       "Western              4      4          4  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#genres.min()    #'min()' applys on string also(based on ASCII values)\n",
    "#genres.mean()   #avg\n",
    "#genres.mode()\n",
    "#genres.std()\n",
    "genres.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Genre\n",
       "Drama     3.540997e+10\n",
       "Action    3.263226e+10\n",
       "Comedy    1.566387e+10\n",
       "Name: Gross, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the top 3 genres by total earning\n",
    "\n",
    "movies.groupby('Genre')\n",
    "movies.groupby('Genre')['Gross']\n",
    "movies.groupby('Genre')['Gross'].sum()   #here its index sorted\n",
    "movies.groupby('Genre')['Gross'].sum().sort_values() #descending\n",
    "movies.groupby('Genre')['Gross'].sum().sort_values(ascending=False)\n",
    "movies.groupby('Genre')['Gross'].sum().sort_values(ascending=False).head(3) #top3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the genre with highest avg IMDB rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Genre\n",
       "Western    8.35\n",
       "Name: IMDB_Rating, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.groupby('Genre')['IMDB_Rating'].mean().sort_values(ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Director\n",
       "Christopher Nolan    11578345\n",
       "Name: No_of_Votes, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find director with most popularity\n",
    "movies.groupby('Director')['No_of_Votes'].sum().sort_values(ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the highest rated movie of each genre\n",
    "#movies.groupby('Genre')['IMDB_Rating'].max().......................solve later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Star1\n",
       "Tom Hanks             12\n",
       "Robert De Niro        11\n",
       "Clint Eastwood        10\n",
       "Al Pacino             10\n",
       "Leonardo DiCaprio      9\n",
       "                      ..\n",
       "Glen Hansard           1\n",
       "Giuseppe Battiston     1\n",
       "Giulietta Masina       1\n",
       "Gerardo Taracena       1\n",
       "Ömer Faruk Sorak       1\n",
       "Name: Series_Title, Length: 660, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find number of movies done by each actor\n",
    "# movies['Star1'].value_counts()\n",
    "\n",
    "movies.groupby('Star1')['Series_Title'].count().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GroupBy Attributes and Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find total number of groups -> len\n",
    "# find items in each group -> size\n",
    "# first()/last() -> nth item\n",
    "# get_group -> vs filtering\n",
    "# groups\n",
    "# describe\n",
    "# sample\n",
    "# nunique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find total number of groups -> len\n",
    "len(movies.groupby('Genre'))    #faster\n",
    "#or\n",
    "movies['Genre'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Genre\n",
       "Action       172\n",
       "Adventure     72\n",
       "Animation     82\n",
       "Biography     88\n",
       "Comedy       155\n",
       "Crime        107\n",
       "Drama        289\n",
       "Family         2\n",
       "Fantasy        2\n",
       "Film-Noir      3\n",
       "Horror        11\n",
       "Mystery       12\n",
       "Thriller       1\n",
       "Western        4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find items in each group -> size                 #preferred as this is groupby's built in function\n",
    "movies.groupby('Genre').size()               #sorted based on index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Drama        289\n",
       "Action       172\n",
       "Comedy       155\n",
       "Crime        107\n",
       "Biography     88\n",
       "Animation     82\n",
       "Adventure     72\n",
       "Mystery       12\n",
       "Horror        11\n",
       "Western        4\n",
       "Film-Noir      3\n",
       "Fantasy        2\n",
       "Family         2\n",
       "Thriller       1\n",
       "Name: Genre, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#or\n",
    "movies['Genre'].value_counts()   #sorted based on values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Series_Title</th>\n",
       "      <th>Released_Year</th>\n",
       "      <th>Runtime</th>\n",
       "      <th>IMDB_Rating</th>\n",
       "      <th>Director</th>\n",
       "      <th>Star1</th>\n",
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       "    <tr>\n",
       "      <th>Genre</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>152</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>Christian Bale</td>\n",
       "      <td>2303232</td>\n",
       "      <td>534858444.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>Interstellar</td>\n",
       "      <td>2014</td>\n",
       "      <td>169</td>\n",
       "      <td>8.6</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>Matthew McConaughey</td>\n",
       "      <td>1512360</td>\n",
       "      <td>188020017.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animation</th>\n",
       "      <td>Sen to Chihiro no kamikakushi</td>\n",
       "      <td>2001</td>\n",
       "      <td>125</td>\n",
       "      <td>8.6</td>\n",
       "      <td>Hayao Miyazaki</td>\n",
       "      <td>Daveigh Chase</td>\n",
       "      <td>651376</td>\n",
       "      <td>10055859.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biography</th>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>1993</td>\n",
       "      <td>195</td>\n",
       "      <td>8.9</td>\n",
       "      <td>Steven Spielberg</td>\n",
       "      <td>Liam Neeson</td>\n",
       "      <td>1213505</td>\n",
       "      <td>96898818.0</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>Gisaengchung</td>\n",
       "      <td>2019</td>\n",
       "      <td>132</td>\n",
       "      <td>8.6</td>\n",
       "      <td>Bong Joon Ho</td>\n",
       "      <td>Kang-ho Song</td>\n",
       "      <td>552778</td>\n",
       "      <td>53367844.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>1972</td>\n",
       "      <td>175</td>\n",
       "      <td>9.2</td>\n",
       "      <td>Francis Ford Coppola</td>\n",
       "      <td>Marlon Brando</td>\n",
       "      <td>1620367</td>\n",
       "      <td>134966411.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>142</td>\n",
       "      <td>9.3</td>\n",
       "      <td>Frank Darabont</td>\n",
       "      <td>Tim Robbins</td>\n",
       "      <td>2343110</td>\n",
       "      <td>28341469.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>E.T. the Extra-Terrestrial</td>\n",
       "      <td>1982</td>\n",
       "      <td>115</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Steven Spielberg</td>\n",
       "      <td>Henry Thomas</td>\n",
       "      <td>372490</td>\n",
       "      <td>435110554.0</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <td>Das Cabinet des Dr. Caligari</td>\n",
       "      <td>1920</td>\n",
       "      <td>76</td>\n",
       "      <td>8.1</td>\n",
       "      <td>Robert Wiene</td>\n",
       "      <td>Werner Krauss</td>\n",
       "      <td>57428</td>\n",
       "      <td>337574718.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film-Noir</th>\n",
       "      <td>The Third Man</td>\n",
       "      <td>1949</td>\n",
       "      <td>104</td>\n",
       "      <td>8.1</td>\n",
       "      <td>Carol Reed</td>\n",
       "      <td>Orson Welles</td>\n",
       "      <td>158731</td>\n",
       "      <td>449191.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horror</th>\n",
       "      <td>Psycho</td>\n",
       "      <td>1960</td>\n",
       "      <td>109</td>\n",
       "      <td>8.5</td>\n",
       "      <td>Alfred Hitchcock</td>\n",
       "      <td>Anthony Perkins</td>\n",
       "      <td>604211</td>\n",
       "      <td>32000000.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mystery</th>\n",
       "      <td>Memento</td>\n",
       "      <td>2000</td>\n",
       "      <td>113</td>\n",
       "      <td>8.4</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>Guy Pearce</td>\n",
       "      <td>1125712</td>\n",
       "      <td>25544867.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>Wait Until Dark</td>\n",
       "      <td>1967</td>\n",
       "      <td>108</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>Audrey Hepburn</td>\n",
       "      <td>27733</td>\n",
       "      <td>17550741.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western</th>\n",
       "      <td>Il buono, il brutto, il cattivo</td>\n",
       "      <td>1966</td>\n",
       "      <td>161</td>\n",
       "      <td>8.8</td>\n",
       "      <td>Sergio Leone</td>\n",
       "      <td>Clint Eastwood</td>\n",
       "      <td>688390</td>\n",
       "      <td>6100000.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Series_Title Released_Year  Runtime  \\\n",
       "Genre                                                               \n",
       "Action                     The Dark Knight          2008      152   \n",
       "Adventure                     Interstellar          2014      169   \n",
       "Animation    Sen to Chihiro no kamikakushi          2001      125   \n",
       "Biography                 Schindler's List          1993      195   \n",
       "Comedy                        Gisaengchung          2019      132   \n",
       "Crime                        The Godfather          1972      175   \n",
       "Drama             The Shawshank Redemption          1994      142   \n",
       "Family          E.T. the Extra-Terrestrial          1982      115   \n",
       "Fantasy       Das Cabinet des Dr. Caligari          1920       76   \n",
       "Film-Noir                    The Third Man          1949      104   \n",
       "Horror                              Psycho          1960      109   \n",
       "Mystery                            Memento          2000      113   \n",
       "Thriller                   Wait Until Dark          1967      108   \n",
       "Western    Il buono, il brutto, il cattivo          1966      161   \n",
       "\n",
       "           IMDB_Rating              Director                Star1  \\\n",
       "Genre                                                               \n",
       "Action             9.0     Christopher Nolan       Christian Bale   \n",
       "Adventure          8.6     Christopher Nolan  Matthew McConaughey   \n",
       "Animation          8.6        Hayao Miyazaki        Daveigh Chase   \n",
       "Biography          8.9      Steven Spielberg          Liam Neeson   \n",
       "Comedy             8.6          Bong Joon Ho         Kang-ho Song   \n",
       "Crime              9.2  Francis Ford Coppola        Marlon Brando   \n",
       "Drama              9.3        Frank Darabont          Tim Robbins   \n",
       "Family             7.8      Steven Spielberg         Henry Thomas   \n",
       "Fantasy            8.1          Robert Wiene        Werner Krauss   \n",
       "Film-Noir          8.1            Carol Reed         Orson Welles   \n",
       "Horror             8.5      Alfred Hitchcock      Anthony Perkins   \n",
       "Mystery            8.4     Christopher Nolan           Guy Pearce   \n",
       "Thriller           7.8         Terence Young       Audrey Hepburn   \n",
       "Western            8.8          Sergio Leone       Clint Eastwood   \n",
       "\n",
       "           No_of_Votes        Gross  Metascore  \n",
       "Genre                                           \n",
       "Action         2303232  534858444.0       84.0  \n",
       "Adventure      1512360  188020017.0       74.0  \n",
       "Animation       651376   10055859.0       96.0  \n",
       "Biography      1213505   96898818.0       94.0  \n",
       "Comedy          552778   53367844.0       96.0  \n",
       "Crime          1620367  134966411.0      100.0  \n",
       "Drama          2343110   28341469.0       80.0  \n",
       "Family          372490  435110554.0       91.0  \n",
       "Fantasy          57428  337574718.0        NaN  \n",
       "Film-Noir       158731     449191.0       97.0  \n",
       "Horror          604211   32000000.0       97.0  \n",
       "Mystery        1125712   25544867.0       80.0  \n",
       "Thriller         27733   17550741.0       81.0  \n",
       "Western         688390    6100000.0       90.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.groupby('Genre').first()  #gives first movie in each (genre) 14 groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Action</th>\n",
       "      <td>Escape from Alcatraz</td>\n",
       "      <td>1979</td>\n",
       "      <td>112</td>\n",
       "      <td>7.6</td>\n",
       "      <td>Don Siegel</td>\n",
       "      <td>Clint Eastwood</td>\n",
       "      <td>121731</td>\n",
       "      <td>43000000.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>Kelly's Heroes</td>\n",
       "      <td>1970</td>\n",
       "      <td>144</td>\n",
       "      <td>7.6</td>\n",
       "      <td>Brian G. Hutton</td>\n",
       "      <td>Clint Eastwood</td>\n",
       "      <td>45338</td>\n",
       "      <td>1378435.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animation</th>\n",
       "      <td>The Jungle Book</td>\n",
       "      <td>1967</td>\n",
       "      <td>78</td>\n",
       "      <td>7.6</td>\n",
       "      <td>Wolfgang Reitherman</td>\n",
       "      <td>Phil Harris</td>\n",
       "      <td>166409</td>\n",
       "      <td>141843612.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biography</th>\n",
       "      <td>Midnight Express</td>\n",
       "      <td>1978</td>\n",
       "      <td>121</td>\n",
       "      <td>7.6</td>\n",
       "      <td>Alan Parker</td>\n",
       "      <td>Brad Davis</td>\n",
       "      <td>73662</td>\n",
       "      <td>35000000.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>Breakfast at Tiffany's</td>\n",
       "      <td>1961</td>\n",
       "      <td>115</td>\n",
       "      <td>7.6</td>\n",
       "      <td>Blake Edwards</td>\n",
       "      <td>Audrey Hepburn</td>\n",
       "      <td>166544</td>\n",
       "      <td>679874270.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>The 39 Steps</td>\n",
       "      <td>1935</td>\n",
       "      <td>86</td>\n",
       "      <td>7.6</td>\n",
       "      <td>Alfred Hitchcock</td>\n",
       "      <td>Robert Donat</td>\n",
       "      <td>51853</td>\n",
       "      <td>302787539.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>Lifeboat</td>\n",
       "      <td>1944</td>\n",
       "      <td>97</td>\n",
       "      <td>7.6</td>\n",
       "      <td>Alfred Hitchcock</td>\n",
       "      <td>Tallulah Bankhead</td>\n",
       "      <td>26471</td>\n",
       "      <td>852142728.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>Willy Wonka &amp; the Chocolate Factory</td>\n",
       "      <td>1971</td>\n",
       "      <td>100</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Mel Stuart</td>\n",
       "      <td>Gene Wilder</td>\n",
       "      <td>178731</td>\n",
       "      <td>4000000.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <td>Nosferatu</td>\n",
       "      <td>1922</td>\n",
       "      <td>94</td>\n",
       "      <td>7.9</td>\n",
       "      <td>F.W. Murnau</td>\n",
       "      <td>Max Schreck</td>\n",
       "      <td>88794</td>\n",
       "      <td>445151978.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film-Noir</th>\n",
       "      <td>Shadow of a Doubt</td>\n",
       "      <td>1943</td>\n",
       "      <td>108</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Alfred Hitchcock</td>\n",
       "      <td>Teresa Wright</td>\n",
       "      <td>59556</td>\n",
       "      <td>123353292.0</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horror</th>\n",
       "      <td>The Others</td>\n",
       "      <td>2001</td>\n",
       "      <td>101</td>\n",
       "      <td>7.6</td>\n",
       "      <td>Alejandro Amenábar</td>\n",
       "      <td>Nicole Kidman</td>\n",
       "      <td>337651</td>\n",
       "      <td>96522687.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mystery</th>\n",
       "      <td>Lost Highway</td>\n",
       "      <td>1997</td>\n",
       "      <td>134</td>\n",
       "      <td>7.6</td>\n",
       "      <td>David Lynch</td>\n",
       "      <td>Bill Pullman</td>\n",
       "      <td>131101</td>\n",
       "      <td>3796699.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>Wait Until Dark</td>\n",
       "      <td>1967</td>\n",
       "      <td>108</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>Audrey Hepburn</td>\n",
       "      <td>27733</td>\n",
       "      <td>17550741.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western</th>\n",
       "      <td>The Outlaw Josey Wales</td>\n",
       "      <td>1976</td>\n",
       "      <td>135</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Clint Eastwood</td>\n",
       "      <td>Clint Eastwood</td>\n",
       "      <td>65659</td>\n",
       "      <td>31800000.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Series_Title Released_Year  Runtime  \\\n",
       "Genre                                                                   \n",
       "Action                    Escape from Alcatraz          1979      112   \n",
       "Adventure                       Kelly's Heroes          1970      144   \n",
       "Animation                      The Jungle Book          1967       78   \n",
       "Biography                     Midnight Express          1978      121   \n",
       "Comedy                  Breakfast at Tiffany's          1961      115   \n",
       "Crime                             The 39 Steps          1935       86   \n",
       "Drama                                 Lifeboat          1944       97   \n",
       "Family     Willy Wonka & the Chocolate Factory          1971      100   \n",
       "Fantasy                              Nosferatu          1922       94   \n",
       "Film-Noir                    Shadow of a Doubt          1943      108   \n",
       "Horror                              The Others          2001      101   \n",
       "Mystery                           Lost Highway          1997      134   \n",
       "Thriller                       Wait Until Dark          1967      108   \n",
       "Western                 The Outlaw Josey Wales          1976      135   \n",
       "\n",
       "           IMDB_Rating             Director              Star1  No_of_Votes  \\\n",
       "Genre                                                                         \n",
       "Action             7.6           Don Siegel     Clint Eastwood       121731   \n",
       "Adventure          7.6      Brian G. Hutton     Clint Eastwood        45338   \n",
       "Animation          7.6  Wolfgang Reitherman        Phil Harris       166409   \n",
       "Biography          7.6          Alan Parker         Brad Davis        73662   \n",
       "Comedy             7.6        Blake Edwards     Audrey Hepburn       166544   \n",
       "Crime              7.6     Alfred Hitchcock       Robert Donat        51853   \n",
       "Drama              7.6     Alfred Hitchcock  Tallulah Bankhead        26471   \n",
       "Family             7.8           Mel Stuart        Gene Wilder       178731   \n",
       "Fantasy            7.9          F.W. Murnau        Max Schreck        88794   \n",
       "Film-Noir          7.8     Alfred Hitchcock      Teresa Wright        59556   \n",
       "Horror             7.6   Alejandro Amenábar      Nicole Kidman       337651   \n",
       "Mystery            7.6          David Lynch       Bill Pullman       131101   \n",
       "Thriller           7.8        Terence Young     Audrey Hepburn        27733   \n",
       "Western            7.8       Clint Eastwood     Clint Eastwood        65659   \n",
       "\n",
       "                 Gross  Metascore  \n",
       "Genre                              \n",
       "Action      43000000.0       76.0  \n",
       "Adventure    1378435.0       50.0  \n",
       "Animation  141843612.0       65.0  \n",
       "Biography   35000000.0       59.0  \n",
       "Comedy     679874270.0       76.0  \n",
       "Crime      302787539.0       93.0  \n",
       "Drama      852142728.0       78.0  \n",
       "Family       4000000.0       67.0  \n",
       "Fantasy    445151978.0        NaN  \n",
       "Film-Noir  123353292.0       94.0  \n",
       "Horror      96522687.0       74.0  \n",
       "Mystery      3796699.0       52.0  \n",
       "Thriller    17550741.0       81.0  \n",
       "Western     31800000.0       69.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.groupby('Genre').last()  #gives last movie in each (genre) 14 groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Series_Title</th>\n",
       "      <th>Released_Year</th>\n",
       "      <th>Runtime</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Genre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>Star Wars: Episode V - The Empire Strikes Back</td>\n",
       "      <td>1980</td>\n",
       "      <td>124</td>\n",
       "      <td>8.7</td>\n",
       "      <td>Irvin Kershner</td>\n",
       "      <td>Mark Hamill</td>\n",
       "      <td>1159315</td>\n",
       "      <td>290475067.0</td>\n",
       "      <td>82.0</td>\n",
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       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>North by Northwest</td>\n",
       "      <td>1959</td>\n",
       "      <td>136</td>\n",
       "      <td>8.3</td>\n",
       "      <td>Alfred Hitchcock</td>\n",
       "      <td>Cary Grant</td>\n",
       "      <td>299198</td>\n",
       "      <td>13275000.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animation</th>\n",
       "      <td>WALL·E</td>\n",
       "      <td>2008</td>\n",
       "      <td>98</td>\n",
       "      <td>8.4</td>\n",
       "      <td>Andrew Stanton</td>\n",
       "      <td>Ben Burtt</td>\n",
       "      <td>999790</td>\n",
       "      <td>223808164.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biography</th>\n",
       "      <td>Braveheart</td>\n",
       "      <td>1995</td>\n",
       "      <td>178</td>\n",
       "      <td>8.3</td>\n",
       "      <td>Mel Gibson</td>\n",
       "      <td>Mel Gibson</td>\n",
       "      <td>959181</td>\n",
       "      <td>75600000.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>The Great Dictator</td>\n",
       "      <td>1940</td>\n",
       "      <td>125</td>\n",
       "      <td>8.4</td>\n",
       "      <td>Charles Chaplin</td>\n",
       "      <td>Charles Chaplin</td>\n",
       "      <td>203150</td>\n",
       "      <td>288475.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>Se7en</td>\n",
       "      <td>1995</td>\n",
       "      <td>127</td>\n",
       "      <td>8.6</td>\n",
       "      <td>David Fincher</td>\n",
       "      <td>Morgan Freeman</td>\n",
       "      <td>1445096</td>\n",
       "      <td>100125643.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>It's a Wonderful Life</td>\n",
       "      <td>1946</td>\n",
       "      <td>130</td>\n",
       "      <td>8.6</td>\n",
       "      <td>Frank Capra</td>\n",
       "      <td>James Stewart</td>\n",
       "      <td>405801</td>\n",
       "      <td>82385199.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horror</th>\n",
       "      <td>Get Out</td>\n",
       "      <td>2017</td>\n",
       "      <td>104</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Jordan Peele</td>\n",
       "      <td>Daniel Kaluuya</td>\n",
       "      <td>492851</td>\n",
       "      <td>176040665.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mystery</th>\n",
       "      <td>Sleuth</td>\n",
       "      <td>1972</td>\n",
       "      <td>138</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Joseph L. Mankiewicz</td>\n",
       "      <td>Laurence Olivier</td>\n",
       "      <td>44748</td>\n",
       "      <td>4081254.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Series_Title Released_Year  \\\n",
       "Genre                                                                     \n",
       "Action     Star Wars: Episode V - The Empire Strikes Back          1980   \n",
       "Adventure                              North by Northwest          1959   \n",
       "Animation                                          WALL·E          2008   \n",
       "Biography                                      Braveheart          1995   \n",
       "Comedy                                 The Great Dictator          1940   \n",
       "Crime                                               Se7en          1995   \n",
       "Drama                               It's a Wonderful Life          1946   \n",
       "Horror                                            Get Out          2017   \n",
       "Mystery                                            Sleuth          1972   \n",
       "\n",
       "           Runtime  IMDB_Rating              Director             Star1  \\\n",
       "Genre                                                                     \n",
       "Action         124          8.7        Irvin Kershner       Mark Hamill   \n",
       "Adventure      136          8.3      Alfred Hitchcock        Cary Grant   \n",
       "Animation       98          8.4        Andrew Stanton         Ben Burtt   \n",
       "Biography      178          8.3            Mel Gibson        Mel Gibson   \n",
       "Comedy         125          8.4       Charles Chaplin   Charles Chaplin   \n",
       "Crime          127          8.6         David Fincher    Morgan Freeman   \n",
       "Drama          130          8.6           Frank Capra     James Stewart   \n",
       "Horror         104          7.7          Jordan Peele    Daniel Kaluuya   \n",
       "Mystery        138          8.0  Joseph L. Mankiewicz  Laurence Olivier   \n",
       "\n",
       "           No_of_Votes        Gross  Metascore  \n",
       "Genre                                           \n",
       "Action         1159315  290475067.0       82.0  \n",
       "Adventure       299198   13275000.0       98.0  \n",
       "Animation       999790  223808164.0       95.0  \n",
       "Biography       959181   75600000.0       68.0  \n",
       "Comedy          203150     288475.0        NaN  \n",
       "Crime          1445096  100125643.0       65.0  \n",
       "Drama           405801   82385199.0       89.0  \n",
       "Horror          492851  176040665.0       85.0  \n",
       "Mystery          44748    4081254.0        NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.groupby('Genre').nth(6)  #7th item in each group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Series_Title</th>\n",
       "      <th>Released_Year</th>\n",
       "      <th>Runtime</th>\n",
       "      <th>Genre</th>\n",
       "      <th>IMDB_Rating</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Psycho</td>\n",
       "      <td>1960</td>\n",
       "      <td>109</td>\n",
       "      <td>Horror</td>\n",
       "      <td>8.5</td>\n",
       "      <td>Alfred Hitchcock</td>\n",
       "      <td>Anthony Perkins</td>\n",
       "      <td>604211</td>\n",
       "      <td>32000000.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Alien</td>\n",
       "      <td>1979</td>\n",
       "      <td>117</td>\n",
       "      <td>Horror</td>\n",
       "      <td>8.4</td>\n",
       "      <td>Ridley Scott</td>\n",
       "      <td>Sigourney Weaver</td>\n",
       "      <td>787806</td>\n",
       "      <td>78900000.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>The Thing</td>\n",
       "      <td>1982</td>\n",
       "      <td>109</td>\n",
       "      <td>Horror</td>\n",
       "      <td>8.1</td>\n",
       "      <td>John Carpenter</td>\n",
       "      <td>Kurt Russell</td>\n",
       "      <td>371271</td>\n",
       "      <td>13782838.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>The Exorcist</td>\n",
       "      <td>1973</td>\n",
       "      <td>122</td>\n",
       "      <td>Horror</td>\n",
       "      <td>8.0</td>\n",
       "      <td>William Friedkin</td>\n",
       "      <td>Ellen Burstyn</td>\n",
       "      <td>362393</td>\n",
       "      <td>232906145.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>Night of the Living Dead</td>\n",
       "      <td>1968</td>\n",
       "      <td>96</td>\n",
       "      <td>Horror</td>\n",
       "      <td>7.9</td>\n",
       "      <td>George A. Romero</td>\n",
       "      <td>Duane Jones</td>\n",
       "      <td>116557</td>\n",
       "      <td>89029.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>The Innocents</td>\n",
       "      <td>1961</td>\n",
       "      <td>100</td>\n",
       "      <td>Horror</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Jack Clayton</td>\n",
       "      <td>Deborah Kerr</td>\n",
       "      <td>27007</td>\n",
       "      <td>2616000.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>Get Out</td>\n",
       "      <td>2017</td>\n",
       "      <td>104</td>\n",
       "      <td>Horror</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Jordan Peele</td>\n",
       "      <td>Daniel Kaluuya</td>\n",
       "      <td>492851</td>\n",
       "      <td>176040665.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>844</th>\n",
       "      <td>Halloween</td>\n",
       "      <td>1978</td>\n",
       "      <td>91</td>\n",
       "      <td>Horror</td>\n",
       "      <td>7.7</td>\n",
       "      <td>John Carpenter</td>\n",
       "      <td>Donald Pleasence</td>\n",
       "      <td>233106</td>\n",
       "      <td>47000000.0</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>The Invisible Man</td>\n",
       "      <td>1933</td>\n",
       "      <td>71</td>\n",
       "      <td>Horror</td>\n",
       "      <td>7.7</td>\n",
       "      <td>James Whale</td>\n",
       "      <td>Claude Rains</td>\n",
       "      <td>30683</td>\n",
       "      <td>298791505.0</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>Saw</td>\n",
       "      <td>2004</td>\n",
       "      <td>103</td>\n",
       "      <td>Horror</td>\n",
       "      <td>7.6</td>\n",
       "      <td>James Wan</td>\n",
       "      <td>Cary Elwes</td>\n",
       "      <td>379020</td>\n",
       "      <td>56000369.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>The Others</td>\n",
       "      <td>2001</td>\n",
       "      <td>101</td>\n",
       "      <td>Horror</td>\n",
       "      <td>7.6</td>\n",
       "      <td>Alejandro Amenábar</td>\n",
       "      <td>Nicole Kidman</td>\n",
       "      <td>337651</td>\n",
       "      <td>96522687.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Series_Title Released_Year  Runtime   Genre  IMDB_Rating  \\\n",
       "49                     Psycho          1960      109  Horror          8.5   \n",
       "75                      Alien          1979      117  Horror          8.4   \n",
       "271                 The Thing          1982      109  Horror          8.1   \n",
       "419              The Exorcist          1973      122  Horror          8.0   \n",
       "544  Night of the Living Dead          1968       96  Horror          7.9   \n",
       "707             The Innocents          1961      100  Horror          7.8   \n",
       "724                   Get Out          2017      104  Horror          7.7   \n",
       "844                 Halloween          1978       91  Horror          7.7   \n",
       "876         The Invisible Man          1933       71  Horror          7.7   \n",
       "932                       Saw          2004      103  Horror          7.6   \n",
       "948                The Others          2001      101  Horror          7.6   \n",
       "\n",
       "               Director             Star1  No_of_Votes        Gross  Metascore  \n",
       "49     Alfred Hitchcock   Anthony Perkins       604211   32000000.0       97.0  \n",
       "75         Ridley Scott  Sigourney Weaver       787806   78900000.0       89.0  \n",
       "271      John Carpenter      Kurt Russell       371271   13782838.0       57.0  \n",
       "419    William Friedkin     Ellen Burstyn       362393  232906145.0       81.0  \n",
       "544    George A. Romero       Duane Jones       116557      89029.0       89.0  \n",
       "707        Jack Clayton      Deborah Kerr        27007    2616000.0       88.0  \n",
       "724        Jordan Peele    Daniel Kaluuya       492851  176040665.0       85.0  \n",
       "844      John Carpenter  Donald Pleasence       233106   47000000.0       87.0  \n",
       "876         James Whale      Claude Rains        30683  298791505.0       87.0  \n",
       "932           James Wan        Cary Elwes       379020   56000369.0       46.0  \n",
       "948  Alejandro Amenábar     Nicole Kidman       337651   96522687.0       74.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to get all items in a particular group\n",
    "movies.groupby('Genre').get_group('Horror')       #faster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Series_Title</th>\n",
       "      <th>Released_Year</th>\n",
       "      <th>Runtime</th>\n",
       "      <th>Genre</th>\n",
       "      <th>IMDB_Rating</th>\n",
       "      <th>Director</th>\n",
       "      <th>Star1</th>\n",
       "      <th>No_of_Votes</th>\n",
       "      <th>Gross</th>\n",
       "      <th>Metascore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Psycho</td>\n",
       "      <td>1960</td>\n",
       "      <td>109</td>\n",
       "      <td>Horror</td>\n",
       "      <td>8.5</td>\n",
       "      <td>Alfred Hitchcock</td>\n",
       "      <td>Anthony Perkins</td>\n",
       "      <td>604211</td>\n",
       "      <td>32000000.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Alien</td>\n",
       "      <td>1979</td>\n",
       "      <td>117</td>\n",
       "      <td>Horror</td>\n",
       "      <td>8.4</td>\n",
       "      <td>Ridley Scott</td>\n",
       "      <td>Sigourney Weaver</td>\n",
       "      <td>787806</td>\n",
       "      <td>78900000.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>The Thing</td>\n",
       "      <td>1982</td>\n",
       "      <td>109</td>\n",
       "      <td>Horror</td>\n",
       "      <td>8.1</td>\n",
       "      <td>John Carpenter</td>\n",
       "      <td>Kurt Russell</td>\n",
       "      <td>371271</td>\n",
       "      <td>13782838.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>The Exorcist</td>\n",
       "      <td>1973</td>\n",
       "      <td>122</td>\n",
       "      <td>Horror</td>\n",
       "      <td>8.0</td>\n",
       "      <td>William Friedkin</td>\n",
       "      <td>Ellen Burstyn</td>\n",
       "      <td>362393</td>\n",
       "      <td>232906145.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>Night of the Living Dead</td>\n",
       "      <td>1968</td>\n",
       "      <td>96</td>\n",
       "      <td>Horror</td>\n",
       "      <td>7.9</td>\n",
       "      <td>George A. Romero</td>\n",
       "      <td>Duane Jones</td>\n",
       "      <td>116557</td>\n",
       "      <td>89029.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>The Innocents</td>\n",
       "      <td>1961</td>\n",
       "      <td>100</td>\n",
       "      <td>Horror</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Jack Clayton</td>\n",
       "      <td>Deborah Kerr</td>\n",
       "      <td>27007</td>\n",
       "      <td>2616000.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>Get Out</td>\n",
       "      <td>2017</td>\n",
       "      <td>104</td>\n",
       "      <td>Horror</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Jordan Peele</td>\n",
       "      <td>Daniel Kaluuya</td>\n",
       "      <td>492851</td>\n",
       "      <td>176040665.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>844</th>\n",
       "      <td>Halloween</td>\n",
       "      <td>1978</td>\n",
       "      <td>91</td>\n",
       "      <td>Horror</td>\n",
       "      <td>7.7</td>\n",
       "      <td>John Carpenter</td>\n",
       "      <td>Donald Pleasence</td>\n",
       "      <td>233106</td>\n",
       "      <td>47000000.0</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>The Invisible Man</td>\n",
       "      <td>1933</td>\n",
       "      <td>71</td>\n",
       "      <td>Horror</td>\n",
       "      <td>7.7</td>\n",
       "      <td>James Whale</td>\n",
       "      <td>Claude Rains</td>\n",
       "      <td>30683</td>\n",
       "      <td>298791505.0</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>Saw</td>\n",
       "      <td>2004</td>\n",
       "      <td>103</td>\n",
       "      <td>Horror</td>\n",
       "      <td>7.6</td>\n",
       "      <td>James Wan</td>\n",
       "      <td>Cary Elwes</td>\n",
       "      <td>379020</td>\n",
       "      <td>56000369.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>The Others</td>\n",
       "      <td>2001</td>\n",
       "      <td>101</td>\n",
       "      <td>Horror</td>\n",
       "      <td>7.6</td>\n",
       "      <td>Alejandro Amenábar</td>\n",
       "      <td>Nicole Kidman</td>\n",
       "      <td>337651</td>\n",
       "      <td>96522687.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Series_Title Released_Year  Runtime   Genre  IMDB_Rating  \\\n",
       "49                     Psycho          1960      109  Horror          8.5   \n",
       "75                      Alien          1979      117  Horror          8.4   \n",
       "271                 The Thing          1982      109  Horror          8.1   \n",
       "419              The Exorcist          1973      122  Horror          8.0   \n",
       "544  Night of the Living Dead          1968       96  Horror          7.9   \n",
       "707             The Innocents          1961      100  Horror          7.8   \n",
       "724                   Get Out          2017      104  Horror          7.7   \n",
       "844                 Halloween          1978       91  Horror          7.7   \n",
       "876         The Invisible Man          1933       71  Horror          7.7   \n",
       "932                       Saw          2004      103  Horror          7.6   \n",
       "948                The Others          2001      101  Horror          7.6   \n",
       "\n",
       "               Director             Star1  No_of_Votes        Gross  Metascore  \n",
       "49     Alfred Hitchcock   Anthony Perkins       604211   32000000.0       97.0  \n",
       "75         Ridley Scott  Sigourney Weaver       787806   78900000.0       89.0  \n",
       "271      John Carpenter      Kurt Russell       371271   13782838.0       57.0  \n",
       "419    William Friedkin     Ellen Burstyn       362393  232906145.0       81.0  \n",
       "544    George A. Romero       Duane Jones       116557      89029.0       89.0  \n",
       "707        Jack Clayton      Deborah Kerr        27007    2616000.0       88.0  \n",
       "724        Jordan Peele    Daniel Kaluuya       492851  176040665.0       85.0  \n",
       "844      John Carpenter  Donald Pleasence       233106   47000000.0       87.0  \n",
       "876         James Whale      Claude Rains        30683  298791505.0       87.0  \n",
       "932           James Wan        Cary Elwes       379020   56000369.0       46.0  \n",
       "948  Alejandro Amenábar     Nicole Kidman       337651   96522687.0       74.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#or\n",
    "movies[movies['Genre'] == 'Horror']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Action': [2, 5, 8, 10, 13, 14, 16, 29, 30, 31, 39, 42, 44, 55, 57, 59, 60, 63, 68, 72, 106, 109, 129, 130, 134, 140, 142, 144, 152, 155, 160, 161, 166, 168, 171, 172, 177, 181, 194, 201, 202, 216, 217, 223, 224, 236, 241, 262, 275, 294, 308, 320, 325, 326, 331, 337, 339, 340, 343, 345, 348, 351, 353, 356, 357, 362, 368, 369, 375, 376, 390, 410, 431, 436, 473, 477, 479, 482, 488, 493, 496, 502, 507, 511, 532, 535, 540, 543, 564, 569, 570, 573, 577, 582, 583, 602, 605, 608, 615, 623, ...], 'Adventure': [21, 47, 93, 110, 114, 116, 118, 137, 178, 179, 191, 193, 209, 226, 231, 247, 267, 273, 281, 300, 301, 304, 306, 323, 329, 361, 366, 377, 402, 406, 415, 426, 458, 470, 497, 498, 506, 513, 514, 537, 549, 552, 553, 566, 576, 604, 609, 618, 638, 647, 675, 681, 686, 692, 711, 713, 739, 755, 781, 797, 798, 851, 873, 884, 912, 919, 947, 957, 964, 966, 984, 991], 'Animation': [23, 43, 46, 56, 58, 61, 66, 70, 101, 135, 146, 151, 158, 170, 197, 205, 211, 213, 219, 229, 230, 242, 245, 246, 270, 330, 332, 358, 367, 378, 386, 389, 394, 395, 399, 401, 405, 409, 469, 499, 510, 516, 518, 522, 578, 586, 592, 595, 596, 599, 633, 640, 643, 651, 665, 672, 694, 728, 740, 741, 744, 756, 758, 761, 771, 783, 796, 799, 822, 828, 843, 875, 891, 892, 902, 906, 920, 956, 971, 976, 986, 992], 'Biography': [7, 15, 18, 35, 38, 54, 102, 107, 131, 139, 147, 157, 159, 173, 176, 212, 215, 218, 228, 235, 243, 263, 276, 282, 290, 298, 317, 328, 338, 342, 346, 359, 360, 365, 372, 373, 385, 411, 416, 418, 424, 429, 484, 525, 536, 542, 545, 575, 579, 587, 600, 606, 614, 622, 632, 635, 644, 649, 650, 657, 671, 673, 684, 729, 748, 753, 757, 759, 766, 770, 779, 809, 810, 815, 820, 831, 849, 858, 877, 882, 897, 910, 915, 923, 940, 949, 952, 987], 'Comedy': [19, 26, 51, 52, 64, 78, 83, 95, 96, 112, 117, 120, 127, 128, 132, 153, 169, 183, 192, 204, 207, 208, 214, 221, 233, 238, 240, 250, 251, 252, 256, 261, 266, 277, 284, 311, 313, 316, 318, 322, 327, 374, 379, 381, 392, 396, 403, 413, 414, 417, 427, 435, 445, 446, 449, 455, 459, 460, 463, 464, 466, 471, 472, 475, 481, 490, 494, 500, 503, 509, 526, 528, 530, 531, 533, 538, 539, 541, 547, 557, 558, 562, 563, 565, 574, 591, 593, 594, 598, 613, 626, 630, 660, 662, 667, 679, 680, 683, 687, 701, ...], 'Crime': [1, 3, 4, 6, 22, 25, 27, 28, 33, 37, 41, 71, 77, 79, 86, 87, 103, 108, 111, 113, 123, 125, 133, 136, 162, 163, 164, 165, 180, 186, 187, 189, 198, 222, 232, 239, 255, 257, 287, 288, 299, 305, 335, 363, 364, 380, 384, 397, 437, 438, 441, 442, 444, 450, 451, 465, 474, 480, 485, 487, 505, 512, 519, 520, 523, 527, 546, 556, 560, 584, 597, 603, 607, 611, 621, 639, 653, 664, 669, 676, 695, 708, 723, 762, 763, 767, 775, 791, 795, 802, 811, 823, 827, 833, 885, 895, 921, 922, 926, 938, ...], 'Drama': [0, 9, 11, 17, 20, 24, 32, 34, 36, 40, 45, 50, 53, 62, 65, 67, 73, 74, 76, 80, 82, 84, 85, 88, 89, 90, 91, 92, 94, 97, 98, 99, 100, 104, 105, 121, 122, 124, 126, 138, 141, 143, 148, 149, 150, 154, 156, 167, 174, 175, 182, 184, 185, 188, 190, 195, 196, 199, 200, 203, 206, 210, 225, 227, 234, 237, 244, 248, 249, 253, 254, 258, 259, 260, 264, 265, 268, 269, 272, 274, 278, 279, 280, 283, 285, 286, 289, 291, 292, 293, 295, 296, 297, 302, 303, 307, 310, 312, 314, 315, ...], 'Family': [688, 698], 'Fantasy': [321, 568], 'Film-Noir': [309, 456, 712], 'Horror': [49, 75, 271, 419, 544, 707, 724, 844, 876, 932, 948], 'Mystery': [69, 81, 119, 145, 220, 393, 420, 714, 829, 899, 959, 961], 'Thriller': [700], 'Western': [12, 48, 115, 691]}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#groups\n",
    "movies.groupby('Genre').groups  #gives a dict-- keys=name of group,value=list of index positions of corresponding movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead tr th {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
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       "    <tr>\n",
       "      <th>Genre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>172.0</td>\n",
       "      <td>129.046512</td>\n",
       "      <td>28.500706</td>\n",
       "      <td>45.0</td>\n",
       "      <td>110.75</td>\n",
       "      <td>127.5</td>\n",
       "      <td>143.25</td>\n",
       "      <td>321.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>7.949419</td>\n",
       "      <td>...</td>\n",
       "      <td>2.674437e+08</td>\n",
       "      <td>936662225.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>73.419580</td>\n",
       "      <td>12.421252</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.00</td>\n",
       "      <td>74.0</td>\n",
       "      <td>82.00</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>72.0</td>\n",
       "      <td>134.111111</td>\n",
       "      <td>33.317320</td>\n",
       "      <td>88.0</td>\n",
       "      <td>109.00</td>\n",
       "      <td>127.0</td>\n",
       "      <td>149.00</td>\n",
       "      <td>228.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>7.937500</td>\n",
       "      <td>...</td>\n",
       "      <td>1.998070e+08</td>\n",
       "      <td>874211619.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>78.437500</td>\n",
       "      <td>12.345393</td>\n",
       "      <td>41.0</td>\n",
       "      <td>69.75</td>\n",
       "      <td>80.5</td>\n",
       "      <td>87.25</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animation</th>\n",
       "      <td>82.0</td>\n",
       "      <td>99.585366</td>\n",
       "      <td>14.530471</td>\n",
       "      <td>71.0</td>\n",
       "      <td>90.00</td>\n",
       "      <td>99.5</td>\n",
       "      <td>106.75</td>\n",
       "      <td>137.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>7.930488</td>\n",
       "      <td>...</td>\n",
       "      <td>2.520612e+08</td>\n",
       "      <td>873839108.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>81.093333</td>\n",
       "      <td>8.813646</td>\n",
       "      <td>61.0</td>\n",
       "      <td>75.00</td>\n",
       "      <td>82.0</td>\n",
       "      <td>87.50</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biography</th>\n",
       "      <td>88.0</td>\n",
       "      <td>136.022727</td>\n",
       "      <td>25.514466</td>\n",
       "      <td>93.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>129.0</td>\n",
       "      <td>146.25</td>\n",
       "      <td>209.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>7.938636</td>\n",
       "      <td>...</td>\n",
       "      <td>9.829924e+07</td>\n",
       "      <td>753585104.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>76.240506</td>\n",
       "      <td>11.028187</td>\n",
       "      <td>48.0</td>\n",
       "      <td>70.50</td>\n",
       "      <td>76.0</td>\n",
       "      <td>84.50</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>155.0</td>\n",
       "      <td>112.129032</td>\n",
       "      <td>22.946213</td>\n",
       "      <td>68.0</td>\n",
       "      <td>96.00</td>\n",
       "      <td>106.0</td>\n",
       "      <td>124.50</td>\n",
       "      <td>188.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>7.901290</td>\n",
       "      <td>...</td>\n",
       "      <td>8.107809e+07</td>\n",
       "      <td>886752933.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>78.720000</td>\n",
       "      <td>11.829160</td>\n",
       "      <td>45.0</td>\n",
       "      <td>72.00</td>\n",
       "      <td>79.0</td>\n",
       "      <td>88.00</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>107.0</td>\n",
       "      <td>126.392523</td>\n",
       "      <td>27.689231</td>\n",
       "      <td>80.0</td>\n",
       "      <td>106.50</td>\n",
       "      <td>122.0</td>\n",
       "      <td>141.50</td>\n",
       "      <td>229.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>8.016822</td>\n",
       "      <td>...</td>\n",
       "      <td>7.102163e+07</td>\n",
       "      <td>790482117.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>77.080460</td>\n",
       "      <td>13.099102</td>\n",
       "      <td>47.0</td>\n",
       "      <td>69.50</td>\n",
       "      <td>77.0</td>\n",
       "      <td>87.00</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>289.0</td>\n",
       "      <td>124.737024</td>\n",
       "      <td>27.740490</td>\n",
       "      <td>64.0</td>\n",
       "      <td>105.00</td>\n",
       "      <td>121.0</td>\n",
       "      <td>137.00</td>\n",
       "      <td>242.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>7.957439</td>\n",
       "      <td>...</td>\n",
       "      <td>1.164461e+08</td>\n",
       "      <td>924558264.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>79.701245</td>\n",
       "      <td>12.744687</td>\n",
       "      <td>28.0</td>\n",
       "      <td>72.00</td>\n",
       "      <td>82.0</td>\n",
       "      <td>89.00</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>2.0</td>\n",
       "      <td>107.500000</td>\n",
       "      <td>10.606602</td>\n",
       "      <td>100.0</td>\n",
       "      <td>103.75</td>\n",
       "      <td>107.5</td>\n",
       "      <td>111.25</td>\n",
       "      <td>115.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.273329e+08</td>\n",
       "      <td>435110554.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>16.970563</td>\n",
       "      <td>67.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>79.0</td>\n",
       "      <td>85.00</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <td>2.0</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>12.727922</td>\n",
       "      <td>76.0</td>\n",
       "      <td>80.50</td>\n",
       "      <td>85.0</td>\n",
       "      <td>89.50</td>\n",
       "      <td>94.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.182577e+08</td>\n",
       "      <td>445151978.0</td>\n",
       "      <td>0.0</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>Film-Noir</th>\n",
       "      <td>3.0</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>102.00</td>\n",
       "      <td>104.0</td>\n",
       "      <td>106.00</td>\n",
       "      <td>108.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.966667</td>\n",
       "      <td>...</td>\n",
       "      <td>6.273068e+07</td>\n",
       "      <td>123353292.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.666667</td>\n",
       "      <td>1.527525</td>\n",
       "      <td>94.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>96.0</td>\n",
       "      <td>96.50</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horror</th>\n",
       "      <td>11.0</td>\n",
       "      <td>102.090909</td>\n",
       "      <td>13.604812</td>\n",
       "      <td>71.0</td>\n",
       "      <td>98.00</td>\n",
       "      <td>103.0</td>\n",
       "      <td>109.00</td>\n",
       "      <td>122.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.909091</td>\n",
       "      <td>...</td>\n",
       "      <td>1.362817e+08</td>\n",
       "      <td>298791505.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>15.362291</td>\n",
       "      <td>46.0</td>\n",
       "      <td>77.50</td>\n",
       "      <td>87.0</td>\n",
       "      <td>88.50</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mystery</th>\n",
       "      <td>12.0</td>\n",
       "      <td>119.083333</td>\n",
       "      <td>14.475423</td>\n",
       "      <td>96.0</td>\n",
       "      <td>110.75</td>\n",
       "      <td>117.5</td>\n",
       "      <td>130.25</td>\n",
       "      <td>138.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.975000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.310949e+08</td>\n",
       "      <td>474203697.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>79.125000</td>\n",
       "      <td>18.604435</td>\n",
       "      <td>52.0</td>\n",
       "      <td>65.25</td>\n",
       "      <td>77.0</td>\n",
       "      <td>98.50</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>1.0</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>108.0</td>\n",
       "      <td>108.00</td>\n",
       "      <td>108.0</td>\n",
       "      <td>108.00</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.755074e+07</td>\n",
       "      <td>17550741.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.0</td>\n",
       "      <td>81.00</td>\n",
       "      <td>81.0</td>\n",
       "      <td>81.00</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western</th>\n",
       "      <td>4.0</td>\n",
       "      <td>148.250000</td>\n",
       "      <td>17.153717</td>\n",
       "      <td>132.0</td>\n",
       "      <td>134.25</td>\n",
       "      <td>148.0</td>\n",
       "      <td>162.00</td>\n",
       "      <td>165.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.350000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.920000e+07</td>\n",
       "      <td>31800000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>78.250000</td>\n",
       "      <td>9.032349</td>\n",
       "      <td>69.0</td>\n",
       "      <td>72.75</td>\n",
       "      <td>77.0</td>\n",
       "      <td>82.50</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Runtime                                                              \\\n",
       "            count        mean        std    min     25%    50%     75%    max   \n",
       "Genre                                                                           \n",
       "Action      172.0  129.046512  28.500706   45.0  110.75  127.5  143.25  321.0   \n",
       "Adventure    72.0  134.111111  33.317320   88.0  109.00  127.0  149.00  228.0   \n",
       "Animation    82.0   99.585366  14.530471   71.0   90.00   99.5  106.75  137.0   \n",
       "Biography    88.0  136.022727  25.514466   93.0  120.00  129.0  146.25  209.0   \n",
       "Comedy      155.0  112.129032  22.946213   68.0   96.00  106.0  124.50  188.0   \n",
       "Crime       107.0  126.392523  27.689231   80.0  106.50  122.0  141.50  229.0   \n",
       "Drama       289.0  124.737024  27.740490   64.0  105.00  121.0  137.00  242.0   \n",
       "Family        2.0  107.500000  10.606602  100.0  103.75  107.5  111.25  115.0   \n",
       "Fantasy       2.0   85.000000  12.727922   76.0   80.50   85.0   89.50   94.0   \n",
       "Film-Noir     3.0  104.000000   4.000000  100.0  102.00  104.0  106.00  108.0   \n",
       "Horror       11.0  102.090909  13.604812   71.0   98.00  103.0  109.00  122.0   \n",
       "Mystery      12.0  119.083333  14.475423   96.0  110.75  117.5  130.25  138.0   \n",
       "Thriller      1.0  108.000000        NaN  108.0  108.00  108.0  108.00  108.0   \n",
       "Western       4.0  148.250000  17.153717  132.0  134.25  148.0  162.00  165.0   \n",
       "\n",
       "          IMDB_Rating            ...         Gross              Metascore  \\\n",
       "                count      mean  ...           75%          max     count   \n",
       "Genre                            ...                                        \n",
       "Action          172.0  7.949419  ...  2.674437e+08  936662225.0     143.0   \n",
       "Adventure        72.0  7.937500  ...  1.998070e+08  874211619.0      64.0   \n",
       "Animation        82.0  7.930488  ...  2.520612e+08  873839108.0      75.0   \n",
       "Biography        88.0  7.938636  ...  9.829924e+07  753585104.0      79.0   \n",
       "Comedy          155.0  7.901290  ...  8.107809e+07  886752933.0     125.0   \n",
       "Crime           107.0  8.016822  ...  7.102163e+07  790482117.0      87.0   \n",
       "Drama           289.0  7.957439  ...  1.164461e+08  924558264.0     241.0   \n",
       "Family            2.0  7.800000  ...  3.273329e+08  435110554.0       2.0   \n",
       "Fantasy           2.0  8.000000  ...  4.182577e+08  445151978.0       0.0   \n",
       "Film-Noir         3.0  7.966667  ...  6.273068e+07  123353292.0       3.0   \n",
       "Horror           11.0  7.909091  ...  1.362817e+08  298791505.0      11.0   \n",
       "Mystery          12.0  7.975000  ...  1.310949e+08  474203697.0       8.0   \n",
       "Thriller          1.0  7.800000  ...  1.755074e+07   17550741.0       1.0   \n",
       "Western           4.0  8.350000  ...  1.920000e+07   31800000.0       4.0   \n",
       "\n",
       "                                                                  \n",
       "                mean        std   min    25%   50%    75%    max  \n",
       "Genre                                                             \n",
       "Action     73.419580  12.421252  33.0  65.00  74.0  82.00   98.0  \n",
       "Adventure  78.437500  12.345393  41.0  69.75  80.5  87.25  100.0  \n",
       "Animation  81.093333   8.813646  61.0  75.00  82.0  87.50   96.0  \n",
       "Biography  76.240506  11.028187  48.0  70.50  76.0  84.50   97.0  \n",
       "Comedy     78.720000  11.829160  45.0  72.00  79.0  88.00   99.0  \n",
       "Crime      77.080460  13.099102  47.0  69.50  77.0  87.00  100.0  \n",
       "Drama      79.701245  12.744687  28.0  72.00  82.0  89.00  100.0  \n",
       "Family     79.000000  16.970563  67.0  73.00  79.0  85.00   91.0  \n",
       "Fantasy          NaN        NaN   NaN    NaN   NaN    NaN    NaN  \n",
       "Film-Noir  95.666667   1.527525  94.0  95.00  96.0  96.50   97.0  \n",
       "Horror     80.000000  15.362291  46.0  77.50  87.0  88.50   97.0  \n",
       "Mystery    79.125000  18.604435  52.0  65.25  77.0  98.50  100.0  \n",
       "Thriller   81.000000        NaN  81.0  81.00  81.0  81.00   81.0  \n",
       "Western    78.250000   9.032349  69.0  72.75  77.0  82.50   90.0  \n",
       "\n",
       "[14 rows x 40 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.groupby('Genre').describe() #a summary of all numerical columns over each groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Series_Title</th>\n",
       "      <th>Released_Year</th>\n",
       "      <th>Runtime</th>\n",
       "      <th>Genre</th>\n",
       "      <th>IMDB_Rating</th>\n",
       "      <th>Director</th>\n",
       "      <th>Star1</th>\n",
       "      <th>No_of_Votes</th>\n",
       "      <th>Gross</th>\n",
       "      <th>Metascore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>The Matrix</td>\n",
       "      <td>1999</td>\n",
       "      <td>136</td>\n",
       "      <td>Action</td>\n",
       "      <td>8.7</td>\n",
       "      <td>Lana Wachowski</td>\n",
       "      <td>Lilly Wachowski</td>\n",
       "      <td>1676426</td>\n",
       "      <td>171479930.0</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>The Wizard of Oz</td>\n",
       "      <td>1939</td>\n",
       "      <td>102</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Victor Fleming</td>\n",
       "      <td>George Cukor</td>\n",
       "      <td>371379</td>\n",
       "      <td>2076020.0</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>Soul</td>\n",
       "      <td>2020</td>\n",
       "      <td>100</td>\n",
       "      <td>Animation</td>\n",
       "      <td>8.1</td>\n",
       "      <td>Pete Docter</td>\n",
       "      <td>Kemp Powers</td>\n",
       "      <td>159171</td>\n",
       "      <td>321826155.0</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>Dog Day Afternoon</td>\n",
       "      <td>1975</td>\n",
       "      <td>125</td>\n",
       "      <td>Biography</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Sidney Lumet</td>\n",
       "      <td>Al Pacino</td>\n",
       "      <td>235652</td>\n",
       "      <td>50000000.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>Love and Death</td>\n",
       "      <td>1975</td>\n",
       "      <td>85</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Woody Allen</td>\n",
       "      <td>Woody Allen</td>\n",
       "      <td>36037</td>\n",
       "      <td>745173387.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>The Girl with the Dragon Tattoo</td>\n",
       "      <td>2011</td>\n",
       "      <td>158</td>\n",
       "      <td>Crime</td>\n",
       "      <td>7.8</td>\n",
       "      <td>David Fincher</td>\n",
       "      <td>Daniel Craig</td>\n",
       "      <td>423010</td>\n",
       "      <td>102515793.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>Creed</td>\n",
       "      <td>2015</td>\n",
       "      <td>133</td>\n",
       "      <td>Drama</td>\n",
       "      <td>7.6</td>\n",
       "      <td>Ryan Coogler</td>\n",
       "      <td>Michael B. Jordan</td>\n",
       "      <td>247666</td>\n",
       "      <td>109767581.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>E.T. the Extra-Terrestrial</td>\n",
       "      <td>1982</td>\n",
       "      <td>115</td>\n",
       "      <td>Family</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Steven Spielberg</td>\n",
       "      <td>Henry Thomas</td>\n",
       "      <td>372490</td>\n",
       "      <td>435110554.0</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Das Cabinet des Dr. Caligari</td>\n",
       "      <td>1920</td>\n",
       "      <td>76</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>8.1</td>\n",
       "      <td>Robert Wiene</td>\n",
       "      <td>Werner Krauss</td>\n",
       "      <td>57428</td>\n",
       "      <td>337574718.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>The Third Man</td>\n",
       "      <td>1949</td>\n",
       "      <td>104</td>\n",
       "      <td>Film-Noir</td>\n",
       "      <td>8.1</td>\n",
       "      <td>Carol Reed</td>\n",
       "      <td>Orson Welles</td>\n",
       "      <td>158731</td>\n",
       "      <td>449191.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>The Innocents</td>\n",
       "      <td>1961</td>\n",
       "      <td>100</td>\n",
       "      <td>Horror</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Jack Clayton</td>\n",
       "      <td>Deborah Kerr</td>\n",
       "      <td>27007</td>\n",
       "      <td>2616000.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>Kahaani</td>\n",
       "      <td>2012</td>\n",
       "      <td>122</td>\n",
       "      <td>Mystery</td>\n",
       "      <td>8.1</td>\n",
       "      <td>Sujoy Ghosh</td>\n",
       "      <td>Vidya Balan</td>\n",
       "      <td>57806</td>\n",
       "      <td>1035953.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>Wait Until Dark</td>\n",
       "      <td>1967</td>\n",
       "      <td>108</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>Audrey Hepburn</td>\n",
       "      <td>27733</td>\n",
       "      <td>17550741.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Once Upon a Time in the West</td>\n",
       "      <td>1968</td>\n",
       "      <td>165</td>\n",
       "      <td>Western</td>\n",
       "      <td>8.5</td>\n",
       "      <td>Sergio Leone</td>\n",
       "      <td>Henry Fonda</td>\n",
       "      <td>302844</td>\n",
       "      <td>5321508.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Series_Title Released_Year  Runtime      Genre  \\\n",
       "14                        The Matrix          1999      136     Action   \n",
       "458                 The Wizard of Oz          1939      102  Adventure   \n",
       "205                             Soul          2020      100  Animation   \n",
       "416                Dog Day Afternoon          1975      125  Biography   \n",
       "846                   Love and Death          1975       85     Comedy   \n",
       "603  The Girl with the Dragon Tattoo          2011      158      Crime   \n",
       "894                            Creed          2015      133      Drama   \n",
       "688       E.T. the Extra-Terrestrial          1982      115     Family   \n",
       "321     Das Cabinet des Dr. Caligari          1920       76    Fantasy   \n",
       "309                    The Third Man          1949      104  Film-Noir   \n",
       "707                    The Innocents          1961      100     Horror   \n",
       "220                          Kahaani          2012      122    Mystery   \n",
       "700                  Wait Until Dark          1967      108   Thriller   \n",
       "48      Once Upon a Time in the West          1968      165    Western   \n",
       "\n",
       "     IMDB_Rating          Director              Star1  No_of_Votes  \\\n",
       "14           8.7    Lana Wachowski    Lilly Wachowski      1676426   \n",
       "458          8.0    Victor Fleming       George Cukor       371379   \n",
       "205          8.1       Pete Docter        Kemp Powers       159171   \n",
       "416          8.0      Sidney Lumet          Al Pacino       235652   \n",
       "846          7.7       Woody Allen        Woody Allen        36037   \n",
       "603          7.8     David Fincher       Daniel Craig       423010   \n",
       "894          7.6      Ryan Coogler  Michael B. Jordan       247666   \n",
       "688          7.8  Steven Spielberg       Henry Thomas       372490   \n",
       "321          8.1      Robert Wiene      Werner Krauss        57428   \n",
       "309          8.1        Carol Reed       Orson Welles       158731   \n",
       "707          7.8      Jack Clayton       Deborah Kerr        27007   \n",
       "220          8.1       Sujoy Ghosh        Vidya Balan        57806   \n",
       "700          7.8     Terence Young     Audrey Hepburn        27733   \n",
       "48           8.5      Sergio Leone        Henry Fonda       302844   \n",
       "\n",
       "           Gross  Metascore  \n",
       "14   171479930.0       73.0  \n",
       "458    2076020.0       92.0  \n",
       "205  321826155.0       83.0  \n",
       "416   50000000.0       86.0  \n",
       "846  745173387.0       89.0  \n",
       "603  102515793.0       71.0  \n",
       "894  109767581.0       82.0  \n",
       "688  435110554.0       91.0  \n",
       "321  337574718.0        NaN  \n",
       "309     449191.0       97.0  \n",
       "707    2616000.0       88.0  \n",
       "220    1035953.0        NaN  \n",
       "700   17550741.0       81.0  \n",
       "48     5321508.0       80.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sample\n",
    "movies.groupby('Genre').sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Series_Title</th>\n",
       "      <th>Released_Year</th>\n",
       "      <th>Runtime</th>\n",
       "      <th>IMDB_Rating</th>\n",
       "      <th>Director</th>\n",
       "      <th>Star1</th>\n",
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       "      <th>Metascore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Genre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>172</td>\n",
       "      <td>61</td>\n",
       "      <td>78</td>\n",
       "      <td>15</td>\n",
       "      <td>123</td>\n",
       "      <td>121</td>\n",
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       "      <td>72</td>\n",
       "      <td>49</td>\n",
       "      <td>58</td>\n",
       "      <td>10</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animation</th>\n",
       "      <td>82</td>\n",
       "      <td>35</td>\n",
       "      <td>41</td>\n",
       "      <td>11</td>\n",
       "      <td>51</td>\n",
       "      <td>77</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biography</th>\n",
       "      <td>88</td>\n",
       "      <td>44</td>\n",
       "      <td>56</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>72</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>155</td>\n",
       "      <td>72</td>\n",
       "      <td>70</td>\n",
       "      <td>11</td>\n",
       "      <td>113</td>\n",
       "      <td>133</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>106</td>\n",
       "      <td>56</td>\n",
       "      <td>65</td>\n",
       "      <td>14</td>\n",
       "      <td>86</td>\n",
       "      <td>85</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>289</td>\n",
       "      <td>83</td>\n",
       "      <td>95</td>\n",
       "      <td>14</td>\n",
       "      <td>211</td>\n",
       "      <td>250</td>\n",
       "      <td>288</td>\n",
       "      <td>287</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film-Noir</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horror</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mystery</th>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Series_Title  Released_Year  Runtime  IMDB_Rating  Director  Star1  \\\n",
       "Genre                                                                           \n",
       "Action              172             61       78           15       123    121   \n",
       "Adventure            72             49       58           10        59     59   \n",
       "Animation            82             35       41           11        51     77   \n",
       "Biography            88             44       56           13        76     72   \n",
       "Comedy              155             72       70           11       113    133   \n",
       "Crime               106             56       65           14        86     85   \n",
       "Drama               289             83       95           14       211    250   \n",
       "Family                2              2        2            1         2      2   \n",
       "Fantasy               2              2        2            2         2      2   \n",
       "Film-Noir             3              3        3            3         3      3   \n",
       "Horror               11             11       10            8        10     11   \n",
       "Mystery              12             11       10            8        10     11   \n",
       "Thriller              1              1        1            1         1      1   \n",
       "Western               4              4        4            4         2      2   \n",
       "\n",
       "           No_of_Votes  Gross  Metascore  \n",
       "Genre                                     \n",
       "Action             172    172         50  \n",
       "Adventure           72     72         33  \n",
       "Animation           82     82         29  \n",
       "Biography           88     88         40  \n",
       "Comedy             155    155         44  \n",
       "Crime              107    107         39  \n",
       "Drama              288    287         52  \n",
       "Family               2      2          2  \n",
       "Fantasy              2      2          0  \n",
       "Film-Noir            3      3          3  \n",
       "Horror              11     11          9  \n",
       "Mystery             12     12          7  \n",
       "Thriller             1      1          1  \n",
       "Western              4      4          4  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.groupby('Genre').nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### agg method"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    " can perform multiple aggregation fuctions at same time on groupby objects\n",
    "#movies.groupby('Genre') ......applying sum() over all numerical function is not a logical thing... applying diff agg.fn over diff columns make sense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Adventure</th>\n",
       "      <td>134.111111</td>\n",
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       "      <td>7.938636</td>\n",
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       "      <td>48.0</td>\n",
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       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>112.129032</td>\n",
       "      <td>7.901290</td>\n",
       "      <td>27620327</td>\n",
       "      <td>1.566387e+10</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>126.392523</td>\n",
       "      <td>8.016822</td>\n",
       "      <td>33533615</td>\n",
       "      <td>8.452632e+09</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>124.737024</td>\n",
       "      <td>7.957439</td>\n",
       "      <td>61367304</td>\n",
       "      <td>3.540997e+10</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>107.500000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>551221</td>\n",
       "      <td>4.391106e+08</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <td>85.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>146222</td>\n",
       "      <td>7.827267e+08</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film-Noir</th>\n",
       "      <td>104.000000</td>\n",
       "      <td>7.966667</td>\n",
       "      <td>367215</td>\n",
       "      <td>1.259105e+08</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horror</th>\n",
       "      <td>102.090909</td>\n",
       "      <td>7.909091</td>\n",
       "      <td>3742556</td>\n",
       "      <td>1.034649e+09</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mystery</th>\n",
       "      <td>119.083333</td>\n",
       "      <td>7.975000</td>\n",
       "      <td>4203004</td>\n",
       "      <td>1.256417e+09</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>108.000000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>27733</td>\n",
       "      <td>1.755074e+07</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western</th>\n",
       "      <td>148.250000</td>\n",
       "      <td>8.350000</td>\n",
       "      <td>1289665</td>\n",
       "      <td>5.822151e+07</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Runtime  IMDB_Rating  No_of_Votes         Gross  Metascore\n",
       "Genre                                                                   \n",
       "Action     129.046512     7.949419     72282412  3.263226e+10       33.0\n",
       "Adventure  134.111111     7.937500     22576163  9.496922e+09       41.0\n",
       "Animation   99.585366     7.930488     21978630  1.463147e+10       61.0\n",
       "Biography  136.022727     7.938636     24006844  8.276358e+09       48.0\n",
       "Comedy     112.129032     7.901290     27620327  1.566387e+10       45.0\n",
       "Crime      126.392523     8.016822     33533615  8.452632e+09       47.0\n",
       "Drama      124.737024     7.957439     61367304  3.540997e+10       28.0\n",
       "Family     107.500000     7.800000       551221  4.391106e+08       67.0\n",
       "Fantasy     85.000000     8.000000       146222  7.827267e+08        NaN\n",
       "Film-Noir  104.000000     7.966667       367215  1.259105e+08       94.0\n",
       "Horror     102.090909     7.909091      3742556  1.034649e+09       46.0\n",
       "Mystery    119.083333     7.975000      4203004  1.256417e+09       52.0\n",
       "Thriller   108.000000     7.800000        27733  1.755074e+07       81.0\n",
       "Western    148.250000     8.350000      1289665  5.822151e+07       69.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#passing dict\n",
    "genres = movies.groupby('Genre')\n",
    "\n",
    "genres.agg({\n",
    "    'Runtime':'mean',\n",
    "    'IMDB_Rating':'mean',\n",
    "    'No_of_Votes':'sum',\n",
    "    'Gross':'sum',\n",
    "    'Metascore':'min'  \n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_9976\\101454270.py:3: FutureWarning: ['Series_Title', 'Released_Year', 'Director', 'Star1'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n",
      "  genres.agg(['min','max','mean'])\n"
     ]
    },
    {
     "data": {
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       "      <th colspan=\"3\" halign=\"left\">Runtime</th>\n",
       "      <th colspan=\"3\" halign=\"left\">IMDB_Rating</th>\n",
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       "      <th>min</th>\n",
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       "      <th>min</th>\n",
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       "      <th>Genre</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>Action</th>\n",
       "      <td>45</td>\n",
       "      <td>321</td>\n",
       "      <td>129.046512</td>\n",
       "      <td>7.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.949419</td>\n",
       "      <td>25312</td>\n",
       "      <td>2303232</td>\n",
       "      <td>420246.581395</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>936662225.0</td>\n",
       "      <td>1.897224e+08</td>\n",
       "      <td>33.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>73.419580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>88</td>\n",
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       "      <td>134.111111</td>\n",
       "      <td>7.6</td>\n",
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       "      <td>100.0</td>\n",
       "      <td>78.437500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animation</th>\n",
       "      <td>71</td>\n",
       "      <td>137</td>\n",
       "      <td>99.585366</td>\n",
       "      <td>7.6</td>\n",
       "      <td>8.6</td>\n",
       "      <td>7.930488</td>\n",
       "      <td>25229</td>\n",
       "      <td>999790</td>\n",
       "      <td>268032.073171</td>\n",
       "      <td>128985.0</td>\n",
       "      <td>873839108.0</td>\n",
       "      <td>1.784326e+08</td>\n",
       "      <td>61.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>81.093333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biography</th>\n",
       "      <td>93</td>\n",
       "      <td>209</td>\n",
       "      <td>136.022727</td>\n",
       "      <td>7.6</td>\n",
       "      <td>8.9</td>\n",
       "      <td>7.938636</td>\n",
       "      <td>27254</td>\n",
       "      <td>1213505</td>\n",
       "      <td>272805.045455</td>\n",
       "      <td>21877.0</td>\n",
       "      <td>753585104.0</td>\n",
       "      <td>9.404952e+07</td>\n",
       "      <td>48.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>76.240506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>68</td>\n",
       "      <td>188</td>\n",
       "      <td>112.129032</td>\n",
       "      <td>7.6</td>\n",
       "      <td>8.6</td>\n",
       "      <td>7.901290</td>\n",
       "      <td>26337</td>\n",
       "      <td>939631</td>\n",
       "      <td>178195.658065</td>\n",
       "      <td>1305.0</td>\n",
       "      <td>886752933.0</td>\n",
       "      <td>1.010572e+08</td>\n",
       "      <td>45.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>78.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>80</td>\n",
       "      <td>229</td>\n",
       "      <td>126.392523</td>\n",
       "      <td>7.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8.016822</td>\n",
       "      <td>27712</td>\n",
       "      <td>1826188</td>\n",
       "      <td>313398.271028</td>\n",
       "      <td>6013.0</td>\n",
       "      <td>790482117.0</td>\n",
       "      <td>7.899656e+07</td>\n",
       "      <td>47.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>77.080460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>64</td>\n",
       "      <td>242</td>\n",
       "      <td>124.737024</td>\n",
       "      <td>7.6</td>\n",
       "      <td>9.3</td>\n",
       "      <td>7.957439</td>\n",
       "      <td>25088</td>\n",
       "      <td>2343110</td>\n",
       "      <td>212343.612457</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>924558264.0</td>\n",
       "      <td>1.225259e+08</td>\n",
       "      <td>28.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>79.701245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>100</td>\n",
       "      <td>115</td>\n",
       "      <td>107.500000</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>178731</td>\n",
       "      <td>372490</td>\n",
       "      <td>275610.500000</td>\n",
       "      <td>4000000.0</td>\n",
       "      <td>435110554.0</td>\n",
       "      <td>2.195553e+08</td>\n",
       "      <td>67.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <td>76</td>\n",
       "      <td>94</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>57428</td>\n",
       "      <td>88794</td>\n",
       "      <td>73111.000000</td>\n",
       "      <td>337574718.0</td>\n",
       "      <td>445151978.0</td>\n",
       "      <td>3.913633e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film-Noir</th>\n",
       "      <td>100</td>\n",
       "      <td>108</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.1</td>\n",
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       "      <td>59556</td>\n",
       "      <td>158731</td>\n",
       "      <td>122405.000000</td>\n",
       "      <td>449191.0</td>\n",
       "      <td>123353292.0</td>\n",
       "      <td>4.197018e+07</td>\n",
       "      <td>94.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>95.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horror</th>\n",
       "      <td>71</td>\n",
       "      <td>122</td>\n",
       "      <td>102.090909</td>\n",
       "      <td>7.6</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.909091</td>\n",
       "      <td>27007</td>\n",
       "      <td>787806</td>\n",
       "      <td>340232.363636</td>\n",
       "      <td>89029.0</td>\n",
       "      <td>298791505.0</td>\n",
       "      <td>9.405902e+07</td>\n",
       "      <td>46.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mystery</th>\n",
       "      <td>96</td>\n",
       "      <td>138</td>\n",
       "      <td>119.083333</td>\n",
       "      <td>7.6</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.975000</td>\n",
       "      <td>33982</td>\n",
       "      <td>1129894</td>\n",
       "      <td>350250.333333</td>\n",
       "      <td>1035953.0</td>\n",
       "      <td>474203697.0</td>\n",
       "      <td>1.047014e+08</td>\n",
       "      <td>52.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>79.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>27733</td>\n",
       "      <td>27733</td>\n",
       "      <td>27733.000000</td>\n",
       "      <td>17550741.0</td>\n",
       "      <td>17550741.0</td>\n",
       "      <td>1.755074e+07</td>\n",
       "      <td>81.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western</th>\n",
       "      <td>132</td>\n",
       "      <td>165</td>\n",
       "      <td>148.250000</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.350000</td>\n",
       "      <td>65659</td>\n",
       "      <td>688390</td>\n",
       "      <td>322416.250000</td>\n",
       "      <td>5321508.0</td>\n",
       "      <td>31800000.0</td>\n",
       "      <td>1.455538e+07</td>\n",
       "      <td>69.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>78.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Runtime                  IMDB_Rating                No_of_Votes  \\\n",
       "              min  max        mean         min  max      mean         min   \n",
       "Genre                                                                       \n",
       "Action         45  321  129.046512         7.6  9.0  7.949419       25312   \n",
       "Adventure      88  228  134.111111         7.6  8.6  7.937500       29999   \n",
       "Animation      71  137   99.585366         7.6  8.6  7.930488       25229   \n",
       "Biography      93  209  136.022727         7.6  8.9  7.938636       27254   \n",
       "Comedy         68  188  112.129032         7.6  8.6  7.901290       26337   \n",
       "Crime          80  229  126.392523         7.6  9.2  8.016822       27712   \n",
       "Drama          64  242  124.737024         7.6  9.3  7.957439       25088   \n",
       "Family        100  115  107.500000         7.8  7.8  7.800000      178731   \n",
       "Fantasy        76   94   85.000000         7.9  8.1  8.000000       57428   \n",
       "Film-Noir     100  108  104.000000         7.8  8.1  7.966667       59556   \n",
       "Horror         71  122  102.090909         7.6  8.5  7.909091       27007   \n",
       "Mystery        96  138  119.083333         7.6  8.4  7.975000       33982   \n",
       "Thriller      108  108  108.000000         7.8  7.8  7.800000       27733   \n",
       "Western       132  165  148.250000         7.8  8.8  8.350000       65659   \n",
       "\n",
       "                                         Gross                             \\\n",
       "               max           mean          min          max          mean   \n",
       "Genre                                                                       \n",
       "Action     2303232  420246.581395       3296.0  936662225.0  1.897224e+08   \n",
       "Adventure  1512360  313557.819444      61001.0  874211619.0  1.319017e+08   \n",
       "Animation   999790  268032.073171     128985.0  873839108.0  1.784326e+08   \n",
       "Biography  1213505  272805.045455      21877.0  753585104.0  9.404952e+07   \n",
       "Comedy      939631  178195.658065       1305.0  886752933.0  1.010572e+08   \n",
       "Crime      1826188  313398.271028       6013.0  790482117.0  7.899656e+07   \n",
       "Drama      2343110  212343.612457       3600.0  924558264.0  1.225259e+08   \n",
       "Family      372490  275610.500000    4000000.0  435110554.0  2.195553e+08   \n",
       "Fantasy      88794   73111.000000  337574718.0  445151978.0  3.913633e+08   \n",
       "Film-Noir   158731  122405.000000     449191.0  123353292.0  4.197018e+07   \n",
       "Horror      787806  340232.363636      89029.0  298791505.0  9.405902e+07   \n",
       "Mystery    1129894  350250.333333    1035953.0  474203697.0  1.047014e+08   \n",
       "Thriller     27733   27733.000000   17550741.0   17550741.0  1.755074e+07   \n",
       "Western     688390  322416.250000    5321508.0   31800000.0  1.455538e+07   \n",
       "\n",
       "          Metascore                    \n",
       "                min    max       mean  \n",
       "Genre                                  \n",
       "Action         33.0   98.0  73.419580  \n",
       "Adventure      41.0  100.0  78.437500  \n",
       "Animation      61.0   96.0  81.093333  \n",
       "Biography      48.0   97.0  76.240506  \n",
       "Comedy         45.0   99.0  78.720000  \n",
       "Crime          47.0  100.0  77.080460  \n",
       "Drama          28.0  100.0  79.701245  \n",
       "Family         67.0   91.0  79.000000  \n",
       "Fantasy         NaN    NaN        NaN  \n",
       "Film-Noir      94.0   97.0  95.666667  \n",
       "Horror         46.0   97.0  80.000000  \n",
       "Mystery        52.0  100.0  79.125000  \n",
       "Thriller       81.0   81.0  81.000000  \n",
       "Western        69.0   90.0  78.250000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#passing list\n",
    "#if u want to perform 3 agg operation over each columns\n",
    "genres.agg(['min','max','mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Runtime</th>\n",
       "      <th>IMDB_Rating</th>\n",
       "      <th colspan=\"2\" halign=\"left\">No_of_Votes</th>\n",
       "      <th>Gross</th>\n",
       "      <th>Metascore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>max</th>\n",
       "      <th>sum</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Genre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>45</td>\n",
       "      <td>129.046512</td>\n",
       "      <td>7.949419</td>\n",
       "      <td>72282412</td>\n",
       "      <td>2303232</td>\n",
       "      <td>3.263226e+10</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>88</td>\n",
       "      <td>134.111111</td>\n",
       "      <td>7.937500</td>\n",
       "      <td>22576163</td>\n",
       "      <td>1512360</td>\n",
       "      <td>9.496922e+09</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animation</th>\n",
       "      <td>71</td>\n",
       "      <td>99.585366</td>\n",
       "      <td>7.930488</td>\n",
       "      <td>21978630</td>\n",
       "      <td>999790</td>\n",
       "      <td>1.463147e+10</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biography</th>\n",
       "      <td>93</td>\n",
       "      <td>136.022727</td>\n",
       "      <td>7.938636</td>\n",
       "      <td>24006844</td>\n",
       "      <td>1213505</td>\n",
       "      <td>8.276358e+09</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>68</td>\n",
       "      <td>112.129032</td>\n",
       "      <td>7.901290</td>\n",
       "      <td>27620327</td>\n",
       "      <td>939631</td>\n",
       "      <td>1.566387e+10</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>80</td>\n",
       "      <td>126.392523</td>\n",
       "      <td>8.016822</td>\n",
       "      <td>33533615</td>\n",
       "      <td>1826188</td>\n",
       "      <td>8.452632e+09</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>64</td>\n",
       "      <td>124.737024</td>\n",
       "      <td>7.957439</td>\n",
       "      <td>61367304</td>\n",
       "      <td>2343110</td>\n",
       "      <td>3.540997e+10</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>100</td>\n",
       "      <td>107.500000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>551221</td>\n",
       "      <td>372490</td>\n",
       "      <td>4.391106e+08</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <td>76</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>146222</td>\n",
       "      <td>88794</td>\n",
       "      <td>7.827267e+08</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film-Noir</th>\n",
       "      <td>100</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>7.966667</td>\n",
       "      <td>367215</td>\n",
       "      <td>158731</td>\n",
       "      <td>1.259105e+08</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horror</th>\n",
       "      <td>71</td>\n",
       "      <td>102.090909</td>\n",
       "      <td>7.909091</td>\n",
       "      <td>3742556</td>\n",
       "      <td>787806</td>\n",
       "      <td>1.034649e+09</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mystery</th>\n",
       "      <td>96</td>\n",
       "      <td>119.083333</td>\n",
       "      <td>7.975000</td>\n",
       "      <td>4203004</td>\n",
       "      <td>1129894</td>\n",
       "      <td>1.256417e+09</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>108</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>27733</td>\n",
       "      <td>27733</td>\n",
       "      <td>1.755074e+07</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western</th>\n",
       "      <td>132</td>\n",
       "      <td>148.250000</td>\n",
       "      <td>8.350000</td>\n",
       "      <td>1289665</td>\n",
       "      <td>688390</td>\n",
       "      <td>5.822151e+07</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Runtime             IMDB_Rating No_of_Votes                  Gross  \\\n",
       "              min        mean        mean         sum      max           sum   \n",
       "Genre                                                                          \n",
       "Action         45  129.046512    7.949419    72282412  2303232  3.263226e+10   \n",
       "Adventure      88  134.111111    7.937500    22576163  1512360  9.496922e+09   \n",
       "Animation      71   99.585366    7.930488    21978630   999790  1.463147e+10   \n",
       "Biography      93  136.022727    7.938636    24006844  1213505  8.276358e+09   \n",
       "Comedy         68  112.129032    7.901290    27620327   939631  1.566387e+10   \n",
       "Crime          80  126.392523    8.016822    33533615  1826188  8.452632e+09   \n",
       "Drama          64  124.737024    7.957439    61367304  2343110  3.540997e+10   \n",
       "Family        100  107.500000    7.800000      551221   372490  4.391106e+08   \n",
       "Fantasy        76   85.000000    8.000000      146222    88794  7.827267e+08   \n",
       "Film-Noir     100  104.000000    7.966667      367215   158731  1.259105e+08   \n",
       "Horror         71  102.090909    7.909091     3742556   787806  1.034649e+09   \n",
       "Mystery        96  119.083333    7.975000     4203004  1129894  1.256417e+09   \n",
       "Thriller      108  108.000000    7.800000       27733    27733  1.755074e+07   \n",
       "Western       132  148.250000    8.350000     1289665   688390  5.822151e+07   \n",
       "\n",
       "          Metascore  \n",
       "                min  \n",
       "Genre                \n",
       "Action         33.0  \n",
       "Adventure      41.0  \n",
       "Animation      61.0  \n",
       "Biography      48.0  \n",
       "Comedy         45.0  \n",
       "Crime          47.0  \n",
       "Drama          28.0  \n",
       "Family         67.0  \n",
       "Fantasy         NaN  \n",
       "Film-Noir      94.0  \n",
       "Horror         46.0  \n",
       "Mystery        52.0  \n",
       "Thriller       81.0  \n",
       "Western        69.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding both the syntax\n",
    "genres.agg(\n",
    "    {\n",
    "        'Runtime':['min','mean'],\n",
    "        'IMDB_Rating':'mean',\n",
    "        'No_of_Votes':['sum','max'],\n",
    "        'Gross':'sum',\n",
    "        'Metascore':'min'\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### looping on groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Series_Title Released_Year  Runtime   Genre  IMDB_Rating  \\\n",
      "2  The Dark Knight          2008      152  Action          9.0   \n",
      "\n",
      "            Director           Star1  No_of_Votes        Gross  Metascore  \n",
      "2  Christopher Nolan  Christian Bale      2303232  534858444.0       84.0  \n",
      "    Series_Title Released_Year  Runtime      Genre  IMDB_Rating  \\\n",
      "21  Interstellar          2014      169  Adventure          8.6   \n",
      "\n",
      "             Director                Star1  No_of_Votes        Gross  \\\n",
      "21  Christopher Nolan  Matthew McConaughey      1512360  188020017.0   \n",
      "\n",
      "    Metascore  \n",
      "21       74.0  \n",
      "                     Series_Title Released_Year  Runtime      Genre  \\\n",
      "23  Sen to Chihiro no kamikakushi          2001      125  Animation   \n",
      "\n",
      "    IMDB_Rating        Director          Star1  No_of_Votes       Gross  \\\n",
      "23          8.6  Hayao Miyazaki  Daveigh Chase       651376  10055859.0   \n",
      "\n",
      "    Metascore  \n",
      "23       96.0  \n",
      "       Series_Title Released_Year  Runtime      Genre  IMDB_Rating  \\\n",
      "7  Schindler's List          1993      195  Biography          8.9   \n",
      "\n",
      "           Director        Star1  No_of_Votes       Gross  Metascore  \n",
      "7  Steven Spielberg  Liam Neeson      1213505  96898818.0       94.0  \n",
      "       Series_Title Released_Year  Runtime   Genre  IMDB_Rating  \\\n",
      "19     Gisaengchung          2019      132  Comedy          8.6   \n",
      "26  La vita è bella          1997      116  Comedy          8.6   \n",
      "\n",
      "           Director            Star1  No_of_Votes       Gross  Metascore  \n",
      "19     Bong Joon Ho     Kang-ho Song       552778  53367844.0       96.0  \n",
      "26  Roberto Benigni  Roberto Benigni       623629  57598247.0       59.0  \n",
      "    Series_Title Released_Year  Runtime  Genre  IMDB_Rating  \\\n",
      "1  The Godfather          1972      175  Crime          9.2   \n",
      "\n",
      "               Director          Star1  No_of_Votes        Gross  Metascore  \n",
      "1  Francis Ford Coppola  Marlon Brando      1620367  134966411.0      100.0  \n",
      "               Series_Title Released_Year  Runtime  Genre  IMDB_Rating  \\\n",
      "0  The Shawshank Redemption          1994      142  Drama          9.3   \n",
      "\n",
      "         Director        Star1  No_of_Votes       Gross  Metascore  \n",
      "0  Frank Darabont  Tim Robbins      2343110  28341469.0       80.0  \n",
      "                            Series_Title Released_Year  Runtime   Genre  \\\n",
      "688           E.T. the Extra-Terrestrial          1982      115  Family   \n",
      "698  Willy Wonka & the Chocolate Factory          1971      100  Family   \n",
      "\n",
      "     IMDB_Rating          Director         Star1  No_of_Votes        Gross  \\\n",
      "688          7.8  Steven Spielberg  Henry Thomas       372490  435110554.0   \n",
      "698          7.8        Mel Stuart   Gene Wilder       178731    4000000.0   \n",
      "\n",
      "     Metascore  \n",
      "688       91.0  \n",
      "698       67.0  \n",
      "                     Series_Title Released_Year  Runtime    Genre  \\\n",
      "321  Das Cabinet des Dr. Caligari          1920       76  Fantasy   \n",
      "\n",
      "     IMDB_Rating      Director          Star1  No_of_Votes        Gross  \\\n",
      "321          8.1  Robert Wiene  Werner Krauss        57428  337574718.0   \n",
      "\n",
      "     Metascore  \n",
      "321        NaN  \n",
      "      Series_Title Released_Year  Runtime      Genre  IMDB_Rating    Director  \\\n",
      "309  The Third Man          1949      104  Film-Noir          8.1  Carol Reed   \n",
      "\n",
      "            Star1  No_of_Votes     Gross  Metascore  \n",
      "309  Orson Welles       158731  449191.0       97.0  \n",
      "   Series_Title Released_Year  Runtime   Genre  IMDB_Rating          Director  \\\n",
      "49       Psycho          1960      109  Horror          8.5  Alfred Hitchcock   \n",
      "\n",
      "              Star1  No_of_Votes       Gross  Metascore  \n",
      "49  Anthony Perkins       604211  32000000.0       97.0  \n",
      "   Series_Title Released_Year  Runtime    Genre  IMDB_Rating  \\\n",
      "69      Memento          2000      113  Mystery          8.4   \n",
      "81  Rear Window          1954      112  Mystery          8.4   \n",
      "\n",
      "             Director          Star1  No_of_Votes       Gross  Metascore  \n",
      "69  Christopher Nolan     Guy Pearce      1125712  25544867.0       80.0  \n",
      "81   Alfred Hitchcock  James Stewart       444074  36764313.0      100.0  \n",
      "        Series_Title Released_Year  Runtime     Genre  IMDB_Rating  \\\n",
      "700  Wait Until Dark          1967      108  Thriller          7.8   \n",
      "\n",
      "          Director           Star1  No_of_Votes       Gross  Metascore  \n",
      "700  Terence Young  Audrey Hepburn        27733  17550741.0       81.0  \n",
      "                       Series_Title Released_Year  Runtime    Genre  \\\n",
      "12  Il buono, il brutto, il cattivo          1966      161  Western   \n",
      "\n",
      "    IMDB_Rating      Director           Star1  No_of_Votes      Gross  \\\n",
      "12          8.8  Sergio Leone  Clint Eastwood       688390  6100000.0   \n",
      "\n",
      "    Metascore  \n",
      "12       90.0  \n"
     ]
    }
   ],
   "source": [
    "#looping over a groupby object gives 'group' & 'data'\n",
    "\n",
    "# Q) find the highest rated movie of each genre\n",
    "for group,data in genres:\n",
    "    print(data[data['IMDB_Rating'] == data['IMDB_Rating'].max()])           #print(type(group),type(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_9976\\2550134662.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append(data[data['IMDB_Rating'] == data['IMDB_Rating'].max()])\n",
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_9976\\2550134662.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append(data[data['IMDB_Rating'] == data['IMDB_Rating'].max()])\n",
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_9976\\2550134662.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append(data[data['IMDB_Rating'] == data['IMDB_Rating'].max()])\n",
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_9976\\2550134662.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append(data[data['IMDB_Rating'] == data['IMDB_Rating'].max()])\n",
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_9976\\2550134662.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append(data[data['IMDB_Rating'] == data['IMDB_Rating'].max()])\n",
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_9976\\2550134662.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append(data[data['IMDB_Rating'] == data['IMDB_Rating'].max()])\n",
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_9976\\2550134662.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append(data[data['IMDB_Rating'] == data['IMDB_Rating'].max()])\n",
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_9976\\2550134662.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append(data[data['IMDB_Rating'] == data['IMDB_Rating'].max()])\n",
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_9976\\2550134662.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append(data[data['IMDB_Rating'] == data['IMDB_Rating'].max()])\n",
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_9976\\2550134662.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append(data[data['IMDB_Rating'] == data['IMDB_Rating'].max()])\n",
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_9976\\2550134662.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append(data[data['IMDB_Rating'] == data['IMDB_Rating'].max()])\n",
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_9976\\2550134662.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append(data[data['IMDB_Rating'] == data['IMDB_Rating'].max()])\n",
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_9976\\2550134662.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append(data[data['IMDB_Rating'] == data['IMDB_Rating'].max()])\n",
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_9976\\2550134662.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append(data[data['IMDB_Rating'] == data['IMDB_Rating'].max()])\n"
     ]
    },
    {
     "data": {
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       "        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>Series_Title</th>\n",
       "      <th>Released_Year</th>\n",
       "      <th>Runtime</th>\n",
       "      <th>Genre</th>\n",
       "      <th>IMDB_Rating</th>\n",
       "      <th>Director</th>\n",
       "      <th>Star1</th>\n",
       "      <th>No_of_Votes</th>\n",
       "      <th>Gross</th>\n",
       "      <th>Metascore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>152</td>\n",
       "      <td>Action</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>Christian Bale</td>\n",
       "      <td>2303232</td>\n",
       "      <td>534858444.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Interstellar</td>\n",
       "      <td>2014</td>\n",
       "      <td>169</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>8.6</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>Matthew McConaughey</td>\n",
       "      <td>1512360</td>\n",
       "      <td>188020017.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Sen to Chihiro no kamikakushi</td>\n",
       "      <td>2001</td>\n",
       "      <td>125</td>\n",
       "      <td>Animation</td>\n",
       "      <td>8.6</td>\n",
       "      <td>Hayao Miyazaki</td>\n",
       "      <td>Daveigh Chase</td>\n",
       "      <td>651376</td>\n",
       "      <td>10055859.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>1993</td>\n",
       "      <td>195</td>\n",
       "      <td>Biography</td>\n",
       "      <td>8.9</td>\n",
       "      <td>Steven Spielberg</td>\n",
       "      <td>Liam Neeson</td>\n",
       "      <td>1213505</td>\n",
       "      <td>96898818.0</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Gisaengchung</td>\n",
       "      <td>2019</td>\n",
       "      <td>132</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>8.6</td>\n",
       "      <td>Bong Joon Ho</td>\n",
       "      <td>Kang-ho Song</td>\n",
       "      <td>552778</td>\n",
       "      <td>53367844.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>La vita è bella</td>\n",
       "      <td>1997</td>\n",
       "      <td>116</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>8.6</td>\n",
       "      <td>Roberto Benigni</td>\n",
       "      <td>Roberto Benigni</td>\n",
       "      <td>623629</td>\n",
       "      <td>57598247.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>1972</td>\n",
       "      <td>175</td>\n",
       "      <td>Crime</td>\n",
       "      <td>9.2</td>\n",
       "      <td>Francis Ford Coppola</td>\n",
       "      <td>Marlon Brando</td>\n",
       "      <td>1620367</td>\n",
       "      <td>134966411.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>142</td>\n",
       "      <td>Drama</td>\n",
       "      <td>9.3</td>\n",
       "      <td>Frank Darabont</td>\n",
       "      <td>Tim Robbins</td>\n",
       "      <td>2343110</td>\n",
       "      <td>28341469.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>E.T. the Extra-Terrestrial</td>\n",
       "      <td>1982</td>\n",
       "      <td>115</td>\n",
       "      <td>Family</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Steven Spielberg</td>\n",
       "      <td>Henry Thomas</td>\n",
       "      <td>372490</td>\n",
       "      <td>435110554.0</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>Willy Wonka &amp; the Chocolate Factory</td>\n",
       "      <td>1971</td>\n",
       "      <td>100</td>\n",
       "      <td>Family</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Mel Stuart</td>\n",
       "      <td>Gene Wilder</td>\n",
       "      <td>178731</td>\n",
       "      <td>4000000.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Das Cabinet des Dr. Caligari</td>\n",
       "      <td>1920</td>\n",
       "      <td>76</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>8.1</td>\n",
       "      <td>Robert Wiene</td>\n",
       "      <td>Werner Krauss</td>\n",
       "      <td>57428</td>\n",
       "      <td>337574718.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>The Third Man</td>\n",
       "      <td>1949</td>\n",
       "      <td>104</td>\n",
       "      <td>Film-Noir</td>\n",
       "      <td>8.1</td>\n",
       "      <td>Carol Reed</td>\n",
       "      <td>Orson Welles</td>\n",
       "      <td>158731</td>\n",
       "      <td>449191.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Psycho</td>\n",
       "      <td>1960</td>\n",
       "      <td>109</td>\n",
       "      <td>Horror</td>\n",
       "      <td>8.5</td>\n",
       "      <td>Alfred Hitchcock</td>\n",
       "      <td>Anthony Perkins</td>\n",
       "      <td>604211</td>\n",
       "      <td>32000000.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Memento</td>\n",
       "      <td>2000</td>\n",
       "      <td>113</td>\n",
       "      <td>Mystery</td>\n",
       "      <td>8.4</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>Guy Pearce</td>\n",
       "      <td>1125712</td>\n",
       "      <td>25544867.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Rear Window</td>\n",
       "      <td>1954</td>\n",
       "      <td>112</td>\n",
       "      <td>Mystery</td>\n",
       "      <td>8.4</td>\n",
       "      <td>Alfred Hitchcock</td>\n",
       "      <td>James Stewart</td>\n",
       "      <td>444074</td>\n",
       "      <td>36764313.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>Wait Until Dark</td>\n",
       "      <td>1967</td>\n",
       "      <td>108</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>Audrey Hepburn</td>\n",
       "      <td>27733</td>\n",
       "      <td>17550741.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Il buono, il brutto, il cattivo</td>\n",
       "      <td>1966</td>\n",
       "      <td>161</td>\n",
       "      <td>Western</td>\n",
       "      <td>8.8</td>\n",
       "      <td>Sergio Leone</td>\n",
       "      <td>Clint Eastwood</td>\n",
       "      <td>688390</td>\n",
       "      <td>6100000.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Series_Title Released_Year Runtime      Genre  \\\n",
       "2                        The Dark Knight          2008     152     Action   \n",
       "21                          Interstellar          2014     169  Adventure   \n",
       "23         Sen to Chihiro no kamikakushi          2001     125  Animation   \n",
       "7                       Schindler's List          1993     195  Biography   \n",
       "19                          Gisaengchung          2019     132     Comedy   \n",
       "26                       La vita è bella          1997     116     Comedy   \n",
       "1                          The Godfather          1972     175      Crime   \n",
       "0               The Shawshank Redemption          1994     142      Drama   \n",
       "688           E.T. the Extra-Terrestrial          1982     115     Family   \n",
       "698  Willy Wonka & the Chocolate Factory          1971     100     Family   \n",
       "321         Das Cabinet des Dr. Caligari          1920      76    Fantasy   \n",
       "309                        The Third Man          1949     104  Film-Noir   \n",
       "49                                Psycho          1960     109     Horror   \n",
       "69                               Memento          2000     113    Mystery   \n",
       "81                           Rear Window          1954     112    Mystery   \n",
       "700                      Wait Until Dark          1967     108   Thriller   \n",
       "12       Il buono, il brutto, il cattivo          1966     161    Western   \n",
       "\n",
       "     IMDB_Rating              Director                Star1 No_of_Votes  \\\n",
       "2            9.0     Christopher Nolan       Christian Bale     2303232   \n",
       "21           8.6     Christopher Nolan  Matthew McConaughey     1512360   \n",
       "23           8.6        Hayao Miyazaki        Daveigh Chase      651376   \n",
       "7            8.9      Steven Spielberg          Liam Neeson     1213505   \n",
       "19           8.6          Bong Joon Ho         Kang-ho Song      552778   \n",
       "26           8.6       Roberto Benigni      Roberto Benigni      623629   \n",
       "1            9.2  Francis Ford Coppola        Marlon Brando     1620367   \n",
       "0            9.3        Frank Darabont          Tim Robbins     2343110   \n",
       "688          7.8      Steven Spielberg         Henry Thomas      372490   \n",
       "698          7.8            Mel Stuart          Gene Wilder      178731   \n",
       "321          8.1          Robert Wiene        Werner Krauss       57428   \n",
       "309          8.1            Carol Reed         Orson Welles      158731   \n",
       "49           8.5      Alfred Hitchcock      Anthony Perkins      604211   \n",
       "69           8.4     Christopher Nolan           Guy Pearce     1125712   \n",
       "81           8.4      Alfred Hitchcock        James Stewart      444074   \n",
       "700          7.8         Terence Young       Audrey Hepburn       27733   \n",
       "12           8.8          Sergio Leone       Clint Eastwood      688390   \n",
       "\n",
       "           Gross  Metascore  \n",
       "2    534858444.0       84.0  \n",
       "21   188020017.0       74.0  \n",
       "23    10055859.0       96.0  \n",
       "7     96898818.0       94.0  \n",
       "19    53367844.0       96.0  \n",
       "26    57598247.0       59.0  \n",
       "1    134966411.0      100.0  \n",
       "0     28341469.0       80.0  \n",
       "688  435110554.0       91.0  \n",
       "698    4000000.0       67.0  \n",
       "321  337574718.0        NaN  \n",
       "309     449191.0       97.0  \n",
       "49    32000000.0       97.0  \n",
       "69    25544867.0       80.0  \n",
       "81    36764313.0      100.0  \n",
       "700   17550741.0       81.0  \n",
       "12     6100000.0       90.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to view in dataframe, make an empty DF\n",
    "df = pd.DataFrame(columns=movies.columns)\n",
    "for group,data in genres:\n",
    "    df = df.append(data[data['IMDB_Rating'] == data['IMDB_Rating'].max()])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### split - apply- combine strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                          Series_Title Released_Year  Runtime  \\\n",
      "2                                      The Dark Knight          2008      152   \n",
      "5        The Lord of the Rings: The Return of the King          2003      201   \n",
      "8                                            Inception          2010      148   \n",
      "10   The Lord of the Rings: The Fellowship of the Ring          2001      178   \n",
      "13               The Lord of the Rings: The Two Towers          2002      179   \n",
      "..                                                 ...           ...      ...   \n",
      "968                                       Falling Down          1993      113   \n",
      "979                                      Lethal Weapon          1987      109   \n",
      "982                                          Mad Max 2          1981       96   \n",
      "983                                       The Warriors          1979       92   \n",
      "985                               Escape from Alcatraz          1979      112   \n",
      "\n",
      "      Genre  IMDB_Rating           Director              Star1  No_of_Votes  \\\n",
      "2    Action          9.0  Christopher Nolan     Christian Bale      2303232   \n",
      "5    Action          8.9      Peter Jackson        Elijah Wood      1642758   \n",
      "8    Action          8.8  Christopher Nolan  Leonardo DiCaprio      2067042   \n",
      "10   Action          8.8      Peter Jackson        Elijah Wood      1661481   \n",
      "13   Action          8.7      Peter Jackson        Elijah Wood      1485555   \n",
      "..      ...          ...                ...                ...          ...   \n",
      "968  Action          7.6    Joel Schumacher    Michael Douglas       171640   \n",
      "979  Action          7.6     Richard Donner         Mel Gibson       236894   \n",
      "982  Action          7.6      George Miller         Mel Gibson       166588   \n",
      "983  Action          7.6        Walter Hill       Michael Beck        93878   \n",
      "985  Action          7.6         Don Siegel     Clint Eastwood       121731   \n",
      "\n",
      "           Gross  Metascore  \n",
      "2    534858444.0       84.0  \n",
      "5    377845905.0       94.0  \n",
      "8    292576195.0       74.0  \n",
      "10   315544750.0       92.0  \n",
      "13   342551365.0       87.0  \n",
      "..           ...        ...  \n",
      "968   40903593.0       56.0  \n",
      "979   65207127.0       68.0  \n",
      "982   12465371.0       77.0  \n",
      "983   22490039.0       65.0  \n",
      "985   43000000.0       76.0  \n",
      "\n",
      "[172 rows x 10 columns]\n",
      "                       Series_Title Released_Year  Runtime      Genre  \\\n",
      "21                     Interstellar          2014      169  Adventure   \n",
      "47               Back to the Future          1985      116  Adventure   \n",
      "93             Inglourious Basterds          2009      153  Adventure   \n",
      "110                        Das Boot          1981      149  Adventure   \n",
      "114           2001: A Space Odyssey          1968      149  Adventure   \n",
      "..                              ...           ...      ...        ...   \n",
      "957  Fear and Loathing in Las Vegas          1998      118  Adventure   \n",
      "964                        Dead Man          1995      121  Adventure   \n",
      "966                       Apollo 13            PG      140  Adventure   \n",
      "984                The Muppet Movie          1979       95  Adventure   \n",
      "991                  Kelly's Heroes          1970      144  Adventure   \n",
      "\n",
      "     IMDB_Rating           Director                Star1  No_of_Votes  \\\n",
      "21           8.6  Christopher Nolan  Matthew McConaughey      1512360   \n",
      "47           8.5    Robert Zemeckis       Michael J. Fox      1058081   \n",
      "93           8.3  Quentin Tarantino            Brad Pitt      1267869   \n",
      "110          8.3  Wolfgang Petersen      Jürgen Prochnow       231855   \n",
      "114          8.3    Stanley Kubrick          Keir Dullea       603517   \n",
      "..           ...                ...                  ...          ...   \n",
      "957          7.6      Terry Gilliam          Johnny Depp       259753   \n",
      "964          7.6       Jim Jarmusch          Johnny Depp        90442   \n",
      "966          7.6         Ron Howard            Tom Hanks       269197   \n",
      "984          7.6      James Frawley           Jim Henson        32802   \n",
      "991          7.6    Brian G. Hutton       Clint Eastwood        45338   \n",
      "\n",
      "           Gross  Metascore  \n",
      "21   188020017.0       74.0  \n",
      "47   210609762.0       87.0  \n",
      "93   120540719.0       69.0  \n",
      "110   11487676.0       86.0  \n",
      "114   56954992.0       84.0  \n",
      "..           ...        ...  \n",
      "957   10680275.0       41.0  \n",
      "964    1037847.0       62.0  \n",
      "966  173837933.0       77.0  \n",
      "984   76657000.0       74.0  \n",
      "991    1378435.0       50.0  \n",
      "\n",
      "[72 rows x 10 columns]\n",
      "                          Series_Title Released_Year  Runtime      Genre  \\\n",
      "23       Sen to Chihiro no kamikakushi          2001      125  Animation   \n",
      "43                       The Lion King          1994       88  Animation   \n",
      "46                      Hotaru no haka          1988       89  Animation   \n",
      "56                      Kimi no na wa.          2016      106  Animation   \n",
      "58   Spider-Man: Into the Spider-Verse          2018      117  Animation   \n",
      "..                                 ...           ...      ...        ...   \n",
      "956                              Mulan          1998       88  Animation   \n",
      "971                  Omohide poro poro          1991      118  Animation   \n",
      "976                 The Little Mermaid          1989       83  Animation   \n",
      "986                     Watership Down          1978       91  Animation   \n",
      "992                    The Jungle Book          1967       78  Animation   \n",
      "\n",
      "     IMDB_Rating             Director             Star1  No_of_Votes  \\\n",
      "23           8.6       Hayao Miyazaki     Daveigh Chase       651376   \n",
      "43           8.5         Roger Allers       Rob Minkoff       942045   \n",
      "46           8.5        Isao Takahata   Tsutomu Tatsumi       235231   \n",
      "56           8.4       Makoto Shinkai  Ryûnosuke Kamiki       194838   \n",
      "58           8.4      Bob Persichetti      Peter Ramsey       375110   \n",
      "..           ...                  ...               ...          ...   \n",
      "956          7.6        Tony Bancroft        Barry Cook       256906   \n",
      "971          7.6        Isao Takahata         Miki Imai        27071   \n",
      "976          7.6         Ron Clements       John Musker       237696   \n",
      "986          7.6         Martin Rosen       John Hubley        33656   \n",
      "992          7.6  Wolfgang Reitherman       Phil Harris       166409   \n",
      "\n",
      "           Gross  Metascore  \n",
      "23    10055859.0       96.0  \n",
      "43   422783777.0       88.0  \n",
      "46   150734678.0       94.0  \n",
      "56     5017246.0       79.0  \n",
      "58   190241310.0       87.0  \n",
      "..           ...        ...  \n",
      "956  120620254.0       71.0  \n",
      "971     453243.0       90.0  \n",
      "976  111543479.0       88.0  \n",
      "986  232841485.0       64.0  \n",
      "992  141843612.0       65.0  \n",
      "\n",
      "[82 rows x 10 columns]\n",
      "          Series_Title Released_Year  Runtime      Genre  IMDB_Rating  \\\n",
      "7     Schindler's List          1993      195  Biography          8.9   \n",
      "15          Goodfellas          1990      146  Biography          8.7   \n",
      "18            Hamilton          2020      160  Biography          8.6   \n",
      "35    The Intouchables          2011      112  Biography          8.5   \n",
      "38         The Pianist          2002      150  Biography          8.5   \n",
      "..                 ...           ...      ...        ...          ...   \n",
      "923     La Vie En Rose          2007      140  Biography          7.6   \n",
      "940  Finding Neverland          2004      106  Biography          7.6   \n",
      "949               Blow          2001      124  Biography          7.6   \n",
      "952      The Hurricane          1999      146  Biography          7.6   \n",
      "987   Midnight Express          1978      121  Biography          7.6   \n",
      "\n",
      "             Director               Star1  No_of_Votes        Gross  Metascore  \n",
      "7    Steven Spielberg         Liam Neeson      1213505   96898818.0       94.0  \n",
      "15    Martin Scorsese      Robert De Niro      1020727   46836394.0       90.0  \n",
      "18        Thomas Kail  Lin-Manuel Miranda        55291  440984783.0       90.0  \n",
      "35    Olivier Nakache       Éric Toledano       760360   13182281.0       57.0  \n",
      "38     Roman Polanski        Adrien Brody       729603   32572577.0       85.0  \n",
      "..                ...                 ...          ...          ...        ...  \n",
      "923     Olivier Dahan    Marion Cotillard        82781   10301706.0       66.0  \n",
      "940      Marc Forster         Johnny Depp       198677   51680613.0       67.0  \n",
      "949         Ted Demme         Johnny Depp       240714   52990775.0       52.0  \n",
      "952    Norman Jewison   Denzel Washington        91557   50668906.0       74.0  \n",
      "987       Alan Parker          Brad Davis        73662   35000000.0       59.0  \n",
      "\n",
      "[88 rows x 10 columns]\n",
      "                                       Series_Title Released_Year  Runtime  \\\n",
      "19                                     Gisaengchung          2019      132   \n",
      "26                                  La vita è bella          1997      116   \n",
      "51                                     Modern Times          1936       87   \n",
      "52                                      City Lights          1931       87   \n",
      "64                                         3 Idiots          2009      170   \n",
      "..                                              ...           ...      ...   \n",
      "977  The Naked Gun: From the Files of Police Squad!          1988       85   \n",
      "978                    Planes, Trains & Automobiles          1987       93   \n",
      "989                                The Long Goodbye          1973      112   \n",
      "994                              A Hard Day's Night          1964       87   \n",
      "995                          Breakfast at Tiffany's          1961      115   \n",
      "\n",
      "      Genre  IMDB_Rating         Director            Star1  No_of_Votes  \\\n",
      "19   Comedy          8.6     Bong Joon Ho     Kang-ho Song       552778   \n",
      "26   Comedy          8.6  Roberto Benigni  Roberto Benigni       623629   \n",
      "51   Comedy          8.5  Charles Chaplin  Charles Chaplin       217881   \n",
      "52   Comedy          8.5  Charles Chaplin  Charles Chaplin       167839   \n",
      "64   Comedy          8.4  Rajkumar Hirani       Aamir Khan       344445   \n",
      "..      ...          ...              ...              ...          ...   \n",
      "977  Comedy          7.6     David Zucker   Leslie Nielsen       152871   \n",
      "978  Comedy          7.6      John Hughes     Steve Martin       124773   \n",
      "989  Comedy          7.6    Robert Altman    Elliott Gould        26337   \n",
      "994  Comedy          7.6   Richard Lester      John Lennon        40351   \n",
      "995  Comedy          7.6    Blake Edwards   Audrey Hepburn       166544   \n",
      "\n",
      "           Gross  Metascore  \n",
      "19    53367844.0       96.0  \n",
      "26    57598247.0       59.0  \n",
      "51      163245.0       96.0  \n",
      "52       19181.0       99.0  \n",
      "64     6532908.0       67.0  \n",
      "..           ...        ...  \n",
      "977   78756177.0       76.0  \n",
      "978   49530280.0       72.0  \n",
      "989     959000.0       87.0  \n",
      "994   13780024.0       96.0  \n",
      "995  679874270.0       76.0  \n",
      "\n",
      "[155 rows x 10 columns]\n",
      "                Series_Title Released_Year  Runtime  Genre  IMDB_Rating  \\\n",
      "1              The Godfather          1972      175  Crime          9.2   \n",
      "3     The Godfather: Part II          1974      202  Crime          9.0   \n",
      "4               12 Angry Men          1957       96  Crime          9.0   \n",
      "6               Pulp Fiction          1994      154  Crime          8.9   \n",
      "22            Cidade de Deus          2002      130  Crime          8.6   \n",
      "..                       ...           ...      ...    ...          ...   \n",
      "958              Funny Games          1997      108  Crime          7.6   \n",
      "960                 Sleepers          1996      147  Crime          7.6   \n",
      "974  The Godfather: Part III          1990      162  Crime          7.6   \n",
      "980             Blood Simple          1984       99  Crime          7.6   \n",
      "999             The 39 Steps          1935       86  Crime          7.6   \n",
      "\n",
      "                 Director           Star1  No_of_Votes        Gross  Metascore  \n",
      "1    Francis Ford Coppola   Marlon Brando      1620367  134966411.0      100.0  \n",
      "3    Francis Ford Coppola       Al Pacino      1129952   57300000.0       90.0  \n",
      "4            Sidney Lumet     Henry Fonda       689845    4360000.0       96.0  \n",
      "6       Quentin Tarantino   John Travolta      1826188  107928762.0       94.0  \n",
      "22     Fernando Meirelles      Kátia Lund       699256    7563397.0       79.0  \n",
      "..                    ...             ...          ...          ...        ...  \n",
      "958        Michael Haneke  Susanne Lothar        65058  217345863.0       69.0  \n",
      "960        Barry Levinson  Robert De Niro       186734   49100000.0       49.0  \n",
      "974  Francis Ford Coppola       Al Pacino       359809   66666062.0       60.0  \n",
      "980             Joel Coen      Ethan Coen        87745    2150000.0       82.0  \n",
      "999      Alfred Hitchcock    Robert Donat        51853  302787539.0       93.0  \n",
      "\n",
      "[107 rows x 10 columns]\n",
      "                        Series_Title Released_Year  Runtime  Genre  \\\n",
      "0           The Shawshank Redemption          1994      142  Drama   \n",
      "9                         Fight Club          1999      139  Drama   \n",
      "11                      Forrest Gump          1994      142  Drama   \n",
      "17   One Flew Over the Cuckoo's Nest          1975      133  Drama   \n",
      "20                   Soorarai Pottru          2020      153  Drama   \n",
      "..                               ...           ...      ...    ...   \n",
      "990                     Giù la testa          1971      157  Drama   \n",
      "993                           Blowup          1966      111  Drama   \n",
      "996                            Giant          1956      201  Drama   \n",
      "997            From Here to Eternity          1953      118  Drama   \n",
      "998                         Lifeboat          1944       97  Drama   \n",
      "\n",
      "     IMDB_Rating                Director              Star1  No_of_Votes  \\\n",
      "0            9.3          Frank Darabont        Tim Robbins      2343110   \n",
      "9            8.8           David Fincher          Brad Pitt      1854740   \n",
      "11           8.8         Robert Zemeckis          Tom Hanks      1809221   \n",
      "17           8.7            Milos Forman     Jack Nicholson       918088   \n",
      "20           8.6           Sudha Kongara             Suriya        54995   \n",
      "..           ...                     ...                ...          ...   \n",
      "990          7.6            Sergio Leone        Rod Steiger        30144   \n",
      "993          7.6  Michelangelo Antonioni     David Hemmings        56513   \n",
      "996          7.6          George Stevens   Elizabeth Taylor        34075   \n",
      "997          7.6          Fred Zinnemann     Burt Lancaster        43374   \n",
      "998          7.6        Alfred Hitchcock  Tallulah Bankhead        26471   \n",
      "\n",
      "           Gross  Metascore  \n",
      "0     28341469.0       80.0  \n",
      "9     37030102.0       66.0  \n",
      "11   330252182.0       82.0  \n",
      "17   112000000.0       83.0  \n",
      "20   556832648.0        NaN  \n",
      "..           ...        ...  \n",
      "990     696690.0       77.0  \n",
      "993  632532802.0       82.0  \n",
      "996  195217415.0       84.0  \n",
      "997   30500000.0       85.0  \n",
      "998  852142728.0       78.0  \n",
      "\n",
      "[289 rows x 10 columns]\n",
      "                            Series_Title Released_Year  Runtime   Genre  \\\n",
      "688           E.T. the Extra-Terrestrial          1982      115  Family   \n",
      "698  Willy Wonka & the Chocolate Factory          1971      100  Family   \n",
      "\n",
      "     IMDB_Rating          Director         Star1  No_of_Votes        Gross  \\\n",
      "688          7.8  Steven Spielberg  Henry Thomas       372490  435110554.0   \n",
      "698          7.8        Mel Stuart   Gene Wilder       178731    4000000.0   \n",
      "\n",
      "     Metascore  \n",
      "688       91.0  \n",
      "698       67.0  \n",
      "                     Series_Title Released_Year  Runtime    Genre  \\\n",
      "321  Das Cabinet des Dr. Caligari          1920       76  Fantasy   \n",
      "568                     Nosferatu          1922       94  Fantasy   \n",
      "\n",
      "     IMDB_Rating      Director          Star1  No_of_Votes        Gross  \\\n",
      "321          8.1  Robert Wiene  Werner Krauss        57428  337574718.0   \n",
      "568          7.9   F.W. Murnau    Max Schreck        88794  445151978.0   \n",
      "\n",
      "     Metascore  \n",
      "321        NaN  \n",
      "568        NaN  \n",
      "           Series_Title Released_Year  Runtime      Genre  IMDB_Rating  \\\n",
      "309       The Third Man          1949      104  Film-Noir          8.1   \n",
      "456  The Maltese Falcon          1941      100  Film-Noir          8.0   \n",
      "712   Shadow of a Doubt          1943      108  Film-Noir          7.8   \n",
      "\n",
      "             Director            Star1  No_of_Votes        Gross  Metascore  \n",
      "309        Carol Reed     Orson Welles       158731     449191.0       97.0  \n",
      "456       John Huston  Humphrey Bogart       148928    2108060.0       96.0  \n",
      "712  Alfred Hitchcock    Teresa Wright        59556  123353292.0       94.0  \n",
      "                 Series_Title Released_Year  Runtime   Genre  IMDB_Rating  \\\n",
      "49                     Psycho          1960      109  Horror          8.5   \n",
      "75                      Alien          1979      117  Horror          8.4   \n",
      "271                 The Thing          1982      109  Horror          8.1   \n",
      "419              The Exorcist          1973      122  Horror          8.0   \n",
      "544  Night of the Living Dead          1968       96  Horror          7.9   \n",
      "707             The Innocents          1961      100  Horror          7.8   \n",
      "724                   Get Out          2017      104  Horror          7.7   \n",
      "844                 Halloween          1978       91  Horror          7.7   \n",
      "876         The Invisible Man          1933       71  Horror          7.7   \n",
      "932                       Saw          2004      103  Horror          7.6   \n",
      "948                The Others          2001      101  Horror          7.6   \n",
      "\n",
      "               Director             Star1  No_of_Votes        Gross  Metascore  \n",
      "49     Alfred Hitchcock   Anthony Perkins       604211   32000000.0       97.0  \n",
      "75         Ridley Scott  Sigourney Weaver       787806   78900000.0       89.0  \n",
      "271      John Carpenter      Kurt Russell       371271   13782838.0       57.0  \n",
      "419    William Friedkin     Ellen Burstyn       362393  232906145.0       81.0  \n",
      "544    George A. Romero       Duane Jones       116557      89029.0       89.0  \n",
      "707        Jack Clayton      Deborah Kerr        27007    2616000.0       88.0  \n",
      "724        Jordan Peele    Daniel Kaluuya       492851  176040665.0       85.0  \n",
      "844      John Carpenter  Donald Pleasence       233106   47000000.0       87.0  \n",
      "876         James Whale      Claude Rains        30683  298791505.0       87.0  \n",
      "932           James Wan        Cary Elwes       379020   56000369.0       46.0  \n",
      "948  Alejandro Amenábar     Nicole Kidman       337651   96522687.0       74.0  \n",
      "          Series_Title Released_Year  Runtime    Genre  IMDB_Rating  \\\n",
      "69             Memento          2000      113  Mystery          8.4   \n",
      "81         Rear Window          1954      112  Mystery          8.4   \n",
      "119            Vertigo          1958      128  Mystery          8.3   \n",
      "145     Shutter Island          2010      138  Mystery          8.2   \n",
      "220            Kahaani          2012      122  Mystery          8.1   \n",
      "393     Twelve Monkeys          1995      129  Mystery          8.0   \n",
      "420             Sleuth          1972      138  Mystery          8.0   \n",
      "714  The Lady Vanishes          1938       96  Mystery          7.8   \n",
      "829          Spoorloos          1988      107  Mystery          7.7   \n",
      "899          El cuerpo          2012      112  Mystery          7.6   \n",
      "959          Dark City          1998      100  Mystery          7.6   \n",
      "961       Lost Highway          1997      134  Mystery          7.6   \n",
      "\n",
      "                 Director                     Star1  No_of_Votes        Gross  \\\n",
      "69      Christopher Nolan                Guy Pearce      1125712   25544867.0   \n",
      "81       Alfred Hitchcock             James Stewart       444074   36764313.0   \n",
      "119      Alfred Hitchcock             James Stewart       364368    3200000.0   \n",
      "145       Martin Scorsese         Leonardo DiCaprio      1129894  128012934.0   \n",
      "220           Sujoy Ghosh               Vidya Balan        57806    1035953.0   \n",
      "393         Terry Gilliam              Bruce Willis       578443   57141459.0   \n",
      "420  Joseph L. Mankiewicz          Laurence Olivier        44748    4081254.0   \n",
      "714      Alfred Hitchcock         Margaret Lockwood        47400  474203697.0   \n",
      "829        George Sluizer  Bernard-Pierre Donnadieu        33982  367916835.0   \n",
      "899           Oriol Paulo             Jose Coronado        57549  140340673.0   \n",
      "959           Alex Proyas              Rufus Sewell       187927   14378331.0   \n",
      "961           David Lynch              Bill Pullman       131101    3796699.0   \n",
      "\n",
      "     Metascore  \n",
      "69        80.0  \n",
      "81       100.0  \n",
      "119      100.0  \n",
      "145       63.0  \n",
      "220        NaN  \n",
      "393       74.0  \n",
      "420        NaN  \n",
      "714       98.0  \n",
      "829        NaN  \n",
      "899        NaN  \n",
      "959       66.0  \n",
      "961       52.0  \n",
      "        Series_Title Released_Year  Runtime     Genre  IMDB_Rating  \\\n",
      "700  Wait Until Dark          1967      108  Thriller          7.8   \n",
      "\n",
      "          Director           Star1  No_of_Votes       Gross  Metascore  \n",
      "700  Terence Young  Audrey Hepburn        27733  17550741.0       81.0  \n",
      "                        Series_Title Released_Year  Runtime    Genre  \\\n",
      "12   Il buono, il brutto, il cattivo          1966      161  Western   \n",
      "48      Once Upon a Time in the West          1968      165  Western   \n",
      "115       Per qualche dollaro in più          1965      132  Western   \n",
      "691           The Outlaw Josey Wales          1976      135  Western   \n",
      "\n",
      "     IMDB_Rating        Director           Star1  No_of_Votes       Gross  \\\n",
      "12           8.8    Sergio Leone  Clint Eastwood       688390   6100000.0   \n",
      "48           8.5    Sergio Leone     Henry Fonda       302844   5321508.0   \n",
      "115          8.3    Sergio Leone  Clint Eastwood       232772  15000000.0   \n",
      "691          7.8  Clint Eastwood  Clint Eastwood        65659  31800000.0   \n",
      "\n",
      "     Metascore  \n",
      "12        90.0  \n",
      "48        80.0  \n",
      "115       74.0  \n",
      "691       69.0  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_9976\\3922993140.py:10: FutureWarning: Not prepending group keys to the result index of transform-like apply. In the future, the group keys will be included in the index, regardless of whether the applied function returns a like-indexed object.\n",
      "To preserve the previous behavior, use\n",
      "\n",
      "\t>>> .groupby(..., group_keys=False)\n",
      "\n",
      "To adopt the future behavior and silence this warning, use \n",
      "\n",
      "\t>>> .groupby(..., group_keys=True)\n",
      "  genres.apply(foo)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Series_Title</th>\n",
       "      <th>Released_Year</th>\n",
       "      <th>Runtime</th>\n",
       "      <th>Genre</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>142</td>\n",
       "      <td>Drama</td>\n",
       "      <td>9.3</td>\n",
       "      <td>Frank Darabont</td>\n",
       "      <td>Tim Robbins</td>\n",
       "      <td>2343110</td>\n",
       "      <td>28341469.0</td>\n",
       "      <td>80.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>1972</td>\n",
       "      <td>175</td>\n",
       "      <td>Crime</td>\n",
       "      <td>9.2</td>\n",
       "      <td>Francis Ford Coppola</td>\n",
       "      <td>Marlon Brando</td>\n",
       "      <td>1620367</td>\n",
       "      <td>134966411.0</td>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>152</td>\n",
       "      <td>Action</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>Christian Bale</td>\n",
       "      <td>2303232</td>\n",
       "      <td>534858444.0</td>\n",
       "      <td>84.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>1974</td>\n",
       "      <td>202</td>\n",
       "      <td>Crime</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Francis Ford Coppola</td>\n",
       "      <td>Al Pacino</td>\n",
       "      <td>1129952</td>\n",
       "      <td>57300000.0</td>\n",
       "      <td>90.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>1957</td>\n",
       "      <td>96</td>\n",
       "      <td>Crime</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Sidney Lumet</td>\n",
       "      <td>Henry Fonda</td>\n",
       "      <td>689845</td>\n",
       "      <td>4360000.0</td>\n",
       "      <td>96.0</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Breakfast at Tiffany's</td>\n",
       "      <td>1961</td>\n",
       "      <td>115</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>7.6</td>\n",
       "      <td>Blake Edwards</td>\n",
       "      <td>Audrey Hepburn</td>\n",
       "      <td>166544</td>\n",
       "      <td>679874270.0</td>\n",
       "      <td>76.0</td>\n",
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       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Giant</td>\n",
       "      <td>1956</td>\n",
       "      <td>201</td>\n",
       "      <td>Drama</td>\n",
       "      <td>7.6</td>\n",
       "      <td>George Stevens</td>\n",
       "      <td>Elizabeth Taylor</td>\n",
       "      <td>34075</td>\n",
       "      <td>195217415.0</td>\n",
       "      <td>84.0</td>\n",
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       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>From Here to Eternity</td>\n",
       "      <td>1953</td>\n",
       "      <td>118</td>\n",
       "      <td>Drama</td>\n",
       "      <td>7.6</td>\n",
       "      <td>Fred Zinnemann</td>\n",
       "      <td>Burt Lancaster</td>\n",
       "      <td>43374</td>\n",
       "      <td>30500000.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Lifeboat</td>\n",
       "      <td>1944</td>\n",
       "      <td>97</td>\n",
       "      <td>Drama</td>\n",
       "      <td>7.6</td>\n",
       "      <td>Alfred Hitchcock</td>\n",
       "      <td>Tallulah Bankhead</td>\n",
       "      <td>26471</td>\n",
       "      <td>852142728.0</td>\n",
       "      <td>78.0</td>\n",
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       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>The 39 Steps</td>\n",
       "      <td>1935</td>\n",
       "      <td>86</td>\n",
       "      <td>Crime</td>\n",
       "      <td>7.6</td>\n",
       "      <td>Alfred Hitchcock</td>\n",
       "      <td>Robert Donat</td>\n",
       "      <td>51853</td>\n",
       "      <td>302787539.0</td>\n",
       "      <td>93.0</td>\n",
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       "<p>1000 rows × 10 columns</p>\n",
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      "text/plain": [
       "                 Series_Title Released_Year  Runtime   Genre  IMDB_Rating  \\\n",
       "0    The Shawshank Redemption          1994      142   Drama          9.3   \n",
       "1               The Godfather          1972      175   Crime          9.2   \n",
       "2             The Dark Knight          2008      152  Action          9.0   \n",
       "3      The Godfather: Part II          1974      202   Crime          9.0   \n",
       "4                12 Angry Men          1957       96   Crime          9.0   \n",
       "..                        ...           ...      ...     ...          ...   \n",
       "995    Breakfast at Tiffany's          1961      115  Comedy          7.6   \n",
       "996                     Giant          1956      201   Drama          7.6   \n",
       "997     From Here to Eternity          1953      118   Drama          7.6   \n",
       "998                  Lifeboat          1944       97   Drama          7.6   \n",
       "999              The 39 Steps          1935       86   Crime          7.6   \n",
       "\n",
       "                 Director              Star1  No_of_Votes        Gross  \\\n",
       "0          Frank Darabont        Tim Robbins      2343110   28341469.0   \n",
       "1    Francis Ford Coppola      Marlon Brando      1620367  134966411.0   \n",
       "2       Christopher Nolan     Christian Bale      2303232  534858444.0   \n",
       "3    Francis Ford Coppola          Al Pacino      1129952   57300000.0   \n",
       "4            Sidney Lumet        Henry Fonda       689845    4360000.0   \n",
       "..                    ...                ...          ...          ...   \n",
       "995         Blake Edwards     Audrey Hepburn       166544  679874270.0   \n",
       "996        George Stevens   Elizabeth Taylor        34075  195217415.0   \n",
       "997        Fred Zinnemann     Burt Lancaster        43374   30500000.0   \n",
       "998      Alfred Hitchcock  Tallulah Bankhead        26471  852142728.0   \n",
       "999      Alfred Hitchcock       Robert Donat        51853  302787539.0   \n",
       "\n",
       "     Metascore  \n",
       "0         80.0  \n",
       "1        100.0  \n",
       "2         84.0  \n",
       "3         90.0  \n",
       "4         96.0  \n",
       "..         ...  \n",
       "995       76.0  \n",
       "996       84.0  \n",
       "997       85.0  \n",
       "998       78.0  \n",
       "999       93.0  \n",
       "\n",
       "[1000 rows x 10 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#genres.apply(min)................ #here built-in-function is applied..we can also apply custom functions\n",
    "\n",
    "#make a function of ur own\n",
    "\n",
    "def foo(group):\n",
    "    print(group)                                                                            #print(type(group))\n",
    "    return group\n",
    "    \n",
    "\n",
    "genres.apply(foo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Genre\n",
       "Action       10\n",
       "Adventure     2\n",
       "Animation     2\n",
       "Biography     9\n",
       "Comedy       14\n",
       "Crime         4\n",
       "Drama        21\n",
       "Family        0\n",
       "Fantasy       0\n",
       "Film-Noir     0\n",
       "Horror        1\n",
       "Mystery       0\n",
       "Thriller      0\n",
       "Western       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q) find number of movies starting with A for each group\n",
    "\n",
    "def foo(group):\n",
    "    return group['Series_Title'].str.startswith('A').sum()\n",
    "\n",
    "genres.apply(foo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_9976\\697731264.py:7: FutureWarning: Not prepending group keys to the result index of transform-like apply. In the future, the group keys will be included in the index, regardless of whether the applied function returns a like-indexed object.\n",
      "To preserve the previous behavior, use\n",
      "\n",
      "\t>>> .groupby(..., group_keys=False)\n",
      "\n",
      "To adopt the future behavior and silence this warning, use \n",
      "\n",
      "\t>>> .groupby(..., group_keys=True)\n",
      "  genres.apply(ranking)\n"
     ]
    },
    {
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       "      <td>The Shawshank Redemption</td>\n",
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       "      <th>2</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>152</td>\n",
       "      <td>Action</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>Christian Bale</td>\n",
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       "      <td>534858444.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>1974</td>\n",
       "      <td>202</td>\n",
       "      <td>Crime</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Francis Ford Coppola</td>\n",
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       "      <td>2.5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>1957</td>\n",
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       "      <td>9.0</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Breakfast at Tiffany's</td>\n",
       "      <td>1961</td>\n",
       "      <td>115</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>7.6</td>\n",
       "      <td>Blake Edwards</td>\n",
       "      <td>Audrey Hepburn</td>\n",
       "      <td>166544</td>\n",
       "      <td>679874270.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Giant</td>\n",
       "      <td>1956</td>\n",
       "      <td>201</td>\n",
       "      <td>Drama</td>\n",
       "      <td>7.6</td>\n",
       "      <td>George Stevens</td>\n",
       "      <td>Elizabeth Taylor</td>\n",
       "      <td>34075</td>\n",
       "      <td>195217415.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>272.5</td>\n",
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       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>From Here to Eternity</td>\n",
       "      <td>1953</td>\n",
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       "      <td>Drama</td>\n",
       "      <td>7.6</td>\n",
       "      <td>Fred Zinnemann</td>\n",
       "      <td>Burt Lancaster</td>\n",
       "      <td>43374</td>\n",
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       "      <td>85.0</td>\n",
       "      <td>272.5</td>\n",
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       "    <tr>\n",
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       "      <td>Lifeboat</td>\n",
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       "      <td>97</td>\n",
       "      <td>Drama</td>\n",
       "      <td>7.6</td>\n",
       "      <td>Alfred Hitchcock</td>\n",
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       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>The 39 Steps</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                 Series_Title Released_Year  Runtime   Genre  IMDB_Rating  \\\n",
       "0    The Shawshank Redemption          1994      142   Drama          9.3   \n",
       "1               The Godfather          1972      175   Crime          9.2   \n",
       "2             The Dark Knight          2008      152  Action          9.0   \n",
       "3      The Godfather: Part II          1974      202   Crime          9.0   \n",
       "4                12 Angry Men          1957       96   Crime          9.0   \n",
       "..                        ...           ...      ...     ...          ...   \n",
       "995    Breakfast at Tiffany's          1961      115  Comedy          7.6   \n",
       "996                     Giant          1956      201   Drama          7.6   \n",
       "997     From Here to Eternity          1953      118   Drama          7.6   \n",
       "998                  Lifeboat          1944       97   Drama          7.6   \n",
       "999              The 39 Steps          1935       86   Crime          7.6   \n",
       "\n",
       "                 Director              Star1  No_of_Votes        Gross  \\\n",
       "0          Frank Darabont        Tim Robbins      2343110   28341469.0   \n",
       "1    Francis Ford Coppola      Marlon Brando      1620367  134966411.0   \n",
       "2       Christopher Nolan     Christian Bale      2303232  534858444.0   \n",
       "3    Francis Ford Coppola          Al Pacino      1129952   57300000.0   \n",
       "4            Sidney Lumet        Henry Fonda       689845    4360000.0   \n",
       "..                    ...                ...          ...          ...   \n",
       "995         Blake Edwards     Audrey Hepburn       166544  679874270.0   \n",
       "996        George Stevens   Elizabeth Taylor        34075  195217415.0   \n",
       "997        Fred Zinnemann     Burt Lancaster        43374   30500000.0   \n",
       "998      Alfred Hitchcock  Tallulah Bankhead        26471  852142728.0   \n",
       "999      Alfred Hitchcock       Robert Donat        51853  302787539.0   \n",
       "\n",
       "     Metascore  IMDB_ranking  \n",
       "0         80.0           1.0  \n",
       "1        100.0           1.0  \n",
       "2         84.0           1.0  \n",
       "3         90.0           2.5  \n",
       "4         96.0           2.5  \n",
       "..         ...           ...  \n",
       "995       76.0         147.0  \n",
       "996       84.0         272.5  \n",
       "997       85.0         272.5  \n",
       "998       78.0         272.5  \n",
       "999       93.0         101.0  \n",
       "\n",
       "[1000 rows x 11 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find ranking of each movie in the group according to IMDB score\n",
    "\n",
    "def ranking(group):\n",
    "    group['IMDB_ranking'] = group['IMDB_Rating'].rank(ascending=False)\n",
    "    return group\n",
    "\n",
    "genres.apply(ranking)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_9976\\1388266725.py:7: FutureWarning: Not prepending group keys to the result index of transform-like apply. In the future, the group keys will be included in the index, regardless of whether the applied function returns a like-indexed object.\n",
      "To preserve the previous behavior, use\n",
      "\n",
      "\t>>> .groupby(..., group_keys=False)\n",
      "\n",
      "To adopt the future behavior and silence this warning, use \n",
      "\n",
      "\t>>> .groupby(..., group_keys=True)\n",
      "  genres.apply(norm)\n"
     ]
    },
    {
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       "      <td>The Godfather: Part II</td>\n",
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       "      <td>Drama</td>\n",
       "      <td>7.6</td>\n",
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       "      <td>The 39 Steps</td>\n",
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       "      <td>Robert Donat</td>\n",
       "      <td>51853</td>\n",
       "      <td>302787539.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Series_Title Released_Year  Runtime   Genre  IMDB_Rating  \\\n",
       "0    The Shawshank Redemption          1994      142   Drama          9.3   \n",
       "1               The Godfather          1972      175   Crime          9.2   \n",
       "2             The Dark Knight          2008      152  Action          9.0   \n",
       "3      The Godfather: Part II          1974      202   Crime          9.0   \n",
       "4                12 Angry Men          1957       96   Crime          9.0   \n",
       "..                        ...           ...      ...     ...          ...   \n",
       "995    Breakfast at Tiffany's          1961      115  Comedy          7.6   \n",
       "996                     Giant          1956      201   Drama          7.6   \n",
       "997     From Here to Eternity          1953      118   Drama          7.6   \n",
       "998                  Lifeboat          1944       97   Drama          7.6   \n",
       "999              The 39 Steps          1935       86   Crime          7.6   \n",
       "\n",
       "                 Director              Star1  No_of_Votes        Gross  \\\n",
       "0          Frank Darabont        Tim Robbins      2343110   28341469.0   \n",
       "1    Francis Ford Coppola      Marlon Brando      1620367  134966411.0   \n",
       "2       Christopher Nolan     Christian Bale      2303232  534858444.0   \n",
       "3    Francis Ford Coppola          Al Pacino      1129952   57300000.0   \n",
       "4            Sidney Lumet        Henry Fonda       689845    4360000.0   \n",
       "..                    ...                ...          ...          ...   \n",
       "995         Blake Edwards     Audrey Hepburn       166544  679874270.0   \n",
       "996        George Stevens   Elizabeth Taylor        34075  195217415.0   \n",
       "997        Fred Zinnemann     Burt Lancaster        43374   30500000.0   \n",
       "998      Alfred Hitchcock  Tallulah Bankhead        26471  852142728.0   \n",
       "999      Alfred Hitchcock       Robert Donat        51853  302787539.0   \n",
       "\n",
       "     Metascore  norm_IMDB  \n",
       "0         80.0      1.000  \n",
       "1        100.0      1.000  \n",
       "2         84.0      1.000  \n",
       "3         90.0      0.875  \n",
       "4         96.0      0.875  \n",
       "..         ...        ...  \n",
       "995       76.0      0.000  \n",
       "996       84.0      0.000  \n",
       "997       85.0      0.000  \n",
       "998       78.0      0.000  \n",
       "999       93.0      0.000  \n",
       "\n",
       "[1000 rows x 11 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find normalized IMDB rating group wise\n",
    "def norm(group):\n",
    "    group['norm_IMDB'] = (group['IMDB_Rating']-group['IMDB_Rating'].min())/(group['IMDB_Rating'].max() - group['IMDB_Rating'].min())\n",
    "    return group\n",
    "\n",
    "\n",
    "genres.apply(norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### groupby on multiple cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "duo = movies.groupby(['Director','Star1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Director             Star1         \n",
       "Aamir Khan           Amole Gupte       1\n",
       "Aaron Sorkin         Eddie Redmayne    1\n",
       "Abdellatif Kechiche  Léa Seydoux       1\n",
       "Abhishek Chaubey     Shahid Kapoor     1\n",
       "Abhishek Kapoor      Amit Sadh         1\n",
       "                                      ..\n",
       "Zaza Urushadze       Lembit Ulfsak     1\n",
       "Zoya Akhtar          Hrithik Roshan    1\n",
       "                     Vijay Varma       1\n",
       "Çagan Irmak          Çetin Tekindor    1\n",
       "Ömer Faruk Sorak     Cem Yilmaz        1\n",
       "Length: 898, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duo.size()  #gives no.of items in each group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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",
       "    .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>Series_Title</th>\n",
       "      <th>Released_Year</th>\n",
       "      <th>Runtime</th>\n",
       "      <th>Genre</th>\n",
       "      <th>IMDB_Rating</th>\n",
       "      <th>Director</th>\n",
       "      <th>Star1</th>\n",
       "      <th>No_of_Votes</th>\n",
       "      <th>Gross</th>\n",
       "      <th>Metascore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Taare Zameen Par</td>\n",
       "      <td>2007</td>\n",
       "      <td>165</td>\n",
       "      <td>Drama</td>\n",
       "      <td>8.4</td>\n",
       "      <td>Aamir Khan</td>\n",
       "      <td>Amole Gupte</td>\n",
       "      <td>168895</td>\n",
       "      <td>1223869.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Series_Title Released_Year  Runtime  Genre  IMDB_Rating    Director  \\\n",
       "65  Taare Zameen Par          2007      165  Drama          8.4  Aamir Khan   \n",
       "\n",
       "          Star1  No_of_Votes      Gross  Metascore  \n",
       "65  Amole Gupte       168895  1223869.0        NaN  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duo.get_group(('Aamir Khan','Amole Gupte')) #give items in specified group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Director        Star1         \n",
       "Akira Kurosawa  Toshirô Mifune    2.999877e+09\n",
       "Name: Gross, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the most earning actor->director combo\n",
    "duo['Gross'].sum().sort_values(ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Star1</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Metascore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>Ellar Coltrane</td>\n",
       "      <td>Drama</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Star1  Genre  Metascore\n",
       "230  Ellar Coltrane  Drama      100.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the best(in-terms of metascore(avg)) actor->genre combo\n",
    "movies.groupby(['Star1','Genre'])['Metascore'].mean().reset_index().sort_values('Metascore',ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nithin\\AppData\\Local\\Temp\\ipykernel_9976\\2140502421.py:2: FutureWarning: ['Series_Title', 'Released_Year', 'Genre'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n",
      "  duo.agg(['min','max','mean'])\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Runtime</th>\n",
       "      <th colspan=\"3\" halign=\"left\">IMDB_Rating</th>\n",
       "      <th colspan=\"3\" halign=\"left\">No_of_Votes</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Gross</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Metascore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Director</th>\n",
       "      <th>Star1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>Aamir Khan</th>\n",
       "      <th>Amole Gupte</th>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>165.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
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       "      <td>168895</td>\n",
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       "      <td>1223869.0</td>\n",
       "      <td>1223869.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>Aaron Sorkin</th>\n",
       "      <th>Eddie Redmayne</th>\n",
       "      <td>129</td>\n",
       "      <td>129</td>\n",
       "      <td>129.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.8</td>\n",
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       "      <td>89896</td>\n",
       "      <td>89896</td>\n",
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       "      <td>853090410.0</td>\n",
       "      <td>853090410.0</td>\n",
       "      <td>853090410.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abdellatif Kechiche</th>\n",
       "      <th>Léa Seydoux</th>\n",
       "      <td>180</td>\n",
       "      <td>180</td>\n",
       "      <td>180.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>138741</td>\n",
       "      <td>138741</td>\n",
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       "      <td>2199675.0</td>\n",
       "      <td>2199675.0</td>\n",
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       "      <td>89.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>89.0</td>\n",
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       "    <tr>\n",
       "      <th>Abhishek Chaubey</th>\n",
       "      <th>Shahid Kapoor</th>\n",
       "      <td>148</td>\n",
       "      <td>148</td>\n",
       "      <td>148.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.8</td>\n",
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       "      <td>27175</td>\n",
       "      <td>27175</td>\n",
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       "      <td>218428303.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Abhishek Kapoor</th>\n",
       "      <th>Amit Sadh</th>\n",
       "      <td>130</td>\n",
       "      <td>130</td>\n",
       "      <td>130.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>32628</td>\n",
       "      <td>32628</td>\n",
       "      <td>32628.0</td>\n",
       "      <td>1122527.0</td>\n",
       "      <td>1122527.0</td>\n",
       "      <td>1122527.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>Zaza Urushadze</th>\n",
       "      <th>Lembit Ulfsak</th>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "      <td>87.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.2</td>\n",
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       "      <td>40382</td>\n",
       "      <td>40382</td>\n",
       "      <td>40382.0</td>\n",
       "      <td>144501.0</td>\n",
       "      <td>144501.0</td>\n",
       "      <td>144501.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Zoya Akhtar</th>\n",
       "      <th>Hrithik Roshan</th>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>155.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.1</td>\n",
       "      <td>67927</td>\n",
       "      <td>67927</td>\n",
       "      <td>67927.0</td>\n",
       "      <td>3108485.0</td>\n",
       "      <td>3108485.0</td>\n",
       "      <td>3108485.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vijay Varma</th>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "      <td>154.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>31886</td>\n",
       "      <td>31886</td>\n",
       "      <td>31886.0</td>\n",
       "      <td>5566534.0</td>\n",
       "      <td>5566534.0</td>\n",
       "      <td>5566534.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Çagan Irmak</th>\n",
       "      <th>Çetin Tekindor</th>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>112.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.3</td>\n",
       "      <td>78925</td>\n",
       "      <td>78925</td>\n",
       "      <td>78925.0</td>\n",
       "      <td>461855363.0</td>\n",
       "      <td>461855363.0</td>\n",
       "      <td>461855363.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ömer Faruk Sorak</th>\n",
       "      <th>Cem Yilmaz</th>\n",
       "      <td>127</td>\n",
       "      <td>127</td>\n",
       "      <td>127.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>56960</td>\n",
       "      <td>56960</td>\n",
       "      <td>56960.0</td>\n",
       "      <td>196206077.0</td>\n",
       "      <td>196206077.0</td>\n",
       "      <td>196206077.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>898 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Runtime             IMDB_Rating            \\\n",
       "                                       min  max   mean         min  max mean   \n",
       "Director            Star1                                                      \n",
       "Aamir Khan          Amole Gupte        165  165  165.0         8.4  8.4  8.4   \n",
       "Aaron Sorkin        Eddie Redmayne     129  129  129.0         7.8  7.8  7.8   \n",
       "Abdellatif Kechiche Léa Seydoux        180  180  180.0         7.7  7.7  7.7   \n",
       "Abhishek Chaubey    Shahid Kapoor      148  148  148.0         7.8  7.8  7.8   \n",
       "Abhishek Kapoor     Amit Sadh          130  130  130.0         7.7  7.7  7.7   \n",
       "...                                    ...  ...    ...         ...  ...  ...   \n",
       "Zaza Urushadze      Lembit Ulfsak       87   87   87.0         8.2  8.2  8.2   \n",
       "Zoya Akhtar         Hrithik Roshan     155  155  155.0         8.1  8.1  8.1   \n",
       "                    Vijay Varma        154  154  154.0         8.0  8.0  8.0   \n",
       "Çagan Irmak         Çetin Tekindor     112  112  112.0         8.3  8.3  8.3   \n",
       "Ömer Faruk Sorak    Cem Yilmaz         127  127  127.0         8.0  8.0  8.0   \n",
       "\n",
       "                                   No_of_Votes                          Gross  \\\n",
       "                                           min     max      mean          min   \n",
       "Director            Star1                                                       \n",
       "Aamir Khan          Amole Gupte         168895  168895  168895.0    1223869.0   \n",
       "Aaron Sorkin        Eddie Redmayne       89896   89896   89896.0  853090410.0   \n",
       "Abdellatif Kechiche Léa Seydoux         138741  138741  138741.0    2199675.0   \n",
       "Abhishek Chaubey    Shahid Kapoor        27175   27175   27175.0  218428303.0   \n",
       "Abhishek Kapoor     Amit Sadh            32628   32628   32628.0    1122527.0   \n",
       "...                                        ...     ...       ...          ...   \n",
       "Zaza Urushadze      Lembit Ulfsak        40382   40382   40382.0     144501.0   \n",
       "Zoya Akhtar         Hrithik Roshan       67927   67927   67927.0    3108485.0   \n",
       "                    Vijay Varma          31886   31886   31886.0    5566534.0   \n",
       "Çagan Irmak         Çetin Tekindor       78925   78925   78925.0  461855363.0   \n",
       "Ömer Faruk Sorak    Cem Yilmaz           56960   56960   56960.0  196206077.0   \n",
       "\n",
       "                                                             Metascore        \\\n",
       "                                            max         mean       min   max   \n",
       "Director            Star1                                                      \n",
       "Aamir Khan          Amole Gupte       1223869.0    1223869.0       NaN   NaN   \n",
       "Aaron Sorkin        Eddie Redmayne  853090410.0  853090410.0      77.0  77.0   \n",
       "Abdellatif Kechiche Léa Seydoux       2199675.0    2199675.0      89.0  89.0   \n",
       "Abhishek Chaubey    Shahid Kapoor   218428303.0  218428303.0       NaN   NaN   \n",
       "Abhishek Kapoor     Amit Sadh         1122527.0    1122527.0      40.0  40.0   \n",
       "...                                         ...          ...       ...   ...   \n",
       "Zaza Urushadze      Lembit Ulfsak      144501.0     144501.0      73.0  73.0   \n",
       "Zoya Akhtar         Hrithik Roshan    3108485.0    3108485.0       NaN   NaN   \n",
       "                    Vijay Varma       5566534.0    5566534.0      65.0  65.0   \n",
       "Çagan Irmak         Çetin Tekindor  461855363.0  461855363.0       NaN   NaN   \n",
       "Ömer Faruk Sorak    Cem Yilmaz      196206077.0  196206077.0       NaN   NaN   \n",
       "\n",
       "                                          \n",
       "                                    mean  \n",
       "Director            Star1                 \n",
       "Aamir Khan          Amole Gupte      NaN  \n",
       "Aaron Sorkin        Eddie Redmayne  77.0  \n",
       "Abdellatif Kechiche Léa Seydoux     89.0  \n",
       "Abhishek Chaubey    Shahid Kapoor    NaN  \n",
       "Abhishek Kapoor     Amit Sadh       40.0  \n",
       "...                                  ...  \n",
       "Zaza Urushadze      Lembit Ulfsak   73.0  \n",
       "Zoya Akhtar         Hrithik Roshan   NaN  \n",
       "                    Vijay Varma     65.0  \n",
       "Çagan Irmak         Çetin Tekindor   NaN  \n",
       "Ömer Faruk Sorak    Cem Yilmaz       NaN  \n",
       "\n",
       "[898 rows x 15 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# agg on multiple groupby\n",
    "duo.agg(['min','max','mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>179073</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179074</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179075</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>run out</td>\n",
       "      <td>KH Pandya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179076</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179077</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>lbw</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179078 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning         batting_team                 bowling_team  \\\n",
       "0              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "1              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "2              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "3              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "4              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "...          ...     ...                  ...                          ...   \n",
       "179073     11415       2  Chennai Super Kings               Mumbai Indians   \n",
       "179074     11415       2  Chennai Super Kings               Mumbai Indians   \n",
       "179075     11415       2  Chennai Super Kings               Mumbai Indians   \n",
       "179076     11415       2  Chennai Super Kings               Mumbai Indians   \n",
       "179077     11415       2  Chennai Super Kings               Mumbai Indians   \n",
       "\n",
       "        over  ball    batsman non_striker      bowler  is_super_over  ...  \\\n",
       "0          1     1  DA Warner    S Dhawan    TS Mills              0  ...   \n",
       "1          1     2  DA Warner    S Dhawan    TS Mills              0  ...   \n",
       "2          1     3  DA Warner    S Dhawan    TS Mills              0  ...   \n",
       "3          1     4  DA Warner    S Dhawan    TS Mills              0  ...   \n",
       "4          1     5  DA Warner    S Dhawan    TS Mills              0  ...   \n",
       "...      ...   ...        ...         ...         ...            ...  ...   \n",
       "179073    20     2  RA Jadeja   SR Watson  SL Malinga              0  ...   \n",
       "179074    20     3  SR Watson   RA Jadeja  SL Malinga              0  ...   \n",
       "179075    20     4  SR Watson   RA Jadeja  SL Malinga              0  ...   \n",
       "179076    20     5  SN Thakur   RA Jadeja  SL Malinga              0  ...   \n",
       "179077    20     6  SN Thakur   RA Jadeja  SL Malinga              0  ...   \n",
       "\n",
       "        bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
       "0              0            0            0             0             0   \n",
       "1              0            0            0             0             0   \n",
       "2              0            0            0             0             4   \n",
       "3              0            0            0             0             0   \n",
       "4              0            0            0             0             0   \n",
       "...          ...          ...          ...           ...           ...   \n",
       "179073         0            0            0             0             1   \n",
       "179074         0            0            0             0             2   \n",
       "179075         0            0            0             0             1   \n",
       "179076         0            0            0             0             2   \n",
       "179077         0            0            0             0             0   \n",
       "\n",
       "        extra_runs  total_runs  player_dismissed dismissal_kind    fielder  \n",
       "0                0           0               NaN            NaN        NaN  \n",
       "1                0           0               NaN            NaN        NaN  \n",
       "2                0           4               NaN            NaN        NaN  \n",
       "3                0           0               NaN            NaN        NaN  \n",
       "4                2           2               NaN            NaN        NaN  \n",
       "...            ...         ...               ...            ...        ...  \n",
       "179073           0           1               NaN            NaN        NaN  \n",
       "179074           0           2               NaN            NaN        NaN  \n",
       "179075           0           1         SR Watson        run out  KH Pandya  \n",
       "179076           0           2               NaN            NaN        NaN  \n",
       "179077           0           0         SN Thakur            lbw        NaN  \n",
       "\n",
       "[179078 rows x 21 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipl = pd.read_csv('deliveries.csv')\n",
    "ipl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179078, 21)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipl.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "batsman\n",
       "V Kohli           5434\n",
       "SK Raina          5415\n",
       "RG Sharma         4914\n",
       "DA Warner         4741\n",
       "S Dhawan          4632\n",
       "CH Gayle          4560\n",
       "MS Dhoni          4477\n",
       "RV Uthappa        4446\n",
       "AB de Villiers    4428\n",
       "G Gambhir         4223\n",
       "Name: batsman_runs, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the top 10 batsman in terms of runs\n",
    "ipl.groupby('batsman')['batsman_runs'].sum().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CH Gayle'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the batsman with max no of sixes\n",
    "\n",
    "#first filter to get rows with only 6 runs\n",
    "six = ipl[ipl['batsman_runs'] == 6]\n",
    "six.groupby('batsman')['batsman_runs'].sum().sort_values(ascending=False).head(1).index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'MS Dhoni'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find batsman with most number of 4's and 6's in last 5 overs\n",
    "ipl_temp = ipl[ipl['over'] > 15]\n",
    "ipl_temp[(ipl_temp['batsman_runs']==4) | (ipl_temp['batsman_runs']==6)]\n",
    "ipl_temp.groupby('batsman')['batsman_runs'].sum().sort_values(ascending=False).head(1).index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>bowling_team</th>\n",
       "      <th>batsman_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Deccan Chargers</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kochi Tuskers Kerala</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Pune Warriors</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Rising Pune Supergiants</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               bowling_team  batsman_runs\n",
       "0       Chennai Super Kings           749\n",
       "1           Deccan Chargers           306\n",
       "2            Delhi Capitals            66\n",
       "3          Delhi Daredevils           763\n",
       "4             Gujarat Lions           283\n",
       "5           Kings XI Punjab           636\n",
       "6      Kochi Tuskers Kerala            50\n",
       "7     Kolkata Knight Riders           675\n",
       "8            Mumbai Indians           628\n",
       "9             Pune Warriors           128\n",
       "10         Rajasthan Royals           370\n",
       "11   Rising Pune Supergiant            83\n",
       "12  Rising Pune Supergiants           188\n",
       "13      Sunrisers Hyderabad           509"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find V Kohli's record against each teams\n",
    "#ipl['batsman'].value_counts()\n",
    "ipl_new = ipl[ipl['batsman'] == 'V Kohli']\n",
    "ipl_new.groupby('bowling_team')['batsman_runs'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ipl_new[(ipl_new['batsman_runs']==4) | (ipl_new['batsman_runs']==6)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q) Create a function that can return the highest score of any batsman\n",
    "\n",
    "temp_df = ipl[ipl['batsman'] == 'V Kohli']\n",
    "temp_df.groupby('match_id')['batsman_runs'].sum().sort_values(ascending=False).head(1).values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def highest(batsman):\n",
    "  temp_df = ipl[ipl['batsman'] == batsman]\n",
    "  return temp_df.groupby('match_id')['batsman_runs'].sum().sort_values(ascending=False).head(1).values[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest('DA Warner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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