Indian-Movie-Analysis2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rc('figure', figsize = (12,6))\n",
    "mpl.rc('font', size = 14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Rating</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Description</th>\n",
       "      <th>Title</th>\n",
       "      <th>Gross</th>\n",
       "      <th>Director</th>\n",
       "      <th>Release</th>\n",
       "      <th>Stars</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Gross (India)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.7</td>\n",
       "      <td>59,702</td>\n",
       "      <td>Biopic of Mahavir Singh Phogat, who taught wre...</td>\n",
       "      <td>Dangal</td>\n",
       "      <td>11,151,900</td>\n",
       "      <td>Nitesh Tiwari</td>\n",
       "      <td>21-Dec-16</td>\n",
       "      <td>Aamir Khan,Sakshi Tanwar,Fatima Sana Shaikh</td>\n",
       "      <td>Action,Biography,Drama,Sport</td>\n",
       "      <td>387.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.2</td>\n",
       "      <td>106,800</td>\n",
       "      <td>P. K. is a comedy of ideas about a stranger in...</td>\n",
       "      <td>PK</td>\n",
       "      <td>10,572,800</td>\n",
       "      <td>Rajkumar Hirani</td>\n",
       "      <td>19-Dec-14</td>\n",
       "      <td>Aamir Khan,Anushka Sharma,Sanjay Dutt</td>\n",
       "      <td>Comedy,Drama,Fantasy,Romance,Sci-Fi</td>\n",
       "      <td>339.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>51,638</td>\n",
       "      <td>A little mute girl from a Pakistan village get...</td>\n",
       "      <td>Bajrangi Bhaijaan</td>\n",
       "      <td>7,655,452</td>\n",
       "      <td>Kabir Khan</td>\n",
       "      <td>17-Jul-15</td>\n",
       "      <td>Salman Khan,Kareena Kapoor Khan,Nawazuddin Sid...</td>\n",
       "      <td>Action,Adventure,Comedy,Drama</td>\n",
       "      <td>320.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>27,251</td>\n",
       "      <td>Sultan is a story of Sultan Ali Khan - a local...</td>\n",
       "      <td>Sultan</td>\n",
       "      <td>6,173</td>\n",
       "      <td>Ali Abbas Zafar</td>\n",
       "      <td>6-Jul-16</td>\n",
       "      <td>Salman Khan,Anushka Sharma,Marko Zaror</td>\n",
       "      <td>Action,Drama,Family,Romance,Sport</td>\n",
       "      <td>300.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.4</td>\n",
       "      <td>36,496</td>\n",
       "      <td>The favourite Indian action franchise is back....</td>\n",
       "      <td>Dhoom 3</td>\n",
       "      <td>8,031,955</td>\n",
       "      <td>Vijay Krishna Acharya</td>\n",
       "      <td>20-Dec-13</td>\n",
       "      <td>Aamir Khan,Katrina Kaif,Abhishek Bachchan</td>\n",
       "      <td>Action,Crime,Drama,Thriller</td>\n",
       "      <td>280.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rating    Votes                                        Description  \\\n",
       "0     8.7   59,702  Biopic of Mahavir Singh Phogat, who taught wre...   \n",
       "1     8.2  106,800  P. K. is a comedy of ideas about a stranger in...   \n",
       "2     8.1   51,638  A little mute girl from a Pakistan village get...   \n",
       "3     7.2   27,251  Sultan is a story of Sultan Ali Khan - a local...   \n",
       "4     5.4   36,496  The favourite Indian action franchise is back....   \n",
       "\n",
       "               Title       Gross               Director    Release  \\\n",
       "0             Dangal  11,151,900          Nitesh Tiwari  21-Dec-16   \n",
       "1                 PK  10,572,800        Rajkumar Hirani  19-Dec-14   \n",
       "2  Bajrangi Bhaijaan   7,655,452             Kabir Khan  17-Jul-15   \n",
       "3             Sultan       6,173        Ali Abbas Zafar   6-Jul-16   \n",
       "4            Dhoom 3   8,031,955  Vijay Krishna Acharya  20-Dec-13   \n",
       "\n",
       "                                               Stars  \\\n",
       "0        Aamir Khan,Sakshi Tanwar,Fatima Sana Shaikh   \n",
       "1              Aamir Khan,Anushka Sharma,Sanjay Dutt   \n",
       "2  Salman Khan,Kareena Kapoor Khan,Nawazuddin Sid...   \n",
       "3             Salman Khan,Anushka Sharma,Marko Zaror   \n",
       "4          Aamir Khan,Katrina Kaif,Abhishek Bachchan   \n",
       "\n",
       "                                 Genre  Gross (India)  \n",
       "0         Action,Biography,Drama,Sport         387.39  \n",
       "1  Comedy,Drama,Fantasy,Romance,Sci-Fi         339.50  \n",
       "2        Action,Adventure,Comedy,Drama         320.34  \n",
       "3    Action,Drama,Family,Romance,Sport         300.45  \n",
       "4          Action,Crime,Drama,Thriller         280.25  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = 'A:\\\\Data Analysis Jupyter\\\\Indian Movies\\\\hindi_movies.csv'\n",
    "data = pd.read_csv(file)\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rating           <class 'numpy.float64'>\n",
       "Votes                      <class 'str'>\n",
       "Description                <class 'str'>\n",
       "Title                      <class 'str'>\n",
       "Gross                      <class 'str'>\n",
       "Director                   <class 'str'>\n",
       "Release                    <class 'str'>\n",
       "Stars                      <class 'str'>\n",
       "Genre                      <class 'str'>\n",
       "Gross (India)    <class 'numpy.float64'>\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data type of each column\n",
    "data.iloc[0].apply(type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning\n",
    "*Votes* and *Gross* is of type `string`, should be converted to `integer`.\n",
    "But `pd.to_numeric()` wont work as strings contain commas and commas are to be removed first. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace every comma with empty string in column votes\n",
    "data['Votes'] = data['Votes'].str.replace(',', '')\n",
    "# convert values under votes column from String to Integer\n",
    "data['Votes'] = pd.to_numeric(data['Votes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Gross'] = data['Gross'].str.replace(',', '')\n",
    "data['Gross'] = pd.to_numeric(data['Gross'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5607, 10)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysing the Rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4798.000000</td>\n",
       "      <td>4798.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.948458</td>\n",
       "      <td>1597.706753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.364512</td>\n",
       "      <td>6843.694953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.100000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>512.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>244050.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rating          Votes\n",
       "count  4798.000000    4798.000000\n",
       "mean      5.948458    1597.706753\n",
       "std       1.364512    6843.694953\n",
       "min       1.100000       5.000000\n",
       "25%       5.000000      21.000000\n",
       "50%       6.100000      70.000000\n",
       "75%       7.000000     512.000000\n",
       "max       9.000000  244050.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a df of movies for which a RATING is present\n",
    "movies_with_rating = data[data['Rating'].isnull() ==  False]\n",
    "# summary stats\n",
    "movies_with_rating[['Rating', 'Votes']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The _Rating_ is calculated as the mean of all the ratings a particular movie has recieved. \n",
    "_number of ratings_ recieved is *Votes*. Minimum no. of Votes a movie has received is 5. It is too low, and might create biased Rating.\n",
    "Therefore, remove such bias and consider Rating as valid for which at least 100 votes have been made. Votes > 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2141.000000</td>\n",
       "      <td>2141.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.930453</td>\n",
       "      <td>3542.204577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.459720</td>\n",
       "      <td>9907.354596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.700000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.900000</td>\n",
       "      <td>248.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.100000</td>\n",
       "      <td>667.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>2504.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.900000</td>\n",
       "      <td>244050.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rating          Votes\n",
       "count  2141.000000    2141.000000\n",
       "mean      5.930453    3542.204577\n",
       "std       1.459720    9907.354596\n",
       "min       1.700000     100.000000\n",
       "25%       4.900000     248.000000\n",
       "50%       6.100000     667.000000\n",
       "75%       7.100000    2504.000000\n",
       "max       8.900000  244050.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# chunk up only Votes >= 100\n",
    "movies_with_rating = movies_with_rating[movies_with_rating['Votes'] >= 100]\n",
    "movies_with_rating[['Rating','Votes']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot Histogram\n",
    "plt.hist(movies_with_rating['Rating'].sort_values(), bins = 20 )\n",
    "plt.title(\"Distribution of Movie Rating\")\n",
    "plt.xlabel(\"Rating\")\n",
    "plt.ylabel(\"Number of Movies\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution of ratings is **negatively skewed**. The negative slope indicated that few movies _drag down_ **mean** *Rating* of the dataset. Thus, **median** is to be used as it gives a better avg when distrubutions are skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot to show rante of ratings\n",
    "plt.boxplot(movies_with_rating['Rating'], vert = False)\n",
    "plt.xlabel('Rating')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "6.1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Median\")\n",
    "movies_with_rating['Rating'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The orange line in boxplot shows the median which is **6.1** and there are no outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysing 'Gross'\n",
    "There are 2 Gross columns, Gross column represents US box earnings and Gross(India) column represents box office collection in India"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box office Gross (USA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "304"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a df of movies where Gross USA figure is present\n",
    "movies_with_gross = data[data['Gross'].isnull() == False]\n",
    "#display number of movies having Gross count\n",
    "movies_with_gross['Gross'].count() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.040000e+02\n",
       "mean     1.039095e+06\n",
       "std      1.617755e+06\n",
       "min      1.954000e+03\n",
       "25%      2.205232e+05\n",
       "50%      5.409520e+05\n",
       "75%      1.159148e+06\n",
       "max      1.387697e+07\n",
       "Name: Gross, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_with_gross['Gross'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot histogram of US gross\n",
    "plt.hist(movies_with_gross['Gross'].sort_values(), bins = 80)\n",
    "plt.title(\"Box Office Gross in USA\")\n",
    "plt.xlabel(\"Box Office Gross in USD\")\n",
    "plt.ylabel('Number of Movies')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distrubution is skewed heavily towards right as very few movies have grossed beyond 0.4 millions. Most of them gross in the range of 0.0 to 0.4 ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(movies_with_gross['Gross'], vert = False)\n",
    "plt.title(\"Boxplot of Box Office Gross USA\")\n",
    "plt.xlabel('Gross in USD')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen, median is quite low. And handful of movies make good money than the average, this is shown by the outliers on the boxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(220523.25, 1159148.25)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding the outliers in the dataset\n",
    "q1 = movies_with_gross['Gross'].quantile(0.25)\n",
    "q3 = movies_with_gross['Gross'].quantile(0.75)\n",
    "q1, q3\n",
    "\n",
    "# 25th percentile of the data 2,20,523.25, 75th percentile data 11,59,148.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "938625.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iqr = q3 - q1 \n",
    "iqr\n",
    "# 9,38,625"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quantiles are lines that divide group into equal groups.\n",
    "Percentiles divide lines into 100 equally sized groups.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "938625.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1159148.25- 220523.25\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### IQR - Intquartile Range.\n",
    "measure of variability, based on dividing a data set into quartiles.\n",
    "\n",
    "The interquartile range iqr is the range of the middle 50% of the values in a data set, which is calculated as the difference between the 75th [upper quartile Q3] and 25th percentile [lower quartile Q1] values.\n",
    "\n",
    "Mild outliers are those lower than the 25th quartile [Q1] minus 1.5 x IQR or greater than the 75th quartile [Q3] plus 1.5 x IQR.\n",
    "\n",
    "Reference - https://www.back2code.me/2017/08/outliers/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Rating  Votes  Description  Title  Gross  Director  Release  Stars  \\\n",
      "0      False  False         True   True   True      True     True   True   \n",
      "1      False  False         True   True   True      True     True   True   \n",
      "2      False  False         True   True   True      True     True   True   \n",
      "3      False  False         True   True  False      True     True   True   \n",
      "4      False  False         True   True   True      True     True   True   \n",
      "5      False  False         True   True  False      True     True   True   \n",
      "6      False  False         True   True  False      True     True   True   \n",
      "7      False  False         True   True   True      True     True   True   \n",
      "8      False  False         True   True   True      True     True   True   \n",
      "10     False  False         True   True  False      True     True   True   \n",
      "11     False  False         True   True   True      True     True   True   \n",
      "12     False  False         True   True   True      True     True   True   \n",
      "13     False  False         True   True  False      True     True   True   \n",
      "14     False  False         True   True  False      True     True   True   \n",
      "18     False  False         True   True  False      True     True   True   \n",
      "19     False  False         True   True   True      True     True   True   \n",
      "21     False  False         True   True  False      True     True   True   \n",
      "22     False  False         True   True  False      True     True   True   \n",
      "27     False  False         True   True  False      True     True   True   \n",
      "28     False  False         True   True   True      True     True   True   \n",
      "29     False  False         True   True  False      True     True   True   \n",
      "30     False  False         True   True   True      True     True   True   \n",
      "31     False  False         True   True  False      True     True   True   \n",
      "32     False  False         True   True  False      True     True   True   \n",
      "33     False  False         True   True  False      True     True   True   \n",
      "34     False  False         True   True  False      True     True   True   \n",
      "36     False  False         True   True   True      True     True   True   \n",
      "37     False  False         True   True  False      True     True   True   \n",
      "38     False  False         True   True  False      True     True   True   \n",
      "40     False  False         True   True  False      True     True   True   \n",
      "...      ...    ...          ...    ...    ...       ...      ...    ...   \n",
      "1509   False  False         True   True  False      True     True   True   \n",
      "1531   False  False         True   True  False      True     True   True   \n",
      "1544   False  False         True   True  False      True     True   True   \n",
      "1551   False  False         True   True  False      True     True   True   \n",
      "1553   False  False         True   True  False      True     True   True   \n",
      "1560   False  False         True   True  False      True     True   True   \n",
      "1589   False  False         True   True  False      True     True   True   \n",
      "1638   False  False         True   True  False      True     True   True   \n",
      "1654   False  False         True   True  False      True     True   True   \n",
      "1663   False  False         True   True  False      True     True   True   \n",
      "1665   False  False         True   True  False      True     True   True   \n",
      "1669   False  False         True   True  False      True     True   True   \n",
      "1711   False  False         True   True  False      True     True   True   \n",
      "1733   False  False         True   True   True      True     True   True   \n",
      "1761   False  False         True   True  False      True     True   True   \n",
      "1786   False  False         True   True  False      True     True   True   \n",
      "1798   False  False         True   True  False      True     True   True   \n",
      "1847   False  False         True   True  False      True     True   True   \n",
      "1867   False  False         True   True  False      True     True   True   \n",
      "1945   False  False         True   True  False      True     True   True   \n",
      "1998   False  False         True   True  False      True     True   True   \n",
      "2059   False  False         True   True  False      True     True   True   \n",
      "2156   False  False         True   True  False      True     True   True   \n",
      "2185   False  False         True   True  False      True     True   True   \n",
      "2502   False  False         True   True  False      True     True   True   \n",
      "2720   False  False         True   True  False      True     True   True   \n",
      "2738   False  False         True   True  False      True     True   True   \n",
      "2940   False  False         True   True  False      True     True   True   \n",
      "3165   False  False         True   True  False      True     True   True   \n",
      "3173   False  False         True   True  False      True     True   True   \n",
      "\n",
      "      Genre  Gross (India)  \n",
      "0      True          False  \n",
      "1      True          False  \n",
      "2      True          False  \n",
      "3      True          False  \n",
      "4      True          False  \n",
      "5      True          False  \n",
      "6      True          False  \n",
      "7      True          False  \n",
      "8      True          False  \n",
      "10     True          False  \n",
      "11     True          False  \n",
      "12     True          False  \n",
      "13     True          False  \n",
      "14     True          False  \n",
      "18     True          False  \n",
      "19     True          False  \n",
      "21     True          False  \n",
      "22     True          False  \n",
      "27     True          False  \n",
      "28     True          False  \n",
      "29     True          False  \n",
      "30     True          False  \n",
      "31     True          False  \n",
      "32     True          False  \n",
      "33     True          False  \n",
      "34     True          False  \n",
      "36     True          False  \n",
      "37     True          False  \n",
      "38     True          False  \n",
      "40     True          False  \n",
      "...     ...            ...  \n",
      "1509   True          False  \n",
      "1531   True          False  \n",
      "1544   True          False  \n",
      "1551   True          False  \n",
      "1553   True          False  \n",
      "1560   True          False  \n",
      "1589   True          False  \n",
      "1638   True          False  \n",
      "1654   True          False  \n",
      "1663   True          False  \n",
      "1665   True          False  \n",
      "1669   True          False  \n",
      "1711   True          False  \n",
      "1733   True          False  \n",
      "1761   True          False  \n",
      "1786   True          False  \n",
      "1798   True          False  \n",
      "1847   True          False  \n",
      "1867   True          False  \n",
      "1945   True          False  \n",
      "1998   True          False  \n",
      "2059   True          False  \n",
      "2156   True          False  \n",
      "2185   True          False  \n",
      "2502   True          False  \n",
      "2720   True          False  \n",
      "2738   True          False  \n",
      "2940   True          False  \n",
      "3165   True          False  \n",
      "3173   True          False  \n",
      "\n",
      "[304 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "print(movies_with_gross > (q3 + 1.5 * iqr))\n",
    "\n",
    "# False indicates valid points, true = presence of outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Rating</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Gross</th>\n",
       "      <th>Gross (India)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>2.800000e+01</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.025000</td>\n",
       "      <td>42729.142857</td>\n",
       "      <td>4.929001e+06</td>\n",
       "      <td>183.310625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.083761</td>\n",
       "      <td>45283.741383</td>\n",
       "      <td>2.897258e+06</td>\n",
       "      <td>103.728969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.700000</td>\n",
       "      <td>1558.000000</td>\n",
       "      <td>2.633885e+06</td>\n",
       "      <td>22.210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.325000</td>\n",
       "      <td>20244.500000</td>\n",
       "      <td>3.069054e+06</td>\n",
       "      <td>109.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.150000</td>\n",
       "      <td>31601.500000</td>\n",
       "      <td>3.739582e+06</td>\n",
       "      <td>161.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.925000</td>\n",
       "      <td>46213.750000</td>\n",
       "      <td>5.610831e+06</td>\n",
       "      <td>240.087500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.700000</td>\n",
       "      <td>244050.000000</td>\n",
       "      <td>1.387697e+07</td>\n",
       "      <td>387.390000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Rating          Votes         Gross  Gross (India)\n",
       "count  28.000000      28.000000  2.800000e+01      16.000000\n",
       "mean    7.025000   42729.142857  4.929001e+06     183.310625\n",
       "std     1.083761   45283.741383  2.897258e+06     103.728969\n",
       "min     4.700000    1558.000000  2.633885e+06      22.210000\n",
       "25%     6.325000   20244.500000  3.069054e+06     109.375000\n",
       "50%     7.150000   31601.500000  3.739582e+06     161.500000\n",
       "75%     7.925000   46213.750000  5.610831e+06     240.087500\n",
       "max     8.700000  244050.000000  1.387697e+07     387.390000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers = movies_with_gross.query('Gross > (@q3 + 1.5*@iqr)')\n",
    "outliers.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**28** out of 304 movies can be considered as outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median gross of all movies = 540952.0\n",
      "Median gross of Outliers = 3739582.5\n",
      "Ratio of mean gross = 4.743551207424231\n",
      "Ratio of median gross = 6.912965475679912\n",
      "Percentage of outliers = 9.210526315789473\n"
     ]
    }
   ],
   "source": [
    "# Display the median value for US Gross (Outliers)\n",
    "print(\"Median gross of all movies = {}\".format(movies_with_gross['Gross'].median()))\n",
    "print(\"Median gross of Outliers = {}\".format(outliers['Gross'].median()))\n",
    "print(\"Ratio of mean gross = {}\".format(outliers['Gross'].mean()/movies_with_gross['Gross'].mean()))\n",
    "print(\"Ratio of median gross = {}\".format(outliers['Gross'].median()/movies_with_gross['Gross'].median()))\n",
    "print(\"Percentage of outliers = {}\".format(outliers['Title'].count()/movies_with_gross['Title'].count() * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a histogram of US gross (Outliers)\n",
    "plt.hist(outliers['Gross'].sort_values(), bins=100)\n",
    "plt.title(\"Box Office Gross (USA) [OUTLIERS]\")\n",
    "plt.xlabel(\"Box Office Gross in USD\")\n",
    "plt.ylabel(\"Number of Movies\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Histogram depicts a positive skewness. Therefore, there exists **outliers within** `outliers`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movies which have a US box office gross above $5 million:\n"
     ]
    },
    {
     "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>Title</th>\n",
       "      <th>Director</th>\n",
       "      <th>Stars</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Gross</th>\n",
       "      <th>Gross (India)</th>\n",
       "      <th>Release</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monsoon Wedding</td>\n",
       "      <td>Mira Nair</td>\n",
       "      <td>Naseeruddin Shah,Lillete Dubey,Shefali Shetty</td>\n",
       "      <td>7.4</td>\n",
       "      <td>13876974.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26 April 2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dangal</td>\n",
       "      <td>Nitesh Tiwari</td>\n",
       "      <td>Aamir Khan,Sakshi Tanwar,Fatima Sana Shaikh</td>\n",
       "      <td>8.7</td>\n",
       "      <td>11151900.0</td>\n",
       "      <td>387.39</td>\n",
       "      <td>21-Dec-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PK</td>\n",
       "      <td>Rajkumar Hirani</td>\n",
       "      <td>Aamir Khan,Anushka Sharma,Sanjay Dutt</td>\n",
       "      <td>8.2</td>\n",
       "      <td>10572800.0</td>\n",
       "      <td>339.50</td>\n",
       "      <td>19-Dec-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dhoom 3</td>\n",
       "      <td>Vijay Krishna Acharya</td>\n",
       "      <td>Aamir Khan,Katrina Kaif,Abhishek Bachchan</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8031955.0</td>\n",
       "      <td>280.25</td>\n",
       "      <td>20-Dec-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bajrangi Bhaijaan</td>\n",
       "      <td>Kabir Khan</td>\n",
       "      <td>Salman Khan,Kareena Kapoor Khan,Nawazuddin Sid...</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7655452.0</td>\n",
       "      <td>320.34</td>\n",
       "      <td>17-Jul-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bajirao Mastani</td>\n",
       "      <td>Sanjay Leela Bhansali</td>\n",
       "      <td>Ranveer Singh,Priyanka Chopra,Deepika Padukone</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6557047.0</td>\n",
       "      <td>184.00</td>\n",
       "      <td>18-Dec-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3 Idiots</td>\n",
       "      <td>Rajkumar Hirani</td>\n",
       "      <td>Aamir Khan,Madhavan,Mona Singh</td>\n",
       "      <td>8.4</td>\n",
       "      <td>6523137.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25-Dec-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Chennai Express</td>\n",
       "      <td>Rohit Shetty</td>\n",
       "      <td>Deepika Padukone,Shah Rukh Khan,Sathyaraj</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5306729.0</td>\n",
       "      <td>226.70</td>\n",
       "      <td>8-Aug-13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Title               Director  \\\n",
       "0    Monsoon Wedding              Mira Nair   \n",
       "1             Dangal          Nitesh Tiwari   \n",
       "2                 PK        Rajkumar Hirani   \n",
       "3            Dhoom 3  Vijay Krishna Acharya   \n",
       "4  Bajrangi Bhaijaan             Kabir Khan   \n",
       "5    Bajirao Mastani  Sanjay Leela Bhansali   \n",
       "6           3 Idiots        Rajkumar Hirani   \n",
       "7    Chennai Express           Rohit Shetty   \n",
       "\n",
       "                                               Stars  Rating       Gross  \\\n",
       "0      Naseeruddin Shah,Lillete Dubey,Shefali Shetty     7.4  13876974.0   \n",
       "1        Aamir Khan,Sakshi Tanwar,Fatima Sana Shaikh     8.7  11151900.0   \n",
       "2              Aamir Khan,Anushka Sharma,Sanjay Dutt     8.2  10572800.0   \n",
       "3          Aamir Khan,Katrina Kaif,Abhishek Bachchan     5.4   8031955.0   \n",
       "4  Salman Khan,Kareena Kapoor Khan,Nawazuddin Sid...     8.1   7655452.0   \n",
       "5     Ranveer Singh,Priyanka Chopra,Deepika Padukone     7.2   6557047.0   \n",
       "6                     Aamir Khan,Madhavan,Mona Singh     8.4   6523137.0   \n",
       "7          Deepika Padukone,Shah Rukh Khan,Sathyaraj     6.0   5306729.0   \n",
       "\n",
       "   Gross (India)        Release  \n",
       "0            NaN  26 April 2002  \n",
       "1         387.39      21-Dec-16  \n",
       "2         339.50      19-Dec-14  \n",
       "3         280.25      20-Dec-13  \n",
       "4         320.34      17-Jul-15  \n",
       "5         184.00      18-Dec-15  \n",
       "6            NaN      25-Dec-09  \n",
       "7         226.70       8-Aug-13  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Outliers within the outliers. \n",
    "print(\"Movies which have a US box office gross above $5 million:\")\n",
    "high_gross_in_us = movies_with_gross[movies_with_gross['Gross'] > 5.0e+6].sort_values('Gross', ascending=False)\n",
    "high_gross_in_us.reset_index(inplace=True)\n",
    "high_gross_in_us[['Title', 'Director', 'Stars', 'Rating', 'Gross', 'Gross (India)', 'Release']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Monsoon Wedding** is the highest gross Indian movie in USA following Dangal and PK."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BOX OFFICE GROSS(INDIA)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    380.000000\n",
       "mean      43.651579\n",
       "std       55.031905\n",
       "min        0.400000\n",
       "25%        7.500000\n",
       "50%       26.535000\n",
       "75%       58.540000\n",
       "max      387.390000\n",
       "Name: Gross (India), dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a df of movies for which Indian Gross Fig is present\n",
    "movies_with_gross_india = data[data['Gross (India)'].isnull() == False ]\n",
    "movies_with_gross_india['Gross (India)'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "380 Movies are present for which Gross Figs in India are given.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a histogram of Indian gross\n",
    "plt.hist(movies_with_gross_india['Gross (India)'].sort_values(), bins=76)\n",
    "plt.title(\"Box Office Gross (India)\")\n",
    "plt.xlabel(\"Box Office Gross in 1e7 INR\")\n",
    "plt.ylabel(\"Number of Movies\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Positively Skewed**. Consider median rather than mean as few outliers increase the avg significantly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a boxplot to show the range of Indian Gross\n",
    "plt.boxplot(movies_with_gross_india['Gross (India)'], vert = False)\n",
    "plt.title('Boxplot of Box Office Gross (India)')\n",
    "plt.xlabel('Gross in 1e7 INR')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Median is quite **low**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Description</th>\n",
       "      <th>Title</th>\n",
       "      <th>Gross</th>\n",
       "      <th>Director</th>\n",
       "      <th>Release</th>\n",
       "      <th>Stars</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Gross (India)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.7</td>\n",
       "      <td>59702.0</td>\n",
       "      <td>Biopic of Mahavir Singh Phogat, who taught wre...</td>\n",
       "      <td>Dangal</td>\n",
       "      <td>11151900.0</td>\n",
       "      <td>Nitesh Tiwari</td>\n",
       "      <td>21-Dec-16</td>\n",
       "      <td>Aamir Khan,Sakshi Tanwar,Fatima Sana Shaikh</td>\n",
       "      <td>Action,Biography,Drama,Sport</td>\n",
       "      <td>387.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.2</td>\n",
       "      <td>106800.0</td>\n",
       "      <td>P. K. is a comedy of ideas about a stranger in...</td>\n",
       "      <td>PK</td>\n",
       "      <td>10572800.0</td>\n",
       "      <td>Rajkumar Hirani</td>\n",
       "      <td>19-Dec-14</td>\n",
       "      <td>Aamir Khan,Anushka Sharma,Sanjay Dutt</td>\n",
       "      <td>Comedy,Drama,Fantasy,Romance,Sci-Fi</td>\n",
       "      <td>339.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>51638.0</td>\n",
       "      <td>A little mute girl from a Pakistan village get...</td>\n",
       "      <td>Bajrangi Bhaijaan</td>\n",
       "      <td>7655452.0</td>\n",
       "      <td>Kabir Khan</td>\n",
       "      <td>17-Jul-15</td>\n",
       "      <td>Salman Khan,Kareena Kapoor Khan,Nawazuddin Sid...</td>\n",
       "      <td>Action,Adventure,Comedy,Drama</td>\n",
       "      <td>320.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>27251.0</td>\n",
       "      <td>Sultan is a story of Sultan Ali Khan - a local...</td>\n",
       "      <td>Sultan</td>\n",
       "      <td>6173.0</td>\n",
       "      <td>Ali Abbas Zafar</td>\n",
       "      <td>6-Jul-16</td>\n",
       "      <td>Salman Khan,Anushka Sharma,Marko Zaror</td>\n",
       "      <td>Action,Drama,Family,Romance,Sport</td>\n",
       "      <td>300.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.4</td>\n",
       "      <td>36496.0</td>\n",
       "      <td>The favourite Indian action franchise is back....</td>\n",
       "      <td>Dhoom 3</td>\n",
       "      <td>8031955.0</td>\n",
       "      <td>Vijay Krishna Acharya</td>\n",
       "      <td>20-Dec-13</td>\n",
       "      <td>Aamir Khan,Katrina Kaif,Abhishek Bachchan</td>\n",
       "      <td>Action,Crime,Drama,Thriller</td>\n",
       "      <td>280.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rating     Votes                                        Description  \\\n",
       "0     8.7   59702.0  Biopic of Mahavir Singh Phogat, who taught wre...   \n",
       "1     8.2  106800.0  P. K. is a comedy of ideas about a stranger in...   \n",
       "2     8.1   51638.0  A little mute girl from a Pakistan village get...   \n",
       "3     7.2   27251.0  Sultan is a story of Sultan Ali Khan - a local...   \n",
       "4     5.4   36496.0  The favourite Indian action franchise is back....   \n",
       "\n",
       "               Title       Gross               Director    Release  \\\n",
       "0             Dangal  11151900.0          Nitesh Tiwari  21-Dec-16   \n",
       "1                 PK  10572800.0        Rajkumar Hirani  19-Dec-14   \n",
       "2  Bajrangi Bhaijaan   7655452.0             Kabir Khan  17-Jul-15   \n",
       "3             Sultan      6173.0        Ali Abbas Zafar   6-Jul-16   \n",
       "4            Dhoom 3   8031955.0  Vijay Krishna Acharya  20-Dec-13   \n",
       "\n",
       "                                               Stars  \\\n",
       "0        Aamir Khan,Sakshi Tanwar,Fatima Sana Shaikh   \n",
       "1              Aamir Khan,Anushka Sharma,Sanjay Dutt   \n",
       "2  Salman Khan,Kareena Kapoor Khan,Nawazuddin Sid...   \n",
       "3             Salman Khan,Anushka Sharma,Marko Zaror   \n",
       "4          Aamir Khan,Katrina Kaif,Abhishek Bachchan   \n",
       "\n",
       "                                 Genre  Gross (India)  \n",
       "0         Action,Biography,Drama,Sport         387.39  \n",
       "1  Comedy,Drama,Fantasy,Romance,Sci-Fi         339.50  \n",
       "2        Action,Adventure,Comedy,Drama         320.34  \n",
       "3    Action,Drama,Family,Romance,Sport         300.45  \n",
       "4          Action,Crime,Drama,Thriller         280.25  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_with_gross_india.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.5 58.53999999999999\n",
      "51.03999999999999\n"
     ]
    }
   ],
   "source": [
    "# Finding the outliers in the dataset\n",
    "q1 = movies_with_gross_india['Gross (India)'].quantile(0.25)\n",
    "q3 = movies_with_gross_india['Gross (India)'].quantile(0.75)\n",
    "print(q1,q3)\n",
    "print(q3 -q1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Rating</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Gross</th>\n",
       "      <th>Gross (India)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>1.170000e+02</td>\n",
       "      <td>117.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.180342</td>\n",
       "      <td>14739.068376</td>\n",
       "      <td>1.360197e+06</td>\n",
       "      <td>81.615983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.318649</td>\n",
       "      <td>15969.287969</td>\n",
       "      <td>1.945435e+06</td>\n",
       "      <td>73.466779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.300000</td>\n",
       "      <td>324.000000</td>\n",
       "      <td>6.108000e+03</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.400000</td>\n",
       "      <td>4179.000000</td>\n",
       "      <td>3.369850e+05</td>\n",
       "      <td>30.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.100000</td>\n",
       "      <td>8526.000000</td>\n",
       "      <td>5.860280e+05</td>\n",
       "      <td>58.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.200000</td>\n",
       "      <td>19699.000000</td>\n",
       "      <td>1.535234e+06</td>\n",
       "      <td>105.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.700000</td>\n",
       "      <td>106800.000000</td>\n",
       "      <td>1.115190e+07</td>\n",
       "      <td>387.390000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Rating          Votes         Gross  Gross (India)\n",
       "count  117.000000     117.000000  1.170000e+02     117.000000\n",
       "mean     6.180342   14739.068376  1.360197e+06      81.615983\n",
       "std      1.318649   15969.287969  1.945435e+06      73.466779\n",
       "min      2.300000     324.000000  6.108000e+03       6.000000\n",
       "25%      5.400000    4179.000000  3.369850e+05      30.900000\n",
       "50%      6.100000    8526.000000  5.860280e+05      58.000000\n",
       "75%      7.200000   19699.000000  1.535234e+06     105.500000\n",
       "max      8.700000  106800.000000  1.115190e+07     387.390000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iqr = q3 - q1\n",
    "outliers = movies_with_gross_india.query('Gross > (@q3 + 1.5 * @iqr)')\n",
    "outliers.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median gross of all movies = 26.535\n",
      "Median gross of outliers = 58.0\n",
      "Ratio of mean gross = 1.8697143350619452\n",
      "Ratio of median gross = 2.185792349726776\n",
      "Percentage of outliers = 30.789473684210527\n"
     ]
    }
   ],
   "source": [
    "# Comparison of all movies versus outliers\n",
    "print(\"Median gross of all movies = {}\".format(movies_with_gross_india['Gross (India)'].median()))\n",
    "print(\"Median gross of outliers = {}\".format(outliers['Gross (India)'].median()))\n",
    "print(\"Ratio of mean gross = {}\".format(outliers['Gross (India)'].mean()/movies_with_gross_india['Gross (India)'].mean()))\n",
    "print(\"Ratio of median gross = {}\".format(outliers['Gross (India)'].median()/movies_with_gross_india['Gross (India)'].median()))\n",
    "print(\"Percentage of outliers = {}\".format(outliers['Title'].count()/movies_with_gross_india['Title'].count() * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**30.8%** are considered as outliers. These movies have a mean that is **1.8** times higher where median is **2.2** times higher.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a histogram of Indian gross (Outliers)\n",
    "plt.hist(outliers['Gross (India)'].sort_values(), bins=76)\n",
    "plt.title(\"Box Office Gross (India) [OUTLIERS]\")\n",
    "plt.xlabel(\"Box Office Gross in 1e7 INR\")\n",
    "plt.ylabel(\"Number of Movies\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ditribution is skewed to the the right and this shows that there exists outliers even within the outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movies which have an Indian box office gross above INR 100cr:\n"
     ]
    },
    {
     "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>Title</th>\n",
       "      <th>Director</th>\n",
       "      <th>Stars</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Gross</th>\n",
       "      <th>Gross (India)</th>\n",
       "      <th>Release</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dangal</td>\n",
       "      <td>Nitesh Tiwari</td>\n",
       "      <td>Aamir Khan,Sakshi Tanwar,Fatima Sana Shaikh</td>\n",
       "      <td>8.7</td>\n",
       "      <td>11151900.0</td>\n",
       "      <td>387.39</td>\n",
       "      <td>21-Dec-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PK</td>\n",
       "      <td>Rajkumar Hirani</td>\n",
       "      <td>Aamir Khan,Anushka Sharma,Sanjay Dutt</td>\n",
       "      <td>8.2</td>\n",
       "      <td>10572800.0</td>\n",
       "      <td>339.50</td>\n",
       "      <td>19-Dec-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bajrangi Bhaijaan</td>\n",
       "      <td>Kabir Khan</td>\n",
       "      <td>Salman Khan,Kareena Kapoor Khan,Nawazuddin Sid...</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7655452.0</td>\n",
       "      <td>320.34</td>\n",
       "      <td>17-Jul-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sultan</td>\n",
       "      <td>Ali Abbas Zafar</td>\n",
       "      <td>Salman Khan,Anushka Sharma,Marko Zaror</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6173.0</td>\n",
       "      <td>300.45</td>\n",
       "      <td>6-Jul-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dhoom 3</td>\n",
       "      <td>Vijay Krishna Acharya</td>\n",
       "      <td>Aamir Khan,Katrina Kaif,Abhishek Bachchan</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8031955.0</td>\n",
       "      <td>280.25</td>\n",
       "      <td>20-Dec-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Krrish 3</td>\n",
       "      <td>Rakesh Roshan</td>\n",
       "      <td>Priyanka Chopra,Hrithik Roshan,Amitabh Bachchan</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2191343.0</td>\n",
       "      <td>240.50</td>\n",
       "      <td>1-Nov-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kick</td>\n",
       "      <td>Sajid Nadiadwala</td>\n",
       "      <td>Salman Khan,Jacqueline Fernandez,Nawazuddin Si...</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2461031.0</td>\n",
       "      <td>233.00</td>\n",
       "      <td>25-Jul-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Chennai Express</td>\n",
       "      <td>Rohit Shetty</td>\n",
       "      <td>Deepika Padukone,Shah Rukh Khan,Sathyaraj</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5306729.0</td>\n",
       "      <td>226.70</td>\n",
       "      <td>8-Aug-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Prem Ratan Dhan Payo</td>\n",
       "      <td>Sooraj R. Barjatya</td>\n",
       "      <td>Salman Khan,Sonam Kapoor,Neil Nitin Mukesh</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4328805.0</td>\n",
       "      <td>207.40</td>\n",
       "      <td>12-Nov-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Happy New Year</td>\n",
       "      <td>Farah Khan</td>\n",
       "      <td>Shah Rukh Khan,Deepika Padukone,Abhishek Bachchan</td>\n",
       "      <td>5.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>205.00</td>\n",
       "      <td>24-Oct-14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Title               Director  \\\n",
       "0                Dangal          Nitesh Tiwari   \n",
       "1                    PK        Rajkumar Hirani   \n",
       "2     Bajrangi Bhaijaan             Kabir Khan   \n",
       "3                Sultan        Ali Abbas Zafar   \n",
       "4               Dhoom 3  Vijay Krishna Acharya   \n",
       "5              Krrish 3          Rakesh Roshan   \n",
       "6                  Kick       Sajid Nadiadwala   \n",
       "7       Chennai Express           Rohit Shetty   \n",
       "8  Prem Ratan Dhan Payo     Sooraj R. Barjatya   \n",
       "9        Happy New Year             Farah Khan   \n",
       "\n",
       "                                               Stars  Rating       Gross  \\\n",
       "0        Aamir Khan,Sakshi Tanwar,Fatima Sana Shaikh     8.7  11151900.0   \n",
       "1              Aamir Khan,Anushka Sharma,Sanjay Dutt     8.2  10572800.0   \n",
       "2  Salman Khan,Kareena Kapoor Khan,Nawazuddin Sid...     8.1   7655452.0   \n",
       "3             Salman Khan,Anushka Sharma,Marko Zaror     7.2      6173.0   \n",
       "4          Aamir Khan,Katrina Kaif,Abhishek Bachchan     5.4   8031955.0   \n",
       "5    Priyanka Chopra,Hrithik Roshan,Amitabh Bachchan     5.3   2191343.0   \n",
       "6  Salman Khan,Jacqueline Fernandez,Nawazuddin Si...     5.5   2461031.0   \n",
       "7          Deepika Padukone,Shah Rukh Khan,Sathyaraj     6.0   5306729.0   \n",
       "8         Salman Khan,Sonam Kapoor,Neil Nitin Mukesh     4.7   4328805.0   \n",
       "9  Shah Rukh Khan,Deepika Padukone,Abhishek Bachchan     5.2         NaN   \n",
       "\n",
       "   Gross (India)    Release  \n",
       "0         387.39  21-Dec-16  \n",
       "1         339.50  19-Dec-14  \n",
       "2         320.34  17-Jul-15  \n",
       "3         300.45   6-Jul-16  \n",
       "4         280.25  20-Dec-13  \n",
       "5         240.50   1-Nov-13  \n",
       "6         233.00  25-Jul-14  \n",
       "7         226.70   8-Aug-13  \n",
       "8         207.40  12-Nov-15  \n",
       "9         205.00  24-Oct-14  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Outliers amongst the outliers\n",
    "print(\"Movies which have an Indian box office gross above INR 100cr:\")\n",
    "high_gross_in_india = movies_with_gross_india[movies_with_gross_india['Gross (India)'] > 100]\n",
    "high_gross_in_india.sort_values('Gross (India)', ascending=False).reset_index(inplace=True)\n",
    "high_gross_in_india[['Title', 'Director', 'Stars', 'Rating', 'Gross', 'Gross (India)', 'Release']] [:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dangal** and **PK** are the highest grossers over **100 Crore INR** in India. Both Aamir Khan starrers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most Popular Genre\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           Action,Biography,Drama,Sport\n",
       "1    Comedy,Drama,Fantasy,Romance,Sci-Fi\n",
       "2          Action,Adventure,Comedy,Drama\n",
       "3      Action,Drama,Family,Romance,Sport\n",
       "4            Action,Crime,Drama,Thriller\n",
       "Name: Genre, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DF of movies for which genre is present\n",
    "movies_with_genre = data[data['Genre'].isnull() == False]\n",
    "data['Genre'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique genres: 21\n"
     ]
    }
   ],
   "source": [
    "# collecting the unique genres into a set\n",
    "unique_genres = set()\n",
    "for genre in movies_with_genre['Genre'].str.split(\",\"):\n",
    "    for g in genre:\n",
    "        unique_genres.add(g)\n",
    "        \n",
    "    \n",
    "print(\"Number of unique genres: {}\".format(len(unique_genres)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are **21** unique genres."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#Grouping by genres\n",
    "movies_by_genre = dict()\n",
    "genre_masks = dict()\n",
    "\n",
    "# Dictionary of masks\n",
    "for genre in unique_genres:\n",
    "    genre_masks[genre] = movies_with_genre['Genre'].apply(lambda x: genre in x)\n",
    "\n",
    "\n",
    "# create dic of movies witg particular genre\n",
    "for genre in unique_genres:\n",
    "    movies_by_genre[genre] = movies_with_genre[genre_masks[genre]]\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of movies in each genre\n",
      "Drama        3400\n",
      "Romance      1658\n",
      "Action       1538\n",
      "Comedy       1170\n",
      "Thriller     1001\n",
      "Crime         873\n",
      "Family        624\n",
      "Music         563\n",
      "Musical       538\n",
      "Adventure     286\n",
      "Mystery       279\n",
      "Fantasy       167\n",
      "Horror        157\n",
      "History       102\n",
      "Biography      60\n",
      "War            48\n",
      "Sport          44\n",
      "Animation      43\n",
      "Sci-Fi         41\n",
      "Western         6\n",
      "News            2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# create a dic mapping of genre and number of movies belong to that genre\n",
    "count_of_movies_by_genre = dict()\n",
    "\n",
    "for genre in unique_genres:\n",
    "    count_of_movies_by_genre[genre] = movies_by_genre[genre]['Title'].count()\n",
    "    \n",
    "count_of_movies_by_genre = pd.Series(count_of_movies_by_genre).sort_values(ascending = False)\n",
    "print(\"Number of movies in each genre\")\n",
    "print(count_of_movies_by_genre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_of_movies_by_genre.plot.bar()\n",
    "plt.title('Popularity of Genres')\n",
    "plt.ylabel('Number of Movies')\n",
    "plt.xlabel('Genre')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Drama** is the most popular genre. **News, Western and Sci-Fi** are the least popular genres in India."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of movies with particular group of genres :\n",
      "\n",
      "Genre\n",
      "Action                                                      237\n",
      "Action,Adventure                                             12\n",
      "Action,Adventure,Biography,Drama,History,Musical,Romance      1\n",
      "Action,Adventure,Comedy                                       1\n",
      "Action,Adventure,Comedy,Crime                                 1\n",
      "Action,Adventure,Comedy,Crime,Drama,Thriller                  1\n",
      "Action,Adventure,Comedy,Crime,Musical                         2\n",
      "Action,Adventure,Comedy,Crime,Sport                           1\n",
      "Action,Adventure,Comedy,Drama                                 6\n",
      "Action,Adventure,Comedy,Drama,Fantasy                         1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Create df which contains grouping of genres and the movies associated with\n",
    "# the grouped genres\n",
    "movies_with_grouped_genres = movies_with_genre.groupby('Genre')\n",
    "print(\"Number of movies with particular group of genres :\\n\")\n",
    "print(movies_with_grouped_genres.size()[:10]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Popular Grouped Genres: \n",
      "\n",
      "Genre\n",
      "Drama                   600\n",
      "Action                  237\n",
      "Drama,Romance           229\n",
      "Drama,Family            203\n",
      "Romance                 180\n",
      "Comedy                  177\n",
      "Comedy,Drama            162\n",
      "Comedy,Drama,Romance    157\n",
      "Action,Crime,Drama      137\n",
      "Action,Drama            124\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Most Popular Grouped Genres: \\n\")\n",
    "print(movies_with_grouped_genres.size().sort_values(ascending = False)[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "most_pop = (movies_with_grouped_genres.size().sort_values(ascending = False)[:10])\n",
    "most_pop.plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly Visible, combinations of **Drama, Romance** and **Drama, Family** are the most popular by far."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of Unique Stars\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5557"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df of movies for which stars info is present\n",
    "movies_with_stars = data[data['Stars'].isnull() == False]\n",
    "# no of movies for which stars info is present\n",
    "movies_with_stars.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are total **5557** diff movies for which an actor or more is present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Unique Stars : 4042\n"
     ]
    }
   ],
   "source": [
    "unique_stars = set()\n",
    "for stars in movies_with_stars['Stars'].str.split(\",\"):\n",
    "    for star in stars:\n",
    "        unique_stars.add(star)\n",
    "# display\n",
    "print(\"Number of Unique Stars : {}\".format(len(unique_stars)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4042** different actor names are present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group movies by stars\n",
    "movies_by_star = dict()\n",
    "\n",
    "# Creating a dictionary of movies accessed by star\n",
    "for star in unique_stars:\n",
    "    movies_by_star[star] = movies_with_stars[movies_with_stars['Stars'].apply(lambda x: star in x )]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary mapping of star and number of movies\n",
    "# that the actor stared in\n",
    "count_of_movies_by_star = dict()\n",
    "\n",
    "for star in unique_stars:\n",
    "    count_of_movies_by_star[star] = movies_by_star[star]['Title'].count()\n",
    "\n",
    "count_of_movies_by_star = pd.Series(count_of_movies_by_star).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of number of starring roles\n",
    "plt.hist(count_of_movies_by_star, bins=100)\n",
    "plt.title('Nmber of Starring Roles')\n",
    "plt.ylabel('Number of Stars')\n",
    "plt.xlabel('Number of Roles')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_of_movies_by_star.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The disributon of starring roles is heavily skewed to the right. Most actors have starred in only 1 movie and hardly anyone has appeared in more than **25** movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of movies by each star (top 20)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Amit                  182\n",
       "Mithun Chakraborty    182\n",
       "Dharmendra            162\n",
       "Amitabh Bachchan      152\n",
       "Jeetendra             137\n",
       "Ashok Kumar           128\n",
       "Rekha                 125\n",
       "Rajesh Khanna         123\n",
       "Sanjay Dutt           115\n",
       "Akshay Kumar          114\n",
       "Chand                 112\n",
       "Vinod Khanna          102\n",
       "Arun                  101\n",
       "Ajay                   98\n",
       "Shashi Kapoor          97\n",
       "Govinda                95\n",
       "Raj Babbar             94\n",
       "Madhu                  94\n",
       "Rishi Kapoor           93\n",
       "Jackie Shroff          93\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The 20 most featured actors\n",
    "print(\"Number of movies by each star (top 20)\")\n",
    "count_of_movies_by_star[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot of the 20 stars with the highest number of roles\n",
    "count_of_movies_by_star[:20].plot.bar()\n",
    "plt.title('Nmber of Starring Roles (top 20)')\n",
    "plt.ylabel('Number of Roles')\n",
    "plt.xlabel('Star')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Actor with **max** no of roles is **Mithun Chakraborty** having stared **182** roles. **Rekha** played the max number of roles as an actress which are **125** roles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of unique directors\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Director\n",
       "[]                      59\n",
       "David Dhawan            43\n",
       "Mahesh Bhatt            38\n",
       "Hrishikesh Mukherjee    35\n",
       "Rama Rao Tatineni       34\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe consisting of moies for which \n",
    "# name of director is present \n",
    "movies_with_director = data[(data['Director'].isnull() == False)]\n",
    "\n",
    "# Group movies by unique direcotrs \n",
    "movies_by_director = movies_with_director.groupby('Director').size().sort_values(ascending=False)\n",
    "\n",
    "movies_by_director.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# '[]' is not a valid value for director there ignore and remove\n",
    "movies_by_director = movies_by_director.drop(labels='[]', axis='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique directors: 2163\n",
      "Director with the highest number of movies: David Dhawan\n",
      "Maximum number of movies directed: 43\n"
     ]
    }
   ],
   "source": [
    "print('Number of unique directors: {}'.format(movies_by_director.size))\n",
    "print('Director with the highest number of movies: {}'.format(movies_by_director.index[0]))\n",
    "print('Maximum number of movies directed: {}'.format(movies_by_director.iloc[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are **2163** different directors, and amongst them **David Dhawan** is the one who has directed the highest number of movies with **43** films to his credit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of movies directed\n",
    "plt.hist(movies_by_director, bins=43)\n",
    "plt.title(\"Number of Movies Directed\")\n",
    "plt.xlabel(\"Number of Movies\")\n",
    "plt.ylabel(\"Number of Directors\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2163.000000\n",
       "mean        2.564956\n",
       "std         3.780985\n",
       "min         1.000000\n",
       "25%         1.000000\n",
       "50%         1.000000\n",
       "75%         2.000000\n",
       "max        43.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_by_director.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram is **skewed to its right** due to *small* number of directors directing a *large* number of movies. \n",
    "The skewness also indicates, most of the directors only get once chance to direct only one movie or two at the max and the careers end. Very few direct 3rd or 4th film and fewer still go on to direct more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}