Assigment1_COMP41680_Sitabja_Ukil_17200734.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sitabja Ukil - 17200734"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COMP41680 -   Assignment 1: Data Collection & Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from urllib.request import urlopen\n",
    "import csv\n",
    "import pandas as pd \n",
    "import json\n",
    "\n",
    "#import for the supporting apis for analysis\n",
    "from itertools import chain\n",
    "from ast import literal_eval\n",
    "import numpy as np\n",
    "\n",
    "#import for visualization api\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1: Identify one or more suitable web APIs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The API chosen for this assignment is https://www.themoviedb.org/documentation/api/discover. This is a very powerful API to get information about movies their ratings and reviews."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am using an API to get information of the movies which are released after 1900. \n",
    "The Api key I have recieved is 3088d30de9df0e8832fd0436bdd62d98"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "api_key= \"3088d30de9df0e8832fd0436bdd62d98\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "base_link = \"https://api.themoviedb.org/3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "movie_link = \"/discover/movie?primary_release_date.gte=1900-09-15\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "movie_genres_link = \"/genre/movie/list?\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The API has a pagination in the response and each page has around 20 movies. Here , I am taking first 20 pages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pages = range(1,41);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2: Collect data from your chosen API(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "functions to get the data from the api"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red' style=\"font-style:italic\">get the data from the given api</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_data_from_api(link,api_key,page,isGenre):\n",
    "    u = urlopen(link+\"&api_key=\"+api_key+\"&page=\"+str(page)); #open the url to read\n",
    "    resp = json.loads(u.read().decode('utf-8')) #load json data from the api \n",
    "    response = []\n",
    "    if (not isGenre): #check which ai is called\n",
    "        response = resp['results'];\n",
    "    else:\n",
    "        response = resp['genres']\n",
    "    return response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red' style=\"font-style:italic\">In movie list genre filed has ids , this method is to chnage those ids to their appropriate name</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#merge the data from two apis, changes individual genre id to its name \n",
    "def change_genreId_name(movie_json,genre_json):\n",
    "    for movie in movie_json:\n",
    "        movie['genre'] = []\n",
    "        movie['genre'] = [obj[\"name\"] for obj in genre_json if obj[\"id\"] in movie['genre_ids'] ]\n",
    "    return movie_json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red' style=\"font-style:italic\">get the filtereder data with respect to the given columns</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_filtered_data(response_json, columns):\n",
    "    filtered_data = []\n",
    "    for response in response_json:\n",
    "        data = {}\n",
    "        for key in response:\n",
    "            if key in columns:\n",
    "                data[key] = response[key];\n",
    "        filtered_data.append(data);\n",
    "    return filtered_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red' style=\"font-style:italic\">Write the filtered data to the csv</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def write_data_to_csv(file_loc,data_json):\n",
    "    is_header = True;\n",
    "    # open a file and write into it, newLine ='' is used to prevent the blanck line after each row\n",
    "    with open(file_loc, \"w\", newline='') as f: \n",
    "        csvwriter = csv.writer(f)\n",
    "        for data in data_json:\n",
    "            if (is_header):\n",
    "                #set the header of the csv\n",
    "                header = data.keys();\n",
    "                csvwriter.writerow(header);\n",
    "                is_header = False\n",
    "            try:\n",
    "                csvwriter.writerow(data.values())\n",
    "            except ValueError:\n",
    "                # rows having onknown character ( not english) are excluded\n",
    "                print('Unknown data found while writing the following row :')\n",
    "                print(data.values())\n",
    "    f.close();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3: Parse the collected data, and store it in an appropriate file format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red' style=\"font-style:italic\">Writting raw data into the data file</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It uses the function defined in the previous steps to collect the raw data and filter it using some predefined filed and store it to an appropriate format. It excludes the rows having unknown characters, which"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unknown data found while writing the following row :\n",
      "dict_values([0, 479363, False, 0, 'Kamen Rider Heisei Generations FINAL: Build & Ex-Aid with Legend Riders', 52.099561, '/cGhdduplj8YdAwg7iPCeGjO1YvZ.jpg', 'ja', '仮面ライダー平成ジェネレーションズFINAL ビルド＆エグゼイドwithレジェンドライダー', [28, 12, 878], '/aVnvDWF7dELkSzYJfzK9yrpvbv9.jpg', False, 'Kamen Riders Build and Ex-Aid team up with the legendary heroes of the Heisei Generation - OOO, Fourze, Gaim, and Ghost.', '2017-12-09', ['Action', 'Adventure', 'Science Fiction']])\n",
      "Unknown data found while writing the following row :\n",
      "dict_values([2, 458741, False, 8.5, 'Mayaanadhi', 37.560329, '/mNxAgWdHPWOAUVcI4kEuXvpo97j.jpg', 'ml', 'മായാനദി', [18, 10749], '/cWmknHr6VVNInXw6BmauPaldMnP.jpg', False, 'A shady money deal goes wrong and in the aftermath Mathan  ends up killing a police officer. Meanwhile in Kochi, his old flame Aparna  an aspiring actress is struggling to prop up her career. Mathan meets up with Aparna and tries to rekindle the past, even as the police hunt for him gets intense.', '2017-12-22', ['Drama', 'Romance']])\n",
      "Unknown data found while writing the following row :\n",
      "dict_values([3, 437543, False, 3, 'The Monkey King 3: Kingdom of Women', 36.812863, '/zMYnxPIkUVaRIuTpS77B8hLGkBB.jpg', 'zh', '西游记·女儿国', [28, 12, 14], '/xCF6Vkq1L5tTGENb07vzlQyXwKv.jpg', False, 'The third installment of the blockbuster fantasy series sees the return of the Monkey King (Aaron Kwok) in his most action-packed adventure yet! While continuing their epic journey to the West, the Monkey King and his companions are taken captive by the Queen of an all-female land, who believes them to be part of an ancient prophecy heralding the fall of her kingdom. With a lot of sorcery and a little bit of charm, the travelers devise a plan to escape. But when their trickery angers the mighty River God, they realize they might just bring about the foretold destruction - unless they can find a way to quell her wrath.', '2018-02-16', ['Action', 'Adventure', 'Fantasy']])\n",
      "Unknown data found while writing the following row :\n",
      "dict_values([2173, 8392, False, 8, 'My Neighbor Totoro', 34.222265, '/2i0OOjvi7CqNQA6ZtYJtL65P9oZ.jpg', 'ja', 'となりのトトロ', [14, 16, 10751], '/fYU3ckVE1zECs42ZO0uSoJpwlXi.jpg', False, 'Two sisters move to the country with their father in order to be closer to their hospitalized mother, and discover the surrounding trees are inhabited by Totoros, magical spirits of the forest. When the youngest runs away from home, the older sister seeks help from the spirits to find her.', '1988-04-16', ['Animation', 'Family', 'Fantasy']])\n",
      "Unknown data found while writing the following row :\n",
      "dict_values([5001, 129, False, 8.4, 'Spirited Away', 33.942001, '/dL11DBPcRhWWnJcFXl9A07MrqTI.jpg', 'ja', '千と千尋の神隠し', [16, 10751, 14], '/djgM2d3e42p9GFQObg6lwK2SVw2.jpg', False, 'A young girl, Chihiro, becomes trapped in a strange new world of spirits. When her parents undergo a mysterious transformation, she must call upon the courage she never knew she had to free her family.', '2001-07-20', ['Animation', 'Family', 'Fantasy']])\n",
      "Unknown data found while writing the following row :\n",
      "dict_values([4150, 68724, False, 6.4, 'Elysium', 32.164109, '/tHkjoAxmhp3Eau1h0Ir7maKMwUz.jpg', 'en', 'Elysium', [878, 28, 18, 53], '/vSbjlX9y8S2hKNURHEO4GsoIOay.jpg', False, 'In the year 2159, two classes of people exist: the very wealthy who live on a pristine man-made space station called Elysium, and the rest, who live on an overpopulated, ruined Earth. Secretary Rhodes (Jodie Foster), a hard line government ofﬁcial, will stop at nothing to enforce anti-immigration laws and preserve the luxurious lifestyle of the citizens of Elysium. That doesn’t stop the people of Earth from trying to get in, by any means they can. When unlucky Max (Matt Damon) is backed into a corner, he agrees to take on a daunting mission that, if successful, will not only save his life, but could bring equality to these polarized worlds.', '2013-08-07', ['Action', 'Drama', 'Science Fiction', 'Thriller']])\n",
      "Unknown data found while writing the following row :\n",
      "dict_values([2, 483877, False, 6, '15+ IQ Krachoot', 30.339268, '/1RxnEJzXYv5GgPszbHNsWTXPC6M.jpg', 'en', '15 ไอคิวกระฉูด', [35], '/fp3Iiap1hblJfqHrBDziqgl9zzB.jpg', False, 'A teenage comedy film about a young Yorkie. \"Rising\" opened the first movie on screen.  The story of fun gangs of teenage age 15+ hormones gurgling at the curiosity to try on their love and sex has spread out.', '2017-08-03', ['Comedy']])\n",
      "Unknown data found while writing the following row :\n",
      "dict_values([2628, 4935, False, 8.2, \"Howl's Moving Castle\", 29.084537, '/iMarB2ior30OAXjPa7QIdeyUfM1.jpg', 'ja', 'ハウルの動く城', [14, 16, 12], '/cGyqa5PxiWSsG7NSw3ufCj0JW9k.jpg', False, 'When Sophie, a shy young woman, is cursed with an old body by a spiteful witch, her only chance of breaking the spell lies with a self-indulgent yet insecure young wizard and his companions in his legged, walking home.', '2004-11-19', ['Adventure', 'Animation', 'Fantasy']])\n",
      "Unknown data found while writing the following row :\n",
      "dict_values([2558, 128, False, 8.3, 'Princess Mononoke', 29.083977, '/gzlJkVfWV5VEG5xK25cvFGJgkDz.jpg', 'ja', 'もののけ姫', [12, 14, 16], '/dWAYPAKlJi0n85P0xVEvQ2FOYLF.jpg', False, 'Ashitaka, a prince of the disappearing Ainu tribe, is cursed by a demonized boar god and must journey to the west to find a cure. Along the way, he encounters San, a young human woman fighting to protect the forest, and Lady Eboshi, who is trying to destroy it. Ashitaka must find a way to bring balance to this conflict.', '1997-07-12', ['Adventure', 'Animation', 'Fantasy']])\n",
      "Unknown data found while writing the following row :\n",
      "dict_values([1077, 346, False, 8.2, 'Seven Samurai', 24.957393, '/v6xrz4fr92KY1oNC3HsEvrsvR1n.jpg', 'ja', '七人の侍', [28, 18], '/qPuNawmymdzSLQ6zAQeeIZxuNfY.jpg', False, \"A samurai answers a village's request for protection after he falls on hard times. The town needs protection from bandits, so the samurai gathers six others to help him teach the people how to defend themselves, and the villagers provide the soldiers with food. A giant battle occurs when 40 bandits attack the village.\", '1954-04-26', ['Action', 'Drama']])\n",
      "Unknown data found while writing the following row :\n",
      "dict_values([2065, 372058, False, 8.5, 'Your Name.', 23.158323, '/xq1Ugd62d23K2knRUx6xxuALTZB.jpg', 'ja', '君の名は。', [10749, 16, 18], '/6vkhRvsRvWpmaRVyCXaxTkIEb7j.jpg', False, 'High schoolers Mitsuha and Taki are complete strangers living separate lives. But one night, they suddenly switch places. Mitsuha wakes up in Taki’s body, and he in hers. This bizarre occurrence continues to happen randomly, and the two must adjust their lives around each other.', '2016-08-26', ['Animation', 'Drama', 'Romance']])\n",
      "Unknown data found while writing the following row :\n",
      "dict_values([2542, 670, False, 8.1, 'Oldboy', 20.876074, '/rIZX6X0MIHYEebk6W4LABT9VP2c.jpg', 'ko', '올드보이', [18, 53, 9648, 28], '/qUxC2uJnHgGKq6Qvy4sYPxy0hoJ.jpg', False, 'With no clue how he came to be imprisoned, drugged and tortured for 15 years, a desperate businessman seeks revenge on his captors.', '2003-09-28', ['Action', 'Drama', 'Mystery', 'Thriller']])\n"
     ]
    }
   ],
   "source": [
    "response_json_movie = []\n",
    "for page in pages:\n",
    "    response_json_movie.extend(get_data_from_api(base_link+movie_link,api_key,page, False))\n",
    "    \n",
    "response_json_genre = get_data_from_api(base_link+movie_genres_link,api_key,1,True)\n",
    "\n",
    "# get the filtered data from the raw json\n",
    "filtered_data = change_genreId_name(response_json_movie, response_json_genre)\n",
    "#write the filtered data to csv file\n",
    "write_data_to_csv('Movie_data.csv',filtered_data);"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "From the exceptions we can see, the rows having characters other than english are excluded. There are 12 such rows which are excluded. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 4: Load and represent the data using an appropriate data structure. Apply any pre-processing steps to clean/filter/combine the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vote_count</th>\n",
       "      <th>id</th>\n",
       "      <th>video</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>title</th>\n",
       "      <th>popularity</th>\n",
       "      <th>poster_path</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>genre_ids</th>\n",
       "      <th>backdrop_path</th>\n",
       "      <th>adult</th>\n",
       "      <th>overview</th>\n",
       "      <th>release_date</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>2097</td>\n",
       "      <td>7446</td>\n",
       "      <td>False</td>\n",
       "      <td>6.5</td>\n",
       "      <td>Tropic Thunder</td>\n",
       "      <td>19.939552</td>\n",
       "      <td>/zoWUdaaWKPDyr9b0il0YcggDWgJ.jpg</td>\n",
       "      <td>en</td>\n",
       "      <td>Tropic Thunder</td>\n",
       "      <td>[28, 35]</td>\n",
       "      <td>/6TdYGyANd7QhcV6gsx4meW8Y9Wf.jpg</td>\n",
       "      <td>False</td>\n",
       "      <td>Vietnam veteran 'Four Leaf' Tayback's memoir, ...</td>\n",
       "      <td>2008-08-09</td>\n",
       "      <td>['Action', 'Comedy']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>3311</td>\n",
       "      <td>854</td>\n",
       "      <td>False</td>\n",
       "      <td>6.7</td>\n",
       "      <td>The Mask</td>\n",
       "      <td>19.919275</td>\n",
       "      <td>/v8x8p441l1Bep8p82pAG6rduBoK.jpg</td>\n",
       "      <td>en</td>\n",
       "      <td>The Mask</td>\n",
       "      <td>[10749, 35, 80, 14]</td>\n",
       "      <td>/kQlLZ8JgzVhFH6hlZovZycQSxbP.jpg</td>\n",
       "      <td>False</td>\n",
       "      <td>When timid bank clerk Stanley Ipkiss discovers...</td>\n",
       "      <td>1994-07-29</td>\n",
       "      <td>['Comedy', 'Crime', 'Fantasy', 'Romance']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785</th>\n",
       "      <td>2490</td>\n",
       "      <td>44896</td>\n",
       "      <td>False</td>\n",
       "      <td>6.6</td>\n",
       "      <td>Rango</td>\n",
       "      <td>19.908856</td>\n",
       "      <td>/afxRbCwwKjrWgGJtsVNs1BW1Dk5.jpg</td>\n",
       "      <td>en</td>\n",
       "      <td>Rango</td>\n",
       "      <td>[16, 35, 10751, 37, 12]</td>\n",
       "      <td>/fX6o5DCdGVEEZ1OT1IourOUkQMb.jpg</td>\n",
       "      <td>False</td>\n",
       "      <td>When Rango, a lost family pet, accidentally wi...</td>\n",
       "      <td>2011-03-02</td>\n",
       "      <td>['Adventure', 'Animation', 'Comedy', 'Family',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>2279</td>\n",
       "      <td>11224</td>\n",
       "      <td>False</td>\n",
       "      <td>6.8</td>\n",
       "      <td>Cinderella</td>\n",
       "      <td>19.893925</td>\n",
       "      <td>/cdf4J7tDhSPAghWWgx9EnwQWBfF.jpg</td>\n",
       "      <td>en</td>\n",
       "      <td>Cinderella</td>\n",
       "      <td>[10751, 14, 16, 10749]</td>\n",
       "      <td>/xSN1cpugrzi8DjpVxtHeRRlbB6Q.jpg</td>\n",
       "      <td>False</td>\n",
       "      <td>Cinderella has faith her dreams of a better li...</td>\n",
       "      <td>1950-03-04</td>\n",
       "      <td>['Animation', 'Family', 'Fantasy', 'Romance']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>1960</td>\n",
       "      <td>324849</td>\n",
       "      <td>False</td>\n",
       "      <td>7.2</td>\n",
       "      <td>The Lego Batman Movie</td>\n",
       "      <td>19.875023</td>\n",
       "      <td>/1pHOqpdCYNmtRVJs6pGKQKttrPm.jpg</td>\n",
       "      <td>en</td>\n",
       "      <td>The Lego Batman Movie</td>\n",
       "      <td>[28, 16, 35, 10751, 14]</td>\n",
       "      <td>/gbO4tWvMC0oLbNvHP9Zth3eUeAA.jpg</td>\n",
       "      <td>False</td>\n",
       "      <td>In the irreverent spirit of fun that made “The...</td>\n",
       "      <td>2017-02-08</td>\n",
       "      <td>['Action', 'Animation', 'Comedy', 'Family', 'F...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     vote_count      id  video  vote_average                  title  \\\n",
       "783        2097    7446  False           6.5         Tropic Thunder   \n",
       "784        3311     854  False           6.7               The Mask   \n",
       "785        2490   44896  False           6.6                  Rango   \n",
       "786        2279   11224  False           6.8             Cinderella   \n",
       "787        1960  324849  False           7.2  The Lego Batman Movie   \n",
       "\n",
       "     popularity                       poster_path original_language  \\\n",
       "783   19.939552  /zoWUdaaWKPDyr9b0il0YcggDWgJ.jpg                en   \n",
       "784   19.919275  /v8x8p441l1Bep8p82pAG6rduBoK.jpg                en   \n",
       "785   19.908856  /afxRbCwwKjrWgGJtsVNs1BW1Dk5.jpg                en   \n",
       "786   19.893925  /cdf4J7tDhSPAghWWgx9EnwQWBfF.jpg                en   \n",
       "787   19.875023  /1pHOqpdCYNmtRVJs6pGKQKttrPm.jpg                en   \n",
       "\n",
       "            original_title                genre_ids  \\\n",
       "783         Tropic Thunder                 [28, 35]   \n",
       "784               The Mask      [10749, 35, 80, 14]   \n",
       "785                  Rango  [16, 35, 10751, 37, 12]   \n",
       "786             Cinderella   [10751, 14, 16, 10749]   \n",
       "787  The Lego Batman Movie  [28, 16, 35, 10751, 14]   \n",
       "\n",
       "                        backdrop_path  adult  \\\n",
       "783  /6TdYGyANd7QhcV6gsx4meW8Y9Wf.jpg  False   \n",
       "784  /kQlLZ8JgzVhFH6hlZovZycQSxbP.jpg  False   \n",
       "785  /fX6o5DCdGVEEZ1OT1IourOUkQMb.jpg  False   \n",
       "786  /xSN1cpugrzi8DjpVxtHeRRlbB6Q.jpg  False   \n",
       "787  /gbO4tWvMC0oLbNvHP9Zth3eUeAA.jpg  False   \n",
       "\n",
       "                                              overview release_date  \\\n",
       "783  Vietnam veteran 'Four Leaf' Tayback's memoir, ...   2008-08-09   \n",
       "784  When timid bank clerk Stanley Ipkiss discovers...   1994-07-29   \n",
       "785  When Rango, a lost family pet, accidentally wi...   2011-03-02   \n",
       "786  Cinderella has faith her dreams of a better li...   1950-03-04   \n",
       "787  In the irreverent spirit of fun that made “The...   2017-02-08   \n",
       "\n",
       "                                                 genre  \n",
       "783                               ['Action', 'Comedy']  \n",
       "784          ['Comedy', 'Crime', 'Fantasy', 'Romance']  \n",
       "785  ['Adventure', 'Animation', 'Comedy', 'Family',...  \n",
       "786      ['Animation', 'Family', 'Fantasy', 'Romance']  \n",
       "787  ['Action', 'Animation', 'Comedy', 'Family', 'F...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('Movie_data.csv',engine='python') # engine=python  is used because without this it was not able to read some characters of the file\n",
    "dataset.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red' style=\"font-style:italic\">Drop Irrelevant columns</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#we already have genres by name we can drop genre_ids column\n",
    "dataset = dataset.drop(['genre_ids'], axis=1)\n",
    "#poster_path and basckdrop_path contain some image path, which are not relevant for this analysis, we can drop those two columns\n",
    "dataset = dataset.drop(['poster_path','backdrop_path'], axis=1)\n",
    "#overview contains description which is not useful for statistical analysis, dropping overview\n",
    "dataset = dataset.drop(['overview'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vote_count</th>\n",
       "      <th>id</th>\n",
       "      <th>video</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>title</th>\n",
       "      <th>popularity</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>adult</th>\n",
       "      <th>release_date</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1070</td>\n",
       "      <td>337167</td>\n",
       "      <td>False</td>\n",
       "      <td>6.2</td>\n",
       "      <td>Fifty Shades Freed</td>\n",
       "      <td>600.256939</td>\n",
       "      <td>en</td>\n",
       "      <td>Fifty Shades Freed</td>\n",
       "      <td>False</td>\n",
       "      <td>2018-02-07</td>\n",
       "      <td>['Drama', 'Romance']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6675</td>\n",
       "      <td>269149</td>\n",
       "      <td>False</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Zootopia</td>\n",
       "      <td>417.963560</td>\n",
       "      <td>en</td>\n",
       "      <td>Zootopia</td>\n",
       "      <td>False</td>\n",
       "      <td>2016-02-11</td>\n",
       "      <td>['Adventure', 'Animation', 'Comedy', 'Family']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3439</td>\n",
       "      <td>284054</td>\n",
       "      <td>False</td>\n",
       "      <td>7.4</td>\n",
       "      <td>Black Panther</td>\n",
       "      <td>298.913231</td>\n",
       "      <td>en</td>\n",
       "      <td>Black Panther</td>\n",
       "      <td>False</td>\n",
       "      <td>2018-02-13</td>\n",
       "      <td>['Action', 'Adventure', 'Fantasy', 'Science Fi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3328</td>\n",
       "      <td>354912</td>\n",
       "      <td>False</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Coco</td>\n",
       "      <td>259.945509</td>\n",
       "      <td>en</td>\n",
       "      <td>Coco</td>\n",
       "      <td>False</td>\n",
       "      <td>2017-10-27</td>\n",
       "      <td>['Adventure', 'Animation', 'Comedy', 'Family']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>506</td>\n",
       "      <td>338970</td>\n",
       "      <td>False</td>\n",
       "      <td>6.1</td>\n",
       "      <td>Tomb Raider</td>\n",
       "      <td>224.314953</td>\n",
       "      <td>en</td>\n",
       "      <td>Tomb Raider</td>\n",
       "      <td>False</td>\n",
       "      <td>2018-03-08</td>\n",
       "      <td>['Action', 'Adventure']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   vote_count      id  video  vote_average               title  popularity  \\\n",
       "0        1070  337167  False           6.2  Fifty Shades Freed  600.256939   \n",
       "1        6675  269149  False           7.7            Zootopia  417.963560   \n",
       "2        3439  284054  False           7.4       Black Panther  298.913231   \n",
       "3        3328  354912  False           7.8                Coco  259.945509   \n",
       "4         506  338970  False           6.1         Tomb Raider  224.314953   \n",
       "\n",
       "  original_language      original_title  adult release_date  \\\n",
       "0                en  Fifty Shades Freed  False   2018-02-07   \n",
       "1                en            Zootopia  False   2016-02-11   \n",
       "2                en       Black Panther  False   2018-02-13   \n",
       "3                en                Coco  False   2017-10-27   \n",
       "4                en         Tomb Raider  False   2018-03-08   \n",
       "\n",
       "                                               genre  \n",
       "0                               ['Drama', 'Romance']  \n",
       "1     ['Adventure', 'Animation', 'Comedy', 'Family']  \n",
       "2  ['Action', 'Adventure', 'Fantasy', 'Science Fi...  \n",
       "3     ['Adventure', 'Animation', 'Comedy', 'Family']  \n",
       "4                            ['Action', 'Adventure']  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red' style=\"font-style:italic\">Check for misisng data</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vote_count           0\n",
       "id                   0\n",
       "video                0\n",
       "vote_average         0\n",
       "title                0\n",
       "popularity           0\n",
       "original_language    0\n",
       "original_title       0\n",
       "adult                0\n",
       "release_date         0\n",
       "genre                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no null value present in the dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red' style=\"font-style:italic\">Check for distinct counts wrt some fields</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "video\n",
       "False    788\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check distinct values for 'video' with counts\n",
    "dataset.groupby('video')['id'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see all the rows have same values for 'video', this column is not giving any relevant information, we can drop this column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = dataset.drop(['video'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "original_language\n",
       "de      4\n",
       "en    765\n",
       "es      1\n",
       "fr      3\n",
       "it      7\n",
       "ko      1\n",
       "no      1\n",
       "pt      1\n",
       "ru      1\n",
       "sv      1\n",
       "ta      2\n",
       "tl      1\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check distinct values for 'original_language' with counts\n",
    "dataset.groupby('original_language')['id'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the movies are in 'en', lets check the proportion of english movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9708121827411168"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset[dataset['original_language'] == 'en']) / len(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As 97% movies in the dataset are of English language, we will restrict our analysis to only English movies. Thus, droping other movies "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = dataset[dataset['original_language']  == 'en']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "adult\n",
       "False    765\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check distinct values for adult columns with counts\n",
    "dataset.groupby('adult')['id'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All rows have same value for 'adult', so we can drop it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = dataset.drop(['adult'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'title' and 'original_title' look similar. Check percentage of dissimilarities between 'title' and 'original_title' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.006535947712418301"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disimilarities = []\n",
    "for index, movie in dataset.iterrows():\n",
    "    if movie['title'] != movie['original_title']:\n",
    "        disimilarities.append((movie['title'], movie['original_title']))\n",
    "#ratio of disimilarities between title and original_title\n",
    "len(disimilarities)/len(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see in very small proprtion of data title and original title differ from each other. This is may be due to different language. However, as the percentage is only 2%, we can easily drop one column because both are highly correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = dataset.drop(['original_title'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vote_count</th>\n",
       "      <th>id</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>title</th>\n",
       "      <th>popularity</th>\n",
       "      <th>original_language</th>\n",
       "      <th>release_date</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1070</td>\n",
       "      <td>337167</td>\n",
       "      <td>6.2</td>\n",
       "      <td>Fifty Shades Freed</td>\n",
       "      <td>600.256939</td>\n",
       "      <td>en</td>\n",
       "      <td>2018-02-07</td>\n",
       "      <td>['Drama', 'Romance']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6675</td>\n",
       "      <td>269149</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Zootopia</td>\n",
       "      <td>417.963560</td>\n",
       "      <td>en</td>\n",
       "      <td>2016-02-11</td>\n",
       "      <td>['Adventure', 'Animation', 'Comedy', 'Family']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3439</td>\n",
       "      <td>284054</td>\n",
       "      <td>7.4</td>\n",
       "      <td>Black Panther</td>\n",
       "      <td>298.913231</td>\n",
       "      <td>en</td>\n",
       "      <td>2018-02-13</td>\n",
       "      <td>['Action', 'Adventure', 'Fantasy', 'Science Fi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3328</td>\n",
       "      <td>354912</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Coco</td>\n",
       "      <td>259.945509</td>\n",
       "      <td>en</td>\n",
       "      <td>2017-10-27</td>\n",
       "      <td>['Adventure', 'Animation', 'Comedy', 'Family']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>506</td>\n",
       "      <td>338970</td>\n",
       "      <td>6.1</td>\n",
       "      <td>Tomb Raider</td>\n",
       "      <td>224.314953</td>\n",
       "      <td>en</td>\n",
       "      <td>2018-03-08</td>\n",
       "      <td>['Action', 'Adventure']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   vote_count      id  vote_average               title  popularity  \\\n",
       "0        1070  337167           6.2  Fifty Shades Freed  600.256939   \n",
       "1        6675  269149           7.7            Zootopia  417.963560   \n",
       "2        3439  284054           7.4       Black Panther  298.913231   \n",
       "3        3328  354912           7.8                Coco  259.945509   \n",
       "4         506  338970           6.1         Tomb Raider  224.314953   \n",
       "\n",
       "  original_language release_date  \\\n",
       "0                en   2018-02-07   \n",
       "1                en   2016-02-11   \n",
       "2                en   2018-02-13   \n",
       "3                en   2017-10-27   \n",
       "4                en   2018-03-08   \n",
       "\n",
       "                                               genre  \n",
       "0                               ['Drama', 'Romance']  \n",
       "1     ['Adventure', 'Animation', 'Comedy', 'Family']  \n",
       "2  ['Action', 'Adventure', 'Fantasy', 'Science Fi...  \n",
       "3     ['Adventure', 'Animation', 'Comedy', 'Family']  \n",
       "4                            ['Action', 'Adventure']  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Round off popularity to 2 decimal place"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset['popularity'] = round(dataset['popularity'],2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "change release date column to release year. All our analysis will be based on the year. In this analysis particular date in irrelevant. Thus extracting year ofrom dates :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset['release_year'] = pd.to_datetime(dataset['release_date']).dt.year #https://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "drop release_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = dataset.drop(['release_date'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As 2018 is not yet completed, we are going to take data till 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rows_drop = dataset[dataset['release_year'] > 2017]\n",
    "dataset = dataset.drop(rows_drop.index, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red' style=\"font-style:italic\">Final cleaned dataset for analysis</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vote_count</th>\n",
       "      <th>id</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>title</th>\n",
       "      <th>popularity</th>\n",
       "      <th>original_language</th>\n",
       "      <th>genre</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6675</td>\n",
       "      <td>269149</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Zootopia</td>\n",
       "      <td>417.96</td>\n",
       "      <td>en</td>\n",
       "      <td>['Adventure', 'Animation', 'Comedy', 'Family']</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3328</td>\n",
       "      <td>354912</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Coco</td>\n",
       "      <td>259.95</td>\n",
       "      <td>en</td>\n",
       "      <td>['Adventure', 'Animation', 'Comedy', 'Family']</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4504</td>\n",
       "      <td>181808</td>\n",
       "      <td>7.2</td>\n",
       "      <td>Star Wars: The Last Jedi</td>\n",
       "      <td>194.03</td>\n",
       "      <td>en</td>\n",
       "      <td>['Action', 'Adventure', 'Fantasy', 'Science Fi...</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5140</td>\n",
       "      <td>284053</td>\n",
       "      <td>7.4</td>\n",
       "      <td>Thor: Ragnarok</td>\n",
       "      <td>189.63</td>\n",
       "      <td>en</td>\n",
       "      <td>['Action', 'Adventure', 'Fantasy']</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2852</td>\n",
       "      <td>399055</td>\n",
       "      <td>7.3</td>\n",
       "      <td>The Shape of Water</td>\n",
       "      <td>173.96</td>\n",
       "      <td>en</td>\n",
       "      <td>['Drama', 'Fantasy', 'Romance']</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   vote_count      id  vote_average                     title  popularity  \\\n",
       "1        6675  269149           7.7                  Zootopia      417.96   \n",
       "3        3328  354912           7.8                      Coco      259.95   \n",
       "6        4504  181808           7.2  Star Wars: The Last Jedi      194.03   \n",
       "7        5140  284053           7.4            Thor: Ragnarok      189.63   \n",
       "8        2852  399055           7.3        The Shape of Water      173.96   \n",
       "\n",
       "  original_language                                              genre  \\\n",
       "1                en     ['Adventure', 'Animation', 'Comedy', 'Family']   \n",
       "3                en     ['Adventure', 'Animation', 'Comedy', 'Family']   \n",
       "6                en  ['Action', 'Adventure', 'Fantasy', 'Science Fi...   \n",
       "7                en                 ['Action', 'Adventure', 'Fantasy']   \n",
       "8                en                    ['Drama', 'Fantasy', 'Romance']   \n",
       "\n",
       "   release_year  \n",
       "1          2016  \n",
       "3          2017  \n",
       "6          2017  \n",
       "7          2017  \n",
       "8          2017  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 5: Analyse and summarise the cleaned dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red' style=\"font-style:italic\">Number of movies released year wise</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets see how the number of movies released has changed over the years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "release_year\n",
       "2013    43\n",
       "2014    47\n",
       "2015    44\n",
       "2016    28\n",
       "2017    77\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by the data by release year and get the count of ids\n",
    "movie_per_year = dataset.groupby(['release_year'])[\"id\"].count()\n",
    "movie_per_year.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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twK0ikuF9fU9UURpjTAyFe99U1R+muv7wkAY/Cz+JG27plxRkkZOV1qulXzoh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\ntu6txx9w5wz3z48sySzzEvy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P9F5fh3u6+aO4UUyXezHeoao/8Z5+/g2wCJgEKG6gtEm4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\nN8aYJGJJ3xhjksj/BzOlXjV7sOVkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d91c101128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the movie_per_year\n",
    "plt.figure()\n",
    "movie_per_year.plot()\n",
    "plt.title('Number of movies per year ( 1900 - 2017 )')\n",
    "plt.xlabel('Release year')\n",
    "plt.ylabel('Number of movies')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "As shown in the above graph, the number of movies released per year has an increasing trend. Till 1970, there are very few movies relaesed per year. Then, from around 1975 the number of movies per year has increased exponentially. We can see a spike in 2017. In 2017 there are around 75 movies released. From the above graph, we can infer the movie industry is showing good progress almost in every year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lets see genre wise how the plot look like."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "get the count of movies groupby genre and release_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>release_year</th>\n",
       "      <th>genre</th>\n",
       "      <th>number_of_movies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>2017</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>2017</td>\n",
       "      <td>History</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>2017</td>\n",
       "      <td>Horror</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>2017</td>\n",
       "      <td>Music</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>2017</td>\n",
       "      <td>Mystery</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>2017</td>\n",
       "      <td>Romance</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>2017</td>\n",
       "      <td>Science Fiction</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>2017</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>2017</td>\n",
       "      <td>War</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>2017</td>\n",
       "      <td>Western</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     release_year            genre  number_of_movies\n",
       "532          2017          Fantasy                11\n",
       "533          2017          History                 5\n",
       "534          2017           Horror                12\n",
       "535          2017            Music                 3\n",
       "536          2017          Mystery                11\n",
       "537          2017          Romance                 5\n",
       "538          2017  Science Fiction                20\n",
       "539          2017         Thriller                27\n",
       "540          2017              War                 3\n",
       "541          2017          Western                 1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#https://stackoverflow.com/questions/49220479/how-to-group-by-according-to-the-values-of-a-list-present-in-a-column-in-datafra?noredirect=1#comment85463311_49220479\n",
    "#https://stackoverflow.com/questions/23119472/in-pandas-python-reading-array-stored-as-string\n",
    "#as genre is a list of string wee need to flatten the list and then use group by. \n",
    "df = pd.DataFrame({\n",
    "      'genre' : list(\n",
    "           chain.from_iterable(dataset['genre'].apply(literal_eval).tolist())\n",
    "       ), \n",
    "       'release_year' : dataset.release_year.repeat(dataset['genre'].apply(literal_eval).str.len())\n",
    "})\n",
    "movie_group = df.groupby(\n",
    "     ['release_year','genre'], sort=True \n",
    " ).size().reset_index(name='number_of_movies')\n",
    "#display movie_group\n",
    "movie_group.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red' style=\"font-style:italic\">Number of movies per genre from </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ETOD/WvnWFp2Zh5mZLcgzkM3MzMXAzMxcDMzMDBcDMzPDxcDMzHAxMDMzvIT1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\ncDEwMzNcDMzMDBcDMzMD/h+hjsqYlwoDSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d91c3aaf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#group by genres\n",
    "group_by_genres = movie_group.groupby(['genre'],as_index=False).sum().sort_values(by=['number_of_movies'])\n",
    "#plot group_by_genres\n",
    "group_by_genres.plot(x='genre',y='number_of_movies',kind='bar')\n",
    "plt.title('Number of movies per genre')\n",
    "plt.ylabel('Number of movies')\n",
    "plt.xlabel('Genres')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "As shown in the above graph, the most number of movies released till date is on 'Action'. Action and Drama were two most common genres. There are very few movies released in 'Western', 'Music' etc.\n",
    "\n",
    "top 5 genres with respect to the number of movies ( from the graph)\n",
    "['Action', 'Drama', 'Adventure', 'Thriller', 'Comedy'] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will continuw our analysis with the top five genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "top_genres = ['Action', 'Drama', 'Adventure', 'Thriller', 'Comedy'] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red' style=\"font-style:italic\">proportion of movies of top 5 genres in last 10 years</font>"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Lets see how the proportion of movies of top 5 genres in last 10 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d91d54f7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get the movies of top 5 genres in last 10 years \n",
    "recent_movie_groups = movie_group[(movie_group['release_year'] > 2007) & (movie_group['genre'].isin(top_genres))]\n",
    "# get the recent movies of top five genres group by genres\n",
    "recent_movie_groups_groupby_genres = recent_movie_groups.groupby(['genre'])['number_of_movies'].sum().reset_index()\n",
    "# plot pie chart\n",
    "plt.pie(recent_movie_groups_groupby_genres['number_of_movies'], labels=recent_movie_groups_groupby_genres['genre'],startangle=90)\n",
    "# Set aspect ratio to be equal so that pie is drawn as a circle.\n",
    "plt.axis('equal')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "From the above visualization, in last years also 'Action' is likely to be the most used genres."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red' style=\"font-style:italic\">Number of movies per genre per year in last 10 years</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6ALeZ2cvAY9GlVhPry0lVz1zyZQ3b67DboCr/fuedt+E+jUWLFrJixQrat9+M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\nJJb/B4wKlz4tIgInAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d91c188d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_movie_groups.pivot_table(index='release_year', columns='genre', values='number_of_movies', \n",
    "                                aggfunc='sum', fill_value=0).plot.bar(stacked=True)\n",
    "plt.title('Number of movies per top 5 genres genre per year in last 10 years')\n",
    "plt.xlabel('years')\n",
    "plt.ylabel('number_of_movies')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The above graph shows the movies per genre per year. the number of movies are not the count of unique movies because a movie has a list of genres. However, from the graph we can infer that the number of movies with genre Thriller is the least among the top 5 genres and the most number of movies have genre as action. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red' style=\"font-style:italic\">Top 5 trending movies as of the day, the data is collected </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to TMDB the popularity mesures depends upon <br>\n",
    "<ul>\n",
    "    \n",
    "        <li>Number of votes for the day </li>\n",
    "        <li>Number of views for the day </li>\n",
    "        <li>Number of users who marked it as a \"favourite\" for the day </li>\n",
    "        <li>Number of users who added it to their \"watchlist\" for the day </li>\n",
    "        <li>Release date </li>\n",
    "        <li>Number of total votes </li>\n",
    "        <li>Previous days score </li>\n",
    "    \n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vote_count</th>\n",
       "      <th>id</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>title</th>\n",
       "      <th>popularity</th>\n",
       "      <th>original_language</th>\n",
       "      <th>genre</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6675</td>\n",
       "      <td>269149</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Zootopia</td>\n",
       "      <td>417.96</td>\n",
       "      <td>en</td>\n",
       "      <td>['Adventure', 'Animation', 'Comedy', 'Family']</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3328</td>\n",
       "      <td>354912</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Coco</td>\n",
       "      <td>259.95</td>\n",
       "      <td>en</td>\n",
       "      <td>['Adventure', 'Animation', 'Comedy', 'Family']</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4504</td>\n",
       "      <td>181808</td>\n",
       "      <td>7.2</td>\n",
       "      <td>Star Wars: The Last Jedi</td>\n",
       "      <td>194.03</td>\n",
       "      <td>en</td>\n",
       "      <td>['Action', 'Adventure', 'Fantasy', 'Science Fi...</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5140</td>\n",
       "      <td>284053</td>\n",
       "      <td>7.4</td>\n",
       "      <td>Thor: Ragnarok</td>\n",
       "      <td>189.63</td>\n",
       "      <td>en</td>\n",
       "      <td>['Action', 'Adventure', 'Fantasy']</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2852</td>\n",
       "      <td>399055</td>\n",
       "      <td>7.3</td>\n",
       "      <td>The Shape of Water</td>\n",
       "      <td>173.96</td>\n",
       "      <td>en</td>\n",
       "      <td>['Drama', 'Fantasy', 'Romance']</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   vote_count      id  vote_average                     title  popularity  \\\n",
       "1        6675  269149           7.7                  Zootopia      417.96   \n",
       "3        3328  354912           7.8                      Coco      259.95   \n",
       "6        4504  181808           7.2  Star Wars: The Last Jedi      194.03   \n",
       "7        5140  284053           7.4            Thor: Ragnarok      189.63   \n",
       "8        2852  399055           7.3        The Shape of Water      173.96   \n",
       "\n",
       "  original_language                                              genre  \\\n",
       "1                en     ['Adventure', 'Animation', 'Comedy', 'Family']   \n",
       "3                en     ['Adventure', 'Animation', 'Comedy', 'Family']   \n",
       "6                en  ['Action', 'Adventure', 'Fantasy', 'Science Fi...   \n",
       "7                en                 ['Action', 'Adventure', 'Fantasy']   \n",
       "8                en                    ['Drama', 'Fantasy', 'Romance']   \n",
       "\n",
       "   release_year  \n",
       "1          2016  \n",
       "3          2017  \n",
       "6          2017  \n",
       "7          2017  \n",
       "8          2017  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.sort_values(by='popularity',ascending = False).head(5)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The table contains the top 5 most popular movie as of the day, the data is collected. As these movies are recently released and highly rated, these 5 are the top most popular movies. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red' style=\"font-style:italic\">Top 10 movies according to the rating (avg vote)</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we will consider movies with number of vote counts  more than 1000 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vote_count</th>\n",
       "      <th>id</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>title</th>\n",
       "      <th>popularity</th>\n",
       "      <th>original_language</th>\n",
       "      <th>genre</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>7331</td>\n",
       "      <td>238</td>\n",
       "      <td>8.5</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>45.14</td>\n",
       "      <td>en</td>\n",
       "      <td>['Crime', 'Drama']</td>\n",
       "      <td>1972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>9773</td>\n",
       "      <td>278</td>\n",
       "      <td>8.5</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>29.57</td>\n",
       "      <td>en</td>\n",
       "      <td>['Crime', 'Drama']</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>5489</td>\n",
       "      <td>424</td>\n",
       "      <td>8.4</td>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>24.61</td>\n",
       "      <td>en</td>\n",
       "      <td>['Drama', 'History', 'War']</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>4249</td>\n",
       "      <td>240</td>\n",
       "      <td>8.4</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>36.09</td>\n",
       "      <td>en</td>\n",
       "      <td>['Crime', 'Drama']</td>\n",
       "      <td>1974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>1491</td>\n",
       "      <td>311</td>\n",
       "      <td>8.3</td>\n",
       "      <td>Once Upon a Time in America</td>\n",
       "      <td>22.11</td>\n",
       "      <td>en</td>\n",
       "      <td>['Crime', 'Drama']</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>3161</td>\n",
       "      <td>539</td>\n",
       "      <td>8.3</td>\n",
       "      <td>Psycho</td>\n",
       "      <td>27.10</td>\n",
       "      <td>en</td>\n",
       "      <td>['Drama', 'Horror', 'Thriller']</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>2579</td>\n",
       "      <td>389</td>\n",
       "      <td>8.3</td>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>25.84</td>\n",
       "      <td>en</td>\n",
       "      <td>['Drama']</td>\n",
       "      <td>1957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>11878</td>\n",
       "      <td>550</td>\n",
       "      <td>8.3</td>\n",
       "      <td>Fight Club</td>\n",
       "      <td>38.64</td>\n",
       "      <td>en</td>\n",
       "      <td>['Drama']</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>3803</td>\n",
       "      <td>510</td>\n",
       "      <td>8.3</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest</td>\n",
       "      <td>29.42</td>\n",
       "      <td>en</td>\n",
       "      <td>['Drama']</td>\n",
       "      <td>1975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>5544</td>\n",
       "      <td>244786</td>\n",
       "      <td>8.3</td>\n",
       "      <td>Whiplash</td>\n",
       "      <td>32.78</td>\n",
       "      <td>en</td>\n",
       "      <td>['Drama']</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     vote_count      id  vote_average                            title  \\\n",
       "81         7331     238           8.5                    The Godfather   \n",
       "274        9773     278           8.5         The Shawshank Redemption   \n",
       "484        5489     424           8.4                 Schindler's List   \n",
       "151        4249     240           8.4           The Godfather: Part II   \n",
       "614        1491     311           8.3      Once Upon a Time in America   \n",
       "365        3161     539           8.3                           Psycho   \n",
       "426        2579     389           8.3                     12 Angry Men   \n",
       "124       11878     550           8.3                       Fight Club   \n",
       "281        3803     510           8.3  One Flew Over the Cuckoo's Nest   \n",
       "200        5544  244786           8.3                         Whiplash   \n",
       "\n",
       "     popularity original_language                            genre  \\\n",
       "81        45.14                en               ['Crime', 'Drama']   \n",
       "274       29.57                en               ['Crime', 'Drama']   \n",
       "484       24.61                en      ['Drama', 'History', 'War']   \n",
       "151       36.09                en               ['Crime', 'Drama']   \n",
       "614       22.11                en               ['Crime', 'Drama']   \n",
       "365       27.10                en  ['Drama', 'Horror', 'Thriller']   \n",
       "426       25.84                en                        ['Drama']   \n",
       "124       38.64                en                        ['Drama']   \n",
       "281       29.42                en                        ['Drama']   \n",
       "200       32.78                en                        ['Drama']   \n",
       "\n",
       "     release_year  \n",
       "81           1972  \n",
       "274          1994  \n",
       "484          1993  \n",
       "151          1974  \n",
       "614          1984  \n",
       "365          1960  \n",
       "426          1957  \n",
       "124          1999  \n",
       "281          1975  \n",
       "200          2014  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset['vote_count'] > 1000].sort_values(by='vote_average',ascending=False ).head(10)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The table shows the top rated movies of all time. From the table we can see Drama movies are highly rated. From this table we can infer, though highest number of movies are of genre action, daram movies are the ones which were managed to get highest average voting. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red' style=\"font-style:italic\">average voting per genre per year</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lets see how the rating changes with the years for perticular genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#https://stackoverflow.com/questions/49220479/how-to-group-by-according-to-the-values-of-a-list-present-in-a-column-in-datafra?noredirect=1#comment85463311_49220479\n",
    "#https://stackoverflow.com/questions/23119472/in-pandas-python-reading-array-stored-as-string\n",
    "df = pd.DataFrame({\n",
    "      'genre' : list(\n",
    "           chain.from_iterable(dataset['genre'].apply(literal_eval).tolist())\n",
    "       ), \n",
    "       'vote_average' : dataset.vote_average.repeat(dataset['genre'].apply(literal_eval).str.len()),\n",
    "       'release_year' : dataset.release_year.repeat(dataset['genre'].apply(literal_eval).str.len())\n",
    "})\n",
    "movie_group_vote_average = df.groupby(\n",
    "     ['release_year','genre'], sort=True \n",
    " )['vote_average'].mean().reset_index()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lets see average voting of the genres for all the years (1900 - 2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre</th>\n",
       "      <th>vote_average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Western</td>\n",
       "      <td>6.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Action</td>\n",
       "      <td>6.824821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>6.832957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Horror</td>\n",
       "      <td>6.874198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>6.934536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Science Fiction</td>\n",
       "      <td>6.958622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>7.014152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>7.129115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Romance</td>\n",
       "      <td>7.159020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Animation</td>\n",
       "      <td>7.163833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Family</td>\n",
       "      <td>7.208578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Music</td>\n",
       "      <td>7.227273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mystery</td>\n",
       "      <td>7.239770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>History</td>\n",
       "      <td>7.262037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Crime</td>\n",
       "      <td>7.305325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>War</td>\n",
       "      <td>7.459091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Drama</td>\n",
       "      <td>7.646763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              genre  vote_average\n",
       "16          Western      6.800000\n",
       "0            Action      6.824821\n",
       "7           Fantasy      6.832957\n",
       "9            Horror      6.874198\n",
       "3            Comedy      6.934536\n",
       "13  Science Fiction      6.958622\n",
       "1         Adventure      7.014152\n",
       "14         Thriller      7.129115\n",
       "12          Romance      7.159020\n",
       "2         Animation      7.163833\n",
       "6            Family      7.208578\n",
       "10            Music      7.227273\n",
       "11          Mystery      7.239770\n",
       "8           History      7.262037\n",
       "4             Crime      7.305325\n",
       "15              War      7.459091\n",
       "5             Drama      7.646763"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_group_vote_average_groupby_genres = movie_group_vote_average.groupby(['genre'])['vote_average'].mean().reset_index()\n",
    "movie_group_vote_average_groupby_genres.sort_values(['vote_average'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OIyIiIiKXaK9Zzeb3TpAYG8qtZ9f5tMK0tDgcdhs7tRtej1IDSkTkE1SfauX5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\nUgJKRERERERERERy9R/55wzsYPEeBgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d91c2114a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the graph\n",
    "genres = movie_group_vote_average_groupby_genres['genre'].tolist()\n",
    "movie_group_vote_average_groupby_genres.plot(x='genre',figsize =(20,5))\n",
    "plt.xticks(np.arange(len(genres)), genres) #np.arrange is used to show all the ticks with uniform space in between\n",
    "plt.ylabel('vote_average')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the table above, average rating of the drama is the highest. Crime movies are also very popular. Action movie ratings are the very low."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the line graph to see the trend of average voting of individual genres (top five according to the number of movies released) year wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2Ph8xTaO9z8cPHzvA9ta5Hwd5oG2Qtm4vFygNOXXLTg0AkyWgM2QADQYDdVW2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\nERFZJf4/+gWGh8xA0VEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d91d6c19b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [] #temporary list is taken to show the individual graph\n",
    "fig = plt.figure(figsize=(15,12))\n",
    "i = 1 # counter\n",
    "for genre in top_genres:\n",
    "    x = movie_group_vote_average[movie_group_vote_average['genre']==genre]\n",
    "    fig.add_subplot(3, 2, i)\n",
    "    plt.xlabel('Year of Release')\n",
    "    plt.ylabel('average_voting')\n",
    "    plt.plot(x['release_year'].tolist(),x['vote_average'].tolist(),'o-',label=genre)\n",
    "    plt.yticks(np.arange(6.0,9.0,0.5))\n",
    "    plt.legend()\n",
    "    i +=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This illustration also shows vote_average of drama in consistently higher than other genres. However the average voting for individual genres decreases with the years. As the number of movie released is increasing, the average quality of the movie decreasing. Thus the average voting is showing a decreasing trend."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot a heat map to see the overall comparison among the top five genres in last 10 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wxuFZz7HkZLgTgA+A24wxrxljvuZgWSIiIiJJz2VzWZxLFK21zdba2cAtQBXw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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d91d75dba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#filter data for last 10 years top genres\n",
    "movie_group_filtered = movie_group_vote_average[(movie_group_vote_average['release_year'] > 2007 ) & (movie_group_vote_average['genre'].isin(top_genres))]\n",
    "df_heat = movie_group_filtered.pivot_table(index='genre', columns='release_year', values='vote_average', aggfunc=np.mean)\n",
    "plt.figure(figsize = (12,6))\n",
    "sns.heatmap(df_heat, annot=True, fmt=\".1f\", cmap=\"YlGnBu\", center=6.5, vmax=8, vmin=6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the heat map, we can see drama has almost consistent dark color. On the other hand action is on the lighter side. Thus, this is also supporting our hypothesis made in the previous illustration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red' style=\"font-style:italic\">number of votes per genre per year</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets see how the number of votes changes over the years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>release_year</th>\n",
       "      <th>genre</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1937</td>\n",
       "      <td>Animation</td>\n",
       "      <td>2586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1937</td>\n",
       "      <td>Family</td>\n",
       "      <td>2586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1937</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>2586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1939</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1939</td>\n",
       "      <td>Drama</td>\n",
       "      <td>1246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1939</td>\n",
       "      <td>Family</td>\n",
       "      <td>2074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1939</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>2074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1939</td>\n",
       "      <td>Romance</td>\n",
       "      <td>1246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1939</td>\n",
       "      <td>War</td>\n",
       "      <td>1246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1941</td>\n",
       "      <td>Drama</td>\n",
       "      <td>1579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   release_year      genre  vote_count\n",
       "0          1937  Animation        2586\n",
       "1          1937     Family        2586\n",
       "2          1937    Fantasy        2586\n",
       "3          1939  Adventure        2074\n",
       "4          1939      Drama        1246\n",
       "5          1939     Family        2074\n",
       "6          1939    Fantasy        2074\n",
       "7          1939    Romance        1246\n",
       "8          1939        War        1246\n",
       "9          1941      Drama        1579"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# flatten the genres list\n",
    "df = pd.DataFrame({\n",
    "      'genre' : list(\n",
    "           chain.from_iterable(dataset['genre'].apply(literal_eval).tolist())\n",
    "       ), \n",
    "       'vote_count' : dataset.vote_count.repeat(dataset['genre'].apply(literal_eval).str.len()),\n",
    "       'release_year' : dataset.release_year.repeat(dataset['genre'].apply(literal_eval).str.len())\n",
    "})\n",
    "#group by the movies according to the genre\n",
    "movie_group_vote_count = df.groupby(\n",
    "     ['release_year','genre'], sort=True \n",
    " )['vote_count'].sum().reset_index()\n",
    "#display movie_group\n",
    "movie_group_vote_count.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot line graphs to see the trend of the count of vote for individual genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QBsDYbhJKgGkjpwOwq3anEko6ntXsnNuKV421cx+d1SwiIoOaivKIiEiflDVEPoMy1NS8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\nD+arki9alSeRRCOtk81kVzIPTH20WVzeyfn+Tgzi6Z6PXeX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wvrV2KpAHvIVztWmsMaav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\nALvhkF3E6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d91d840898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [] #temporaray list to plot the graphs\n",
    "fig = plt.figure(figsize=(15,12))\n",
    "i = 1\n",
    "for genre in top_genres:\n",
    "    x = movie_group_vote_count[movie_group_vote_count['genre']==genre]\n",
    "    fig.add_subplot(3, 2, i)\n",
    "    plt.xlabel('Year of Release')\n",
    "    plt.ylabel('Number of Votes')\n",
    "    plt.plot(x['release_year'].tolist(),x['vote_count'].tolist(),'go-',label=genre)\n",
    "    plt.yticks(range(0,130000,20000))\n",
    "    plt.legend()\n",
    "    i +=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with the years number of movie release is increasing, number of vote count has also increased. Action movies hasve the highest number of vote and comedy has the lowest number of votes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot a heat map to make a coparison of vote count among top genres in last 10 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sbFm4Ooypo9pzK1HHZ1N7AHDyQgJLvj3D5chkNi3si9kMB0PjOHmh+CRSn2fi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\n3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2OPFj4f7Dx4kZ5tagLQIzqSbQcL9zF1t13xqXQzz+1tzXN7ao6NbLuDXIeTXIeTtFwb\nIX4+jGpeizsun9tt9kLbo1P1innN8lfaXeD7o6sHzu3bDyfQvUWB/c/Ph6kPtade1cv7nxWH08me\n40l0aFwFPx8vggN8aFgjBH02hXEDmnDXzcZxmZlj7Ks5VjsZOTYyzIu3+JRsQoMKtzzsOJJIz5aX\n978abDO7+5QdJXMf0tLOk6Ps7/Tgsq/i5WVhSv8mxKVk8a9B0QBsOZXMB2uPcuBCGksf7ogTo9/n\nllPJHE3MYPpfWjAkuiZeXhae/88+cu1O3lxxiK/ub0eOzU5qtjGKHmDu8LaMXrCLedtPM31gCxY/\n2AGr3cmEpXuvU6qie3/nMSZ3aszgxjVIs9qYtEGTlmvj0KUM5vZvjdMJG+KS2RGfwvHUTN64OYq7\nG1bDy2Jh8majv97cA2f4uFdzcu1OErJyeW2L0Uz1Se8WPLlmH4sPn+ONm6OY3a8VVoeTSRsOujWG\nRVtP8d6QaBb9tQs4nUxcuPuqvrnXs3jrKaYNbcPgTrXxtlh4fuHu684/d8xNjJ61hXkbTzJ9qLFe\nq93BhPk7ixpKIY+3q0NYBV+ebF+XJ9sbg1Vm7j7D/Ltak2W1s/nsJdacNL706lcM5IzZL/SyBfvO\n8UbPxnx7bxssFnhpjbG9XlpziA/7NcXLAutPJ7Pb7Bc9986WjF4Wy7zYOKb3acKiu6OxOhxMWHHA\nrXFd6c1fDvD2XS0Z0bEOadk2xi+69t9/+m+HeeeuFozoVAdfLwsvfBd73WXPfagDo7/azrytp5h+\nbysWj+lkHFfXWcf/4ufDF5nevwmLB0Xj42XhtTVHyLFfeWkGiVlW3ll/nHn3tALgx0PxeaPxXXlx\n5SFm3NYMbwusO5nMbrNpcd49rXj4uxi+2hvHB/2b8M3gNuTaHYz/2b3bavWec3SMqsLSV/viZYHJ\n83aSkW3j3dEdsdocZOXamDR7O4lpOUxbEsOcZ7sDsGzbGQ6dTeXTZQd4f2wneraqgd3hZOLMrQDM\n/EUz65nu5FrtXEzJZtLs7QDMebY7Yz5cz/zVR3nvkY4snNQLq93BU59ucWtc9SNDOW2O/PfysvDK\nqPbEJWTwz+d6ALB1/wVmLNrLtPm7mP1ybwB+2niSw6eNhLRBZGhe39HLXvxkM1wyu/oAAAylSURB\nVB8/352cXDuHz6SwcKVxvM15uTdj3l7D/OWHeO+Jzix44xasNgdPz1jv1piuNH3nMSbf1JhBUTVI\nz7Xxgnlu35eYxrxbW+NwGhUSm85dQidn8LfOUdzVqBreFguvbsrvix0R4EfKFa1an/ZuwRNr9rHo\n0Dne6BzFnFuMc/sL6917bl+99xwdoyJY+nIf4ztnvrn/jeqA1e4gK9fOpDnbSUjN4cuVh1n4Qi+8\nLBamL40l1+Zg8frjTBvVkUFd6+PtZeH/Zm8j1+Zg6sI9fPlMd3KsDlIzc5k4y+h+N+eZboyZsZ75\na47y3ugOLHyhF1abg6fMblyibLM4b7C26b+llKoJvANUBRYDe7XWN3TWqvfGcs8UqgRVbBBQ0kXw\niJRd7q3xKRXqh/7xPGWQ81xmSRfBIxyVy9+x5bvXs7VzJcVSpm7Nc2OCBrr3zgmlRfpq190Ayrqj\nXwwq8erFNOvKIuc4Ib59SjwOd/Nkk/1nwCzAF1iH8axUIYQQQog/rVI2yr7U8GRCGqC1XgU4tdYa\n487/QgghhBB/Yl5u+Cl/PBlVtlKqP+Bt3hRVElIhhBBCCHEVTw5qGgtMwxht/xzwmAfXJYQQQghR\n6l1+MpwozO0JqfnoKIB44AF3L18IIYQQouyShNQVT9SQasCJ8Re/PJLs8u/uffC7EEIIIUQZUl4H\nJRWV2xNSrXV9dy9TCCGEEKJ8KJ+DkorKE032m8ivGS1Ea93Z3esTQgghhBBlmyea7Id6YJlCCCGE\nEGWeNNm75omEtJ/WeqZSaipX15S+6IH1CSGEEEKUCTLK3jVPJKSnzf8PA3YPLF8IIYQQooyShNQV\nTwxq+tX8dajW+hZ3L18IIYQQoqyyyKAmlzx5Y/xkpdRfgEOAA0BrfciD6xNCCCGEEGWQJxPSqsDT\nBV47gd4eXJ8QQgghRCknTfaueCwh1Vr38tSyhRBCCCHKIhnU5JrHElKl1KvAE4Dt8jStdaSn1ieE\nEEIIUfpJQuqKJ5vs7wTqaq2zPLgOIYQQQghRxnkyIY0HrB5cvhBCCCFEmSKj7F3zxKNDvzZ/rQbs\nUkrFYt4gX2s93N3rE0IIIYQoO6TJ3hVP1JD2BQZ5YLlCCCGEEGWaPDrUNU8kpPu01ms9sFwhhBBC\niDJNRtm75omEtIFS6i1Xb2it5Vn2QgghhBCiEE8kpJmA9sByhRBCCCHKOBnU5IonEtLzWusvPbBc\nIYQQQogyTfqQuuaJhHSHB5YphBBCCFEOSELqitsTUq31c+5ephBCCCFEeSCDmlyTjgxCCCGEEKJE\nefJJTUIIIYQQohCpC3RFElIhhBBCiGIig5pcszidzpIugxBCCCGE+BOTemMhhBBCCFGiJCEVQggh\nhBAlShJSIYQQQghRoiQhFUIIIYQQJUoSUiGEEEIIUaIkIRVCCCGEECWqXN6HVCnlC8wC6gH+wN+A\n/cAcwAnEAn/VWjuUUmOARwEb8Det9Y9KqTBgARAM5AAjtNbnizuOK7khrnBgHhAKJAJjtNbxxR1H\nQf9NTOb8VYANQCutdbZSKgAjpqpAGvCg1vpiMYdxlaLGVWA5dwODtNbDi7P8rrhhW4WRv//5Ac9o\nrTcVcxhXcUNcQcDXQCUgF2MfPFvMYRTixv2vCbAFqFZweklxw7ayAGeAw+YiN2mtJxVnDK64IS5v\n4H2gvfn5KVrrH4s5jELcENMLwK3m4ioC1bXW1YszBlG8ymsN6QggUWvdDWOH/gjjYH3ZnGYBBiql\nqgPjgS5Af2CqUsofeAiIMeddCDxf/CG4VNS4XgTWa627Av8A3iqBGK50QzEBKKX6A8uBgielx8jf\nVnOBl4ux7NdT1LhQSs0AplJ6jtOixvQMsFJr3QPjGPu4+Ip+XUWNawywQ2vdHSPhnliMZb8Wd+x/\nocB0jIvy0qKocTUEdmqte5o/JZ6Mmooa10jAV2vdxZyvUTGW/VqKFJPW+u3L2wnjIuKB4i2+KG6l\n5YvO3RYDr5i/WzBqCdsBa81pPwN9gY7ABq11jtY6BTgCtAJigBBz3lDAWkzl/iNFjauZOQ8YV6Jd\ni6nc13OjMQE4zN+TCny+K/CLi3lLWlHjAtiIkXCXFkWN6QPgX+bvPkCJ17iZihSX1vpD4E3zZR3g\nkofLeyOKFJNZk/gZxkVsZjGU90YVdR9sB9RUSq1WSv2klFKeL/INKWpc/YGzSqllwOfAD54u8A1w\nxzkQpdQ9QLLWerlHSytKXLlsstdapwMopUKAJRi1ZtO01pcfS5UGhGEkmykFPnp5+kXgFqXUfiAc\n6FZMRb8uN8S1G/gLsMv8P7B4Sn5t/0VMaK1XmPMWXETBWPPmLWluiAut9UKlVM9iKvIfKmpMWutL\n5rTqGDWJTxVX2a/HTdvKrpRaBbQE+hVPya/NDTFNBpZprfeUnpzNLXGdA6ZqrRcrpbpi7Icdiqf0\n1+aGuCIwakXvALoDs83/S4w7jivTJGCYp8srSl55rSFFKVUbWA18pbX+GuMK7LIQjFqMVPJrQgtO\nnwy8q7VuBtwCfFMshb4BRYxrKlBPKbUOo1/P6eIo8x+5wZiupWCsfzRvsSpiXKVSUWNSSrUEVgIv\naq3XXm/e4uSObaW17o1x8VoqzhdFjGkEMFoptQajGbXU1E4VMa7twPcAWuv1QKRZG1ziihhXIvCj\n1tppHldRnivpjXPD+aIZcElrfcRzpRSlRblMSJVS1TBOoP+ntZ5lTt5VoLbpNuB3YCvQTSlVwRxw\n0RSjo3Uy+bVu8Ri1cCXODXF1Bz43+7odwWi2L1H/RUzXsgEYcIPzFhs3xFXqFDUm88tlMTBca/3z\nteYrbm6Ia5JSaqT5Mh2we6qsN6qoMWmtGxXov3ce48K8xLnhuJqMWTOvlGoNnC5QY1di3BDXeszz\noBnXKQ8V9Ya56RzYl/xuZqKcK5dN9hj9nioBryilLvdhmQD8XSnlBxwAlpjNbH/HOCi8gJfM0X2v\nADOVUo8DvhiDFkqDosalgblms8hZYHSxR3C1G4rpOp//BPhSKbUeY4RziY9GNxU1rtKoqDFNBSoA\nM8x9MEVrPdCD5b1RRY1rFsY+OBrwBh72ZGFvUHnc/6Docb0NzFNK3Y7Rp/EhD5b1v1HUuD4HPlFK\nbcborznOk4W9Qe7YBxWwwnNFFKWJxeks8YtDIYQQQgjxJ1Yum+yFEEIIIUTZIQmpEEIIIYQoUZKQ\nCiGEEEKIEiUJqRBCCCGEKFGSkAohhBBCiBIlCakQolgopR5SSs0p6XIIIYQofSQhFUIIIYQQJaq8\n3hhfCFGMzKevvItxY/gTGE8samG+fkdr/e8r5u8AfAAEAgnAo1rr40qpHsCb5vRKwETzuePDgYkY\nT0E6DowwH/bwAjDYXM+vGE+FcXlzZfPG9X201sPN15OBbOBj86dQeZVSocAXQC0gElgHPAD0KBBr\nrNb6wSL86YQQQiA1pEII94kCegOHgR1a63YYj6t9SSnV4PJM5lNaZmI8QrQtMB3jSTMATwKPmNNH\nA6+a0/8G3GIu8yDQRCl1K9AO6AC0AWoC91+nfAuBPkqpYPP55fcDXwEvX6O8twO7tdY3A42Bm4G2\nBWOVZFQIIdxDakiFEO6itdYpSqm+QKBSapQ5PQhoXmC+KKAh8B/zEaIAoeb/I4A7lFKDgJuAYHP6\nD8AGpdR3wDda691KqWlAJ2CHOU8A13mGt9Y6XSn1E3AvcAw4qrWOu1Z5zVrSjkqpp4CmQOUC5dFa\n65T/4m8jhBDiOiQhFUK4S5b5vzdGk/pOAKVUNSCJ/NpLb+CY1jrafN8bqGa+9zuwGlgDrAS+BtBa\nT1BKfYFRazlPKTXFXM6HWuv3zeVUxHg++fXMwqgRPQbMuV55lVJPAvcBnwG/YTTpW66IVQghhBtI\nk70Qwt1WAY8BKKVqAHuBOgXePwiEK6W6ma9HAV8rpcIxak9f1Vr/BNwCeCulfJRSh4EErfVUYC5G\nE/0qYKTZBO8DfIeRQF6T1vp3jD6hvcz5r1fefsC/tNbzAScQjZG8CiGEcDNJSIUQ7vYaEKCUisVI\n9iZqrY9eflNrnQMMAqYrpfYCDwKjtdZJGH1L9ymldgFVMQY3+WP0Jf1NKbUdo5/n+1rrH4BvgC1A\nLLAb+PIGyrcUWGWW43rl/RCYrJTaCfwT2AjU/1//KEIIIa7N4nS6HJAqhBDlijmQyQ+j+X3C5SZ6\nIYQQJU/6kAohyg2lVACw6Rpvf4BR6/m5JKNCCFG6SA2pEEIIIYQoUdKHVAghhBBClChJSIUQQggh\nRImShFQIIYQQQpQoSUiFEEIIIUSJkoRUCCGEEEKUKElIhRBCCCFEifp/H+g62DpsSiEAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d91d6048d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#filter data for last 10 years top genres\n",
    "movie_group_filtered = movie_group_vote_count[(movie_group_vote_count['release_year'] > 2007 ) & (movie_group_vote_count['genre'].isin(top_genres))]\n",
    "df_heat = movie_group_filtered.pivot_table(index='genre', columns='release_year', values='vote_count', aggfunc=np.mean)\n",
    "plt.figure(figsize = (12,6))\n",
    "sns.heatmap(df_heat, annot=True, fmt=\".1f\", cmap=\"YlGnBu\", center=6.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Action has dark cells representing higher value, whereas comedy has lighter cells representing lower values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the analysis we can draw the conclusion as follows:<br/>\n",
    "<ul>\n",
    "    <li> The number of movies released per year has an increasing trend. </li>\n",
    "    <li> The movies of genres drama, crime are highly rated over the years. </li>\n",
    "    <li> As the number of movies increases, the number of votes for each genres also increase</li>\n",
    "    <li> The quality of the movie decreases with the years (as the average vote is decreasing for each individual genres) </li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so, lets verify our last two hypothesis with the correlation matrices. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red' style=\"font-style:italic\">correlation between number of movies and vote count and average_vote</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_of_movies</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>average ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>number_of_movies</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.95495</td>\n",
       "      <td>-0.727162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vote_count</th>\n",
       "      <td>0.954950</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>-0.696650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average ratings</th>\n",
       "      <td>-0.727162</td>\n",
       "      <td>-0.69665</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  number_of_movies  vote_count  average ratings\n",
       "number_of_movies          1.000000     0.95495        -0.727162\n",
       "vote_count                0.954950     1.00000        -0.696650\n",
       "average ratings          -0.727162    -0.69665         1.000000"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of movies and average vote count per year for top 5 genres\n",
    "vote_count_per_year = dataset.groupby(['release_year'])['vote_count'].sum()\n",
    "average_rating_per_year =  dataset.groupby(['release_year'])['vote_average'].mean()\n",
    "number_of_movies_per_year = dataset.groupby(['release_year'])['id'].count()\n",
    "\n",
    "#concat the e counts to form the dataframe\n",
    "d = pd.concat([ number_of_movies_per_year.to_frame(),vote_count_per_year.to_frame(),average_rating_per_year.to_frame()], axis=1)\n",
    "d.columns = ['number_of_movies', 'vote_count', 'average ratings']\n",
    "\n",
    "#correlation among the columns\n",
    "corr = d.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d91d71a7b8>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w0pIkVcPRg5KkalhpSZKqYdKSJFVj0KQlSarFgElLklSLQS+YK0mqRX+Fowd7JzuAqSwi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d91d509320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the correlation\n",
    "sns.heatmap(corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above correlation plot it is clear that number of movies and vote count is highly correlated and voting average and number of movies are negatively correlated. Thus with the increase of number of movies the average quality of movies is decreasing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Further statistical analysis can be done to find the relationship among the genres (eg. which set of genres are most likely to succeed as a group). However, drama and crime together most likely to be the best rated genres."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not much analysis is done for the column 'popularity'. Further analysis can be carried out for that column to show the trend of 'popularity' based on the number of search , downloads."
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}