Investigate_a_Dataset_TMDb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Project: Investigate a Dataset : TMDb Dataset\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>\n",
    "\n",
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "> The dataset selected is the TMDb dataset that contains the data on movies and ratings.\n",
    "\n",
    "> Trends to be explored here are:\n",
    "- Popularity over the years\n",
    "- Revenue over the years\n",
    "- Runtime over the years\n",
    "\n",
    "> Associations and Effects [over period spans] to be evaluated are:\n",
    "- Runtime vs Popularity \n",
    "- Runtime vs Revenue \n",
    "- Revenue vs Popularity\n",
    "\n",
    "Some questions to be answered are:\n",
    "- Over the decades, what are the popular runtimes?\n",
    "- Spanning the time periods, is revenue proportional to popularity? \n",
    "- Does runtime affect popularity? \n",
    "\n",
    "> The investigations conducted here are exploratory and any associations will be tentative at best, as only visualization and basic correlations are attempted in this project. Any relation inferred will be used only as a pointer for conducting further detailed, statistically backed analyses. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use this cell to set up import statements for all of the packages that you\n",
    "#   plan to use.\n",
    "\n",
    "# Remember to include a 'magic word' so that your visualizations are plotted\n",
    "#   inline with the notebook. See this page for more:\n",
    "#   http://ipython.readthedocs.io/en/stable/interactive/magics.html\n",
    "\n",
    "#import statements for packages to be used\n",
    "import pandas as pd\n",
    "import csv # read and write csv files\n",
    "from datetime import datetime # operations to parse dates\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "> Data is relatively clean. One duplicate record is deleted.\n",
    "\n",
    "### General Properties\n",
    "**Observations**\n",
    "- Rows: 10866 \n",
    "- Columns: 21\n",
    "- Null values  present in variables: [as inspected in code below]\n",
    "> - imdb_id \n",
    "> - cast\n",
    "> - homepage\n",
    "> - director\n",
    "> - tagline\n",
    "> - keywords\n",
    "> - overview\n",
    "> - genres\n",
    "> - production_companies\n",
    "\n",
    "**Steps to Take:**\n",
    "- imdb_id are significant to identify the records uniquely. Only 10 of 10865 are missing. Hence, we only drop the records with this value as missing. \n",
    "- Other values with missing values are not significant, hence, retained 'as-is'.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10866 entries, 0 to 10865\n",
      "Data columns (total 21 columns):\n",
      "id                      10866 non-null int64\n",
      "imdb_id                 10856 non-null object\n",
      "popularity              10866 non-null float64\n",
      "budget                  10866 non-null int64\n",
      "revenue                 10866 non-null int64\n",
      "original_title          10866 non-null object\n",
      "cast                    10790 non-null object\n",
      "homepage                2936 non-null object\n",
      "director                10822 non-null object\n",
      "tagline                 8042 non-null object\n",
      "keywords                9373 non-null object\n",
      "overview                10862 non-null object\n",
      "runtime                 10866 non-null int64\n",
      "genres                  10843 non-null object\n",
      "production_companies    9836 non-null object\n",
      "release_date            10866 non-null object\n",
      "vote_count              10866 non-null int64\n",
      "vote_average            10866 non-null float64\n",
      "release_year            10866 non-null int64\n",
      "budget_adj              10866 non-null float64\n",
      "revenue_adj             10866 non-null float64\n",
      "dtypes: float64(4), int64(6), object(11)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# Load your data and print out a few lines. Perform operations to inspect data\n",
    "#   types and look for instances of missing or possibly errant data.\n",
    "df = pd.read_csv('tmdb_movies.csv', encoding='ISO-8859-1')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10865 entries, 0 to 10865\n",
      "Data columns (total 21 columns):\n",
      "id                      10865 non-null int64\n",
      "imdb_id                 10855 non-null object\n",
      "popularity              10865 non-null float64\n",
      "budget                  10865 non-null int64\n",
      "revenue                 10865 non-null int64\n",
      "original_title          10865 non-null object\n",
      "cast                    10789 non-null object\n",
      "homepage                2936 non-null object\n",
      "director                10821 non-null object\n",
      "tagline                 8041 non-null object\n",
      "keywords                9372 non-null object\n",
      "overview                10861 non-null object\n",
      "runtime                 10865 non-null int64\n",
      "genres                  10842 non-null object\n",
      "production_companies    9835 non-null object\n",
      "release_date            10865 non-null object\n",
      "vote_count              10865 non-null int64\n",
      "vote_average            10865 non-null float64\n",
      "release_year            10865 non-null int64\n",
      "budget_adj              10865 non-null float64\n",
      "revenue_adj             10865 non-null float64\n",
      "dtypes: float64(4), int64(6), object(11)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                         0\n",
       "imdb_id                   10\n",
       "popularity                 0\n",
       "budget                     0\n",
       "revenue                    0\n",
       "original_title             0\n",
       "cast                      76\n",
       "homepage                7929\n",
       "director                  44\n",
       "tagline                 2824\n",
       "keywords                1493\n",
       "overview                   4\n",
       "runtime                    0\n",
       "genres                    23\n",
       "production_companies    1030\n",
       "release_date               0\n",
       "vote_count                 0\n",
       "vote_average               0\n",
       "release_year               0\n",
       "budget_adj                 0\n",
       "revenue_adj                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning (Dropping rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10855 entries, 0 to 10865\n",
      "Data columns (total 21 columns):\n",
      "id                      10855 non-null int64\n",
      "imdb_id                 10855 non-null object\n",
      "popularity              10855 non-null float64\n",
      "budget                  10855 non-null int64\n",
      "revenue                 10855 non-null int64\n",
      "original_title          10855 non-null object\n",
      "cast                    10779 non-null object\n",
      "homepage                2934 non-null object\n",
      "director                10815 non-null object\n",
      "tagline                 8038 non-null object\n",
      "keywords                9368 non-null object\n",
      "overview                10852 non-null object\n",
      "runtime                 10855 non-null int64\n",
      "genres                  10834 non-null object\n",
      "production_companies    9830 non-null object\n",
      "release_date            10855 non-null object\n",
      "vote_count              10855 non-null int64\n",
      "vote_average            10855 non-null float64\n",
      "release_year            10855 non-null int64\n",
      "budget_adj              10855 non-null float64\n",
      "revenue_adj             10855 non-null float64\n",
      "dtypes: float64(4), int64(6), object(11)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# After discussing the structure of the data and any problems that need to be\n",
    "#   cleaned, perform those cleaning steps in the second part of this section.\n",
    "# As stated earlier, we have removed the single duplicate record.\n",
    "# Here, we are removing rows with null values in imdb_id column\n",
    "df.dropna(subset=['imdb_id'], inplace=True)  \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>homepage</th>\n",
       "      <th>director</th>\n",
       "      <th>tagline</th>\n",
       "      <th>...</th>\n",
       "      <th>overview</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>release_date</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135397</td>\n",
       "      <td>tt0369610</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>http://www.jurassicworld.com/</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>The park is open.</td>\n",
       "      <td>...</td>\n",
       "      <td>Twenty-two years after the events of Jurassic ...</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Universal Studios|Amblin Entertainment|Legenda...</td>\n",
       "      <td>6/9/15</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>137999939.3</td>\n",
       "      <td>1.392446e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76341</td>\n",
       "      <td>tt1392190</td>\n",
       "      <td>28.419936</td>\n",
       "      <td>150000000</td>\n",
       "      <td>378436354</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...</td>\n",
       "      <td>http://www.madmaxmovie.com/</td>\n",
       "      <td>George Miller</td>\n",
       "      <td>What a Lovely Day.</td>\n",
       "      <td>...</td>\n",
       "      <td>An apocalyptic story set in the furthest reach...</td>\n",
       "      <td>120</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Village Roadshow Pictures|Kennedy Miller Produ...</td>\n",
       "      <td>5/13/15</td>\n",
       "      <td>6185</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2015</td>\n",
       "      <td>137999939.3</td>\n",
       "      <td>3.481613e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>262500</td>\n",
       "      <td>tt2908446</td>\n",
       "      <td>13.112507</td>\n",
       "      <td>110000000</td>\n",
       "      <td>295238201</td>\n",
       "      <td>Insurgent</td>\n",
       "      <td>Shailene Woodley|Theo James|Kate Winslet|Ansel...</td>\n",
       "      <td>http://www.thedivergentseries.movie/#insurgent</td>\n",
       "      <td>Robert Schwentke</td>\n",
       "      <td>One Choice Can Destroy You</td>\n",
       "      <td>...</td>\n",
       "      <td>Beatrice Prior must confront her inner demons ...</td>\n",
       "      <td>119</td>\n",
       "      <td>Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Summit Entertainment|Mandeville Films|Red Wago...</td>\n",
       "      <td>3/18/15</td>\n",
       "      <td>2480</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2015</td>\n",
       "      <td>101199955.5</td>\n",
       "      <td>2.716190e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>140607</td>\n",
       "      <td>tt2488496</td>\n",
       "      <td>11.173104</td>\n",
       "      <td>200000000</td>\n",
       "      <td>2068178225</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>Harrison Ford|Mark Hamill|Carrie Fisher|Adam D...</td>\n",
       "      <td>http://www.starwars.com/films/star-wars-episod...</td>\n",
       "      <td>J.J. Abrams</td>\n",
       "      <td>Every generation has a story.</td>\n",
       "      <td>...</td>\n",
       "      <td>Thirty years after defeating the Galactic Empi...</td>\n",
       "      <td>136</td>\n",
       "      <td>Action|Adventure|Science Fiction|Fantasy</td>\n",
       "      <td>Lucasfilm|Truenorth Productions|Bad Robot</td>\n",
       "      <td>12/15/15</td>\n",
       "      <td>5292</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>183999919.0</td>\n",
       "      <td>1.902723e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>168259</td>\n",
       "      <td>tt2820852</td>\n",
       "      <td>9.335014</td>\n",
       "      <td>190000000</td>\n",
       "      <td>1506249360</td>\n",
       "      <td>Furious 7</td>\n",
       "      <td>Vin Diesel|Paul Walker|Jason Statham|Michelle ...</td>\n",
       "      <td>http://www.furious7.com/</td>\n",
       "      <td>James Wan</td>\n",
       "      <td>Vengeance Hits Home</td>\n",
       "      <td>...</td>\n",
       "      <td>Deckard Shaw seeks revenge against Dominic Tor...</td>\n",
       "      <td>137</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "      <td>Universal Pictures|Original Film|Media Rights ...</td>\n",
       "      <td>4/1/15</td>\n",
       "      <td>2947</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2015</td>\n",
       "      <td>174799923.1</td>\n",
       "      <td>1.385749e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id    imdb_id  popularity     budget     revenue  \\\n",
       "0  135397  tt0369610   32.985763  150000000  1513528810   \n",
       "1   76341  tt1392190   28.419936  150000000   378436354   \n",
       "2  262500  tt2908446   13.112507  110000000   295238201   \n",
       "3  140607  tt2488496   11.173104  200000000  2068178225   \n",
       "4  168259  tt2820852    9.335014  190000000  1506249360   \n",
       "\n",
       "                 original_title  \\\n",
       "0                Jurassic World   \n",
       "1            Mad Max: Fury Road   \n",
       "2                     Insurgent   \n",
       "3  Star Wars: The Force Awakens   \n",
       "4                     Furious 7   \n",
       "\n",
       "                                                cast  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   \n",
       "1  Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...   \n",
       "2  Shailene Woodley|Theo James|Kate Winslet|Ansel...   \n",
       "3  Harrison Ford|Mark Hamill|Carrie Fisher|Adam D...   \n",
       "4  Vin Diesel|Paul Walker|Jason Statham|Michelle ...   \n",
       "\n",
       "                                            homepage          director  \\\n",
       "0                      http://www.jurassicworld.com/   Colin Trevorrow   \n",
       "1                        http://www.madmaxmovie.com/     George Miller   \n",
       "2     http://www.thedivergentseries.movie/#insurgent  Robert Schwentke   \n",
       "3  http://www.starwars.com/films/star-wars-episod...       J.J. Abrams   \n",
       "4                           http://www.furious7.com/         James Wan   \n",
       "\n",
       "                         tagline      ...       \\\n",
       "0              The park is open.      ...        \n",
       "1             What a Lovely Day.      ...        \n",
       "2     One Choice Can Destroy You      ...        \n",
       "3  Every generation has a story.      ...        \n",
       "4            Vengeance Hits Home      ...        \n",
       "\n",
       "                                            overview runtime  \\\n",
       "0  Twenty-two years after the events of Jurassic ...     124   \n",
       "1  An apocalyptic story set in the furthest reach...     120   \n",
       "2  Beatrice Prior must confront her inner demons ...     119   \n",
       "3  Thirty years after defeating the Galactic Empi...     136   \n",
       "4  Deckard Shaw seeks revenge against Dominic Tor...     137   \n",
       "\n",
       "                                      genres  \\\n",
       "0  Action|Adventure|Science Fiction|Thriller   \n",
       "1  Action|Adventure|Science Fiction|Thriller   \n",
       "2         Adventure|Science Fiction|Thriller   \n",
       "3   Action|Adventure|Science Fiction|Fantasy   \n",
       "4                      Action|Crime|Thriller   \n",
       "\n",
       "                                production_companies release_date vote_count  \\\n",
       "0  Universal Studios|Amblin Entertainment|Legenda...       6/9/15       5562   \n",
       "1  Village Roadshow Pictures|Kennedy Miller Produ...      5/13/15       6185   \n",
       "2  Summit Entertainment|Mandeville Films|Red Wago...      3/18/15       2480   \n",
       "3          Lucasfilm|Truenorth Productions|Bad Robot     12/15/15       5292   \n",
       "4  Universal Pictures|Original Film|Media Rights ...       4/1/15       2947   \n",
       "\n",
       "   vote_average  release_year   budget_adj   revenue_adj  \n",
       "0           6.5          2015  137999939.3  1.392446e+09  \n",
       "1           7.1          2015  137999939.3  3.481613e+08  \n",
       "2           6.3          2015  101199955.5  2.716190e+08  \n",
       "3           7.5          2015  183999919.0  1.902723e+09  \n",
       "4           7.3          2015  174799923.1  1.385749e+09  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save new clean datasets for next section\n",
    "df.to_csv('data_imdb.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "### Research Question 1 \n",
    "- How are runtimes, popularity and revenues trending over time?\n",
    "- Are the variables: runtimes, budgets, revenues related to popularity?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10855 entries, 0 to 10854\n",
      "Data columns (total 21 columns):\n",
      "id                      10855 non-null int64\n",
      "imdb_id                 10855 non-null object\n",
      "popularity              10855 non-null float64\n",
      "budget                  10855 non-null int64\n",
      "revenue                 10855 non-null int64\n",
      "original_title          10855 non-null object\n",
      "cast                    10779 non-null object\n",
      "homepage                2934 non-null object\n",
      "director                10815 non-null object\n",
      "tagline                 8038 non-null object\n",
      "keywords                9368 non-null object\n",
      "overview                10852 non-null object\n",
      "runtime                 10855 non-null int64\n",
      "genres                  10834 non-null object\n",
      "production_companies    9830 non-null object\n",
      "release_date            10855 non-null object\n",
      "vote_count              10855 non-null int64\n",
      "vote_average            10855 non-null float64\n",
      "release_year            10855 non-null int64\n",
      "budget_adj              10855 non-null float64\n",
      "revenue_adj             10855 non-null float64\n",
      "dtypes: float64(4), int64(6), object(11)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# Importing data from newly cleaned dataset\n",
    "df_imdb = pd.read_csv('data_imdb.csv')\n",
    "df_imdb.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_new = df_imdb.groupby('release_year').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEICAYAAABPgw/pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDt1xHKcg/H+zBxv59xc3RwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ed9f0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_new.hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEWCAYAAABhffzLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4+A3M8uMg9/MLDMOfjOzzPx/ULSj/XCtZNwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ed9f278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_new['runtime'].hist()\n",
    "plt.xlabel('Runtime')\n",
    "plt.title('Runtime Over the Years');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     56.000000\n",
       "mean     105.714644\n",
       "std        5.508458\n",
       "min       96.179331\n",
       "25%      101.910776\n",
       "50%      105.678699\n",
       "75%      107.593247\n",
       "max      124.343750\n",
       "Name: runtime, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['runtime'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations about Runtimes:\n",
    "- As seen in the plots and functions above, popular runtimes over the years are between 104 and 107 minutes.\n",
    "- The distribution is right skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEWCAYAAAB2X2wCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noZuZZcKFbmaWCRe6mVkm/g9FrOwAYvS85AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119c60ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_new['popularity'].hist()\n",
    "plt.xlabel('Popularity')\n",
    "plt.title('Popularity Over the Years');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    56.000000\n",
       "mean      0.559693\n",
       "std       0.128434\n",
       "min       0.304112\n",
       "25%       0.469625\n",
       "50%       0.546928\n",
       "75%       0.626934\n",
       "max       1.032126\n",
       "Name: popularity, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['popularity'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations about Popularity Ratings:\n",
    "- As evident from histogram and quartile percentages, maximum ratings received fall in the 0.47 to 0.62 ranges. \n",
    "- The distribution is skewed to right.\n",
    "- It is observed that no values lie in the range 0.73 to 0.89. This needs further scrutiny."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAEWCAYAAABG030jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5v8DaJGqfl8qzWIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119cc1780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_new['revenue'].hist()\n",
    "plt.xlabel('Revenue')\n",
    "plt.title('Revenue Over the Years');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5.600000e+01\n",
       "mean     3.076766e+07\n",
       "std      1.574209e+07\n",
       "min      1.842102e+06\n",
       "25%      1.815642e+07\n",
       "50%      3.257984e+07\n",
       "75%      4.293171e+07\n",
       "max      5.549569e+07\n",
       "Name: revenue, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['revenue'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Observations of Revenues:\n",
    "From the plots and histogram, we can see that:\n",
    "- Distribution is left skewed.\n",
    "- Most movie revenues fall in the 3.257984e+07 to 4.293171e+07 ranges. `"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "> **Tip**: Finally, summarize your findings and the results that have been performed. Make sure that you are clear with regards to the limitations of your exploration. If you haven't done any statistical tests, do not imply any statistical conclusions. And make sure you avoid implying causation from correlation!\n",
    "\n",
    "> **Tip**: Once you are satisfied with your work, you should save a copy of the report in HTML or PDF form. Before exporting your report, check over it to make sure that the flow of the report is complete. You should probably remove all of the \"Tip\" quotes like this one so that the presentation is as tidy as possible. It's also a good idea to look over the project rubric, found on the project submission page at the end of the lesson.\n",
    "\n",
    "> To export the report to the workspace, you should run the code cell below. If it worked correctly, you should get a return code of 0, and you should see the generated .html file in the workspace directory (click on the jupyter icon in the upper left). Alternatively, you can download the html report via the **File** > **Download as** submenu and then manually upload it to the workspace directory. Once you've done this, you can submit your project by clicking on the \"Submit Project\" button in the lower right. Congratulations!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Investigate_a_Dataset.ipynb'])"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}