{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Project: Investigate The Movie Database (TMDb) \n",
"\n",
"--by Lu Tang\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>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='intro'></a>\n",
"## Introduction\n",
"\n",
"> **Dataset**: This data set contains information about 10,000 movies collected from The Movie Database (TMDb), including user ratings and revenue. Data can be download from[here](https://d17h27t6h515a5.cloudfront.net/topher/2017/October/59dd1c4c_tmdb-movies/tmdb-movies.csv).\n",
"> The final two columns ending with “_adj” show the budget and revenue of the associated movie in terms of 2010 dollars, accounting for inflation over time.\n",
"\n",
"> **The project aims to explore the following questions:**\n",
"> - Question 1: What are the trend for movie industry? Are movie industry making more money over years\n",
"> - Question 2: Are newer movies more popular?\n",
"> - Question 3: What are the top 5 most common movie generes that associated with high revenue?\n",
"> - Question 4. Is it possible to make extremely high profit movies with low budget?\n",
"> - Question 5: What are the top 10 rated movies? and how is their profitibility?"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# import library that will be used in this project \n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='wrangling'></a>\n",
"## Data Wrangling\n",
"\n",
"### General Properties"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(10866, 21)\n"
]
},
{
"data": {
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"<div>\n",
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" vertical-align: middle;\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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>keywords</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>monster|dna|tyrannosaurus rex|velociraptor|island</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>1.379999e+08</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>future|chase|post-apocalyptic|dystopia|australia</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>1.379999e+08</td>\n",
" <td>3.481613e+08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id imdb_id popularity budget revenue original_title \\\n",
"0 135397 tt0369610 32.985763 150000000 1513528810 Jurassic World \n",
"1 76341 tt1392190 28.419936 150000000 378436354 Mad Max: Fury Road \n",
"\n",
" cast \\\n",
"0 Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi... \n",
"1 Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic... \n",
"\n",
" homepage director tagline \\\n",
"0 http://www.jurassicworld.com/ Colin Trevorrow The park is open. \n",
"1 http://www.madmaxmovie.com/ George Miller What a Lovely Day. \n",
"\n",
" keywords \\\n",
"0 monster|dna|tyrannosaurus rex|velociraptor|island \n",
"1 future|chase|post-apocalyptic|dystopia|australia \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",
"\n",
" genres \\\n",
"0 Action|Adventure|Science Fiction|Thriller \n",
"1 Action|Adventure|Science Fiction|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",
"\n",
" vote_average release_year budget_adj revenue_adj \n",
"0 6.5 2015 1.379999e+08 1.392446e+09 \n",
"1 7.1 2015 1.379999e+08 3.481613e+08 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# loading data\n",
"tmdb=pd.read_csv('tmdb-movies.csv')\n",
"# show number of rows and columns\n",
"print(tmdb.shape)\n",
"# to avoid truncated output \n",
"pd.options.display.max_columns = 150 \n",
"# show first 2 rows\n",
"tmdb.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> **Initial observation**: \n",
">- Our focus will be analyzing movie properties associated with high revenue, some columns are irrelavant for our analysis, e.g `id`,`imdb_id`, `homepage`, `tagline`, `keywords, overview, production_companies, release_date` (since we already have `release_year`).\n",
">- we can also remove `budget` and `revenue`, since we have `budget_adj` and `revenue_adj` to analyze."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>popularity</th>\n",
" <th>original_title</th>\n",
" <th>cast</th>\n",
" <th>director</th>\n",
" <th>runtime</th>\n",
" <th>genres</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>32.985763</td>\n",
" <td>Jurassic World</td>\n",
" <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
" <td>Colin Trevorrow</td>\n",
" <td>124</td>\n",
" <td>Action|Adventure|Science Fiction|Thriller</td>\n",
" <td>5562</td>\n",
" <td>6.5</td>\n",
" <td>2015</td>\n",
" <td>1.379999e+08</td>\n",
" <td>1.392446e+09</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" popularity original_title \\\n",
"0 32.985763 Jurassic World \n",
"\n",
" cast director \\\n",
"0 Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi... Colin Trevorrow \n",
"\n",
" runtime genres vote_count \\\n",
"0 124 Action|Adventure|Science Fiction|Thriller 5562 \n",
"\n",
" vote_average release_year budget_adj revenue_adj \n",
"0 6.5 2015 1.379999e+08 1.392446e+09 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Drop extraneous columns \n",
"drop_col=['id','imdb_id','homepage','tagline','keywords','overview','production_companies','release_date','budget','revenue']\n",
"tmdb = tmdb.drop(drop_col, axis=1)\n",
"# check the result\n",
"tmdb.head(1)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 10866 entries, 0 to 10865\n",
"Data columns (total 11 columns):\n",
"popularity 10866 non-null float64\n",
"original_title 10866 non-null object\n",
"cast 10790 non-null object\n",
"director 10822 non-null object\n",
"runtime 10866 non-null int64\n",
"genres 10843 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(3), object(4)\n",
"memory usage: 933.9+ KB\n"
]
}
],
"source": [
"# check data type and missing values\n",
"tmdb.info()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>popularity</th>\n",
" <th>original_title</th>\n",
" <th>cast</th>\n",
" <th>director</th>\n",
" <th>runtime</th>\n",
" <th>genres</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>count</th>\n",
" <td>10866.000000</td>\n",
" <td>10866</td>\n",
" <td>10790</td>\n",
" <td>10822</td>\n",
" <td>10866.000000</td>\n",
" <td>10843</td>\n",
" <td>10866.000000</td>\n",
" <td>10866.000000</td>\n",
" <td>10866.000000</td>\n",
" <td>1.086600e+04</td>\n",
" <td>1.086600e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
" <td>NaN</td>\n",
" <td>10571</td>\n",
" <td>10719</td>\n",
" <td>5067</td>\n",
" <td>NaN</td>\n",
" <td>2039</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
" <td>NaN</td>\n",
" <td>Hamlet</td>\n",
" <td>Louis C.K.</td>\n",
" <td>Woody Allen</td>\n",
" <td>NaN</td>\n",
" <td>Drama</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>NaN</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>45</td>\n",
" <td>NaN</td>\n",
" <td>712</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.646441</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>102.070863</td>\n",
" <td>NaN</td>\n",
" <td>217.389748</td>\n",
" <td>5.974922</td>\n",
" <td>2001.322658</td>\n",
" <td>1.755104e+07</td>\n",
" <td>5.136436e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.000185</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>31.381405</td>\n",
" <td>NaN</td>\n",
" <td>575.619058</td>\n",
" <td>0.935142</td>\n",
" <td>12.812941</td>\n",
" <td>3.430616e+07</td>\n",
" <td>1.446325e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000065</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>10.000000</td>\n",
" <td>1.500000</td>\n",
" <td>1960.000000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.207583</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>90.000000</td>\n",
" <td>NaN</td>\n",
" <td>17.000000</td>\n",
" <td>5.400000</td>\n",
" <td>1995.000000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.383856</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>99.000000</td>\n",
" <td>NaN</td>\n",
" <td>38.000000</td>\n",
" <td>6.000000</td>\n",
" <td>2006.000000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.713817</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>111.000000</td>\n",
" <td>NaN</td>\n",
" <td>145.750000</td>\n",
" <td>6.600000</td>\n",
" <td>2011.000000</td>\n",
" <td>2.085325e+07</td>\n",
" <td>3.369710e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>32.985763</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>900.000000</td>\n",
" <td>NaN</td>\n",
" <td>9767.000000</td>\n",
" <td>9.200000</td>\n",
" <td>2015.000000</td>\n",
" <td>4.250000e+08</td>\n",
" <td>2.827124e+09</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" popularity original_title cast director runtime \\\n",
"count 10866.000000 10866 10790 10822 10866.000000 \n",
"unique NaN 10571 10719 5067 NaN \n",
"top NaN Hamlet Louis C.K. Woody Allen NaN \n",
"freq NaN 4 6 45 NaN \n",
"mean 0.646441 NaN NaN NaN 102.070863 \n",
"std 1.000185 NaN NaN NaN 31.381405 \n",
"min 0.000065 NaN NaN NaN 0.000000 \n",
"25% 0.207583 NaN NaN NaN 90.000000 \n",
"50% 0.383856 NaN NaN NaN 99.000000 \n",
"75% 0.713817 NaN NaN NaN 111.000000 \n",
"max 32.985763 NaN NaN NaN 900.000000 \n",
"\n",
" genres vote_count vote_average release_year budget_adj \\\n",
"count 10843 10866.000000 10866.000000 10866.000000 1.086600e+04 \n",
"unique 2039 NaN NaN NaN NaN \n",
"top Drama NaN NaN NaN NaN \n",
"freq 712 NaN NaN NaN NaN \n",
"mean NaN 217.389748 5.974922 2001.322658 1.755104e+07 \n",
"std NaN 575.619058 0.935142 12.812941 3.430616e+07 \n",
"min NaN 10.000000 1.500000 1960.000000 0.000000e+00 \n",
"25% NaN 17.000000 5.400000 1995.000000 0.000000e+00 \n",
"50% NaN 38.000000 6.000000 2006.000000 0.000000e+00 \n",
"75% NaN 145.750000 6.600000 2011.000000 2.085325e+07 \n",
"max NaN 9767.000000 9.200000 2015.000000 4.250000e+08 \n",
"\n",
" revenue_adj \n",
"count 1.086600e+04 \n",
"unique NaN \n",
"top NaN \n",
"freq NaN \n",
"mean 5.136436e+07 \n",
"std 1.446325e+08 \n",
"min 0.000000e+00 \n",
"25% 0.000000e+00 \n",
"50% 0.000000e+00 \n",
"75% 3.369710e+07 \n",
"max 2.827124e+09 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check statistical information \n",
"tmdb.describe(include='all')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"> **Insights**:\n",
">- Some columns contain NaN values, but the amount is not significant; we don't need to drop all the nulls at the beginning.\n",
">- Data type are all correct.\n",
">- The minimum `runtime` is 0, which is impossible, and some movies have extremely long runtime, we will investigate the outlier data\n",
">- `budget_adj` and `revenue_adj` have minimum and median value as 0 too, which is odd, and the difference from 75% to maximum is huge, we need to investigate in the later anaysis process\n",
">- `popularity ` , `vote_count` has very uneven distribution, with some extreme high value data. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data Cleaning "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This dataseat is generally clean, column names are also clear and with preferred snakecase. For some string columns that contains '|', we will clean and analyze in the later part specific to the question we want to answer."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**1. Remove duplicated data**"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check how many rows are duplicated\n",
"sum(tmdb.duplicated())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Drop duplicated rows\n",
"tmdb.drop_duplicates(inplace=True)\n",
"\n",
"# douch check the results\n",
"sum(tmdb.duplicated())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**2. Cleaning abnormal data for runtime**"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"31"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Find out how many rows are 0 for runtime\n",
"sum(tmdb[\"runtime\"]==0)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Since it is impossible to have runtime as 0, we will remove these.\n",
"tmdb=tmdb[tmdb[\"runtime\"]>0]\n",
"\n",
"#double check the result\n",
"sum(tmdb[\"runtime\"]==0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3. Cleaning abnormal data for budget**"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5668"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(tmdb[\"budget_adj\"]==0)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# It is impossible to make a movie without any budget, we will remove these data\n",
"tmdb=tmdb[tmdb[\"budget_adj\"]>0]\n",
"\n",
"# Double check the result\n",
"sum(tmdb[\"budget_adj\"]==0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**4. Cleaning abnormal data for revenue**"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1312"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(tmdb[\"revenue_adj\"]==0)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Since we will analyze moview revenue, movies with zero revenue may contain incorrect information, we will remove these data\n",
"tmdb=tmdb[tmdb[\"revenue_adj\"]>0]\n",
"\n",
"# Double check the result\n",
"sum(tmdb[\"revenue_adj\"]==0)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(3854, 11)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>popularity</th>\n",
" <th>original_title</th>\n",
" <th>cast</th>\n",
" <th>director</th>\n",
" <th>runtime</th>\n",
" <th>genres</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>32.985763</td>\n",
" <td>Jurassic World</td>\n",
" <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
" <td>Colin Trevorrow</td>\n",
" <td>124</td>\n",
" <td>Action|Adventure|Science Fiction|Thriller</td>\n",
" <td>5562</td>\n",
" <td>6.5</td>\n",
" <td>2015</td>\n",
" <td>1.379999e+08</td>\n",
" <td>1.392446e+09</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" popularity original_title \\\n",
"0 32.985763 Jurassic World \n",
"\n",
" cast director \\\n",
"0 Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi... Colin Trevorrow \n",
"\n",
" runtime genres vote_count \\\n",
"0 124 Action|Adventure|Science Fiction|Thriller 5562 \n",
"\n",
" vote_average release_year budget_adj revenue_adj \n",
"0 6.5 2015 1.379999e+08 1.392446e+09 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Double check the cleaning result\n",
"print(tmdb.shape)\n",
"tmdb.head(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='eda'></a>\n",
"## Exploratory Data Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Find pattern and visualize relationship"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**1_1. Explore relations with `revenue_adj`**"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Correlation heatmap for whole movie data')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plot a heatmap to see correlation with `revenue_adj` for each columns\n",
"plt.figure(figsize=(8,5))\n",
"sns.heatmap(tmdb.corr(),annot=True,cmap='coolwarm')\n",
"plt.xticks(rotation=45)\n",
"plt.title('Correlation heatmap for whole movie data')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Analysis:**\n",
">- `revenue_adj` is positive-correlated with `popularity`, `vote_count` and `budget_adj`, which makes sense, the more popular, the more vote_count and and more revenues. And high budget movies are expected with high revenue too.\n",
">- `popularity` and `vote_count` are strongly correlated eith each other.\n",
">- `runtime`, `vote_average` and `release_year` do not have strong relation with any other columns. In fact `release_year` is slighly negative-correlated with `revenue_adj`. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**1_2. Plotting charts to find out the distribution for the variables that do not have strong correlation with movie revenue, i.e. `runtime, vote_average, release_year`.**"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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8PZer0WJ7KW8U4+SIGBMRY4YOHVoQmpmZtWLDZitGxP2SvgDMBM4B9pIk4LSI+FGTbSypDUu6APhpHl1ISk41OwGL8nBP5WZm1kHNngN5g6RvAvcAbwOOjIg98/A3m12YpGGV0XcBtSu0pgHHSNpE0m7ASGAGcCcwUtJukjYmnWif1uzyzMysfZo9Avkv4ALS0caztcKIWJSPSlYj6RJgLDBE0kLgDGCspNGkbqj5wEdzO3MlXQ78HngBODEiXsztnARcDwwCpkTE3FZfpJmZ9b9mE8g44NnKh/oGwKYR8UxEXNxohog4tkHx93paQER8BfhKg/JrgWubjNPMzNaSZq/CugHYrDK+eS4zM7P1VLMJZNOIWFEbycObtyckMzPrBs0mkKcl7V0bkfQm4Nle6puZ2Tqu2XMgJwNXSKpdQjsMeG97QjIzs27QVAKJiDsl7QG8lvTjvnsj4q9tjczMzAa0pn9ICOwDDM/z7CWJiLioLVGZmdmA11QCkXQx6RYks4AXc3EATiBmZuupZo9AxgCjIqLhfajMzGz90+xVWHOAV7UzEDMz6y7NHoEMAX4vaQbwXK0wIt7ZlqjMzGzAazaBTGpnEGZm1n2avYz3Vkm7AiMj4gZJm5NubmhmZuupZm/n/hHgSuD8XLQj8ON2BWVmZgNfsyfRTwQOAJZD+nMpYLt2BWVmZgNfswnkuYh4vjYiaUN6+GtZMzNbPzR7Ev1WSacBm+X/Qv848JP2hWXri+ETf9aR5c4/64iOLNdsXdLsEchEYBlwN+lfBK8FGv4ToZmZrR+avQrrJdJf2l7Q3nDMzKxbNHsvrAdpcM4jIkb0e0RmZtYVWrkXVs2mwNHANv0fjpmZdYumzoFExGOVx8MR8S3gbW2OzczMBrBmu7D2roxuQDoi2aotEZmZWVdotgvr65XhF4D5wN/3ezRmZtY1mr0K663tDsTMzLpLs11Yn+5tekR8o3/CMTOzbtHKVVj7ANPy+JHAbcCCdgRlZmYDXyt/KLV3RDwFIGkScEVEfLhdgZmZ2cDW7K1MdgGer4w/Dwzv92jMzKxrNHsEcjEwQ9LVpF+kvwu4qG1RmZnZgNfsVVhfkXQd8He56IMR8dv2hWVmZgNds11YAJsDyyPibGChpN3aFJOZmXWBZv/S9gzgc8CpuWgj4PvtCsrMzAa+Zo9A3gW8E3gaICIW4VuZmJmt15pNIM9HRJBv6S5pi/aFZGZm3aDZBHK5pPOBwZI+AtyA/1zKzGy91uzt3L8GXAlcBbwWOD0i/rO3eSRNkbRU0pxK2TaSpku6Pz9vncsl6RxJ8yTNrt79V9KEXP9+SRNKXqSZmfW/PhOIpEGSboiI6RHxLxFxSkRMb6LtC4HD6somAjdGxEjgxjwOcDgwMj9OAM7Ny94GOAPYD9gXOKOWdMzMrLP6TCAR8SLwjKRXttJwRNwGPF5XPB6YmoenAkdVyi+K5HZSV9kw4FBgekQ8HhF/BqazelIyM7MOaPaX6H8B7pY0nXwlFkBEfLLF5W0fEYvzvIslbZfLd2TVGzMuzGU9la9G0gmkoxd22WWXFsMyM7NWNZtAfpYf7aIGZdFL+eqFEZOByQBjxoxpWMfMzPpPrwlE0i4R8VBETO2tXguWSBqWjz6GAUtz+UJg50q9nYBFuXxsXfkt/RSLmZmtgb7Ogfy4NiDpqn5Y3jSgdiXVBOCaSvlx+Wqs/YEnc1fX9cAhkrbOJ88PyWVmZtZhfXVhVbuQRrTSsKRLSEcPQyQtJF1NdRbpNyXHAw8BR+fq1wLjgHnAM8AHASLicUlfAu7M9c6MiPoT82Zm1gF9JZDoYbhPEXFsD5MOalA3gBN7aGcKMKWVZZuZWfv1lUDeKGk56UhkszxMHo+IeEVbozMzswGr1wQSEYPWViBmZtZdWvk/EDMzs5WcQMzMrIgTiJmZFXECMTOzIk4gZmZWxAnEzMyKOIGYmVkRJxAzMyviBGJmZkWcQMzMrIgTiJmZFXECMTOzIk4gZmZWxAnEzMyKOIGYmVkRJxAzMyviBGJmZkWcQMzMrIgTiJmZFXECMTOzIk4gZmZWxAnEzMyKOIGYmVkRJxAzMyviBGJmZkWcQMzMrIgTiJmZFXECMTOzIk4gZmZWxAnEzMyKOIGYmVkRJxAzMyviBGJmZkU6kkAkzZd0t6RZkmbmsm0kTZd0f37eOpdL0jmS5kmaLWnvTsRsZmar6uQRyFsjYnREjMnjE4EbI2IkcGMeBzgcGJkfJwDnrvVIzcxsNQOpC2s8MDUPTwWOqpRfFMntwGBJwzoRoJmZvaxTCSSAX0i6S9IJuWz7iFgMkJ+3y+U7Agsq8y7MZauQdIKkmZJmLlu2rI2hm5kZwIYdWu4BEbFI0nbAdEn39lJXDcpitYKIycBkgDFjxqw23czM+ldHjkAiYlF+XgpcDewLLKl1TeXnpbn6QmDnyuw7AYvWXrRmZtbIWk8gkraQtFVtGDgEmANMAybkahOAa/LwNOC4fDXW/sCTta4uMzPrnE50YW0PXC2ptvwfRsTPJd0JXC7peOAh4Ohc/1pgHDAPeAb44NoP2czM6q31BBIRDwBvbFD+GHBQg/IATlwLoZmZWQsG0mW8ZmbWRZxAzMysiBOImZkVcQIxM7MiTiBmZlbECcTMzIo4gZiZWREnEDMzK+IEYmZmRZxAzMysiBOImZkVcQIxM7MiTiBmZlbECcTMzIo4gZiZWREnEDMzK+IEYmZmRZxAzMysiBOImZkVcQIxM7MiTiBmZlbECcTMzIo4gZiZWREnEDMzK+IEYmZmRTbsdABmnTB84s86tuz5Zx3RsWWb9ScfgZiZWREnEDMzK+IEYmZmRZxAzMysiBOImZkVcQIxM7MiTiBmZlbECcTMzIr4h4QDSCd/3GZm1qquSSCSDgPOBgYB342IszocklmRTn1R8C/grb91RReWpEHAt4HDgVHAsZJGdTYqM7P1W7ccgewLzIuIBwAkXQqMB37f0ajMuojv/2X9rVsSyI7Agsr4QmC/agVJJwAn5NEVku4DhgCPrpUI+59j7wzH3gb6ap9VBmzsTViXYt+1lZm7JYGoQVmsMhIxGZi8ykzSzIgY087A2sWxd4Zj7wzH3hlrGntXnAMhHXHsXBnfCVjUoVjMzIzuSSB3AiMl7SZpY+AYYFqHYzIzW691RRdWRLwg6STgetJlvFMiYm4Ts07uu8qA5dg7w7F3hmPvjDWKXRHRdy0zM7M63dKFZWZmA4wTiJmZFVknE4ikwyTdJ2mepImdjqcvkuZLulvSLEkzc9k2kqZLuj8/b93pOAEkTZG0VNKcSlnDWJWck7fDbEl7dy7ylbE2in+SpIfz+p8laVxl2qk5/vskHdqZqEHSzpJulnSPpLmSPpXLB/y67yX2Ab/ecyybSpoh6Xc5/i/m8t0k3ZHX/WX5Ah8kbZLH5+Xpwwdg7BdKerCy7kfn8tb2m4hYpx6kk+x/BEYAGwO/A0Z1Oq4+Yp4PDKkr+3dgYh6eCHy103HmWN4M7A3M6StWYBxwHel3PPsDdwzQ+CcBpzSoOyrvP5sAu+X9alCH4h4G7J2HtwL+kOMb8Ou+l9gH/HrP8QjYMg9vBNyR1+nlwDG5/Dzgn/Lwx4Hz8vAxwGUDMPYLgfc0qN/SfrMuHoGsvO1JRDwP1G570m3GA1Pz8FTgqA7GslJE3AY8XlfcU6zjgYsiuR0YLGnY2om0sR7i78l44NKIeC4iHgTmkfavtS4iFkfEb/LwU8A9pDs0DPh130vsPRkw6x0gr8MVeXSj/AjgbcCVubx+3de2yZXAQZIa/Ri67XqJvSct7TfrYgJpdNuT3nbWgSCAX0i6K9+SBWD7iFgM6Q0IbNex6PrWU6zdtC1OyofsUyrdhQMy/twlshfp22RXrfu62KFL1rukQZJmAUuB6aSjoici4oVcpRrjyvjz9CeBbdduxC+rjz0iauv+K3ndf1PSJrmspXW/LiaQPm97MgAdEBF7k+42fKKkN3c6oH7SLdviXODVwGhgMfD1XD7g4pe0JXAVcHJELO+taoOygRZ716z3iHgxIkaT7oKxL7Bno2r5eUDFXx+7pNcDpwJ7APsA2wCfy9Vbin1dTCBdd9uTiFiUn5cCV5N20CW1Q8f8vLRzEfapp1i7YltExJL8JnsJuICXu0sGVPySNiJ9AP8gIn6Ui7ti3TeKvVvWe1VEPAHcQjo/MFhS7cfY1RhXxp+nv5Lmu03bphL7YblbMSLiOeC/KVz362IC6arbnkjaQtJWtWHgEGAOKeYJudoE4JrORNiUnmKdBhyXr+zYH3iy1t0ykNT18b6LtP4hxX9MvqpmN2AkMGNtxwfp6hjge8A9EfGNyqQBv+57ir0b1juApKGSBufhzYCDSedxbgbek6vVr/vaNnkPcFPkM9RrWw+x31v50iHSuZvqum9+v+nU1QHtfJCuJPgDqZ/y852Op49YR5CuOPkdMLcWL6nP9Ebg/vy8TadjzXFdQupu+Cvp28rxPcVKOhz+dt4OdwNjBmj8F+f4Zuc30LBK/c/n+O8DDu9g3AeSuhJmA7PyY1w3rPteYh/w6z3H8gbgtznOOcDpuXwEKbHNA64ANsnlm+bxeXn6iAEY+0153c8Bvs/LV2q1tN/4ViZmZlZkXezCMjOztcAJxMzMijiBmJlZEScQMzMr4gRiZmZFnEBsnSYpJF1cGd9Q0jJJPy1s72OSjmuh/gfy8mZJulfSP5cst9LeyZI2r4xfW7vO32xtcwKxdd3TwOvzj6gA3g48XNpYRJwXERe1ONtlkW4lcQDweUk79zVDL04GViaQiBgX6RfGZmudE4itD64DjsjDx5J+TAis/D+NH+ebyt0u6Q2SNlD6j5bBlXrzJG2v9B8Wp+SyV0v6eb4J5i8l7dFbEBHxGOnHZbVfAV8oqfZLZiStyM9jJd0i6cp81PKD/MvgTwI7ADdLujnXnS9piKThue53Jc3J8xws6X+U/q9i31x/i3zjwjsl/VZSN96p2gYIJxBbH1xKujXGpqRf5t5RmfZF4LcR8QbgNNKtrF8i3ZbiXQCS9gPmR8SSunYnA5+IiDcBpwDf6S0ISbuQfqU8u4mY9yIdbYwi/eL5gIg4h3RfordGxFsbzLM7cHZ+jXsA/0D6Ffgp+bVB+oX3TRGxD/BW4D/yLXTMWuYEYuu8iJgNDCcdfVxbN/lA0i01iIibgG0lvRK4DHhvrnNMHl8p31n2b4ErlG6VfT75yKKB90qaCzwAnB0Rf2ki7BkRsTAns1k5/r48GBF353nmAjdGutXE3ZX5DwEm5phvISW0XZpo22w1G/ZdxWydMA34GjCWVf+boafbV/8a2F3SUNLN5r5cV2cD0v9BjG5i2ZdFxEmS/gb4maTrIuIR4IXcTu2mdhtX5nmuMvwizb1Xq/O8VBl/qTK/gHdHxH1NtGfWKx+B2PpiCnBmRNxdV34b8D5I5x6ARyNief7mfjXwDdJdZB+rzhTp/ywelHR0nleS3thbABHxa9LRzqdy0XzgTXl4POnf4vryFOlvYUtdD3wiJywk7bUGbdl6zgnE1gu5O+jsBpMmAWMkzQbO4uXbcEPqtno/dd1XFe8DjpdUu5NyMyekvwp8MN/C/wLgLZJmAPuRrhjry2TgutpJ9AJfIiWq2ZLm5HGzIr4br5mZFfERiJmZFXECMTOzIk4gZmZWxAnEzMyKOIGYmVkRJxAzMyviBGJmZkX+D1Gt3aeJu+e8AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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FERGXAJcAtLe390ecZmZWQ9XE8RDwTrp8yu+jDqAjIu5O89dSJI6nO4egJI0FVpbqjy+tvyOwHDNrGW0zb250CNaPql6OOwp4RNKtkuZ0PvrSYET8AVgm6T2paDLwCDAHmJbKpgE3puk5wLEq7As86/MbZmaNU/WI4+x+bvdLwA8lbQosAY6nSGLXSJoOLAWOTHVvobgUdzHF5bjH93MsZmaWoVLiiIhfSdoJmBgRt0kaAQzta6MRcR/FeZOuJteoG8CJfW3LzMz6V9Xbqv81xbmI76aiccBP6hWUmZk1r6rnOE4EPgqsgzd/1GlMj2uYmdlGqWrieCV9UQ8AScOocUmsmZlt/Komjl9JOgMYnn5r/MfAT+sXlpmZNauqiWMmxbe9HwS+QHGlk3/5z8xsEKp6VdUbFD8d+736hmNmZs2u6r2qnqD2bT526feIzMysqeXcq6rTZhRfztu2/8MxM7NmV/X3OP6n9Ph9RHwLOLDOsZmZWROqOlQ1qTQ7hOIIZMu6RGRmZk2t6lDVv5WmXyP9tGu/R2NmZk2v6lVVf1bvQMzMrDVUHao6taflEfHN/gnHzMyaXc5VVR+m+G0MgE8Cd/DWn3Q1M7NBoGriGAVMiojnACSdDfw4Ij5fr8DMzKw5Vb3lyATg1dL8q0Bbv0djZmZNr+oRxxXA7yTdQPEN8iOA2XWLyszMmlbVq6rOkfQz4GOp6PiI+K/6hWVmZs2q6hEHwAhgXURcLmm0pJ0j4ol6BWZm/att5s2NDsE2ElV/OvYs4DTg9FS0CXBlvYIyM7PmVfXk+BHAp4AXACJiOb7liJnZoFQ1cbwaEUG6tbqkzesXkpmZNbOqieMaSd8FRkr6a+A2/KNOZmaDUtWrqs5PvzW+DngP8A8RMbeukZmZWVPqNXFIGgrcGhEHAU4WZmaDXK9DVRHxOvCipK0HIB4zM2tyVb/H8TLwoKS5pCurACLiy3WJyszMmlbVxHFzepiZ2SDXY+KQNCEilkbErIEKyMzMmltv5zh+0jkh6bo6x2JmZi2gt8Sh0vQu9QzEzMxaQ2+JI7qZ3mCShkr6L0k3pfmdJd0taZGkqyVtmsrfkeYXp+Vt/RmHmZnl6S1x7CVpnaTngD3T9DpJz0lat4FtnwwsLM2fB1wQEROBNcD0VD4dWBMRuwIXpHpmZtYgPSaOiBgaEVtFxJYRMSxNd85v1ddGJe0IfAL4fpoXcCBwbaoyCzg8TU9N86Tlk1N9MzNrgKr3qupv3wK+DryR5rcD1kbEa2m+AxiXpscBywDS8mdT/beQNEPSfEnzV61aVc/YzcwGtQFPHJL+HFgZEQvKxTWqRoVl6wsiLomI9ohoHz16dD9EamZmteT8AmB/+SjwKUlTgM2ArSiOQEZKGpaOKnYElqf6HcB4oEPSMGBrYPXAh21mZtCAI46IOD0idoyINuAo4BcRcQzwS+DTqdo04MY0PSfNk5b/Iv02iJmZNUCjznHUchpwqqTFFOcwLk3llwLbpfJTgZkNis/MzGjMUNWbIuJ24PY0vQTYu0adl4EjBzQwMzPrVjMdcZiZWQtw4jAzsyxOHGZmlsWJw8zMsjhxmJlZFicOMzPL4sRhZmZZnDjMzCyLE4eZmWVx4jAzsyxOHGZmlsWJw8zMsjhxmJlZFicOMzPL4sRhZmZZnDjMzCyLE4eZmWVx4jAzsyxOHGZmlsWJw8zMsjhxmJlZlmGNDsBssGmbeXOjQzDbID7iMDOzLE4cZmaWxYnDzMyyOHGYmVkWJw4zM8viq6psUPKVTWZ95yMOMzPL4sRhZmZZnDjMzCzLgCcOSeMl/VLSQkkPSzo5lW8raa6kRenvNqlcki6StFjSA5ImDXTMZma2XiOOOF4DvhoRuwH7AidK2h2YCcyLiInAvDQPcBgwMT1mAN8e+JDNzKzTgCeOiFgREfem6eeAhcA4YCowK1WbBRyepqcCs6NwFzBS0tgBDtvMzJKGnuOQ1AZ8ELgb2D4iVkCRXIAxqdo4YFlptY5U1nVbMyTNlzR/1apV9QzbzGxQa1jikLQFcB1wSkSs66lqjbJ4W0HEJRHRHhHto0eP7q8wzcysi4YkDkmbUCSNH0bE9an46c4hqPR3ZSrvAMaXVt8RWD5QsZqZ2Vs14qoqAZcCCyPim6VFc4BpaXoacGOp/Nh0ddW+wLOdQ1pmZjbwGnHLkY8C/wd4UNJ9qewM4FzgGknTgaXAkWnZLcAUYDHwInD8wIZrZmZlA544IuJOap+3AJhco34AJ9Y1KDMzq8zfHDczsyxOHGZmlsWJw8zMsjhxmJlZFicOMzPL4sRhZmZZnDjMzCyLE4eZmWVx4jAzsyxOHGZmlsWJw8zMsjhxmJlZFicOMzPL0ojbqpu9qW3mzY0Owcwy+YjDzMyyOHGYmVkWJw4zM8vixGFmZlmcOMzMLIsTh5mZZfHluOZLYs0si484zMwsixOHmZllceIwM7MsThxmZpbFJ8ebiE9Sm1krcOKowW/gZmbd81CVmZllceIwM7MsThxmZpbFicPMzLK0TOKQdKikxyQtljSz0fGYmQ1WLZE4JA0F/h04DNgdOFrS7o2NysxscGqJxAHsDSyOiCUR8SrwI2Bqg2MyMxuUWuV7HOOAZaX5DmCfcgVJM4AZafZ5SY9tQHujgGc2YP1m5r61ro25f+5bP9F5G7T6TlUqtUriUI2yeMtMxCXAJf3SmDQ/Itr7Y1vNxn1rXRtz/9y31tIqQ1UdwPjS/I7A8gbFYmY2qLVK4rgHmChpZ0mbAkcBcxock5nZoNQSQ1UR8Zqkk4BbgaHAZRHxcB2b7JchryblvrWujbl/7lsLUUT0XsvMzCxplaEqMzNrEk4cZmaWZVAkDkmXSVop6aFS2V6SfivpQUk/lbRVadmeadnDaflmqfxDaX6xpIsk1bpMeEDl9E3SMZLuKz3ekPSBtKzp+gbZ/dtE0qxUvlDS6aV1mu6WNZl921TS5an8fkkHlNZpuudO0nhJv0zPw8OSTk7l20qaK2lR+rtNKleKfbGkByRNKm1rWqq/SNK0RvWpFE9u396bntNXJH2ty7aa7nVZSURs9A9gP2AS8FCp7B5g/zR9AvDPaXoY8ACwV5rfDhiapn8HfITieyU/Aw5rpb51We/9wJLSfNP1rQ/P3WeBH6XpEcCTQBvFBRWPA7sAmwL3A7u3WN9OBC5P02OABcCQZn3ugLHApDS9JfDfFLcL+gYwM5XPBM5L01NS7AL2Be5O5dsCS9LfbdL0Ni3WtzHAh4FzgK+VttOUr8sqj0FxxBERdwCruxS/B7gjTc8F/jJNHwI8EBH3p3X/JyJelzQW2CoifhvFsz4bOLz+0fcss29lRwNXATRr3yC7fwFsLmkYMBx4FVhHk96yJrNvuwPz0norgbVAe7M+dxGxIiLuTdPPAQsp7gAxFZiVqs1ifaxTgdlRuAsYmfr2cWBuRKyOiDUU++TQAezK2+T2LSJWRsQ9wB+7bKopX5dVDIrE0Y2HgE+l6SNZ/wXDdwMh6VZJ90r6eiofR/FFxE4dqawZdde3ss+QEget1Tfovn/XAi8AK4ClwPkRsZrat6xp1v5117f7gamShknaGfhQWtb0z52kNuCDwN3A9hGxAoo3YIpP49D9c9TUz13FvnWnqfvWk8GcOE4ATpS0gOJw89VUPgz4U+CY9PcISZOpcNuTJtJd3wCQtA/wYkR0jq23Ut+g+/7tDbwO7ADsDHxV0i60Vv+669tlFG8s84FvAb8BXqPJ+yZpC+A64JSIWNdT1Rpl0UN5w2X0rdtN1Chrir71piW+AFgPEfEoxbAUkt4NfCIt6gB+FRHPpGW3UIxDX0lxq5NOTXvbkx761uko1h9tQNHnlugb9Ni/zwI/j4g/Aisl/Rpop/hU1xK3rOmubxHxGvCVznqSfgMsAtbQpM+dpE0o3lh/GBHXp+KnJY2NiBVpKGplKu/utkIdwAFdym+vZ9xVZPatOy17K6VBe8QhaUz6OwQ4E/hOWnQrsKekEWmsfH/gkXTo+ZykfdNVK8cCNzYg9F710LfOsiMpxlOBNw+rW6Jv0GP/lgIHpit0Nqc4yfooLXTLmu76ll6Pm6fpg4HXIqJpX5cplkuBhRHxzdKiOUDnlVHTWB/rHODY9NztCzyb+nYrcIikbdJVSoeksobpQ9+60zKvy7dp9Nn5gXhQfLpeQXFyqgOYDpxMcTXEfwPnkr5Fn+p/DniYYrz5G6Xy9lT2OHBxeZ0W6tsBwF01ttN0fcvtH7AF8OP03D0C/F1pO1NS/ceBv290v/rQtzbgMYoTsbcBOzXzc0cxzBsUVyjelx5TKK5SnEdxtDQP2DbVF8WPtT0OPAi0l7Z1ArA4PY5vwb69Mz2/6yguauiguKChKV+XVR6+5YiZmWUZtENVZmbWN04cZmaWxYnDzMyyOHGYmVkWJw4zM8vixGFmZlmcOMzMLMv/Am4opiy4kDwlAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plotting distribution for 'runtime'\n",
"tmdb['runtime'].plot.hist(title='Runtime distribution for the whole movies data')\n",
"plt.xlabel('Movie Runtime')\n",
"plt.show()\n",
"\n",
"# plotting distribution for 'vote_average'\n",
"tmdb['vote_average'].plot.hist(title='Vote_average distribution for the whole movies data')\n",
"plt.xlabel('Movie Vote_average')\n",
"plt.show()\n",
"\n",
"# plotting distribution for 'release_year'\n",
"tmdb['release_year'].plot.hist(title=('Movie Release_year Distribution'))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Conclusion:**\n",
">- Most movies have median length from about 100 minutes to 180 minutes.\n",
">- `vote_average` has a distribution that looks like normal with average around 6.\n",
">- There are more movies produced over time."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**1_3. Plotting scatter chart to explore detailed relationship between `popularity` and `vote_count`, and find out outliers.** "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1a15a02400>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plotting relation for 'popularity' and 'vote_count'\n",
"tmdb.plot.scatter(x='popularity',y='vote_count')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Analysis:**\n",
">- From the scatter chart, we can see `popularity` and `vote_count` have strong positive correlation, same result as the heatmap; however, we can also notice there are three movies rated extremely high popularity, but vote count is not extremely high.\n",
">- From the heatmap, both `popularity` and `vote_count` are positively correlated with moview revenue. If we run regression model to predict movie revenues, we need to choose of one of them as an independent variable, but this is beyond the scope of this project."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**1_4. Plotting scatter charts to furthur explore the relation with `revenue_adj` for the varibles of `popularity`, `vote_count` and `budget_adj`.**"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1a15f83240>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"tmdb.plot.scatter(x='popularity', y='revenue_adj')\n",
"tmdb.plot.scatter(x='vote_count',y='revenue_adj')\n",
"tmdb.plot.scatter(x='budget_adj',y='revenue_adj')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Analysis:**\n",
">- In general, the three variabels(`popularity`, `vote_count` and `budget_adj`) are all positively correlated with `revenue_adj`, but the correlation is not very strong, which is the same conclusion from the heatmap;\n",
">- There are many outlier data, some movies with extremely high popularity and high vote_count do not have extremely high revenue. These movies maybe controversial, and popularity and vote_count alone are not good indicator for movie success.\n",
">- Also, some extremely high budget movies do not have very high revenue, which means they maybe losing money."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Explore Answers for research questions "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 1. What are the profitibility trend for movie industry? "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1a15f79908>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# create a column for profit\n",
"tmdb['profit']=tmdb['revenue_adj']-tmdb['budget_adj']\n",
"# plot a box chart for the profit column\n",
"tmdb['profit'].plot.box()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Analysis:**\n",
">- Some movies are losing money; others, however have huge profit."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>popularity</th>\n",
" <th>runtime</th>\n",
" <th>vote_count</th>\n",
" <th>vote_average</th>\n",
" <th>budget_adj</th>\n",
" <th>revenue_adj</th>\n",
" <th>profit</th>\n",
" </tr>\n",
" <tr>\n",
" <th>release_year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1960</th>\n",
" <td>1.324513</td>\n",
" <td>130.0</td>\n",
" <td>372.6</td>\n",
" <td>7.40</td>\n",
" <td>3.068179e+07</td>\n",
" <td>1.902299e+08</td>\n",
" <td>1.595481e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1961</th>\n",
" <td>0.787718</td>\n",
" <td>132.5</td>\n",
" <td>191.4</td>\n",
" <td>6.62</td>\n",
" <td>2.818516e+07</td>\n",
" <td>2.463622e+08</td>\n",
" <td>2.181770e+08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" popularity runtime vote_count vote_average budget_adj \\\n",
"release_year \n",
"1960 1.324513 130.0 372.6 7.40 3.068179e+07 \n",
"1961 0.787718 132.5 191.4 6.62 2.818516e+07 \n",
"\n",
" revenue_adj profit \n",
"release_year \n",
"1960 1.902299e+08 1.595481e+08 \n",
"1961 2.463622e+08 2.181770e+08 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a dataframe that group by year and calculate the mean value\n",
"year_mean=tmdb.groupby('release_year').mean()\n",
"year_mean.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x1a15cba828>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plotting line chart for the average profit groupby each year\n",
"plt.plot(year_mean.index, year_mean['profit'],label='Profit')\n",
"plt.xlabel('years')\n",
"plt.ylabel('In terms of 2010 dollars')\n",
"plt.title('Profit over years')\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Analysis:**\n",
">- Surprisingly, the average profit per year started to decrease since about 1980; there is less profit to making a movie compared with three decades ago.\n",
">- In the earlier years from 1960 to 1980, film industry have higher profit but with very high fluctuation too. Even though the profit is lower since 1989, but the profit trend is more stable. Probably in the earlier years, film industry is relatively new, and high risk is associated with high profit."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 2. Are newer movies more popular?"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x1a15fd7d30>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Since popularuty variable has some outlier data, we use vote_count to count for popularity.\n",
"plt.plot(year_mean.index, year_mean['vote_count'], label='vote_count')\n",
"plt.xlabel('years')\n",
"plt.ylabel('vote_count')\n",
"plt.title('Movie vote_count over years')\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Analysis:**\n",
">- Yes. There is clear trend that the newer movies received more vote count and are more popular. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 3. What are the top 5 most common movie generes that associated with high revenue?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**1. Find out the movies with very high revenues**"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"min 2.370705e+00\n",
"50% 6.173068e+07\n",
"80% 2.033811e+08\n",
"90% 3.538761e+08\n",
"max 2.827124e+09\n",
"Name: revenue_adj, dtype: float64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check the distribution for 'revenue_adj'\n",
"tmdb.revenue_adj.describe([.8,.9]).iloc[3:]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"low 1550\n",
"very_low 1271\n",
"very_high 517\n",
"high 516\n",
"Name: revenue_level, dtype: int64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create an ordinal data column to categorize movies with different levels of revenues\n",
"bin_edge=[0, 2.872138e+07, 1.496016e+08, 2.880722e+08, 3e+09]\n",
"bin_names=['very_low','low','high','very_high']\n",
"tmdb['revenue_level']=pd.cut(tmdb.revenue_adj,bin_edge,labels=bin_names)\n",
"tmdb['revenue_level'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">- Half of the movies have very_low or even negative revenue\n",
">- There are **517 movies with very high_revenue**, let's focus on these movies."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>popularity</th>\n",
" <th>original_title</th>\n",
" <th>cast</th>\n",
" <th>director</th>\n",
" <th>runtime</th>\n",
" <th>genres</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",
" <th>profit</th>\n",
" <th>revenue_level</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>32.985763</td>\n",
" <td>Jurassic World</td>\n",
" <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
" <td>Colin Trevorrow</td>\n",
" <td>124</td>\n",
" <td>Action|Adventure|Science Fiction|Thriller</td>\n",
" <td>5562</td>\n",
" <td>6.5</td>\n",
" <td>2015</td>\n",
" <td>1.379999e+08</td>\n",
" <td>1.392446e+09</td>\n",
" <td>1.254446e+09</td>\n",
" <td>very_high</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>28.419936</td>\n",
" <td>Mad Max: Fury Road</td>\n",
" <td>Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...</td>\n",
" <td>George Miller</td>\n",
" <td>120</td>\n",
" <td>Action|Adventure|Science Fiction|Thriller</td>\n",
" <td>6185</td>\n",
" <td>7.1</td>\n",
" <td>2015</td>\n",
" <td>1.379999e+08</td>\n",
" <td>3.481613e+08</td>\n",
" <td>2.101614e+08</td>\n",
" <td>very_high</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>11.173104</td>\n",
" <td>Star Wars: The Force Awakens</td>\n",
" <td>Harrison Ford|Mark Hamill|Carrie Fisher|Adam D...</td>\n",
" <td>J.J. Abrams</td>\n",
" <td>136</td>\n",
" <td>Action|Adventure|Science Fiction|Fantasy</td>\n",
" <td>5292</td>\n",
" <td>7.5</td>\n",
" <td>2015</td>\n",
" <td>1.839999e+08</td>\n",
" <td>1.902723e+09</td>\n",
" <td>1.718723e+09</td>\n",
" <td>very_high</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>9.335014</td>\n",
" <td>Furious 7</td>\n",
" <td>Vin Diesel|Paul Walker|Jason Statham|Michelle ...</td>\n",
" <td>James Wan</td>\n",
" <td>137</td>\n",
" <td>Action|Crime|Thriller</td>\n",
" <td>2947</td>\n",
" <td>7.3</td>\n",
" <td>2015</td>\n",
" <td>1.747999e+08</td>\n",
" <td>1.385749e+09</td>\n",
" <td>1.210949e+09</td>\n",
" <td>very_high</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>9.110700</td>\n",
" <td>The Revenant</td>\n",
" <td>Leonardo DiCaprio|Tom Hardy|Will Poulter|Domhn...</td>\n",
" <td>Alejandro González Iñárritu</td>\n",
" <td>156</td>\n",
" <td>Western|Drama|Adventure|Thriller</td>\n",
" <td>3929</td>\n",
" <td>7.2</td>\n",
" <td>2015</td>\n",
" <td>1.241999e+08</td>\n",
" <td>4.903142e+08</td>\n",
" <td>3.661143e+08</td>\n",
" <td>very_high</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" popularity original_title \\\n",
"0 32.985763 Jurassic World \n",
"1 28.419936 Mad Max: Fury Road \n",
"3 11.173104 Star Wars: The Force Awakens \n",
"4 9.335014 Furious 7 \n",
"5 9.110700 The Revenant \n",
"\n",
" cast \\\n",
"0 Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi... \n",
"1 Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic... \n",
"3 Harrison Ford|Mark Hamill|Carrie Fisher|Adam D... \n",
"4 Vin Diesel|Paul Walker|Jason Statham|Michelle ... \n",
"5 Leonardo DiCaprio|Tom Hardy|Will Poulter|Domhn... \n",
"\n",
" director runtime \\\n",
"0 Colin Trevorrow 124 \n",
"1 George Miller 120 \n",
"3 J.J. Abrams 136 \n",
"4 James Wan 137 \n",
"5 Alejandro González Iñárritu 156 \n",
"\n",
" genres vote_count vote_average \\\n",
"0 Action|Adventure|Science Fiction|Thriller 5562 6.5 \n",
"1 Action|Adventure|Science Fiction|Thriller 6185 7.1 \n",
"3 Action|Adventure|Science Fiction|Fantasy 5292 7.5 \n",
"4 Action|Crime|Thriller 2947 7.3 \n",
"5 Western|Drama|Adventure|Thriller 3929 7.2 \n",
"\n",
" release_year budget_adj revenue_adj profit revenue_level \n",
"0 2015 1.379999e+08 1.392446e+09 1.254446e+09 very_high \n",
"1 2015 1.379999e+08 3.481613e+08 2.101614e+08 very_high \n",
"3 2015 1.839999e+08 1.902723e+09 1.718723e+09 very_high \n",
"4 2015 1.747999e+08 1.385749e+09 1.210949e+09 very_high \n",
"5 2015 1.241999e+08 4.903142e+08 3.661143e+08 very_high "
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a dataframe that only contains movies with very high revenues\n",
"very_high_revenue = tmdb[tmdb['revenue_level']=='very_high']\n",
"# View top 5 very_high revenue movies\n",
"very_high_revenue.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**2. Find out among the very_high profit movies, what kinds of generes are the most common ones.** "
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
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"text/plain": [
" 0 1 2 3 4\n",
"0 Action Adventure Science Fiction Thriller None\n",
"1 Action Adventure Science Fiction Thriller None\n",
"3 Action Adventure Science Fiction Fantasy None\n",
"4 Action Crime Thriller None None\n",
"5 Western Drama Adventure Thriller None"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# separate the generes with '|' and make it a new table\n",
"generes = very_high_revenue['genres'].str.split('|', expand=True)\n",
"# view the first 5 rows of the new table\n",
"generes.head()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
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" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Drama</th>\n",
" <td>61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Family</th>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fantasy</th>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>History</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Horror</th>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Music</th>\n",
" <td>6</td>\n",
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" <th>Mystery</th>\n",
" <td>3</td>\n",
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" <th>Romance</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Science Fiction</th>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Thriller</th>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>War</th>\n",
" <td>3</td>\n",
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],
"text/plain": [
" 0\n",
"Action 121\n",
"Adventure 116\n",
"Animation 44\n",
"Comedy 63\n",
"Crime 12\n",
"Drama 61\n",
"Family 12\n",
"Fantasy 26\n",
"History 3\n",
"Horror 9\n",
"Music 6\n",
"Mystery 3\n",
"Romance 6\n",
"Science Fiction 21\n",
"Thriller 9\n",
"War 3\n",
"Western 2"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Count the frequency for each genere in column 0 in the generes table, sorted as index.\n",
"col_0 = generes.loc[:,0].value_counts().sort_index()\n",
"# Convert the pandas Series to a data frame \n",
"df_0 = pd.DataFrame(data=col_0, index=col_0.index)\n",
"# display the new dataframe\n",
"df_0"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"# do the same for other columns:\n",
"col_1=generes.loc[:,1].value_counts().sort_index()\n",
"df_1=pd.DataFrame(data=col_1, index=col_1.index)\n",
"\n",
"col_2=generes.loc[:,2].value_counts().sort_index()\n",
"df_2=pd.DataFrame(data=col_2, index=col_2.index)\n",
"\n",
"col_3=generes.loc[:,3].value_counts().sort_index()\n",
"df_3=pd.DataFrame(data=col_3, index=col_3.index)\n",
"\n",
"col_4=generes.loc[:,4].value_counts().sort_index()\n",
"df_4=pd.DataFrame(data=col_4, index=col_4.index)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
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" <th>Family</th>\n",
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" <td>9.0</td>\n",
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" <tr>\n",
" <th>Fantasy</th>\n",
" <td>26</td>\n",
" <td>42</td>\n",
" <td>23</td>\n",
" <td>15.0</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>History</th>\n",
" <td>3</td>\n",
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" <td>4</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Horror</th>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Music</th>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mystery</th>\n",
" <td>3</td>\n",
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" <td>7</td>\n",
" <td>10.0</td>\n",
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" <tr>\n",
" <th>Science Fiction</th>\n",
" <td>21</td>\n",
" <td>18</td>\n",
" <td>37</td>\n",
" <td>32.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Thriller</th>\n",
" <td>9</td>\n",
" <td>38</td>\n",
" <td>66</td>\n",
" <td>28.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>War</th>\n",
" <td>3</td>\n",
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" <td>11</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
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" <tr>\n",
" <th>Western</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
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],
"text/plain": [
" 0 1 2 3 4\n",
"Action 121 79 33 3.0 4.0\n",
"Adventure 116 74 36 14.0 1.0\n",
"Animation 44 23 9 3.0 2.0\n",
"Comedy 63 44 37 13.0 5.0\n",
"Crime 12 16 19 11.0 2.0\n",
"Drama 61 52 32 5.0 1.0\n",
"Family 12 36 40 28.0 9.0\n",
"Fantasy 26 42 23 15.0 10.0\n",
"History 3 5 4 1.0 NaN\n",
"Horror 9 5 3 1.0 1.0\n",
"Music 6 6 3 2.0 4.0\n",
"Mystery 3 13 7 10.0 2.0\n",
"Romance 6 29 20 10.0 5.0\n",
"Science Fiction 21 18 37 32.0 6.0\n",
"Thriller 9 38 66 28.0 7.0\n",
"War 3 1 11 4.0 1.0\n",
"Western 2 2 3 NaN NaN"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# join the other 4 dataframe together\n",
"generes_join=df_0.join(df_1).join(df_2).join(df_3).join(df_4)\n",
"generes_join"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
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" <th>Fantasy</th>\n",
" <td>26</td>\n",
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" <td>10.0</td>\n",
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" <tr>\n",
" <th>History</th>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Horror</th>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
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" <td>6</td>\n",
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" <td>2.0</td>\n",
" <td>4.0</td>\n",
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" <th>Mystery</th>\n",
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" <td>10.0</td>\n",
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" <tr>\n",
" <th>Romance</th>\n",
" <td>6</td>\n",
" <td>29</td>\n",
" <td>20</td>\n",
" <td>10.0</td>\n",
" <td>5.0</td>\n",
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" <tr>\n",
" <th>Science Fiction</th>\n",
" <td>21</td>\n",
" <td>18</td>\n",
" <td>37</td>\n",
" <td>32.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Thriller</th>\n",
" <td>9</td>\n",
" <td>38</td>\n",
" <td>66</td>\n",
" <td>28.0</td>\n",
" <td>7.0</td>\n",
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" <tr>\n",
" <th>War</th>\n",
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" <td>4.0</td>\n",
" <td>1.0</td>\n",
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" <tr>\n",
" <th>Western</th>\n",
" <td>2</td>\n",
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" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4\n",
"Action 121 79 33 3.0 4.0\n",
"Adventure 116 74 36 14.0 1.0\n",
"Animation 44 23 9 3.0 2.0\n",
"Comedy 63 44 37 13.0 5.0\n",
"Crime 12 16 19 11.0 2.0\n",
"Drama 61 52 32 5.0 1.0\n",
"Family 12 36 40 28.0 9.0\n",
"Fantasy 26 42 23 15.0 10.0\n",
"History 3 5 4 1.0 0.0\n",
"Horror 9 5 3 1.0 1.0\n",
"Music 6 6 3 2.0 4.0\n",
"Mystery 3 13 7 10.0 2.0\n",
"Romance 6 29 20 10.0 5.0\n",
"Science Fiction 21 18 37 32.0 6.0\n",
"Thriller 9 38 66 28.0 7.0\n",
"War 3 1 11 4.0 1.0\n",
"Western 2 2 3 0.0 0.0"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# fill null value with 0\n",
"generes_join.fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
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"text/plain": [
" 0 1 2 3 4 sum_generes\n",
"Adventure 116 74 36 14.0 1.0 241.0\n",
"Action 121 79 33 3.0 4.0 240.0\n",
"Comedy 63 44 37 13.0 5.0 162.0\n",
"Drama 61 52 32 5.0 1.0 151.0\n",
"Thriller 9 38 66 28.0 7.0 148.0"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# calculate the sum of each genere's frequency and create a new column for the sum\n",
"generes_join['sum_generes'] = generes_join.sum(axis=1)\n",
"# sort value based on frequency sum and show top 5\n",
"generes_join.sort_values('sum_generes', ascending=False).head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Analysis**\n",
">- We can see among these very_high_revenue movies, the generes with **'Action', 'Adventure', 'Comedy','Drama', 'Thriller'** are top five generes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 4. Is it possible to make high profit movies with low budget?"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"30016111.9054567"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# find out the median value of budget for the whole data\n",
"tmdb.budget_adj.median()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of low budget movies 1927\n"
]
},
{
"data": {
"text/plain": [
"very_low 1020\n",
"low 746\n",
"high 100\n",
"very_high 61\n",
"Name: revenue_level, dtype: int64"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create a table for low_budget movies\n",
"low_budget=tmdb[tmdb['budget_adj']<30016111.9054567]\n",
"# check how many rows are in this table\n",
"print(f'Number of low budget movies {low_budget.shape[0]}')\n",
"# view the distribution of 'revenue_level' in low_budget movie table\n",
"low_budget['revenue_level'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0316554229372081"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Percentage of very_high revenue movies with low_budget\n",
"61/1927"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Analysis:**\n",
">- There are only 3% of the movies that are made with low budget but yeilded very high revenue; we can conclude that most low budget movies does not yeild high revenue, and from the heatmap earlier, we can see the budget and revenue are positively correlated, higher budget movies generally yield higher revenue."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 5. What are the top 10 rated movies? and how is their profitibility?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since some movies have more vote_count, we can not directly compare a movie rated 10 with only 3 counts to the movie rated 7 with 100 counts. We will use IMDB'a definition to calculated **weighted average for movie rating score.**"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The minimum votes required to be listed in the chart is: 1371.7000000000003\n"
]
}
],
"source": [
"# m is the minimum votes required to be listed in the chart;\n",
"m= tmdb['vote_count'].quantile(0.9)\n",
"print(f'The minimum votes required to be listed in the chart is: {m}')"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The mean vote across the whole report is 527.7202906071614\n"
]
}
],
"source": [
"# C is the mean vote across the whole report\n",
"C=tmdb['vote_count'].mean()\n",
"print(f'The mean vote across the whole report is {C}')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column and row number for the table of top 10% highest rated movies: (386, 13)\n"
]
}
],
"source": [
"# Create a table for top 10% highest rated movies\n",
"top_10_percent_movies = tmdb.copy().loc[tmdb['vote_count'] >= m]\n",
"print(f'Column and row number for the table of top 10% highest rated movies: {top_10_percent_movies.shape}')"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"def weighted_rating(x, m=m, C=C):\n",
" v = x['vote_count']\n",
" R = x['vote_average']\n",
" # Calculation based on the IMDB formula\n",
" return (v/(v+m) * R) + (m/(m+v) * C)\n",
"\n",
"# Define a new feature 'score' and calculate its value with `weighted_rating()`\n",
"top_10_percent_movies['score'] = top_10_percent_movies.apply(weighted_rating, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>popularity</th>\n",
" <th>original_title</th>\n",
" <th>cast</th>\n",
" <th>director</th>\n",
" <th>runtime</th>\n",
" <th>genres</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",
" <th>profit</th>\n",
" <th>revenue_level</th>\n",
" <th>score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2912</th>\n",
" <td>1.499784</td>\n",
" <td>Cloverfield</td>\n",
" <td>Lizzy Caplan|Jessica Lucas|Odette Annable|Mich...</td>\n",
" <td>Matt Reeves</td>\n",
" <td>85</td>\n",
" <td>Action|Thriller|Science Fiction</td>\n",
" <td>1373</td>\n",
" <td>6.4</td>\n",
" <td>2008</td>\n",
" <td>2.531967e+07</td>\n",
" <td>1.729475e+08</td>\n",
" <td>1.476279e+08</td>\n",
" <td>high</td>\n",
" <td>266.936686</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2643</th>\n",
" <td>2.449323</td>\n",
" <td>The Mummy Returns</td>\n",
" <td>Brendan Fraser|Rachel Weisz|John Hannah|Arnold...</td>\n",
" <td>Stephen Sommers</td>\n",
" <td>130</td>\n",
" <td>Action|Adventure|Drama|Fantasy|Horror</td>\n",
" <td>1372</td>\n",
" <td>5.8</td>\n",
" <td>2001</td>\n",
" <td>1.206858e+08</td>\n",
" <td>5.332507e+08</td>\n",
" <td>4.125649e+08</td>\n",
" <td>very_high</td>\n",
" <td>266.731612</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6980</th>\n",
" <td>2.175284</td>\n",
" <td>Ocean's Twelve</td>\n",
" <td>George Clooney|Brad Pitt|Catherine Zeta-Jones|...</td>\n",
" <td>Steven Soderbergh</td>\n",
" <td>125</td>\n",
" <td>Thriller|Crime</td>\n",
" <td>1376</td>\n",
" <td>6.4</td>\n",
" <td>2004</td>\n",
" <td>1.269890e+08</td>\n",
" <td>4.187685e+08</td>\n",
" <td>2.917795e+08</td>\n",
" <td>very_high</td>\n",
" <td>266.652226</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7884</th>\n",
" <td>2.484654</td>\n",
" <td>Ghostbusters</td>\n",
" <td>Bill Murray|Dan Aykroyd|Sigourney Weaver|Harol...</td>\n",
" <td>Ivan Reitman</td>\n",
" <td>107</td>\n",
" <td>Fantasy|Action|Comedy|Science Fiction|Family</td>\n",
" <td>1383</td>\n",
" <td>7.2</td>\n",
" <td>1984</td>\n",
" <td>6.297126e+07</td>\n",
" <td>6.196634e+08</td>\n",
" <td>5.566921e+08</td>\n",
" <td>very_high</td>\n",
" <td>266.392537</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>5.337064</td>\n",
" <td>Southpaw</td>\n",
" <td>Jake Gyllenhaal|Rachel McAdams|Forest Whitaker...</td>\n",
" <td>Antoine Fuqua</td>\n",
" <td>123</td>\n",
" <td>Action|Drama</td>\n",
" <td>1386</td>\n",
" <td>7.3</td>\n",
" <td>2015</td>\n",
" <td>2.759999e+07</td>\n",
" <td>8.437300e+07</td>\n",
" <td>5.677302e+07</td>\n",
" <td>low</td>\n",
" <td>266.160831</td>\n",
" </tr>\n",
" <tr>\n",
" <th>658</th>\n",
" <td>3.813740</td>\n",
" <td>Exodus: Gods and Kings</td>\n",
" <td>Christian Bale|Joel Edgerton|John Turturro|Aar...</td>\n",
" <td>Ridley Scott</td>\n",
" <td>153</td>\n",
" <td>Adventure|Drama|Action</td>\n",
" <td>1377</td>\n",
" <td>5.6</td>\n",
" <td>2014</td>\n",
" <td>1.289527e+08</td>\n",
" <td>2.468817e+08</td>\n",
" <td>1.179290e+08</td>\n",
" <td>high</td>\n",
" <td>266.156773</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3416</th>\n",
" <td>1.499109</td>\n",
" <td>Rango</td>\n",
" <td>Johnny Depp|Isla Fisher|Ned Beatty|Bill Nighy|...</td>\n",
" <td>Gore Verbinski</td>\n",
" <td>107</td>\n",
" <td>Animation|Comedy|Family|Western|Adventure</td>\n",
" <td>1385</td>\n",
" <td>6.5</td>\n",
" <td>2011</td>\n",
" <td>1.308687e+08</td>\n",
" <td>2.382049e+08</td>\n",
" <td>1.073362e+08</td>\n",
" <td>high</td>\n",
" <td>265.852803</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6631</th>\n",
" <td>0.890909</td>\n",
" <td>The Pursuit of Happyness</td>\n",
" <td>Will Smith|Jaden Smith|Thandie Newton|Brian Ho...</td>\n",
" <td>Gabriele Muccino</td>\n",
" <td>117</td>\n",
" <td>Drama</td>\n",
" <td>1392</td>\n",
" <td>7.5</td>\n",
" <td>2006</td>\n",
" <td>5.949180e+07</td>\n",
" <td>3.321560e+08</td>\n",
" <td>2.726642e+08</td>\n",
" <td>very_high</td>\n",
" <td>265.699578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6578</th>\n",
" <td>1.603140</td>\n",
" <td>Blood Diamond</td>\n",
" <td>Leonardo DiCaprio|Djimon Hounsou|Jennifer Conn...</td>\n",
" <td>Edward Zwick</td>\n",
" <td>143</td>\n",
" <td>Drama|Thriller|Action</td>\n",
" <td>1394</td>\n",
" <td>7.2</td>\n",
" <td>2006</td>\n",
" <td>1.081669e+08</td>\n",
" <td>1.848334e+08</td>\n",
" <td>7.666646e+07</td>\n",
" <td>high</td>\n",
" <td>265.361653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10094</th>\n",
" <td>0.142486</td>\n",
" <td>Home Alone</td>\n",
" <td>Macaulay Culkin|Joe Pesci|Daniel Stern|John He...</td>\n",
" <td>Chris Columbus</td>\n",
" <td>103</td>\n",
" <td>Comedy|Family</td>\n",
" <td>1393</td>\n",
" <td>7.0</td>\n",
" <td>1990</td>\n",
" <td>3.004017e+07</td>\n",
" <td>7.955384e+08</td>\n",
" <td>7.654982e+08</td>\n",
" <td>very_high</td>\n",
" <td>265.354260</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" popularity original_title \\\n",
"2912 1.499784 Cloverfield \n",
"2643 2.449323 The Mummy Returns \n",
"6980 2.175284 Ocean's Twelve \n",
"7884 2.484654 Ghostbusters \n",
"21 5.337064 Southpaw \n",
"658 3.813740 Exodus: Gods and Kings \n",
"3416 1.499109 Rango \n",
"6631 0.890909 The Pursuit of Happyness \n",
"6578 1.603140 Blood Diamond \n",
"10094 0.142486 Home Alone \n",
"\n",
" cast director \\\n",
"2912 Lizzy Caplan|Jessica Lucas|Odette Annable|Mich... Matt Reeves \n",
"2643 Brendan Fraser|Rachel Weisz|John Hannah|Arnold... Stephen Sommers \n",
"6980 George Clooney|Brad Pitt|Catherine Zeta-Jones|... Steven Soderbergh \n",
"7884 Bill Murray|Dan Aykroyd|Sigourney Weaver|Harol... Ivan Reitman \n",
"21 Jake Gyllenhaal|Rachel McAdams|Forest Whitaker... Antoine Fuqua \n",
"658 Christian Bale|Joel Edgerton|John Turturro|Aar... Ridley Scott \n",
"3416 Johnny Depp|Isla Fisher|Ned Beatty|Bill Nighy|... Gore Verbinski \n",
"6631 Will Smith|Jaden Smith|Thandie Newton|Brian Ho... Gabriele Muccino \n",
"6578 Leonardo DiCaprio|Djimon Hounsou|Jennifer Conn... Edward Zwick \n",
"10094 Macaulay Culkin|Joe Pesci|Daniel Stern|John He... Chris Columbus \n",
"\n",
" runtime genres vote_count \\\n",
"2912 85 Action|Thriller|Science Fiction 1373 \n",
"2643 130 Action|Adventure|Drama|Fantasy|Horror 1372 \n",
"6980 125 Thriller|Crime 1376 \n",
"7884 107 Fantasy|Action|Comedy|Science Fiction|Family 1383 \n",
"21 123 Action|Drama 1386 \n",
"658 153 Adventure|Drama|Action 1377 \n",
"3416 107 Animation|Comedy|Family|Western|Adventure 1385 \n",
"6631 117 Drama 1392 \n",
"6578 143 Drama|Thriller|Action 1394 \n",
"10094 103 Comedy|Family 1393 \n",
"\n",
" vote_average release_year budget_adj revenue_adj profit \\\n",
"2912 6.4 2008 2.531967e+07 1.729475e+08 1.476279e+08 \n",
"2643 5.8 2001 1.206858e+08 5.332507e+08 4.125649e+08 \n",
"6980 6.4 2004 1.269890e+08 4.187685e+08 2.917795e+08 \n",
"7884 7.2 1984 6.297126e+07 6.196634e+08 5.566921e+08 \n",
"21 7.3 2015 2.759999e+07 8.437300e+07 5.677302e+07 \n",
"658 5.6 2014 1.289527e+08 2.468817e+08 1.179290e+08 \n",
"3416 6.5 2011 1.308687e+08 2.382049e+08 1.073362e+08 \n",
"6631 7.5 2006 5.949180e+07 3.321560e+08 2.726642e+08 \n",
"6578 7.2 2006 1.081669e+08 1.848334e+08 7.666646e+07 \n",
"10094 7.0 1990 3.004017e+07 7.955384e+08 7.654982e+08 \n",
"\n",
" revenue_level score \n",
"2912 high 266.936686 \n",
"2643 very_high 266.731612 \n",
"6980 very_high 266.652226 \n",
"7884 very_high 266.392537 \n",
"21 low 266.160831 \n",
"658 high 266.156773 \n",
"3416 high 265.852803 \n",
"6631 very_high 265.699578 \n",
"6578 high 265.361653 \n",
"10094 very_high 265.354260 "
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# show the top 10 rated movies\n",
"top_10_percent_movies.sort_values('score',ascending=False).head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">- Above are the movies with highest rating score, as we can see, most have high or very_high revenue too, but it is different from the top_10_profit movies."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"very_high 242\n",
"high 75\n",
"low 64\n",
"very_low 5\n",
"Name: revenue_level, dtype: int64"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"top_10_percent_movies['revenue_level'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'q_movies' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-41-ad86873dbdbb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Count for percentage of the one with low or very_low revenue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mq_movies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'q_movies' is not defined"
]
}
],
"source": [
"# Count for percentage of the one with low or very_low revenue\n",
"(64+5)/q_movies.shape[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Analysis**\n",
">- About 17% of the highly rated movies have low revenue, most have very_high revenue.\n",
">- This can confirm that a good rating score can yield high revenue."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='conclusions'></a>\n",
"## Conclusions\n",
"\n",
"In this project we did comprehensive analysis on the movie database with a focus on movie revenues and other properties like generes and rating score. We did initial data exploration and answered all the questions \n",
"\n",
"**Summarize some of the featured findings:**\n",
">- Although much more movies are produced over time, movies industry is getting more stable and annual average profit is lower compared with movies made 3 decades ago.\n",
">- Popular movies have more vote_count and also have higher revenues, and newer movies are more popular.\n",
">- 'Action', 'Adventure', 'Comedy','Drama', 'Thriller' are the most common generes for movies with very high revenues\n",
">- In general, higher budget can yield higher revenues; movies made with low budget can have moderately high revenue, but successful rate is lower.\n",
">- After calculatig weighted average rating score, we found the top 10 percent highest rated movies. Most of these movies have high revenues, about 17% have low revenues. \n",
">- Higher popularity, higher budget, higher rating score and certain types of genes ('Action', 'Adventure', 'Comedy','Drama', 'Thriller') are all associated with movies that have high revenues.\n",
"\n",
"**Limitation of the project**\n",
">- As we see in data cleaning process, about half of the data is deleted because it contains 0 value for runtime, budget or revenue. If we can have a more complete data, the analysis can be more accurate.\n",
">- Since the project is main focusing on movie revenue analysis, and we find popularity, budget, rating score and genes can have big impact on revenue, but these information is not enough to make revenue prediction as there might be other factors that can affect movie revenue but not included in this dataset."
]
}
],
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