project 2/investigate-a-dataset-template.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Investigate a 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>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "> This is a project to invesigate a dataset from the International Movie Database (IMDB). This will entail three phases: wrangling which involves cleaning the data, Exploratory Data Analysis to observe trends in the data and Reporting the observation. This will be done using popular python libraies like NumPy, Pandas and Matplotlib. \n",
    "We will answer the following research questions:\n",
    "<li>What movies have the highest and lowest profits</li>\n",
    "<li>What movies have the highest and lowest revenues</li>\n",
    "<li>What movies have the highest and lowest budgets</li>\n",
    "<li>What movies have the highest and least runtimes</li>\n",
    "<li>What are the top 5 actors</li>\n",
    "<li>What are the top 5 movie genres</li>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use this cell to set up import statements for all of the packages that you\n",
    "#   plan to use.\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"white\")\n",
    "import operator\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "> In this section of the report, data is checked for cleanliness, and then further cleaned for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   id                    10866 non-null  int64  \n",
      " 1   imdb_id               10856 non-null  object \n",
      " 2   popularity            10866 non-null  float64\n",
      " 3   budget                10866 non-null  int64  \n",
      " 4   revenue               10866 non-null  int64  \n",
      " 5   original_title        10866 non-null  object \n",
      " 6   cast                  10790 non-null  object \n",
      " 7   homepage              2936 non-null   object \n",
      " 8   director              10822 non-null  object \n",
      " 9   tagline               8042 non-null   object \n",
      " 10  keywords              9373 non-null   object \n",
      " 11  overview              10862 non-null  object \n",
      " 12  runtime               10866 non-null  int64  \n",
      " 13  genres                10843 non-null  object \n",
      " 14  production_companies  9836 non-null   object \n",
      " 15  release_date          10866 non-null  object \n",
      " 16  vote_count            10866 non-null  int64  \n",
      " 17  vote_average          10866 non-null  float64\n",
      " 18  release_year          10866 non-null  int64  \n",
      " 19  budget_adj            10866 non-null  float64\n",
      " 20  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",
    "df = pd.read_csv(\"tmdb-movies.csv\")\n",
    "#   types and look for instances of missing or possibly errant data.\n",
    "df.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1: Remove undesired columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "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",
    "#During this phase, we shall be performing the following steps.\n",
    "\n",
    "df = df.drop(['imdb_id', 'popularity', 'budget_adj', 'revenue_adj', 'homepage', 'keywords', 'overview', 'production_companies', 'vote_count', 'vote_average'],1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>tagline</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>release_date</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135397</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>Colin Trevorrow</td>\n",
       "      <td>The park is open.</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>6/9/15</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76341</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>George Miller</td>\n",
       "      <td>What a Lovely Day.</td>\n",
       "      <td>120</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>5/13/15</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id     budget     revenue      original_title  \\\n",
       "0  135397  150000000  1513528810      Jurassic World   \n",
       "1   76341  150000000   378436354  Mad Max: Fury Road   \n",
       "\n",
       "                                                cast         director  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...  Colin Trevorrow   \n",
       "1  Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...    George Miller   \n",
       "\n",
       "              tagline  runtime                                     genres  \\\n",
       "0   The park is open.      124  Action|Adventure|Science Fiction|Thriller   \n",
       "1  What a Lovely Day.      120  Action|Adventure|Science Fiction|Thriller   \n",
       "\n",
       "  release_date  release_year  \n",
       "0       6/9/15          2015  \n",
       "1      5/13/15          2015  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2: Remove duplicate records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10865, 11)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Removing duplicate records from the dataset\n",
    "df.drop_duplicates(inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3: Remove null values from desired columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['cast'].isnull() == False]\n",
    "df = df[df['genres'].isnull() == False]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4: Remove zero values from revenue and budget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['budget', 'revenue']] = df[['budget', 'revenue']].replace(0, np.NAN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Step 5: Remove rows with NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exploring the dataframe, the budget and the revenue columns contain zeros, this is undesired so we choose\n",
    "#a strategy of replacing it with the mean\n",
    "#getting the mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "> Here we will compute statistics and create visualizations with the goal of addressing each research question. We will look at one variable at a time, and then further follow it up by looking at relationships between variables.\n",
    "\n",
    "### Summary of Movies with the highest and lowest profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Insert a new column to the dataframe called profit\n",
    "df.insert(2,'profit',df['revenue']-df['budget'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1386</th>\n",
       "      <th>2244</th>\n",
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       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>19995</td>\n",
       "      <td>46528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>budget</th>\n",
       "      <td>2.37e+08</td>\n",
       "      <td>4.25e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profit</th>\n",
       "      <td>2.54451e+09</td>\n",
       "      <td>-4.13912e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue</th>\n",
       "      <td>2.78151e+09</td>\n",
       "      <td>1.10876e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>original_title</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>The Warrior's Way</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cast</th>\n",
       "      <td>Sam Worthington|Zoe Saldana|Sigourney Weaver|S...</td>\n",
       "      <td>Kate Bosworth|Jang Dong-gun|Geoffrey Rush|Dann...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director</th>\n",
       "      <td>James Cameron</td>\n",
       "      <td>Sngmoo Lee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tagline</th>\n",
       "      <td>Enter the World of Pandora.</td>\n",
       "      <td>Assassin. Hero. Legend.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>runtime</th>\n",
       "      <td>162</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres</th>\n",
       "      <td>Action|Adventure|Fantasy|Science Fiction</td>\n",
       "      <td>Adventure|Fantasy|Action|Western|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_date</th>\n",
       "      <td>12/10/09</td>\n",
       "      <td>12/2/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_year</th>\n",
       "      <td>2009</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             1386  \\\n",
       "id                                                          19995   \n",
       "budget                                                   2.37e+08   \n",
       "profit                                                2.54451e+09   \n",
       "revenue                                               2.78151e+09   \n",
       "original_title                                             Avatar   \n",
       "cast            Sam Worthington|Zoe Saldana|Sigourney Weaver|S...   \n",
       "director                                            James Cameron   \n",
       "tagline                               Enter the World of Pandora.   \n",
       "runtime                                                       162   \n",
       "genres                   Action|Adventure|Fantasy|Science Fiction   \n",
       "release_date                                             12/10/09   \n",
       "release_year                                                 2009   \n",
       "\n",
       "                                                             2244  \n",
       "id                                                          46528  \n",
       "budget                                                   4.25e+08  \n",
       "profit                                               -4.13912e+08  \n",
       "revenue                                               1.10876e+07  \n",
       "original_title                                  The Warrior's Way  \n",
       "cast            Kate Bosworth|Jang Dong-gun|Geoffrey Rush|Dann...  \n",
       "director                                               Sngmoo Lee  \n",
       "tagline                                   Assassin. Hero. Legend.  \n",
       "runtime                                                       100  \n",
       "genres                  Adventure|Fantasy|Action|Western|Thriller  \n",
       "release_date                                              12/2/10  \n",
       "release_year                                                 2010  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#implementing the profit calculator\n",
    "def range_calculator(col):\n",
    "   higehest_profit_info=pd.DataFrame(df.loc[df[col].idxmax()])\n",
    "   lowest_profit_info=pd.DataFrame(df.loc[df[col].idxmin()])\n",
    "   info=pd.concat([higehest_profit_info, lowest_profit_info], axis=1)\n",
    "   return info\n",
    "range_calculator('profit')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of Movies with the Highest and Lowest Budget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2244</th>\n",
       "      <th>2618</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>46528</td>\n",
       "      <td>39964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>budget</th>\n",
       "      <td>4.25e+08</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profit</th>\n",
       "      <td>-4.13912e+08</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue</th>\n",
       "      <td>1.10876e+07</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>original_title</th>\n",
       "      <td>The Warrior's Way</td>\n",
       "      <td>Lost &amp; Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cast</th>\n",
       "      <td>Kate Bosworth|Jang Dong-gun|Geoffrey Rush|Dann...</td>\n",
       "      <td>David Spade|Sophie Marceau|Ever Carradine|Step...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director</th>\n",
       "      <td>Sngmoo Lee</td>\n",
       "      <td>Jeff Pollack</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tagline</th>\n",
       "      <td>Assassin. Hero. Legend.</td>\n",
       "      <td>A comedy about a guy who would do anything to ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>runtime</th>\n",
       "      <td>100</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres</th>\n",
       "      <td>Adventure|Fantasy|Action|Western|Thriller</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_date</th>\n",
       "      <td>12/2/10</td>\n",
       "      <td>4/23/99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_year</th>\n",
       "      <td>2010</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             2244  \\\n",
       "id                                                          46528   \n",
       "budget                                                   4.25e+08   \n",
       "profit                                               -4.13912e+08   \n",
       "revenue                                               1.10876e+07   \n",
       "original_title                                  The Warrior's Way   \n",
       "cast            Kate Bosworth|Jang Dong-gun|Geoffrey Rush|Dann...   \n",
       "director                                               Sngmoo Lee   \n",
       "tagline                                   Assassin. Hero. Legend.   \n",
       "runtime                                                       100   \n",
       "genres                  Adventure|Fantasy|Action|Western|Thriller   \n",
       "release_date                                              12/2/10   \n",
       "release_year                                                 2010   \n",
       "\n",
       "                                                             2618  \n",
       "id                                                          39964  \n",
       "budget                                                          1  \n",
       "profit                                                         99  \n",
       "revenue                                                       100  \n",
       "original_title                                       Lost & Found  \n",
       "cast            David Spade|Sophie Marceau|Ever Carradine|Step...  \n",
       "director                                             Jeff Pollack  \n",
       "tagline         A comedy about a guy who would do anything to ...  \n",
       "runtime                                                        95  \n",
       "genres                                             Comedy|Romance  \n",
       "release_date                                              4/23/99  \n",
       "release_year                                                 1999  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate summary of movies with the highest and lowest budget using range_calculator\n",
    "range_calculator('budget')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of Movies with the Highest and Lowest Revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1386</th>\n",
       "      <th>8142</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>19995</td>\n",
       "      <td>2293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>budget</th>\n",
       "      <td>2.37e+08</td>\n",
       "      <td>6e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profit</th>\n",
       "      <td>2.54451e+09</td>\n",
       "      <td>-6e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue</th>\n",
       "      <td>2.78151e+09</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>original_title</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>Mallrats</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cast</th>\n",
       "      <td>Sam Worthington|Zoe Saldana|Sigourney Weaver|S...</td>\n",
       "      <td>Jason Lee|Jeremy London|Shannen Doherty|Claire...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director</th>\n",
       "      <td>James Cameron</td>\n",
       "      <td>Kevin Smith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tagline</th>\n",
       "      <td>Enter the World of Pandora.</td>\n",
       "      <td>They're not there to shop. They're not there t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>runtime</th>\n",
       "      <td>162</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres</th>\n",
       "      <td>Action|Adventure|Fantasy|Science Fiction</td>\n",
       "      <td>Romance|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_date</th>\n",
       "      <td>12/10/09</td>\n",
       "      <td>10/20/95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_year</th>\n",
       "      <td>2009</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             1386  \\\n",
       "id                                                          19995   \n",
       "budget                                                   2.37e+08   \n",
       "profit                                                2.54451e+09   \n",
       "revenue                                               2.78151e+09   \n",
       "original_title                                             Avatar   \n",
       "cast            Sam Worthington|Zoe Saldana|Sigourney Weaver|S...   \n",
       "director                                            James Cameron   \n",
       "tagline                               Enter the World of Pandora.   \n",
       "runtime                                                       162   \n",
       "genres                   Action|Adventure|Fantasy|Science Fiction   \n",
       "release_date                                             12/10/09   \n",
       "release_year                                                 2009   \n",
       "\n",
       "                                                             8142  \n",
       "id                                                           2293  \n",
       "budget                                                      6e+06  \n",
       "profit                                                     -6e+06  \n",
       "revenue                                                         2  \n",
       "original_title                                           Mallrats  \n",
       "cast            Jason Lee|Jeremy London|Shannen Doherty|Claire...  \n",
       "director                                              Kevin Smith  \n",
       "tagline         They're not there to shop. They're not there t...  \n",
       "runtime                                                        94  \n",
       "genres                                             Romance|Comedy  \n",
       "release_date                                             10/20/95  \n",
       "release_year                                                 1995  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate summary of movies with the highest and lowest revenue using range_calculator\n",
    "range_calculator('revenue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of Movies with the Highest and Lowest Runtimes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2107</th>\n",
       "      <th>8005</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>43434</td>\n",
       "      <td>14813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>budget</th>\n",
       "      <td>1.8e+07</td>\n",
       "      <td>3e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profit</th>\n",
       "      <td>-1.71287e+07</td>\n",
       "      <td>1.8e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue</th>\n",
       "      <td>871279</td>\n",
       "      <td>2.1e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>original_title</th>\n",
       "      <td>Carlos</td>\n",
       "      <td>Mickey's Christmas Carol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cast</th>\n",
       "      <td>Edgar RamÃ­rez|Alexander Scheer|Fadi Abi Samra...</td>\n",
       "      <td>Alan Young|Wayne Allwine|Clarence Nash|Hal Smi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director</th>\n",
       "      <td>Olivier Assayas</td>\n",
       "      <td>Burny Mattinson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tagline</th>\n",
       "      <td>The man who hijacked the world</td>\n",
       "      <td>He's back! Mickey Mouse - in his first new mot...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>runtime</th>\n",
       "      <td>338</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres</th>\n",
       "      <td>Crime|Drama|Thriller|History</td>\n",
       "      <td>Family|Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_date</th>\n",
       "      <td>5/19/10</td>\n",
       "      <td>10/19/83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_year</th>\n",
       "      <td>2010</td>\n",
       "      <td>1983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             2107  \\\n",
       "id                                                          43434   \n",
       "budget                                                    1.8e+07   \n",
       "profit                                               -1.71287e+07   \n",
       "revenue                                                    871279   \n",
       "original_title                                             Carlos   \n",
       "cast            Edgar RamÃ­rez|Alexander Scheer|Fadi Abi Samra...   \n",
       "director                                          Olivier Assayas   \n",
       "tagline                            The man who hijacked the world   \n",
       "runtime                                                       338   \n",
       "genres                               Crime|Drama|Thriller|History   \n",
       "release_date                                              5/19/10   \n",
       "release_year                                                 2010   \n",
       "\n",
       "                                                             8005  \n",
       "id                                                          14813  \n",
       "budget                                                      3e+06  \n",
       "profit                                                    1.8e+07  \n",
       "revenue                                                   2.1e+07  \n",
       "original_title                           Mickey's Christmas Carol  \n",
       "cast            Alan Young|Wayne Allwine|Clarence Nash|Hal Smi...  \n",
       "director                                          Burny Mattinson  \n",
       "tagline         He's back! Mickey Mouse - in his first new mot...  \n",
       "runtime                                                        26  \n",
       "genres                                           Family|Animation  \n",
       "release_date                                             10/19/83  \n",
       "release_year                                                 1983  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate summary of movies with the highest and lowest revenue using range_calculator\n",
    "range_calculator('runtime')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting a BoxPlot of runtime of movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>217.0</td>\n",
       "      <td>2.765101e+07</td>\n",
       "      <td>112000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy|Romance</th>\n",
       "      <td>96.0</td>\n",
       "      <td>2.980325e+07</td>\n",
       "      <td>100000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>208.0</td>\n",
       "      <td>2.303725e+07</td>\n",
       "      <td>130000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama|Romance</th>\n",
       "      <td>92.0</td>\n",
       "      <td>1.984334e+07</td>\n",
       "      <td>105000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horror|Thriller</th>\n",
       "      <td>80.0</td>\n",
       "      <td>1.192879e+07</td>\n",
       "      <td>65000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count          mean          max\n",
       "genres                                           \n",
       "Comedy           217.0  2.765101e+07  112000000.0\n",
       "Comedy|Romance    96.0  2.980325e+07  100000000.0\n",
       "Drama            208.0  2.303725e+07  130000000.0\n",
       "Drama|Romance     92.0  1.984334e+07  105000000.0\n",
       "Horror|Thriller   80.0  1.192879e+07   65000000.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_1 = df.groupby(['genres'])[['id']].count().sort_values(by=['id'], ascending=False)[0:5]\n",
    "df_new = df[df['genres'].isin(df_1.index.values.tolist())]\n",
    "df_new1 = df_new[df_new['budget']>0]\n",
    "df_new1.boxplot(column=['budget'], by =['genres'], rot=90).set_yscale('log')\n",
    "pd.DataFrame(df_new1.groupby(['genres'])['budget'].describe().loc[:, ['count','mean','max']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy|Romance</th>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama|Romance</th>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horror|Thriller</th>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id\n",
       "genres              \n",
       "Comedy           217\n",
       "Comedy|Romance    96\n",
       "Drama            208\n",
       "Drama|Romance     92\n",
       "Horror|Thriller   80"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xx7zrtutu6Vm3+O46L7zwQu1tRGvWrEmS7LHHHunZs2dmzZq13iKwy5cv3+gtR4MGDcrSpUvzn//5n7XryiTJM888kyuuuCLXX399vV6VsrXOR33o2bNndt1119x0002ZN2/eJs/JlhxHkyZNMnDgwDz00EP57W9/mw9+8IMbXYj57fr165cWLVrkZz/7Wf785z+v99oPfvCD3HDDDbU/17548eLceOONGTt27HrbvfTSS3nzzTc3WJ8HALZlbkcCgO3UFVdckaFDh+ab3/xmpk6dmg4dOuTpp5/O/fffnz333DPf+ta36rzP3XbbLc2aNcvs2bPzne98J/369XvXRVo3Zu+9907nzp3zyCOPZM8996z9tZ+N+fjHP5599tknt99+e1555ZV07NgxL774YqZNm5addtopVVVVWbp0ae32o0aNypAhQ3LhhRfm6KOPzl577ZV77rknjRpt+P8pnXvuubnvvvty44035qGHHsphhx2W1157LXfeeWdWrlyZH/zgB+96NciW2hrnoz7stNNO6d27dyZPnpxmzZrlqKOO2uT2W3IcJ5xwQsaNG5dnnnkmI0aMeM+ZWrVqldGjR+crX/lKBg0alKOPPjp77rln5syZk8cffzydO3fOmWeemSQ5+uij87GPfSy/+MUv8vTTT+ejH/1oli9fXhvjRo4cuQXfCgA0DFfCAMB2av/998/EiRPz2c9+Ns8880zGjRuXBQsWZNiwYfn1r3+9wS/vbI5mzZpl1KhRed/73pebbrops2bNqvM++vfvnySbvBUpeWtdkOuvvz79+/fPvHnzMm7cuDzxxBM54YQT8pvf/CYdO3bM3Llzs2LFiiRJu3btcvPNN2fgwIF58MEHM3HixHTq1Ck/+tGPNtj3zjvvnBtuuCEjR47Mm2++mZtuuikzZ85M165dc8MNN7zn7TJbYmucj/qy7pwceeSR7xmftuQ4unTpkv333z/Je9+KtM6nPvWpjBs3LocffnjuvffejBs3LitWrMgXv/jF/PznP69dn6ZZs2b5yU9+knPOOSdLlizJ+PHjc+edd6ZLly658cYbN/prYgCwraqqrLvZGgAAAICtxpUwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFNGnoAbaW7t27Z5999mnoMQAAAIB/Ic8//3xmz5690dd22Aizzz775LbbbmvoMQAAAIB/IYMHD37X19yOBAAAAFCACAMAAABQgAgDAAAAUIAIAwAAAFCACAMAAABQgAgDAAAAUIAIAwAAAFCACAMAAABQgAgDAAAAUIAIAwAAAFCACAMAAABQgAgDAAAAUIAIAwAAAFCACAMAAABQgAgDAAAAUIAIAwAAAFCACAMAAABQgAgDAAAAUIAIAwAAAFCACAMAAABQgAgDAAAAUIAIAwAAAFCACAMAAABQgAgDAAAAUIAIAwAAAFCACAMAAABQgAgDAAAAUIAIAwAAAFCACAMAAABQgAizHXlj9dqGHoHN4DwBAACwMU0aegA2385NG2f/S+9o6DF4Dwu+e2xDjwAAAMA2yJUwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAMA/5Y3Vaxt6BDaD8wQADa9JQw8AAGzfdm7aOPtfekdDj8F7WPDdYxt6BAD4l+dKGAAAAIACRBgAAACAAkQYAAAAgAJEGAAAAIACRBgAAACAAkQYAAAAgAJEGAAAAIACRBgAAACAAkQYAAAAgAJEGAAAAIACRBgAAACAAkQYAAAAgAJEGAAAAIACRBgAAACAAkQYAAAAgAJEGAAAAIACRBgAAACAApo01AevXr06l112WZ5//vmsWrUq5513Xj784Q/n0ksvTVVVVT7ykY/k3//939OoUaNcffXVmTFjRpo0aZLLLrsshxxySEONDQAAALBFGizC/OY3v0nr1q3zgx/8IK+88koGDRqUjh075sILL0z37t0zatSoTJs2LW3bts2cOXMyYcKEvPjiixk5cmQmTpzYUGMDAAAAbJEGizDHHHNMBgwYUPu4cePGmTdvXg477LAkSa9evXL//fenffv26dGjR6qqqtK2bdusXbs2S5YsSZs2bRpqdAAAAIA6a7A1YZo3b54WLVpk+fLlueCCC3LhhRemUqmkqqqq9vVly5Zl+fLladGixXrvW7ZsWUONDQAAALBFGnRh3hdffDHDhw/Ppz/96Rx//PFp1Ogf46xYsSKtWrVKixYtsmLFivWeb9myZUOMCwAAALDFGizCvPTSSznzzDNz8cUX56STTkqSdOrUKbNnz06S/O53v0u3bt3StWvX3HfffampqckLL7yQmpoatyIBAAAA250GWxPmxz/+cV577bVce+21ufbaa5MkX//61zN69OhceeWVOeCAAzJgwIA0btw43bp1yymnnJKampqMGjWqoUYGAAAA2GINFmG+8Y1v5Bvf+MYGz48bN26D50aOHJmRI0eWGAsAAABgq2jQNWEAAAAA/lWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAU0eIR59NFHM2zYsCTJvHnz0rNnzwwbNizDhg3LpEmTkiRXX311TjrppAwZMiSPPfZYQ44LAAAAsEWaNOSHX3fddfnNb36TXXbZJUnyxBNP5POf/3zOPPPM2m3mzZuXOXPmZMKECXnxxRczcuTITJw4saFGBgAAANgiDXolzH777ZcxY8bUPv7jH/+YGTNmZOjQobnsssuyfPnyPPTQQ+nRo0eqqqrStm3brF27NkuWLGnAqQEAAADqrkEjzIABA9KkyT8uxjnkkEPy1a9+NePHj0+7du1yzTXXZPny5WnRokXtNs2bN8+yZcsaYlwAAACALdbga8K8Xb9+/XLwwQfX/vsTTzyRFi1aZMWKFbXbrFixIi1btmyoEQEAAAC2yDYVYc4666zahXcfeOCBHHTQQenatWvuu+++1NTU5IUXXkhNTU3atGnTwJMCAAAA1E2DLsz7Tt/85jfzrW99K02bNs3uu++eb33rW2nRokW6deuWU045JTU1NRk1alRDjwkAAABQZw0eYfbdd9/ccsstSZKDDjoov/zlLzfYZuTIkRk5cmTp0QAAAADqzTZ1OxIAAADAjkqEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKECEAQAAAChAhAEAAAAoQIQBAAAAKKDeI8yqVauyYMGC+t4tAAAAwHatThHmwAMPzDXXXLPJba6++uqcfPLJ/9RQAAAAADuaJpt68Y9//GMWLVpU+7hSqeTZZ5/NtGnTNrr96tWrM2PGjKxZs6Z+pwQAAADYzm0ywrz66qs5//zzU1VVlSSpqqrKpEmTMmnSpHd9T6VSycCBA+t3SgAAAIDt3CYjzJFHHplRo0ZlyZIlqVQqueaaa3LooYeme/fuG92+adOm2WuvvUQYAAAAgHfYZIRJklNPPbX23+fMmZPPfOYzOfHEE7fqUAAAAAA7mveMMG934403bq05AAAAAHZodYowSfL666/nzjvvzIIFC7Jq1apUKpUNtqmqqsqll15aLwMCAAAA7AjqFGHmz5+f008/Pa+99tpG48s6IgwAAADA+uoUYa688sq8+uqr+exnP5tevXqlZcuWtb+cBAAAAMC7q1OEmTt3bvr06ZMrrrhia80DAAAAsENqVKeNGzXKAQccsLVmAQAAANhh1SnCdOvWLXPnzt1aswAAAADssOoUYS6++OIsWLAgo0ePzqJFi7bWTAAAAAA7nDqtCXP55ZenVatWGT9+fMaPH5+ddtopzZo122C7qqqqzJ49u96GBAAAANje1SnC/PWvf02S7L333ltlGAAAAIAdVZ0izPTp07fWHAAAAAA7tDqtCQMAAADAlqnTlTDTpk3b7G379u1b52EAAAAAdlR1ijDnn39+qqqqNmvbJ598cosGAgAAANgR1UuEWblyZRYuXJiZM2emS5cuOf300+ttQAAAAIAdQZ0izMiRIzf5+hNPPJFTTz01y5Yt+6eGAgAAANjR1OvCvJ06dcoxxxyTsWPH1uduAQAAALZ79f7rSLvttlv+8pe/1PduAQAAALZr9RphlixZkilTpmSPPfaoz90CAAAAbPfqtJybqj4AACAASURBVCbMiBEjNvp8pVLJ66+/nsceeyyvv/56zj///HoZDgAAAGBHUacIc/fdd2/y9fe9730544wzct555/1TQwEAAADsaOoUYaZNm7bR56uqqtK0adO8//3vT6NG9b7MDAAAAMB2r04RZp999tlacwAAAADs0OoUYdaZO3duJk6cmKeeeiorV65M69at85GPfCQnnHBCunXrVt8zAgAAAGz36hxhfvjDH+ZnP/tZKpVKkmSXXXbJggUL8vDDD2fChAk599xzc9FFF9X7oAAAAADbszot4DJp0qRcd911+fCHP5yf/OQnmTt3bh5++OE8+uijGTt2bDp06JCf/vSn77mALwAAAMC/mjpFmBtuuCF77LFHbrjhhvTu3TstWrRIkjRr1ixHHHFExo4dm9133z033njjVhkWAAAAYHtVpwjz1FNPpU+fPtltt902+nqbNm3Sp0+fPPnkk/UyHAAAAMCOYqv8nvTq1au3xm4BAAAAtlt1ijAdOnTIPffck6VLl2709SVLlmT69Onp0KFDvQwHAAAAsKOoU4QZPnx4Fi9enLPOOitz5szJmjVrkiTLly/PzJkzc8YZZ+Tll1/OaaedtlWGBQAAANhe1eknqgcOHJjHH388119/fU4//fQ0atQozZo1yxtvvJEkqVQq+fznP5/jjjtuqwwLAAAAsL2qU4RJkksuuSR9+/bNbbfdlvnz52fFihVp3rx5OnbsmMGDB6dbt25bY04AAACA7VqdI0ySdOvWTWwBAAAAqIPNXhPm2WefzSuvvLLR16666qo89NBD9TYUAAAAwI7mPSPMqlWrctFFF+W4447LzJkzN3h98eLFufbaa3Paaafl/PPPz/Lly7fKoAAAAADbs01GmLVr1+bss8/O5MmT84EPfCC77bbbBtvssssu+cpXvpL99tsv06ZNyxe+8IVUKpWtNjAAAADA9miTEeaXv/xl5syZkxNOOCFTp05N7969N9imRYsWOfvss3P77benb9++eeihh3LrrbdutYEBAAAAtkebjDD/+7//m7Zt2+bb3/52mjTZ9Bq+O++8c773ve9lt912y69//et6HRIAAABge7fJCPOnP/0pPXr0SNOmTTdrZy1atMiRRx6Zp556ql6GAwAAANhRvOeaMC1btqzTDvfaa6+sWbPmnxoKAAAAYEezyQiz9957Z+HChXXa4cKFC7PXXnv9U0MBAAAA7Gg2GWEOPfTQ/O53v8vixYs3a2eLFy/OjBkz0qFDh3oZDgAAAGBHsckIM2TIkKxatSoXXHBBli9fvskdLV++PCNHjszq1aszZMiQeh0SAAAAYHu3yQjTqVOnfOELX8jDDz+cY445Jj/60Y/y2GOPZdmyZampqckrr7ySRx99NNdcc0369++fRx55JIMHD84RRxxRan4AAACA7cKmf3c6yQUXXJCmTZvm2muvzVVXXZWrrrpqg20qlUqaNm2ac845JxdddNFWGRQAAABge/aeEaaqqipf/OIXM3DgwPzqV7/Kvffem0WLFuW1115L69at065du/Ts2TPHHXdc2rVrV2JmAAAAgO3Oe0aYdfbff/9cdNFFrnQBAAAA2AKbXBMGAAAAgPohwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAU0OAR5tFHH82wYcOSJH/5y1/yuc99Lqeeemr+/d//PTU1NUmSq6++OieddFKGDBmSxx57rCHHBQAAANgiDRphrrvuunzjG9/Im2++mST5zne+kwsvvDA33XRTKpVKpk2blnnz5mXOnDmZMGFCrrzyylx++eUNOTIAAADAFmnQCLPffvtlzJgxtY/nzZuXww47LEnSq1ev/P73v89DDz2UHj16pKqqKm3bts3atWuzZMmShhoZAAAAYIs0aIQZMGBAmjRpUvu4UqmkqqoqSdK8efMsW7Ysy5cvT4sWLWq3Wfc8AAAAwPakwdeEebtGjf4xzooVK9KqVau0aNEiK1asWO/5li1bNsR4AAAAAFtsm4ownTp1yuzZs5Mkv/vd79KtW7d07do19913X2pqavLCCy+kpqYmbdq0aeBJAQAAAOqmyXtvUs4ll1yS//f//l+uvPLKHHDAARkwYEAaN26cbt265ZRTTklNTU1GjRrV0GMCAAAA1FmDR5h99903t9xyS5Kkffv2GTdu3AbbjBw5MiNHjiw9GgAAAEC92aZuRwIAAADYUYkwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDDAv6Q3Vq9t6BHYDM4TAAA7kiYNPQBAQ9i5aePsf+kdDT0G72HBd49t6BEAAKDeuBIGAAAAoAARBgAAAKAAEQYAAACgABEGAAAAoAARBgAAAKAAEQYAAACgABEGAAAAoAARBgAAAKAAEQYAAACgABEGAAAAoAARBgAAthFvrF7b0COwGZwnYEs1aegBAACAt+zctHH2v/SOhh6D97Dgu8c29AjAdsqVMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAAAAWIMAAAAAAFiDAAAAAABYgwAAAA7HDeWL22oUdgM/yrnacmDT0AAAAA1LedmzbO/pfe0dBj8B4WfPfYhh6hKFfCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFCDCAAAAABQgwgAAAAAUIMIAAAAAFNCkoQfYmBNPPDEtW7ZMkuy777455ZRT8u1vfzuNGzdOjx49MmLEiAaeEAAAAKButrkI8+abbyZJbrzxxtrnPv3pT2fMmDFp165dzj333MybNy8HHXRQQ40IAAAAUGfb3O1I8+fPz8qVK3PmmWdm+PDhefDBB7Nq1arst99+qaqqSo8ePfLAAw809JgAAAAAdbLNXQmz884756yzzsrJJ5+cBQsW5JxzzkmrVq1qX2/evHmee+65BpwQAAAAoO6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      "text/plain": [
       "<Figure size 1368x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#giving the figure size(width, height)\n",
    "plt.figure(figsize=(19,12))\n",
    "plt.xlabel('Profit made from Movies', fontsize = 20)\n",
    "plt.title('Profit Made from Movies', fontsize=20)\n",
    "plt.ylabel('Count', fontsize=20)\n",
    "# plt.show()\n",
    "df_new1.groupby(['genres'])['id'].count().plot(kind='bar', by=['genres'])\n",
    "df_new1.groupby(['genres'])[['id']].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A box plot of genres and budget "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>217.0</td>\n",
       "      <td>2.765101e+07</td>\n",
       "      <td>112000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy|Romance</th>\n",
       "      <td>96.0</td>\n",
       "      <td>2.980325e+07</td>\n",
       "      <td>100000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>208.0</td>\n",
       "      <td>2.303725e+07</td>\n",
       "      <td>130000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama|Romance</th>\n",
       "      <td>92.0</td>\n",
       "      <td>1.984334e+07</td>\n",
       "      <td>105000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horror|Thriller</th>\n",
       "      <td>80.0</td>\n",
       "      <td>1.192879e+07</td>\n",
       "      <td>65000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count          mean          max\n",
       "genres                                           \n",
       "Comedy           217.0  2.765101e+07  112000000.0\n",
       "Comedy|Romance    96.0  2.980325e+07  100000000.0\n",
       "Drama            208.0  2.303725e+07  130000000.0\n",
       "Drama|Romance     92.0  1.984334e+07  105000000.0\n",
       "Horror|Thriller   80.0  1.192879e+07   65000000.0"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_1 = df.groupby(['genres'])[['id']].count().sort_values(by=['id'], ascending=False)[0:5]\n",
    "df_new = df[df['genres'].isin(df_1.index.values.tolist())]\n",
    "df_new1 = df_new[df_new['budget']>0]\n",
    "df_new1.boxplot(column=['budget'], by =['genres'], rot=90).set_yscale('log')\n",
    "pd.DataFrame(df_new1.groupby(['genres'])['budget'].describe().loc[:, ['count','mean','max']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 5 Movies Genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#A functions that strips individual items based on the \"|\"\n",
    "#and then creates a dictionary of items to produce top 5 items from the dictionary\n",
    "\n",
    "def calculate_top_5(column):\n",
    "    list_stuffs = {}\n",
    "\n",
    "    stuffs = df[column]\n",
    "    stuffs = stuffs.str.split(\"|\")\n",
    "    stuffs = np.array(stuffs)\n",
    "    for itemList in stuffs:\n",
    "        for stuff in itemList:\n",
    "                stuff = stuff.lstrip() #trim the whitespaces\n",
    "                if stuff not in list_stuffs:\n",
    "                    list_stuffs[stuff] = 1\n",
    "                else:\n",
    "                    list_stuffs[stuff] += 1\n",
    "\n",
    "\n",
    "\n",
    "    sorted_list_stuffs = sorted(list_stuffs.items(), key = operator.itemgetter(1), reverse = True)\n",
    "    return sorted_list_stuffs[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Drama', 1572),\n",
       " ('Comedy', 1255),\n",
       " ('Thriller', 1149),\n",
       " ('Action', 1046),\n",
       " ('Adventure', 720)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_top_5('genres')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 5 Actors that Appear Most"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Robert De Niro', 50),\n",
       " ('Bruce Willis', 44),\n",
       " ('Samuel L. Jackson', 43),\n",
       " ('Nicolas Cage', 41),\n",
       " ('Matt Damon', 35)]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_top_5('cast')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Between Datasets using a Pair plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres</th>\n",
       "      <th>profit</th>\n",
       "      <th>revenue</th>\n",
       "      <th>budget</th>\n",
       "      <th>runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Action</th>\n",
       "      <th>-58067057.0</th>\n",
       "      <th>76932943.0</th>\n",
       "      <th>135000000.0</th>\n",
       "      <th>121</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-39255262.0</th>\n",
       "      <th>54744738.0</th>\n",
       "      <th>94000000.0</th>\n",
       "      <th>116</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-14988268.0</th>\n",
       "      <th>55011732.0</th>\n",
       "      <th>70000000.0</th>\n",
       "      <th>139</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-14177139.0</th>\n",
       "      <th>30822861.0</th>\n",
       "      <th>45000000.0</th>\n",
       "      <th>99</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2716629.0</th>\n",
       "      <th>3283371.0</th>\n",
       "      <th>6000000.0</th>\n",
       "      <th>99</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western|Drama</th>\n",
       "      <th>-16803693.0</th>\n",
       "      <th>1196307.0</th>\n",
       "      <th>18000000.0</th>\n",
       "      <th>115</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western|Drama|Adventure|Thriller</th>\n",
       "      <th>397950503.0</th>\n",
       "      <th>532950503.0</th>\n",
       "      <th>135000000.0</th>\n",
       "      <th>156</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western|History</th>\n",
       "      <th>5795189.0</th>\n",
       "      <th>15795189.0</th>\n",
       "      <th>10000000.0</th>\n",
       "      <th>99</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western|History|War</th>\n",
       "      <th>-119180039.0</th>\n",
       "      <th>25819961.0</th>\n",
       "      <th>145000000.0</th>\n",
       "      <th>137</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western|Thriller</th>\n",
       "      <th>-6993853.0</th>\n",
       "      <th>6147.0</th>\n",
       "      <th>7000000.0</th>\n",
       "      <th>95</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3572 rows × 0 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [(Action, -58067057.0, 76932943.0, 135000000.0, 121), (Action, -39255262.0, 54744738.0, 94000000.0, 116), (Action, -14988268.0, 55011732.0, 70000000.0, 139), (Action, -14177139.0, 30822861.0, 45000000.0, 99), (Action, -2716629.0, 3283371.0, 6000000.0, 99), (Action, 608545.0, 15608545.0, 15000000.0, 96), (Action, 6546197.0, 46546197.0, 40000000.0, 84), (Action, 7465299.0, 45465299.0, 38000000.0, 111), (Action, 10306119.0, 11806119.0, 1500000.0, 92), (Action, 13681566.0, 56681566.0, 43000000.0, 97), (Action, 27784194.0, 87784194.0, 60000000.0, 127), (Action, 30795814.0, 58795814.0, 28000000.0, 102), (Action, 50416905.0, 85416905.0, 35000000.0, 100), (Action, 69185582.0, 98185582.0, 29000000.0, 102), (Action, 82944923.0, 162944923.0, 80000000.0, 128), (Action, 96000000.0, 136000000.0, 40000000.0, 110), (Action, 110669540.0, 310669540.0, 200000000.0, 140), (Action, 207448382.0, 277448382.0, 70000000.0, 124), (Action, 532330139.0, 682330139.0, 150000000.0, 131), (Action|Adventure, -79482678.0, 18517322.0, 98000000.0, 119), (Action|Adventure, -8720548.0, 61279452.0, 70000000.0, 99), (Action|Adventure, -1000000.0, 7500000.0, 8500000.0, 90), (Action|Adventure, 27400547.0, 57400547.0, 30000000.0, 95), (Action|Adventure, 79563118.0, 84563118.0, 5000000.0, 117), (Action|Adventure, 143400000.0, 243400000.0, 100000000.0, 99), (Action|Adventure|Animation|Family|Fantasy, 424987477.0, 554987477.0, 130000000.0, 90), (Action|Adventure|Animation|Family|Science Fiction, 539442092.0, 631442092.0, 92000000.0, 115), (Action|Adventure|Animation|Science Fiction, 1731128.0, 5231128.0, 3500000.0, 75), (Action|Adventure|Animation|Science Fiction|Thriller, 18428063.0, 48428063.0, 30000000.0, 79), (Action|Adventure|Comedy, -47781920.0, 17218080.0, 65000000.0, 100), (Action|Adventure|Comedy, -37821105.0, 72178895.0, 110000000.0, 120), (Action|Adventure|Comedy, -12335441.0, 47664559.0, 60000000.0, 112), (Action|Adventure|Comedy, -12261773.0, 238227.0, 12500000.0, 100), (Action|Adventure|Comedy, -3890000.0, 6110000.0, 10000000.0, 90), (Action|Adventure|Comedy, 8408614.0, 63408614.0, 55000000.0, 104), (Action|Adventure|Comedy, 11482931.0, 25482931.0, 14000000.0, 95), (Action|Adventure|Comedy, 23898845.0, 53898845.0, 30000000.0, 105), (Action|Adventure|Comedy, 24350000.0, 29950000.0, 5600000.0, 138), (Action|Adventure|Comedy, 33000000.0, 108000000.0, 75000000.0, 114), (Action|Adventure|Comedy, 45000000.0, 71000000.0, 26000000.0, 106), (Action|Adventure|Comedy, 54817939.0, 79817939.0, 25000000.0, 94), (Action|Adventure|Comedy, 60803676.0, 110803676.0, 50000000.0, 104), (Action|Adventure|Comedy, 66865564.0, 86865564.0, 20000000.0, 86), (Action|Adventure|Comedy|Crime, 10342161.0, 20342161.0, 10000000.0, 84), (Action|Adventure|Comedy|Crime, 59649227.0, 78247647.0, 18598420.0, 101), (Action|Adventure|Comedy|Crime, 139175788.0, 259175788.0, 120000000.0, 106), (Action|Adventure|Comedy|Crime|Drama, 13256975.0, 20256975.0, 7000000.0, 96), (Action|Adventure|Comedy|Crime|Drama, 145444603.0, 285444603.0, 140000000.0, 127), (Action|Adventure|Comedy|Crime|Thriller, -23012437.0, 31987563.0, 55000000.0, 117), (Action|Adventure|Comedy|Crime|Thriller, 13664962.0, 71664962.0, 58000000.0, 111), (Action|Adventure|Comedy|Crime|Thriller, 172105545.0, 264105545.0, 92000000.0, 98), (Action|Adventure|Comedy|Crime|Thriller, 202853986.0, 227853986.0, 25000000.0, 114), (Action|Adventure|Comedy|Drama, 15754284.0, 25754284.0, 10000000.0, 108), (Action|Adventure|Comedy|Drama|Family, 49141030.0, 61141030.0, 12000000.0, 93), (Action|Adventure|Comedy|Drama|Mystery, -10730514.0, 119269486.0, 130000000.0, 124), (Action|Adventure|Comedy|Drama|Science Fiction, -16625257.0, 374743.0, 17000000.0, 144), (Action|Adventure|Comedy|Drama|Science Fiction, 52104681.0, 222104681.0, 170000000.0, 106), (Action|Adventure|Comedy|Drama|Western, 98033791.0, 124033791.0, 26000000.0, 112), (Action|Adventure|Comedy|Family, -33328495.0, 16671505.0, 50000000.0, 99), (Action|Adventure|Comedy|Family, -1481903.0, 22518097.0, 24000000.0, 100), (Action|Adventure|Comedy|Family, 77597242.0, 107597242.0, 30000000.0, 94), (Action|Adventure|Comedy|Family, 81723358.0, 119723358.0, 38000000.0, 100), (Action|Adventure|Comedy|Family, 157293131.0, 183293131.0, 26000000.0, 91), (Action|Adventure|Comedy|Family|Fantasy, 21.0, 42.0, 21.0, 96), (Action|Adventure|Comedy|Family|Fantasy, 464480841.0, 574480841.0, 110000000.0, 108), (Action|Adventure|Comedy|Family|Science Fiction, 159011982.0, 197011982.0, 38000000.0, 84), (Action|Adventure|Comedy|Family|Thriller, -906293.0, 5093707.0, 6000000.0, 86), (Action|Adventure|Comedy|Romance, 71773200.0, 96773200.0, 25000000.0, 106), (Action|Adventure|Comedy|Romance, 76572238.0, 86572238.0, 10000000.0, 106), (Action|Adventure|Comedy|Romance, 94000000.0, 164000000.0, 70000000.0, 98), (Action|Adventure|Comedy|Science Fiction, -1158691.0, 2341309.0, 3500000.0, 89), (Action|Adventure|Comedy|Science Fiction, 102055768.0, 159055768.0, 57000000.0, 115), (Action|Adventure|Comedy|Science Fiction, 301818803.0, 441818803.0, 140000000.0, 88), (Action|Adventure|Comedy|Science Fiction, 499390539.0, 589390539.0, 90000000.0, 98), (Action|Adventure|Comedy|Thriller, -36438863.0, 33561137.0, 70000000.0, 97), (Action|Adventure|Comedy|Thriller, -4022705.0, 65977295.0, 70000000.0, 116), (Action|Adventure|Comedy|Thriller, -3703816.0, 1296184.0, 5000000.0, 106), (Action|Adventure|Comedy|Thriller, -2388046.0, 22611954.0, 25000000.0, 84), (Action|Adventure|Comedy|Thriller, 14007143.0, 27007143.0, 13000000.0, 91), (Action|Adventure|Comedy|Thriller, 50631118.0, 69631118.0, 19000000.0, 95), (Action|Adventure|Comedy|Thriller|Crime, -43279825.0, 18720175.0, 62000000.0, 125), (Action|Adventure|Comedy|Western, 38323487.0, 88323487.0, 50000000.0, 115), (Action|Adventure|Crime, -8490253.0, 9747.0, 8500000.0, 99), (Action|Adventure|Crime, 1000000.0, 29000000.0, 28000000.0, 103), (Action|Adventure|Crime, 635674609.0, 880674609.0, 245000000.0, 148), (Action|Adventure|Crime|Drama, 11480851.0, 41480851.0, 30000000.0, 125), (Action|Adventure|Crime|Drama|Fantasy, -3189887.0, 26810113.0, 30000000.0, 102), (Action|Adventure|Crime|Drama|Mystery, -5452319.0, 44547681.0, 50000000.0, 140), (Action|Adventure|Crime|Drama|Romance, -7505520.0, 2494480.0, 10000000.0, 94), (Action|Adventure|Crime|Drama|Thriller, -24805000.0, 25195000.0, 50000000.0, 97), (Action|Adventure|Crime|Drama|Thriller, -17617826.0, 382174.0, 18000000.0, 98), (Action|Adventure|Crime|Drama|Thriller, 2923000.0, 5923000.0, 3000000.0, 88), (Action|Adventure|Crime|Drama|Thriller, 3048693.0, 5048693.0, 2000000.0, 104), (Action|Adventure|Crime|Drama|Thriller, 13174566.0, 73174566.0, 60000000.0, 101), (Action|Adventure|Crime|Mystery|Thriller, -1420000.0, 23580000.0, 25000000.0, 97), (Action|Adventure|Crime|Science Fiction|Thriller, -11303790.0, 10696210.0, 22000000.0, 104), (Action|Adventure|Crime|Science Fiction|Thriller, 10681173.0, 45681173.0, 35000000.0, 117), (Action|Adventure|Crime|Thriller, -44000000.0, 16000000.0, 60000000.0, 105), (Action|Adventure|Crime|Thriller, -19696928.0, 30303072.0, 50000000.0, 132), (Action|Adventure|Crime|Thriller, 10664721.0, 65664721.0, 55000000.0, 105), ...]\n",
       "\n",
       "[3572 rows x 0 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Numeric columns from dataframe \n",
    "pair_plot_df = df[['revenue', 'budget', 'profit', 'runtime', 'genres']]     \n",
    "pair_plot = sns.pairplot(pair_plot_df, diag_kind=\"reg\")\n",
    "pair_plot_df.groupby(['genres','profit','revenue','budget','runtime']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    ">THe dataset is very rich with lots of information about a lot of movies. The data is well structured making it a kind of easy to wrangle. This dataset can help movie producers make sound business decisions and also assist viewers decide on a choice of movies. Recommender systems can even be built from the dataset and other interesting Machine Learning models that can be used to achieve numerous stuffs.\n",
    "From the analysis we were able to clean a movie Dataset with so much details that gave a lot of insights about the movies on the IMDB.\n",
    "We highlighted some research questions in the introduction section of this project and answered them based on the dataset in the EDA section and depicted them using appropriate charts. The observations from the data are as follows:\n",
    "<li>The movies with the highest profit is Avatar where as The Warrior's ways had the lowest profit (they made a loss).</li>\n",
    "<li> From the data, the profit made is impacted by the runtime and the budget for the movies. This implies that when producing any movie, ensure that you have enough budget for the movie in order to cater for relevant needs for the movie </li>\n",
    "<li> Comedy is the most successful genre. People tend to watch movie that have much humor in them. From my personal experience, if a movie does not have elements of humor in it, I turn it down. It is reasonable that horror is one of the least watched movie among the successful genre. Horror/thrillers are rated so the number of people that watch it are limited.</li>\n",
    "### Limitations of analysis\n",
    "> Other interesting things that can be done from the data is to check the most appearance of actors in movies. Then further compare with the actors in most successful movies/genres in terms of profits; this can simply be used as a yardstick to determine top actors and see roles they can perform based on their genres. Unfortunately this dataset doesn't provide such ratings for actors genrally even though there could be a seperate dataset for that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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