data.ipynb

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    "colab": {
      "name": "data.ipynb",
      "provenance": [],
      "collapsed_sections": [
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        "Ko9rZyNuW4Yu",
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        "hFpWgm1lW4Zo"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/phu0n9/BigData_movie/blob/master/data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jsoUwQu6s1sj"
      },
      "source": [
        "# RMIT University in Vietnam\n",
        "# Course: EEET2574 Big Data in Engineering\n",
        "# Semester: 2020B\n",
        "# Assessment: Big Data Project\n",
        "# Authors: \n",
        "#   Tran Dam Quan           - s3678708\n",
        "#   Nguyen Huynh Anh Phuong - s3695662\n",
        "#   Le Nguyen Thien Phu     - s3639855\n",
        "#   Tran Phi                - s3636649\n",
        "# Date: 27/09/2020"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aw3Af2kRXHDa",
        "outputId": "16c60a75-9f05-4391-e2ff-af01a8010eba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vWSsUIcafgo-",
        "outputId": "10c42189-1463-49c0-f119-c1cd5e09d7fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "!ls \"/content/drive/My Drive/Colab Notebooks/Group Assignment\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " cast.csv\t genre_point.csv     movies.csv\t\t   title.tsv\n",
            " cast_data.csv\t'IMDb movies.csv'    movies_metadata.csv\n",
            " data.ipynb\t'IMDb ratings.csv'   name.tsv\n",
            " extract.ipynb\t modeling.ipynb      statistic.ipynb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3iXrVVY0oEuD"
      },
      "source": [
        "# Importing libraries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9FHlIJgMoJn1"
      },
      "source": [
        "## Importing general library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "djHz1inhW4U0"
      },
      "source": [
        "import pandas as pd \n",
        "import numpy as np\n",
        "from scipy import stats\n",
        "import datetime\n",
        "import sys"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9hgrBJM3oOd2"
      },
      "source": [
        "## Importing plotting library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_CgVmMupoT7a"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.graph_objects as go"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PcDT0xhIW4U_"
      },
      "source": [
        "# Merge AllMoviesClean & Imdb Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nBpTLmxbW4VA",
        "scrolled": true,
        "outputId": "5ff6a20a-3571-43c4-c3c9-94a7f8675f88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        }
      },
      "source": [
        "imdb_df = pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/Group Assignment/IMDb movies.csv\", delimiter=\",\")\n",
        "imdb_df = imdb_df.rename(columns={'imdb_title_id':'imdb_id'})\n",
        "imdb_df = imdb_df.drop(columns=['description','metascore','production_company','country','language','writer','director','actors','usa_gross_income','year','original_title','avg_vote','votes'])\n",
        "imdb_df['year'] = pd.DatetimeIndex(imdb_df['date_published']).year\n",
        "imdb_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>imdb_id</th>\n",
              "      <th>title</th>\n",
              "      <th>date_published</th>\n",
              "      <th>genre</th>\n",
              "      <th>duration</th>\n",
              "      <th>budget</th>\n",
              "      <th>worlwide_gross_income</th>\n",
              "      <th>reviews_from_users</th>\n",
              "      <th>reviews_from_critics</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000574</td>\n",
              "      <td>The Story of the Kelly Gang</td>\n",
              "      <td>1906-12-26</td>\n",
              "      <td>Biography, Crime, Drama</td>\n",
              "      <td>70</td>\n",
              "      <td>$ 2250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1906</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>Den sorte drøm</td>\n",
              "      <td>1911-08-19</td>\n",
              "      <td>Drama</td>\n",
              "      <td>53</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0002101</td>\n",
              "      <td>Cleopatra</td>\n",
              "      <td>1912-11-13</td>\n",
              "      <td>Drama, History</td>\n",
              "      <td>100</td>\n",
              "      <td>$ 45000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0002130</td>\n",
              "      <td>L'Inferno</td>\n",
              "      <td>1911-03-06</td>\n",
              "      <td>Adventure, Drama, Fantasy</td>\n",
              "      <td>68</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0002199</td>\n",
              "      <td>From the Manger to the Cross; or, Jesus of Naz...</td>\n",
              "      <td>1913</td>\n",
              "      <td>Biography, Drama</td>\n",
              "      <td>60</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1913</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "     imdb_id  ...  year\n",
              "0  tt0000574  ...  1906\n",
              "1  tt0001892  ...  1911\n",
              "2  tt0002101  ...  1912\n",
              "3  tt0002130  ...  1911\n",
              "4  tt0002199  ...  1913\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CzbS6zkZW4VN",
        "scrolled": true,
        "outputId": "a21538e9-bd35-416f-dffd-06faf96f77a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678
        }
      },
      "source": [
        "meta_data = pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/Group Assignment/movies_metadata.csv\", delimiter=\",\",low_memory=False)\n",
        "meta_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>adult</th>\n",
              "      <th>belongs_to_collection</th>\n",
              "      <th>budget</th>\n",
              "      <th>genres</th>\n",
              "      <th>homepage</th>\n",
              "      <th>id</th>\n",
              "      <th>imdb_id</th>\n",
              "      <th>original_language</th>\n",
              "      <th>original_title</th>\n",
              "      <th>overview</th>\n",
              "      <th>popularity</th>\n",
              "      <th>poster_path</th>\n",
              "      <th>production_companies</th>\n",
              "      <th>production_countries</th>\n",
              "      <th>release_date</th>\n",
              "      <th>revenue</th>\n",
              "      <th>runtime</th>\n",
              "      <th>spoken_languages</th>\n",
              "      <th>status</th>\n",
              "      <th>tagline</th>\n",
              "      <th>title</th>\n",
              "      <th>video</th>\n",
              "      <th>vote_average</th>\n",
              "      <th>vote_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>{'id': 10194, 'name': 'Toy Story Collection', ...</td>\n",
              "      <td>30000000</td>\n",
              "      <td>[{'id': 16, 'name': 'Animation'}, {'id': 35, '...</td>\n",
              "      <td>http://toystory.disney.com/toy-story</td>\n",
              "      <td>862</td>\n",
              "      <td>tt0114709</td>\n",
              "      <td>en</td>\n",
              "      <td>Toy Story</td>\n",
              "      <td>Led by Woody, Andy's toys live happily in his ...</td>\n",
              "      <td>21.946943</td>\n",
              "      <td>/rhIRbceoE9lR4veEXuwCC2wARtG.jpg</td>\n",
              "      <td>[{'name': 'Pixar Animation Studios', 'id': 3}]</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-10-30</td>\n",
              "      <td>373554033.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Toy Story</td>\n",
              "      <td>False</td>\n",
              "      <td>7.7</td>\n",
              "      <td>5415.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>65000000</td>\n",
              "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8844</td>\n",
              "      <td>tt0113497</td>\n",
              "      <td>en</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>When siblings Judy and Peter discover an encha...</td>\n",
              "      <td>17.015539</td>\n",
              "      <td>/vzmL6fP7aPKNKPRTFnZmiUfciyV.jpg</td>\n",
              "      <td>[{'name': 'TriStar Pictures', 'id': 559}, {'na...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-12-15</td>\n",
              "      <td>262797249.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
              "      <td>Released</td>\n",
              "      <td>Roll the dice and unleash the excitement!</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>False</td>\n",
              "      <td>6.9</td>\n",
              "      <td>2413.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>{'id': 119050, 'name': 'Grumpy Old Men Collect...</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'id': 10749, 'name': 'Romance'}, {'id': 35, ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15602</td>\n",
              "      <td>tt0113228</td>\n",
              "      <td>en</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>A family wedding reignites the ancient feud be...</td>\n",
              "      <td>11.7129</td>\n",
              "      <td>/6ksm1sjKMFLbO7UY2i6G1ju9SML.jpg</td>\n",
              "      <td>[{'name': 'Warner Bros.', 'id': 6194}, {'name'...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-12-22</td>\n",
              "      <td>0.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>Still Yelling. Still Fighting. Still Ready for...</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>False</td>\n",
              "      <td>6.5</td>\n",
              "      <td>92.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16000000</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31357</td>\n",
              "      <td>tt0114885</td>\n",
              "      <td>en</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>Cheated on, mistreated and stepped on, the wom...</td>\n",
              "      <td>3.859495</td>\n",
              "      <td>/16XOMpEaLWkrcPqSQqhTmeJuqQl.jpg</td>\n",
              "      <td>[{'name': 'Twentieth Century Fox Film Corporat...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-12-22</td>\n",
              "      <td>81452156.0</td>\n",
              "      <td>127.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>Friends are the people who let you be yourself...</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>False</td>\n",
              "      <td>6.1</td>\n",
              "      <td>34.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>{'id': 96871, 'name': 'Father of the Bride Col...</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11862</td>\n",
              "      <td>tt0113041</td>\n",
              "      <td>en</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>Just when George Banks has recovered from his ...</td>\n",
              "      <td>8.387519</td>\n",
              "      <td>/e64sOI48hQXyru7naBFyssKFxVd.jpg</td>\n",
              "      <td>[{'name': 'Sandollar Productions', 'id': 5842}...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-02-10</td>\n",
              "      <td>76578911.0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>Just When His World Is Back To Normal... He's ...</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>False</td>\n",
              "      <td>5.7</td>\n",
              "      <td>173.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   adult  ... vote_count\n",
              "0  False  ...     5415.0\n",
              "1  False  ...     2413.0\n",
              "2  False  ...       92.0\n",
              "3  False  ...       34.0\n",
              "4  False  ...      173.0\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Md78xUxgW4VU",
        "scrolled": true
      },
      "source": [
        "meta_data = meta_data[['imdb_id','popularity','overview']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K441zPeknD3x",
        "outputId": "bb529d62-0954-4f5b-8828-732426014b1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "meta_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>imdb_id</th>\n",
              "      <th>popularity</th>\n",
              "      <th>overview</th>\n",
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              "      <td>21.946943</td>\n",
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              "      <td>tt0113497</td>\n",
              "      <td>17.015539</td>\n",
              "      <td>When siblings Judy and Peter discover an encha...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0113228</td>\n",
              "      <td>11.7129</td>\n",
              "      <td>A family wedding reignites the ancient feud be...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0114885</td>\n",
              "      <td>3.859495</td>\n",
              "      <td>Cheated on, mistreated and stepped on, the wom...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0113041</td>\n",
              "      <td>8.387519</td>\n",
              "      <td>Just when George Banks has recovered from his ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     imdb_id popularity                                           overview\n",
              "0  tt0114709  21.946943  Led by Woody, Andy's toys live happily in his ...\n",
              "1  tt0113497  17.015539  When siblings Judy and Peter discover an encha...\n",
              "2  tt0113228    11.7129  A family wedding reignites the ancient feud be...\n",
              "3  tt0114885   3.859495  Cheated on, mistreated and stepped on, the wom...\n",
              "4  tt0113041   8.387519  Just when George Banks has recovered from his ..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LlnUYLNuW4Vc",
        "scrolled": true,
        "outputId": "4be1d6bd-9446-4d90-ad89-f075e1df15a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        }
      },
      "source": [
        "merge_df = pd.merge(imdb_df,meta_data,how='inner',on='imdb_id')\n",
        "merge_df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 34521 entries, 0 to 34520\n",
            "Data columns (total 12 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   imdb_id                34521 non-null  object \n",
            " 1   title                  34521 non-null  object \n",
            " 2   date_published         34521 non-null  object \n",
            " 3   genre                  34521 non-null  object \n",
            " 4   duration               34521 non-null  int64  \n",
            " 5   budget                 13733 non-null  object \n",
            " 6   worlwide_gross_income  17770 non-null  object \n",
            " 7   reviews_from_users     33754 non-null  float64\n",
            " 8   reviews_from_critics   33303 non-null  float64\n",
            " 9   year                   34521 non-null  int64  \n",
            " 10  popularity             34519 non-null  object \n",
            " 11  overview               33932 non-null  object \n",
            "dtypes: float64(2), int64(2), object(8)\n",
            "memory usage: 3.4+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "awJvWFqvW4Vi"
      },
      "source": [
        "dataframe = merge_df.drop(merge_df[(merge_df['budget'].isnull()) | (merge_df['worlwide_gross_income'].isnull())].index)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlB9ug3BW4Vr",
        "scrolled": true,
        "outputId": "56e5942d-f1c5-4542-8e20-abae20a0fe62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 982
        }
      },
      "source": [
        "dataframe"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>imdb_id</th>\n",
              "      <th>title</th>\n",
              "      <th>date_published</th>\n",
              "      <th>genre</th>\n",
              "      <th>duration</th>\n",
              "      <th>budget</th>\n",
              "      <th>worlwide_gross_income</th>\n",
              "      <th>reviews_from_users</th>\n",
              "      <th>reviews_from_critics</th>\n",
              "      <th>year</th>\n",
              "      <th>popularity</th>\n",
              "      <th>overview</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>tt0010323</td>\n",
              "      <td>Das Cabinet des Dr. Caligari</td>\n",
              "      <td>1920-02-27</td>\n",
              "      <td>Fantasy, Horror, Mystery</td>\n",
              "      <td>76</td>\n",
              "      <td>$ 18000</td>\n",
              "      <td>$ 8811</td>\n",
              "      <td>218.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>1920</td>\n",
              "      <td>7.191271</td>\n",
              "      <td>The Cabinet of Dr. Caligari is eerie and expre...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>tt0012190</td>\n",
              "      <td>The Four Horsemen of the Apocalypse</td>\n",
              "      <td>1923-01-08</td>\n",
              "      <td>Drama, Romance, War</td>\n",
              "      <td>150</td>\n",
              "      <td>$ 800000</td>\n",
              "      <td>$ 9183673</td>\n",
              "      <td>44.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1923</td>\n",
              "      <td>1.717444</td>\n",
              "      <td>Set immediately before World War I, it deals w...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>tt0012349</td>\n",
              "      <td>The Kid</td>\n",
              "      <td>1921-02-06</td>\n",
              "      <td>Comedy, Drama, Family</td>\n",
              "      <td>68</td>\n",
              "      <td>$ 250000</td>\n",
              "      <td>$ 26916</td>\n",
              "      <td>151.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>1921</td>\n",
              "      <td>8.168456</td>\n",
              "      <td>Considered one of Charlie Chaplin's best films...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>tt0014624</td>\n",
              "      <td>A Woman of Paris: A Drama of Fate</td>\n",
              "      <td>1923-11-04</td>\n",
              "      <td>Drama, Romance</td>\n",
              "      <td>82</td>\n",
              "      <td>$ 351000</td>\n",
              "      <td>$ 11233</td>\n",
              "      <td>36.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1923</td>\n",
              "      <td>1.74686</td>\n",
              "      <td>When Marie St. Clair believes she has been jil...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>tt0015864</td>\n",
              "      <td>The Gold Rush</td>\n",
              "      <td>1925-07-13</td>\n",
              "      <td>Adventure, Comedy, Drama</td>\n",
              "      <td>95</td>\n",
              "      <td>$ 923000</td>\n",
              "      <td>$ 26916</td>\n",
              "      <td>175.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>1925</td>\n",
              "      <td>6.202041</td>\n",
              "      <td>A lone prospector ventures into Alaska looking...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34503</th>\n",
              "      <td>tt6389344</td>\n",
              "      <td>Tri bogatyrya i Morskoy tsar</td>\n",
              "      <td>2017-01-01</td>\n",
              "      <td>Animation, Action, Adventure</td>\n",
              "      <td>75</td>\n",
              "      <td>$ 4000000</td>\n",
              "      <td>$ 13816357</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.468664</td>\n",
              "      <td>No plot overview available</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34507</th>\n",
              "      <td>tt6569546</td>\n",
              "      <td>Vurdalaki</td>\n",
              "      <td>2017-02-22</td>\n",
              "      <td>Action, Fantasy, Horror</td>\n",
              "      <td>90</td>\n",
              "      <td>RUR 162000000</td>\n",
              "      <td>$ 782438</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2017</td>\n",
              "      <td>9.334447</td>\n",
              "      <td>The story takes place in 18th century russia. ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34510</th>\n",
              "      <td>tt6574272</td>\n",
              "      <td>Phoenix Forgotten</td>\n",
              "      <td>2017-04-21</td>\n",
              "      <td>Horror, Mystery, Sci-Fi</td>\n",
              "      <td>87</td>\n",
              "      <td>$ 2800000</td>\n",
              "      <td>$ 3697729</td>\n",
              "      <td>68.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>2017</td>\n",
              "      <td>12.560942</td>\n",
              "      <td>20 years after three teenagers disappeared in ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34511</th>\n",
              "      <td>tt6597454</td>\n",
              "      <td>Selfie</td>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>85</td>\n",
              "      <td>EUR 10000</td>\n",
              "      <td>$ 184395</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.313827</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34515</th>\n",
              "      <td>tt6690310</td>\n",
              "      <td>Fei fan ren wu</td>\n",
              "      <td>2017-04-06</td>\n",
              "      <td>Action, Crime</td>\n",
              "      <td>117</td>\n",
              "      <td>$ 23000000</td>\n",
              "      <td>$ 22757764</td>\n",
              "      <td>8.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2017</td>\n",
              "      <td>2.552316</td>\n",
              "      <td>‘Extraordinary Mission’ is a new Chinese actio...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9377 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         imdb_id  ...                                           overview\n",
              "83     tt0010323  ...  The Cabinet of Dr. Caligari is eerie and expre...\n",
              "110    tt0012190  ...  Set immediately before World War I, it deals w...\n",
              "112    tt0012349  ...  Considered one of Charlie Chaplin's best films...\n",
              "163    tt0014624  ...  When Marie St. Clair believes she has been jil...\n",
              "205    tt0015864  ...  A lone prospector ventures into Alaska looking...\n",
              "...          ...  ...                                                ...\n",
              "34503  tt6389344  ...                         No plot overview available\n",
              "34507  tt6569546  ...  The story takes place in 18th century russia. ...\n",
              "34510  tt6574272  ...  20 years after three teenagers disappeared in ...\n",
              "34511  tt6597454  ...                                                NaN\n",
              "34515  tt6690310  ...  ‘Extraordinary Mission’ is a new Chinese actio...\n",
              "\n",
              "[9377 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fObuMAUuW4Vw"
      },
      "source": [
        "# Plot graphs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEQaboQiW4Vx",
        "outputId": "15ac2d07-0e22-4dc7-b45a-e61aaf6c6a1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "movie_count = dataframe.groupby('year',as_index=False)['imdb_id'].count().rename(columns={'imdb_id':'count'})\n",
        "movie_count.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>year</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1920</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1921</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1923</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1925</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1927</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   year  count\n",
              "0  1920      1\n",
              "1  1921      1\n",
              "2  1923      2\n",
              "3  1925      1\n",
              "4  1927      3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HHeDscRUW4V2",
        "outputId": "6b3e6144-2ea2-4ace-9147-2cbcc8f0c444",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "fig = go.Figure(data=go.Scatter(x=movie_count['year'],y=movie_count['count'],mode='lines'))\n",
        "fig.update_layout(title='Movies through 1920 to 2019',xaxis_title=\"Year\",yaxis_title=\"Number of movies\")\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"6a705ccc-57ef-44b7-bf04-0bd492b3a6fb\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
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              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
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              "                    Plotly.newPlot(\n",
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\"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Movies through 1920 to 2019\"}, \"xaxis\": {\"title\": {\"text\": \"Year\"}}, \"yaxis\": {\"title\": {\"text\": \"Number of movies\"}}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('6a705ccc-57ef-44b7-bf04-0bd492b3a6fb');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I430FnzBW4V-"
      },
      "source": [
        "# Drop movies before 1980s and after 2017\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lFB7eVcGW4V_"
      },
      "source": [
        "`Second Golden Age of Holywood`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "is9VNEqqShZx",
        "outputId": "af1de406-b498-49ac-cb4e-e256dff52615",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        }
      },
      "source": [
        "dataframe['Month'] = pd.DatetimeIndex(dataframe['date_published']).month\n",
        "dataframe['Day'] = pd.DatetimeIndex(dataframe['date_published']).day\n",
        "dataframe['date_published'] = pd.to_datetime(dataframe['date_published'])\n",
        "dataframe['day_of_week'] = dataframe['date_published'].dt.day_name()\n",
        "dataframe['month_name'] = dataframe['date_published'].dt.month_name()\n",
        "dataframe.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>imdb_id</th>\n",
              "      <th>title</th>\n",
              "      <th>date_published</th>\n",
              "      <th>genre</th>\n",
              "      <th>duration</th>\n",
              "      <th>budget</th>\n",
              "      <th>worlwide_gross_income</th>\n",
              "      <th>reviews_from_users</th>\n",
              "      <th>reviews_from_critics</th>\n",
              "      <th>year</th>\n",
              "      <th>popularity</th>\n",
              "      <th>overview</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>day_of_week</th>\n",
              "      <th>month_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>tt0010323</td>\n",
              "      <td>Das Cabinet des Dr. Caligari</td>\n",
              "      <td>1920-02-27</td>\n",
              "      <td>Fantasy, Horror, Mystery</td>\n",
              "      <td>76</td>\n",
              "      <td>$ 18000</td>\n",
              "      <td>$ 8811</td>\n",
              "      <td>218.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>1920</td>\n",
              "      <td>7.191271</td>\n",
              "      <td>The Cabinet of Dr. Caligari is eerie and expre...</td>\n",
              "      <td>2</td>\n",
              "      <td>27</td>\n",
              "      <td>Friday</td>\n",
              "      <td>February</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>tt0012190</td>\n",
              "      <td>The Four Horsemen of the Apocalypse</td>\n",
              "      <td>1923-01-08</td>\n",
              "      <td>Drama, Romance, War</td>\n",
              "      <td>150</td>\n",
              "      <td>$ 800000</td>\n",
              "      <td>$ 9183673</td>\n",
              "      <td>44.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1923</td>\n",
              "      <td>1.717444</td>\n",
              "      <td>Set immediately before World War I, it deals w...</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>Monday</td>\n",
              "      <td>January</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>tt0012349</td>\n",
              "      <td>The Kid</td>\n",
              "      <td>1921-02-06</td>\n",
              "      <td>Comedy, Drama, Family</td>\n",
              "      <td>68</td>\n",
              "      <td>$ 250000</td>\n",
              "      <td>$ 26916</td>\n",
              "      <td>151.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>1921</td>\n",
              "      <td>8.168456</td>\n",
              "      <td>Considered one of Charlie Chaplin's best films...</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>February</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>tt0014624</td>\n",
              "      <td>A Woman of Paris: A Drama of Fate</td>\n",
              "      <td>1923-11-04</td>\n",
              "      <td>Drama, Romance</td>\n",
              "      <td>82</td>\n",
              "      <td>$ 351000</td>\n",
              "      <td>$ 11233</td>\n",
              "      <td>36.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1923</td>\n",
              "      <td>1.74686</td>\n",
              "      <td>When Marie St. Clair believes she has been jil...</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>November</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>tt0015864</td>\n",
              "      <td>The Gold Rush</td>\n",
              "      <td>1925-07-13</td>\n",
              "      <td>Adventure, Comedy, Drama</td>\n",
              "      <td>95</td>\n",
              "      <td>$ 923000</td>\n",
              "      <td>$ 26916</td>\n",
              "      <td>175.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>1925</td>\n",
              "      <td>6.202041</td>\n",
              "      <td>A lone prospector ventures into Alaska looking...</td>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "      <td>Monday</td>\n",
              "      <td>July</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       imdb_id                                title  ... day_of_week month_name\n",
              "83   tt0010323         Das Cabinet des Dr. Caligari  ...      Friday   February\n",
              "110  tt0012190  The Four Horsemen of the Apocalypse  ...      Monday    January\n",
              "112  tt0012349                              The Kid  ...      Sunday   February\n",
              "163  tt0014624    A Woman of Paris: A Drama of Fate  ...      Sunday   November\n",
              "205  tt0015864                        The Gold Rush  ...      Monday       July\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "btUjFwayW4WA"
      },
      "source": [
        "year_df = dataframe[(dataframe['year'] >= 1980) & (dataframe['year'] <= 2017)]\n",
        "year_df.reset_index(drop=True,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UlWzzlOVW4WG",
        "outputId": "92fe692a-d40b-4aa2-813e-63a94fbd061d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        }
      },
      "source": [
        "year_df.loc[:,'popularity'] = year_df['popularity'].replace(',',\".\",regex=True).astype(float)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:966: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L92Q6il5W4WO",
        "scrolled": true,
        "outputId": "0ea31e9f-1039-4cd9-a5f0-d6483c00830f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "year_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>imdb_id</th>\n",
              "      <th>title</th>\n",
              "      <th>date_published</th>\n",
              "      <th>genre</th>\n",
              "      <th>duration</th>\n",
              "      <th>budget</th>\n",
              "      <th>worlwide_gross_income</th>\n",
              "      <th>reviews_from_users</th>\n",
              "      <th>reviews_from_critics</th>\n",
              "      <th>year</th>\n",
              "      <th>popularity</th>\n",
              "      <th>overview</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>day_of_week</th>\n",
              "      <th>month_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0035423</td>\n",
              "      <td>Kate &amp; Leopold</td>\n",
              "      <td>2002-04-05</td>\n",
              "      <td>Comedy, Fantasy, Romance</td>\n",
              "      <td>118</td>\n",
              "      <td>$ 48000000</td>\n",
              "      <td>$ 76019048</td>\n",
              "      <td>330.0</td>\n",
              "      <td>127.0</td>\n",
              "      <td>2002</td>\n",
              "      <td>7.196356</td>\n",
              "      <td>When her scientist ex-boyfriend discovers a po...</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>Friday</td>\n",
              "      <td>April</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0038733</td>\n",
              "      <td>A Matter of Life and Death</td>\n",
              "      <td>2017-12-08</td>\n",
              "      <td>Comedy, Drama, Fantasy</td>\n",
              "      <td>104</td>\n",
              "      <td>GBP 320000</td>\n",
              "      <td>$ 124241</td>\n",
              "      <td>168.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>2017</td>\n",
              "      <td>6.486938</td>\n",
              "      <td>When a young airman miraculously survives bail...</td>\n",
              "      <td>12</td>\n",
              "      <td>8</td>\n",
              "      <td>Friday</td>\n",
              "      <td>December</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0066921</td>\n",
              "      <td>A Clockwork Orange</td>\n",
              "      <td>2000-03-17</td>\n",
              "      <td>Crime, Drama, Sci-Fi</td>\n",
              "      <td>136</td>\n",
              "      <td>$ 2200000</td>\n",
              "      <td>$ 26903440</td>\n",
              "      <td>1481.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>2000</td>\n",
              "      <td>17.112594</td>\n",
              "      <td>Demonic gang-leader Alex goes on the spree of ...</td>\n",
              "      <td>3</td>\n",
              "      <td>17</td>\n",
              "      <td>Friday</td>\n",
              "      <td>March</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0067962</td>\n",
              "      <td>Warnung vor einer heiligen Nutte</td>\n",
              "      <td>1983-07-15</td>\n",
              "      <td>Comedy, Drama</td>\n",
              "      <td>103</td>\n",
              "      <td>DEM 1100000</td>\n",
              "      <td>$ 8144</td>\n",
              "      <td>12.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>1983</td>\n",
              "      <td>1.147121</td>\n",
              "      <td>Tensions between members of a film crew build ...</td>\n",
              "      <td>7</td>\n",
              "      <td>15</td>\n",
              "      <td>Friday</td>\n",
              "      <td>July</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0077288</td>\n",
              "      <td>La cage aux folles</td>\n",
              "      <td>1980-11-07</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>97</td>\n",
              "      <td>FRF 7000000</td>\n",
              "      <td>$ 20424259</td>\n",
              "      <td>43.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>1980</td>\n",
              "      <td>3.140011</td>\n",
              "      <td>Two gay men living in St. Tropez have their li...</td>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "      <td>Friday</td>\n",
              "      <td>November</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8870</th>\n",
              "      <td>tt6389344</td>\n",
              "      <td>Tri bogatyrya i Morskoy tsar</td>\n",
              "      <td>2017-01-01</td>\n",
              "      <td>Animation, Action, Adventure</td>\n",
              "      <td>75</td>\n",
              "      <td>$ 4000000</td>\n",
              "      <td>$ 13816357</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.468664</td>\n",
              "      <td>No plot overview available</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>January</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8871</th>\n",
              "      <td>tt6569546</td>\n",
              "      <td>Vurdalaki</td>\n",
              "      <td>2017-02-22</td>\n",
              "      <td>Action, Fantasy, Horror</td>\n",
              "      <td>90</td>\n",
              "      <td>RUR 162000000</td>\n",
              "      <td>$ 782438</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2017</td>\n",
              "      <td>9.334447</td>\n",
              "      <td>The story takes place in 18th century russia. ...</td>\n",
              "      <td>2</td>\n",
              "      <td>22</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>February</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8872</th>\n",
              "      <td>tt6574272</td>\n",
              "      <td>Phoenix Forgotten</td>\n",
              "      <td>2017-04-21</td>\n",
              "      <td>Horror, Mystery, Sci-Fi</td>\n",
              "      <td>87</td>\n",
              "      <td>$ 2800000</td>\n",
              "      <td>$ 3697729</td>\n",
              "      <td>68.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>2017</td>\n",
              "      <td>12.560942</td>\n",
              "      <td>20 years after three teenagers disappeared in ...</td>\n",
              "      <td>4</td>\n",
              "      <td>21</td>\n",
              "      <td>Friday</td>\n",
              "      <td>April</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8873</th>\n",
              "      <td>tt6597454</td>\n",
              "      <td>Selfie</td>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>85</td>\n",
              "      <td>EUR 10000</td>\n",
              "      <td>$ 184395</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.313827</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
              "      <td>23</td>\n",
              "      <td>Friday</td>\n",
              "      <td>June</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8874</th>\n",
              "      <td>tt6690310</td>\n",
              "      <td>Fei fan ren wu</td>\n",
              "      <td>2017-04-06</td>\n",
              "      <td>Action, Crime</td>\n",
              "      <td>117</td>\n",
              "      <td>$ 23000000</td>\n",
              "      <td>$ 22757764</td>\n",
              "      <td>8.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2017</td>\n",
              "      <td>2.552316</td>\n",
              "      <td>‘Extraordinary Mission’ is a new Chinese actio...</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>April</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8875 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        imdb_id                             title  ... day_of_week month_name\n",
              "0     tt0035423                    Kate & Leopold  ...      Friday      April\n",
              "1     tt0038733        A Matter of Life and Death  ...      Friday   December\n",
              "2     tt0066921                A Clockwork Orange  ...      Friday      March\n",
              "3     tt0067962  Warnung vor einer heiligen Nutte  ...      Friday       July\n",
              "4     tt0077288                La cage aux folles  ...      Friday   November\n",
              "...         ...                               ...  ...         ...        ...\n",
              "8870  tt6389344      Tri bogatyrya i Morskoy tsar  ...      Sunday    January\n",
              "8871  tt6569546                         Vurdalaki  ...   Wednesday   February\n",
              "8872  tt6574272                 Phoenix Forgotten  ...      Friday      April\n",
              "8873  tt6597454                            Selfie  ...      Friday       June\n",
              "8874  tt6690310                    Fei fan ren wu  ...    Thursday      April\n",
              "\n",
              "[8875 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aue13cO1W4X3"
      },
      "source": [
        "# Calculating profit and ROI\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VHqU7VeFZWAH",
        "outputId": "6224b53e-e644-4585-b527-8f550d0f2704",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "year_df['worlwide_gross_income'] = year_df.loc[:,'worlwide_gross_income'].str.replace('$ ','',regex=False)\n",
        "year_df['worlwide_gross_income'] = year_df.loc[:,'worlwide_gross_income'].apply(pd.to_numeric)\n",
        "year_df['worlwide_gross_income'] = year_df.loc[:,'worlwide_gross_income'].astype(np.int64)\n",
        "year_df = year_df.rename(columns={'worlwide_gross_income':'revenue'})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "76IqhWhsW4X4",
        "scrolled": true
      },
      "source": [
        "new_df = year_df\n",
        "new_df['currency_code'] = new_df['budget'].str.replace(' \\d+', '')\n",
        "new_df.drop(new_df[new_df['currency_code']!='$'].index,inplace=True)\n",
        "new_df.drop(columns='currency_code',inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRIUHGopXGTZ"
      },
      "source": [
        "new_df['budget'] = new_df['budget'].str.replace('$ ','',regex=False)\n",
        "new_df['budget'] = new_df['budget'].apply(pd.to_numeric)\n",
        "new_df['budget'] = new_df['budget'].astype(np.int64)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CqH7ZGP7W4Yj"
      },
      "source": [
        "***\n",
        "**`profit = worldwide gross - budget`**\n",
        "\n",
        "**`ROI = (profit/ budget)* 100`**\n",
        "***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mb1T9uGBW4Yk"
      },
      "source": [
        "new_df['profit'] = new_df['revenue'] - new_df['budget']\n",
        "new_df['ROI'] = (new_df['profit']/ new_df['budget'])*100"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3J7dkS9eW4Wa"
      },
      "source": [
        "# Actor, actress, director, writer and Genre"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o6zEtuhzW4Wb",
        "scrolled": true,
        "outputId": "f2035283-46cb-4f87-dd96-644cbdfd9ca8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "imdb_cast = pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/Group Assignment/cast.csv\", delimiter=\",\",low_memory=False)\n",
        "imdb_cast"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "    .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>imdb_id</th>\n",
              "      <th>imdb_name_id</th>\n",
              "      <th>category</th>\n",
              "      <th>primaryName</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>nm0005690</td>\n",
              "      <td>director</td>\n",
              "      <td>William K.L. Dickson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>nm0005690</td>\n",
              "      <td>director</td>\n",
              "      <td>William K.L. Dickson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0000006</td>\n",
              "      <td>nm0005690</td>\n",
              "      <td>director</td>\n",
              "      <td>William K.L. Dickson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000007</td>\n",
              "      <td>nm0005690</td>\n",
              "      <td>director</td>\n",
              "      <td>William K.L. Dickson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0000008</td>\n",
              "      <td>nm0005690</td>\n",
              "      <td>director</td>\n",
              "      <td>William K.L. Dickson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811316</th>\n",
              "      <td>tt9916856</td>\n",
              "      <td>nm10538650</td>\n",
              "      <td>actress</td>\n",
              "      <td>Anna Fitoussi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811317</th>\n",
              "      <td>tt9916856</td>\n",
              "      <td>nm10538646</td>\n",
              "      <td>actor</td>\n",
              "      <td>Andreas Demmel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811318</th>\n",
              "      <td>tt9916856</td>\n",
              "      <td>nm10538647</td>\n",
              "      <td>actress</td>\n",
              "      <td>Kathrin Knöpfle</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811319</th>\n",
              "      <td>tt9916856</td>\n",
              "      <td>nm10538645</td>\n",
              "      <td>director</td>\n",
              "      <td>Johan Planefeldt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811320</th>\n",
              "      <td>tt9916856</td>\n",
              "      <td>nm6951431</td>\n",
              "      <td>writer</td>\n",
              "      <td>Johan Planefeldt</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23811321 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            imdb_id imdb_name_id  category           primaryName\n",
              "0         tt0000001    nm0005690  director  William K.L. Dickson\n",
              "1         tt0000005    nm0005690  director  William K.L. Dickson\n",
              "2         tt0000006    nm0005690  director  William K.L. Dickson\n",
              "3         tt0000007    nm0005690  director  William K.L. Dickson\n",
              "4         tt0000008    nm0005690  director  William K.L. Dickson\n",
              "...             ...          ...       ...                   ...\n",
              "23811316  tt9916856   nm10538650   actress         Anna Fitoussi\n",
              "23811317  tt9916856   nm10538646     actor        Andreas Demmel\n",
              "23811318  tt9916856   nm10538647   actress       Kathrin Knöpfle\n",
              "23811319  tt9916856   nm10538645  director      Johan Planefeldt\n",
              "23811320  tt9916856    nm6951431    writer      Johan Planefeldt\n",
              "\n",
              "[23811321 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vm4jMUNUW4Wi",
        "scrolled": true,
        "outputId": "3f14f176-cc7e-4dca-fedb-b2b0dc17ae0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "cast = pd.merge(imdb_cast,new_df,on='imdb_id',how='inner').loc[:,('imdb_id','imdb_name_id','category','primaryName','popularity','revenue','profit')]\n",
        "cast['revenue1'] = cast['revenue']\n",
        "cast"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>imdb_id</th>\n",
              "      <th>imdb_name_id</th>\n",
              "      <th>category</th>\n",
              "      <th>primaryName</th>\n",
              "      <th>popularity</th>\n",
              "      <th>revenue</th>\n",
              "      <th>profit</th>\n",
              "      <th>revenue1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0162661</td>\n",
              "      <td>nm0410331</td>\n",
              "      <td>writer</td>\n",
              "      <td>Washington Irving</td>\n",
              "      <td>11.072361</td>\n",
              "      <td>206071502</td>\n",
              "      <td>106071502</td>\n",
              "      <td>206071502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0162661</td>\n",
              "      <td>nm0002091</td>\n",
              "      <td>actor</td>\n",
              "      <td>Michael Gambon</td>\n",
              "      <td>11.072361</td>\n",
              "      <td>206071502</td>\n",
              "      <td>106071502</td>\n",
              "      <td>206071502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0162661</td>\n",
              "      <td>nm0001669</td>\n",
              "      <td>actress</td>\n",
              "      <td>Miranda Richardson</td>\n",
              "      <td>11.072361</td>\n",
              "      <td>206071502</td>\n",
              "      <td>106071502</td>\n",
              "      <td>206071502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0162661</td>\n",
              "      <td>nm0000318</td>\n",
              "      <td>director</td>\n",
              "      <td>Tim Burton</td>\n",
              "      <td>11.072361</td>\n",
              "      <td>206071502</td>\n",
              "      <td>106071502</td>\n",
              "      <td>206071502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0162661</td>\n",
              "      <td>nm0000136</td>\n",
              "      <td>actor</td>\n",
              "      <td>Johnny Depp</td>\n",
              "      <td>11.072361</td>\n",
              "      <td>206071502</td>\n",
              "      <td>106071502</td>\n",
              "      <td>206071502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47883</th>\n",
              "      <td>tt5777418</td>\n",
              "      <td>nm5815221</td>\n",
              "      <td>writer</td>\n",
              "      <td>Paco Bardales</td>\n",
              "      <td>0.122363</td>\n",
              "      <td>1619054</td>\n",
              "      <td>1319054</td>\n",
              "      <td>1619054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47884</th>\n",
              "      <td>tt5777418</td>\n",
              "      <td>nm8176825</td>\n",
              "      <td>actor</td>\n",
              "      <td>Bruno Espejo</td>\n",
              "      <td>0.122363</td>\n",
              "      <td>1619054</td>\n",
              "      <td>1319054</td>\n",
              "      <td>1619054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47885</th>\n",
              "      <td>tt5777418</td>\n",
              "      <td>nm8189535</td>\n",
              "      <td>actress</td>\n",
              "      <td>Lupita Mora</td>\n",
              "      <td>0.122363</td>\n",
              "      <td>1619054</td>\n",
              "      <td>1319054</td>\n",
              "      <td>1619054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47886</th>\n",
              "      <td>tt5777418</td>\n",
              "      <td>nm8189508</td>\n",
              "      <td>actor</td>\n",
              "      <td>Eduardo Ramos</td>\n",
              "      <td>0.122363</td>\n",
              "      <td>1619054</td>\n",
              "      <td>1319054</td>\n",
              "      <td>1619054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47887</th>\n",
              "      <td>tt5777418</td>\n",
              "      <td>nm0349609</td>\n",
              "      <td>actor</td>\n",
              "      <td>Willy Gutiérrez</td>\n",
              "      <td>0.122363</td>\n",
              "      <td>1619054</td>\n",
              "      <td>1319054</td>\n",
              "      <td>1619054</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>47888 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         imdb_id imdb_name_id  category  ...    revenue     profit   revenue1\n",
              "0      tt0162661    nm0410331    writer  ...  206071502  106071502  206071502\n",
              "1      tt0162661    nm0002091     actor  ...  206071502  106071502  206071502\n",
              "2      tt0162661    nm0001669   actress  ...  206071502  106071502  206071502\n",
              "3      tt0162661    nm0000318  director  ...  206071502  106071502  206071502\n",
              "4      tt0162661    nm0000136     actor  ...  206071502  106071502  206071502\n",
              "...          ...          ...       ...  ...        ...        ...        ...\n",
              "47883  tt5777418    nm5815221    writer  ...    1619054    1319054    1619054\n",
              "47884  tt5777418    nm8176825     actor  ...    1619054    1319054    1619054\n",
              "47885  tt5777418    nm8189535   actress  ...    1619054    1319054    1619054\n",
              "47886  tt5777418    nm8189508     actor  ...    1619054    1319054    1619054\n",
              "47887  tt5777418    nm0349609     actor  ...    1619054    1319054    1619054\n",
              "\n",
              "[47888 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FVw3htDakwpW",
        "outputId": "c5ae0590-f9aa-4500-ca2f-94a1bcc02733",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "cast_data = cast.loc[:,('imdb_id','imdb_name_id','category','popularity','profit')].groupby('imdb_name_id',as_index=False).agg({'imdb_id':np.size,'popularity':np.mean,'profit':np.mean}).rename(columns={'imdb_id':'count'})\n",
        "cast_data = cast_data[cast_data['count'] > 1]\n",
        "cast_data = pd.merge(new_df[['imdb_id','year']],cast.loc[:,('imdb_id','imdb_name_id','category','primaryName')],how='inner',on='imdb_id').merge(cast_data,how='inner',on='imdb_name_id')\n",
        "cast_data = cast_data[(cast_data['year'] >= 1980) & (cast_data['year'] <= 2017)]\n",
        "cast_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\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>imdb_id</th>\n",
              "      <th>year</th>\n",
              "      <th>imdb_name_id</th>\n",
              "      <th>category</th>\n",
              "      <th>primaryName</th>\n",
              "      <th>count</th>\n",
              "      <th>popularity</th>\n",
              "      <th>profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0035423</td>\n",
              "      <td>2002</td>\n",
              "      <td>nm0000212</td>\n",
              "      <td>actress</td>\n",
              "      <td>Meg Ryan</td>\n",
              "      <td>19</td>\n",
              "      <td>7.776708</td>\n",
              "      <td>4.180543e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0090660</td>\n",
              "      <td>1986</td>\n",
              "      <td>nm0000212</td>\n",
              "      <td>actress</td>\n",
              "      <td>Meg Ryan</td>\n",
              "      <td>19</td>\n",
              "      <td>7.776708</td>\n",
              "      <td>4.180543e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0093260</td>\n",
              "      <td>1987</td>\n",
              "      <td>nm0000212</td>\n",
              "      <td>actress</td>\n",
              "      <td>Meg Ryan</td>\n",
              "      <td>19</td>\n",
              "      <td>7.776708</td>\n",
              "      <td>4.180543e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0098635</td>\n",
              "      <td>1990</td>\n",
              "      <td>nm0000212</td>\n",
              "      <td>actress</td>\n",
              "      <td>Meg Ryan</td>\n",
              "      <td>19</td>\n",
              "      <td>7.776708</td>\n",
              "      <td>4.180543e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0099892</td>\n",
              "      <td>1990</td>\n",
              "      <td>nm0000212</td>\n",
              "      <td>actress</td>\n",
              "      <td>Meg Ryan</td>\n",
              "      <td>19</td>\n",
              "      <td>7.776708</td>\n",
              "      <td>4.180543e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34734</th>\n",
              "      <td>tt5909128</td>\n",
              "      <td>2016</td>\n",
              "      <td>nm2672655</td>\n",
              "      <td>writer</td>\n",
              "      <td>Oleg Malovichko</td>\n",
              "      <td>2</td>\n",
              "      <td>5.938218</td>\n",
              "      <td>-3.398430e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34735</th>\n",
              "      <td>tt5842624</td>\n",
              "      <td>2016</td>\n",
              "      <td>nm2108643</td>\n",
              "      <td>actor</td>\n",
              "      <td>Eddie Peng</td>\n",
              "      <td>2</td>\n",
              "      <td>24.285211</td>\n",
              "      <td>6.798467e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34736</th>\n",
              "      <td>tt6044910</td>\n",
              "      <td>2016</td>\n",
              "      <td>nm2108643</td>\n",
              "      <td>actor</td>\n",
              "      <td>Eddie Peng</td>\n",
              "      <td>2</td>\n",
              "      <td>24.285211</td>\n",
              "      <td>6.798467e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34737</th>\n",
              "      <td>tt5842624</td>\n",
              "      <td>2016</td>\n",
              "      <td>nm8458426</td>\n",
              "      <td>writer</td>\n",
              "      <td>Wai Ching Tam</td>\n",
              "      <td>2</td>\n",
              "      <td>24.285211</td>\n",
              "      <td>6.798467e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34738</th>\n",
              "      <td>tt6044910</td>\n",
              "      <td>2016</td>\n",
              "      <td>nm8458426</td>\n",
              "      <td>writer</td>\n",
              "      <td>Wai Ching Tam</td>\n",
              "      <td>2</td>\n",
              "      <td>24.285211</td>\n",
              "      <td>6.798467e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>34739 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         imdb_id  year imdb_name_id  ... count popularity        profit\n",
              "0      tt0035423  2002    nm0000212  ...    19   7.776708  4.180543e+07\n",
              "1      tt0090660  1986    nm0000212  ...    19   7.776708  4.180543e+07\n",
              "2      tt0093260  1987    nm0000212  ...    19   7.776708  4.180543e+07\n",
              "3      tt0098635  1990    nm0000212  ...    19   7.776708  4.180543e+07\n",
              "4      tt0099892  1990    nm0000212  ...    19   7.776708  4.180543e+07\n",
              "...          ...   ...          ...  ...   ...        ...           ...\n",
              "34734  tt5909128  2016    nm2672655  ...     2   5.938218 -3.398430e+06\n",
              "34735  tt5842624  2016    nm2108643  ...     2  24.285211  6.798467e+07\n",
              "34736  tt6044910  2016    nm2108643  ...     2  24.285211  6.798467e+07\n",
              "34737  tt5842624  2016    nm8458426  ...     2  24.285211  6.798467e+07\n",
              "34738  tt6044910  2016    nm8458426  ...     2  24.285211  6.798467e+07\n",
              "\n",
              "[34739 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cR9B1sk4o2Uc"
      },
      "source": [
        "cast_for_plot = cast_data.drop(columns='imdb_id').drop_duplicates()\n",
        "cast_for_plot.to_csv('/content/drive/My Drive/Colab Notebooks/Group Assignment/cast_data.csv',index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M4dBbSnUqcq_"
      },
      "source": [
        "## Collaboration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dudkmAUaqgm1"
      },
      "source": [
        "### Actor vs actress"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "skXeAwOcqi9F",
        "outputId": "205e34be-93b2-44b7-fd45-530e8b67019c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "actor_actress = pd.merge(cast[cast['category']=='actor'].loc[:,('imdb_id','imdb_name_id','primaryName')],cast[cast['category']=='actress'].loc[:,('imdb_id','imdb_name_id','primaryName','profit')],how='inner',on='imdb_id').groupby(['primaryName_x','primaryName_y'],as_index=False).agg({'imdb_id':np.size,'profit':np.mean}).rename(columns={'primaryName_x':'actor','primaryName_y':'actress','imdb_id':'count'})\n",
        "actor_actress = actor_actress[actor_actress['count'] >= 5].sort_values('count',ascending=False).reset_index(drop=True)\n",
        "actor_actress"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>actor</th>\n",
              "      <th>actress</th>\n",
              "      <th>count</th>\n",
              "      <th>profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>George Clooney</td>\n",
              "      <td>Julia Roberts</td>\n",
              "      <td>7</td>\n",
              "      <td>9.954275e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Daniel Radcliffe</td>\n",
              "      <td>Emma Watson</td>\n",
              "      <td>6</td>\n",
              "      <td>8.136241e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Johnny Depp</td>\n",
              "      <td>Helena Bonham Carter</td>\n",
              "      <td>6</td>\n",
              "      <td>2.591897e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Rupert Grint</td>\n",
              "      <td>Emma Watson</td>\n",
              "      <td>6</td>\n",
              "      <td>8.136241e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Eric Stuart</td>\n",
              "      <td>Rachael Lillis</td>\n",
              "      <td>5</td>\n",
              "      <td>6.865795e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Eric Stuart</td>\n",
              "      <td>Veronica Taylor</td>\n",
              "      <td>5</td>\n",
              "      <td>6.865795e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Robert Pattinson</td>\n",
              "      <td>Kristen Stewart</td>\n",
              "      <td>5</td>\n",
              "      <td>5.920635e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Sam Rockwell</td>\n",
              "      <td>Drew Barrymore</td>\n",
              "      <td>5</td>\n",
              "      <td>1.499766e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              actor               actress  count        profit\n",
              "0    George Clooney         Julia Roberts      7  9.954275e+07\n",
              "1  Daniel Radcliffe           Emma Watson      6  8.136241e+08\n",
              "2       Johnny Depp  Helena Bonham Carter      6  2.591897e+08\n",
              "3      Rupert Grint           Emma Watson      6  8.136241e+08\n",
              "4       Eric Stuart        Rachael Lillis      5  6.865795e+07\n",
              "5       Eric Stuart       Veronica Taylor      5  6.865795e+07\n",
              "6  Robert Pattinson       Kristen Stewart      5  5.920635e+08\n",
              "7      Sam Rockwell        Drew Barrymore      5  1.499766e+06"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8kcn4X5zql2X"
      },
      "source": [
        "### Actor vs directors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XyFkCD5Kqpjx",
        "outputId": "5776bfa0-a1f6-4663-c0d2-ef86b6a12951",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "source": [
        "actor_director = pd.merge(cast[cast['category']=='actor'].loc[:,('imdb_id','imdb_name_id','primaryName','profit')],cast[cast['category']=='director'].loc[:,('imdb_id','imdb_name_id','primaryName')],how='inner',on='imdb_id').groupby(['primaryName_x','primaryName_y'],as_index=False).agg({'imdb_id':np.size,'profit':np.mean}).rename(columns={'primaryName_x':'actor','primaryName_y':'director','imdb_id':'count'})\n",
        "actor_director = actor_director[actor_director['count'] >= 5].sort_values('count',ascending=False).reset_index(drop=True)\n",
        "actor_director.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>actor</th>\n",
              "      <th>director</th>\n",
              "      <th>count</th>\n",
              "      <th>profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adam Sandler</td>\n",
              "      <td>Dennis Dugan</td>\n",
              "      <td>8</td>\n",
              "      <td>1.256039e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Johnny Depp</td>\n",
              "      <td>Tim Burton</td>\n",
              "      <td>8</td>\n",
              "      <td>1.982080e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mel Gibson</td>\n",
              "      <td>Richard Donner</td>\n",
              "      <td>6</td>\n",
              "      <td>1.512086e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Ethan Hawke</td>\n",
              "      <td>Richard Linklater</td>\n",
              "      <td>6</td>\n",
              "      <td>8.158182e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>George Clooney</td>\n",
              "      <td>Steven Soderbergh</td>\n",
              "      <td>6</td>\n",
              "      <td>1.385729e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Ian McKellen</td>\n",
              "      <td>Peter Jackson</td>\n",
              "      <td>6</td>\n",
              "      <td>8.193179e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Antonio Banderas</td>\n",
              "      <td>Robert Rodriguez</td>\n",
              "      <td>6</td>\n",
              "      <td>7.369857e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Tom Hanks</td>\n",
              "      <td>Ron Howard</td>\n",
              "      <td>5</td>\n",
              "      <td>2.958502e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Russell Crowe</td>\n",
              "      <td>Ridley Scott</td>\n",
              "      <td>5</td>\n",
              "      <td>1.397367e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Robert De Niro</td>\n",
              "      <td>Martin Scorsese</td>\n",
              "      <td>5</td>\n",
              "      <td>4.424447e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              actor           director  count        profit\n",
              "0      Adam Sandler       Dennis Dugan      8  1.256039e+08\n",
              "1       Johnny Depp         Tim Burton      8  1.982080e+08\n",
              "2        Mel Gibson     Richard Donner      6  1.512086e+08\n",
              "3       Ethan Hawke  Richard Linklater      6  8.158182e+06\n",
              "4    George Clooney  Steven Soderbergh      6  1.385729e+08\n",
              "5      Ian McKellen      Peter Jackson      6  8.193179e+08\n",
              "6  Antonio Banderas   Robert Rodriguez      6  7.369857e+07\n",
              "7         Tom Hanks         Ron Howard      5  2.958502e+08\n",
              "8     Russell Crowe       Ridley Scott      5  1.397367e+08\n",
              "9    Robert De Niro    Martin Scorsese      5  4.424447e+07"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ErLbKvNqqme"
      },
      "source": [
        "### Actor vs writer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0H-5i6c7qtNX",
        "outputId": "bfd78a85-9f20-480d-ef51-152e454fb8b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "source": [
        "actor_writer = pd.merge(cast[cast['category']=='actor'].loc[:,('imdb_id','imdb_name_id','primaryName','profit')],cast[cast['category']=='writer'].loc[:,('imdb_id','imdb_name_id','primaryName')],how='inner',on='imdb_id').groupby(['primaryName_x','primaryName_y'],as_index=False).agg({'imdb_id':np.size,'profit':np.mean}).rename(columns={'primaryName_x':'actor','primaryName_y':'writer','imdb_id':'count'})\n",
        "actor_writer = actor_writer[actor_writer['count'] >= 5].sort_values('count',ascending=False).reset_index(drop=True)\n",
        "actor_writer.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>actor</th>\n",
              "      <th>writer</th>\n",
              "      <th>count</th>\n",
              "      <th>profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adam Sandler</td>\n",
              "      <td>Tim Herlihy</td>\n",
              "      <td>10</td>\n",
              "      <td>1.065890e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Daniel Radcliffe</td>\n",
              "      <td>J.K. Rowling</td>\n",
              "      <td>7</td>\n",
              "      <td>8.192618e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Rupert Grint</td>\n",
              "      <td>J.K. Rowling</td>\n",
              "      <td>7</td>\n",
              "      <td>8.192618e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Ian McKellen</td>\n",
              "      <td>Philippa Boyens</td>\n",
              "      <td>6</td>\n",
              "      <td>8.193179e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Vin Diesel</td>\n",
              "      <td>Gary Scott Thompson</td>\n",
              "      <td>6</td>\n",
              "      <td>6.475866e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Rupert Grint</td>\n",
              "      <td>Steve Kloves</td>\n",
              "      <td>6</td>\n",
              "      <td>8.243753e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Robert Englund</td>\n",
              "      <td>Wes Craven</td>\n",
              "      <td>6</td>\n",
              "      <td>3.810193e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Paul Walker</td>\n",
              "      <td>Gary Scott Thompson</td>\n",
              "      <td>6</td>\n",
              "      <td>5.099775e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Leonard Nimoy</td>\n",
              "      <td>Gene Roddenberry</td>\n",
              "      <td>6</td>\n",
              "      <td>8.981277e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Johnny Depp</td>\n",
              "      <td>Terry Rossio</td>\n",
              "      <td>6</td>\n",
              "      <td>5.704197e+08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              actor               writer  count        profit\n",
              "0      Adam Sandler          Tim Herlihy     10  1.065890e+08\n",
              "1  Daniel Radcliffe         J.K. Rowling      7  8.192618e+08\n",
              "2      Rupert Grint         J.K. Rowling      7  8.192618e+08\n",
              "3      Ian McKellen      Philippa Boyens      6  8.193179e+08\n",
              "4        Vin Diesel  Gary Scott Thompson      6  6.475866e+08\n",
              "5      Rupert Grint         Steve Kloves      6  8.243753e+08\n",
              "6    Robert Englund           Wes Craven      6  3.810193e+07\n",
              "7       Paul Walker  Gary Scott Thompson      6  5.099775e+08\n",
              "8     Leonard Nimoy     Gene Roddenberry      6  8.981277e+07\n",
              "9       Johnny Depp         Terry Rossio      6  5.704197e+08"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J6SH_kuxqt3x"
      },
      "source": [
        "### Actress vs director"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MbWhTBYEqx13",
        "outputId": "45607ea9-5fd8-45eb-c4d5-d79665918e16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "source": [
        "actress_director = pd.merge(cast[cast['category']=='actress'].loc[:,('imdb_id','imdb_name_id','primaryName','profit')],cast[cast['category']=='director'].loc[:,('imdb_id','imdb_name_id','primaryName')],how='inner',on='imdb_id').groupby(['primaryName_x','primaryName_y'],as_index=False).agg({'imdb_id':np.size,'profit':np.mean}).rename(columns={'primaryName_x':'actress','primaryName_y':'director','imdb_id':'count'})\n",
        "actress_director = actress_director[actress_director['count'] >= 5].sort_values('count',ascending=False).reset_index(drop=True)\n",
        "actress_director.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>actress</th>\n",
              "      <th>director</th>\n",
              "      <th>count</th>\n",
              "      <th>profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Helena Bonham Carter</td>\n",
              "      <td>Tim Burton</td>\n",
              "      <td>6</td>\n",
              "      <td>2.813155e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mia Farrow</td>\n",
              "      <td>Woody Allen</td>\n",
              "      <td>6</td>\n",
              "      <td>9.148305e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Veronica Taylor</td>\n",
              "      <td>Kunihiko Yuyama</td>\n",
              "      <td>6</td>\n",
              "      <td>7.453984e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Veronica Taylor</td>\n",
              "      <td>Michael Haigney</td>\n",
              "      <td>6</td>\n",
              "      <td>7.453984e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Catherine Keener</td>\n",
              "      <td>Nicole Holofcener</td>\n",
              "      <td>5</td>\n",
              "      <td>7.012507e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Jessica Alba</td>\n",
              "      <td>Robert Rodriguez</td>\n",
              "      <td>5</td>\n",
              "      <td>3.606144e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Milla Jovovich</td>\n",
              "      <td>Paul W.S. Anderson</td>\n",
              "      <td>5</td>\n",
              "      <td>1.629779e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Rachael Lillis</td>\n",
              "      <td>Kunihiko Yuyama</td>\n",
              "      <td>5</td>\n",
              "      <td>6.865795e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Rachael Lillis</td>\n",
              "      <td>Michael Haigney</td>\n",
              "      <td>5</td>\n",
              "      <td>6.865795e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Rica Matsumoto</td>\n",
              "      <td>Kunihiko Yuyama</td>\n",
              "      <td>5</td>\n",
              "      <td>6.271887e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                actress            director  count        profit\n",
              "0  Helena Bonham Carter          Tim Burton      6  2.813155e+08\n",
              "1            Mia Farrow         Woody Allen      6  9.148305e+05\n",
              "2       Veronica Taylor     Kunihiko Yuyama      6  7.453984e+07\n",
              "3       Veronica Taylor     Michael Haigney      6  7.453984e+07\n",
              "4      Catherine Keener   Nicole Holofcener      5  7.012507e+06\n",
              "5          Jessica Alba    Robert Rodriguez      5  3.606144e+07\n",
              "6        Milla Jovovich  Paul W.S. Anderson      5  1.629779e+08\n",
              "7        Rachael Lillis     Kunihiko Yuyama      5  6.865795e+07\n",
              "8        Rachael Lillis     Michael Haigney      5  6.865795e+07\n",
              "9        Rica Matsumoto     Kunihiko Yuyama      5  6.271887e+07"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-U9S71nPqyZy"
      },
      "source": [
        "### Actress vs writer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Crz8-UDhq1Dq",
        "outputId": "6b7e1fec-5047-4044-c719-2aa3d489a662",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "source": [
        "actress_writer = pd.merge(cast[cast['category']=='actress'].loc[:,('imdb_id','imdb_name_id','primaryName','profit')],cast[cast['category']=='writer'].loc[:,('imdb_id','imdb_name_id','primaryName')],how='inner',on='imdb_id').groupby(['primaryName_x','primaryName_y'],as_index=False).agg({'imdb_id':np.size,'profit':np.mean}).rename(columns={'primaryName_x':'actress','primaryName_y':'writer','imdb_id':'count'})\n",
        "actress_writer = actress_writer[actress_writer['count'] >= 5].sort_values('count',ascending=False).reset_index(drop=True)\n",
        "actress_writer.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        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>actress</th>\n",
              "      <th>writer</th>\n",
              "      <th>count</th>\n",
              "      <th>profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Emma Watson</td>\n",
              "      <td>J.K. Rowling</td>\n",
              "      <td>6</td>\n",
              "      <td>8.136241e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Veronica Taylor</td>\n",
              "      <td>Norman J. Grossfeld</td>\n",
              "      <td>6</td>\n",
              "      <td>7.453984e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Veronica Taylor</td>\n",
              "      <td>Takeshi Shudo</td>\n",
              "      <td>6</td>\n",
              "      <td>7.453984e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Emma Watson</td>\n",
              "      <td>Steve Kloves</td>\n",
              "      <td>5</td>\n",
              "      <td>8.186329e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Jamie Lee Curtis</td>\n",
              "      <td>Debra Hill</td>\n",
              "      <td>5</td>\n",
              "      <td>3.057284e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Kristen Stewart</td>\n",
              "      <td>Melissa Rosenberg</td>\n",
              "      <td>5</td>\n",
              "      <td>5.920635e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Kristen Stewart</td>\n",
              "      <td>Stephenie Meyer</td>\n",
              "      <td>5</td>\n",
              "      <td>5.920635e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Rachael Lillis</td>\n",
              "      <td>Norman J. Grossfeld</td>\n",
              "      <td>5</td>\n",
              "      <td>6.865795e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Rachael Lillis</td>\n",
              "      <td>Takeshi Shudo</td>\n",
              "      <td>5</td>\n",
              "      <td>6.865795e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Rica Matsumoto</td>\n",
              "      <td>Norman J. Grossfeld</td>\n",
              "      <td>5</td>\n",
              "      <td>6.271887e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            actress               writer  count        profit\n",
              "0       Emma Watson         J.K. Rowling      6  8.136241e+08\n",
              "1   Veronica Taylor  Norman J. Grossfeld      6  7.453984e+07\n",
              "2   Veronica Taylor        Takeshi Shudo      6  7.453984e+07\n",
              "3       Emma Watson         Steve Kloves      5  8.186329e+08\n",
              "4  Jamie Lee Curtis           Debra Hill      5  3.057284e+07\n",
              "5   Kristen Stewart    Melissa Rosenberg      5  5.920635e+08\n",
              "6   Kristen Stewart      Stephenie Meyer      5  5.920635e+08\n",
              "7    Rachael Lillis  Norman J. Grossfeld      5  6.865795e+07\n",
              "8    Rachael Lillis        Takeshi Shudo      5  6.865795e+07\n",
              "9    Rica Matsumoto  Norman J. Grossfeld      5  6.271887e+07"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rqeWms0Vq358"
      },
      "source": [
        "### Director vs Writer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yTrgo3avrBMx",
        "outputId": "e6a6dc51-cb15-4c4b-847a-3018caf1cb0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "source": [
        "director_writer = pd.merge(cast[cast['category']=='director'].loc[:,('imdb_id','imdb_name_id','primaryName','profit')],cast[cast['category']=='writer'].loc[:,('imdb_id','imdb_name_id','primaryName')],how='inner',on='imdb_id').groupby(['primaryName_x','primaryName_y'],as_index=False).agg({'imdb_id':np.size,'profit':np.mean}).rename(columns={'primaryName_x':'director','primaryName_y':'writer','imdb_id':'count'})\n",
        "director_writer = director_writer[director_writer['count'] >= 5].sort_values('count',ascending=False).reset_index(drop=True)\n",
        "director_writer.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\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>director</th>\n",
              "      <th>writer</th>\n",
              "      <th>count</th>\n",
              "      <th>profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Peter Jackson</td>\n",
              "      <td>Fran Walsh</td>\n",
              "      <td>11</td>\n",
              "      <td>4.802452e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Ang Lee</td>\n",
              "      <td>James Schamus</td>\n",
              "      <td>8</td>\n",
              "      <td>3.690469e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Peter Jackson</td>\n",
              "      <td>Philippa Boyens</td>\n",
              "      <td>8</td>\n",
              "      <td>6.610058e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Kunihiko Yuyama</td>\n",
              "      <td>Norman J. Grossfeld</td>\n",
              "      <td>6</td>\n",
              "      <td>7.453984e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Peter Jackson</td>\n",
              "      <td>J.R.R. Tolkien</td>\n",
              "      <td>6</td>\n",
              "      <td>8.193179e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Michael Haigney</td>\n",
              "      <td>Takeshi Shudo</td>\n",
              "      <td>6</td>\n",
              "      <td>7.453984e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Michael Haigney</td>\n",
              "      <td>Norman J. Grossfeld</td>\n",
              "      <td>6</td>\n",
              "      <td>7.453984e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Kunihiko Yuyama</td>\n",
              "      <td>Takeshi Shudo</td>\n",
              "      <td>6</td>\n",
              "      <td>7.453984e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>James Ivory</td>\n",
              "      <td>Ruth Prawer Jhabvala</td>\n",
              "      <td>6</td>\n",
              "      <td>-2.581750e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Danny Boyle</td>\n",
              "      <td>John Hodge</td>\n",
              "      <td>6</td>\n",
              "      <td>2.117331e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          director                writer  count        profit\n",
              "0    Peter Jackson            Fran Walsh     11  4.802452e+08\n",
              "1          Ang Lee         James Schamus      8  3.690469e+07\n",
              "2    Peter Jackson       Philippa Boyens      8  6.610058e+08\n",
              "3  Kunihiko Yuyama   Norman J. Grossfeld      6  7.453984e+07\n",
              "4    Peter Jackson        J.R.R. Tolkien      6  8.193179e+08\n",
              "5  Michael Haigney         Takeshi Shudo      6  7.453984e+07\n",
              "6  Michael Haigney   Norman J. Grossfeld      6  7.453984e+07\n",
              "7  Kunihiko Yuyama         Takeshi Shudo      6  7.453984e+07\n",
              "8      James Ivory  Ruth Prawer Jhabvala      6 -2.581750e+06\n",
              "9      Danny Boyle            John Hodge      6  2.117331e+07"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2GLqE3hzqCPt"
      },
      "source": [
        "## Popularity"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5dSTWZS-W4Wm"
      },
      "source": [
        "### actor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "arOqq4VTW4Wn",
        "scrolled": true,
        "outputId": "d7cdf144-8aeb-47e4-b971-9ec0214f5e36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "actor = cast[cast['category'] == 'actor'].loc[:,('imdb_name_id','imdb_id','popularity')].groupby('imdb_name_id').agg({'popularity':np.mean,'imdb_id':np.size}).reset_index().rename(columns={'popularity':'actor_pop','imdb_id':'actors_in_movie'})\n",
        "actor = pd.merge(cast.loc[:,('imdb_id','imdb_name_id')],actor,how='inner',on='imdb_name_id').rename(columns={'imdb_name_id':'actor_id'})\n",
        "actor.drop(actor[actor['actors_in_movie'] == 1].index,inplace=True)\n",
        "actor"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>imdb_id</th>\n",
              "      <th>actor_id</th>\n",
              "      <th>actor_pop</th>\n",
              "      <th>actors_in_movie</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0162661</td>\n",
              "      <td>nm0002091</td>\n",
              "      <td>12.361554</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0340012</td>\n",
              "      <td>nm0002091</td>\n",
              "      <td>12.361554</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0280707</td>\n",
              "      <td>nm0002091</td>\n",
              "      <td>12.361554</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0117002</td>\n",
              "      <td>nm0002091</td>\n",
              "      <td>12.361554</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt1441951</td>\n",
              "      <td>nm0002091</td>\n",
              "      <td>12.361554</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18926</th>\n",
              "      <td>tt4457820</td>\n",
              "      <td>nm0069783</td>\n",
              "      <td>2.388444</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18964</th>\n",
              "      <td>tt1853739</td>\n",
              "      <td>nm1410105</td>\n",
              "      <td>5.907742</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18965</th>\n",
              "      <td>tt2383068</td>\n",
              "      <td>nm1410105</td>\n",
              "      <td>5.907742</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18983</th>\n",
              "      <td>tt0390384</td>\n",
              "      <td>nm1503403</td>\n",
              "      <td>7.578442</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18984</th>\n",
              "      <td>tt2084989</td>\n",
              "      <td>nm1503403</td>\n",
              "      <td>7.578442</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14534 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         imdb_id   actor_id  actor_pop  actors_in_movie\n",
              "0      tt0162661  nm0002091  12.361554                8\n",
              "1      tt0340012  nm0002091  12.361554                8\n",
              "2      tt0280707  nm0002091  12.361554                8\n",
              "3      tt0117002  nm0002091  12.361554                8\n",
              "4      tt1441951  nm0002091  12.361554                8\n",
              "...          ...        ...        ...              ...\n",
              "18926  tt4457820  nm0069783   2.388444                2\n",
              "18964  tt1853739  nm1410105   5.907742                2\n",
              "18965  tt2383068  nm1410105   5.907742                2\n",
              "18983  tt0390384  nm1503403   7.578442                2\n",
              "18984  tt2084989  nm1503403   7.578442                2\n",
              "\n",
              "[14534 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LIpmHDE4W4Ws",
        "scrolled": true,
        "outputId": "ee455feb-c966-4df0-c7a6-f154265ff2ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "actor_pop = actor.groupby('imdb_id',as_index=False).agg({'actor_pop':sum,'actor_id':np.size}).rename(columns={'actor_id':'actor_movie_count'})\n",
        "actor_pop['actor_movie_pop'] = actor_pop['actor_pop'] / actor_pop['actor_movie_count']\n",
        "actor_pop.drop(['actor_pop'],axis=1,inplace=True)\n",
        "actor_pop"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>imdb_id</th>\n",
              "      <th>actor_movie_count</th>\n",
              "      <th>actor_movie_pop</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0035423</td>\n",
              "      <td>3</td>\n",
              "      <td>8.748445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0066921</td>\n",
              "      <td>2</td>\n",
              "      <td>9.998665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0077651</td>\n",
              "      <td>1</td>\n",
              "      <td>9.634168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0078767</td>\n",
              "      <td>2</td>\n",
              "      <td>8.030415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0079116</td>\n",
              "      <td>2</td>\n",
              "      <td>12.406851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6401</th>\n",
              "      <td>tt6070530</td>\n",
              "      <td>1</td>\n",
              "      <td>0.840625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6402</th>\n",
              "      <td>tt6083388</td>\n",
              "      <td>3</td>\n",
              "      <td>3.947110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6403</th>\n",
              "      <td>tt6113488</td>\n",
              "      <td>1</td>\n",
              "      <td>7.750526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6404</th>\n",
              "      <td>tt6265828</td>\n",
              "      <td>1</td>\n",
              "      <td>7.920034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6405</th>\n",
              "      <td>tt6389344</td>\n",
              "      <td>4</td>\n",
              "      <td>2.559049</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6406 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        imdb_id  actor_movie_count  actor_movie_pop\n",
              "0     tt0035423                  3         8.748445\n",
              "1     tt0066921                  2         9.998665\n",
              "2     tt0077651                  1         9.634168\n",
              "3     tt0078767                  2         8.030415\n",
              "4     tt0079116                  2        12.406851\n",
              "...         ...                ...              ...\n",
              "6401  tt6070530                  1         0.840625\n",
              "6402  tt6083388                  3         3.947110\n",
              "6403  tt6113488                  1         7.750526\n",
              "6404  tt6265828                  1         7.920034\n",
              "6405  tt6389344                  4         2.559049\n",
              "\n",
              "[6406 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FZaTDHFSW4Wx"
      },
      "source": [
        "### actress"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "em0INlFrW4Wy",
        "scrolled": true,
        "outputId": "bc786e3c-6c65-4d7c-d8e0-565643e767a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "actress = cast[cast['category'] == 'actress'].loc[:,('imdb_name_id','imdb_id','popularity')].groupby('imdb_name_id').agg({'popularity':np.mean,'imdb_id':np.size}).reset_index().rename(columns={'popularity':'actress_pop','imdb_id':'actresses_in_movie'})\n",
        "actress = pd.merge(cast.loc[:,('imdb_id','imdb_name_id')],actress,how='inner',on='imdb_name_id').rename(columns={'imdb_name_id':'actress_id'})\n",
        "actress.drop(actress[actress['actresses_in_movie'] == 1].index,inplace=True)\n",
        "actress"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>imdb_id</th>\n",
              "      <th>actress_id</th>\n",
              "      <th>actress_pop</th>\n",
              "      <th>actresses_in_movie</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0162661</td>\n",
              "      <td>nm0001669</td>\n",
              "      <td>7.470140</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0293508</td>\n",
              "      <td>nm0001669</td>\n",
              "      <td>7.470140</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0160429</td>\n",
              "      <td>nm0001669</td>\n",
              "      <td>7.470140</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0116745</td>\n",
              "      <td>nm0001669</td>\n",
              "      <td>7.470140</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0092965</td>\n",
              "      <td>nm0001669</td>\n",
              "      <td>7.470140</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9840</th>\n",
              "      <td>tt1202532</td>\n",
              "      <td>nm1108521</td>\n",
              "      <td>0.501595</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9951</th>\n",
              "      <td>tt1190539</td>\n",
              "      <td>nm1320089</td>\n",
              "      <td>6.718975</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9952</th>\n",
              "      <td>tt1646959</td>\n",
              "      <td>nm1320089</td>\n",
              "      <td>6.718975</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>tt2383068</td>\n",
              "      <td>nm1541272</td>\n",
              "      <td>6.677236</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>tt2084989</td>\n",
              "      <td>nm1541272</td>\n",
              "      <td>6.677236</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7411 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        imdb_id actress_id  actress_pop  actresses_in_movie\n",
              "0     tt0162661  nm0001669     7.470140                  12\n",
              "1     tt0293508  nm0001669     7.470140                  12\n",
              "2     tt0160429  nm0001669     7.470140                  12\n",
              "3     tt0116745  nm0001669     7.470140                  12\n",
              "4     tt0092965  nm0001669     7.470140                  12\n",
              "...         ...        ...          ...                 ...\n",
              "9840  tt1202532  nm1108521     0.501595                   2\n",
              "9951  tt1190539  nm1320089     6.718975                   2\n",
              "9952  tt1646959  nm1320089     6.718975                   2\n",
              "9995  tt2383068  nm1541272     6.677236                   2\n",
              "9996  tt2084989  nm1541272     6.677236                   2\n",
              "\n",
              "[7411 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i5LBcL5JW4W3",
        "scrolled": true,
        "outputId": "a95171fd-d9b2-40c7-9fc6-75e342591cc9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "actress_pop = actress.groupby('imdb_id',as_index=False).agg({'actress_pop':sum,'actress_id':np.size}).rename(columns={'actress_id':'actress_movie_count'})\n",
        "actress_pop['actress_movie_pop'] = actress_pop['actress_pop'] / actress_pop['actress_movie_count']\n",
        "actress_pop.drop(['actress_pop'],axis=1,inplace=True)\n",
        "actress_pop"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>imdb_id</th>\n",
              "      <th>actress_movie_count</th>\n",
              "      <th>actress_movie_pop</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0035423</td>\n",
              "      <td>1</td>\n",
              "      <td>7.776708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0077651</td>\n",
              "      <td>1</td>\n",
              "      <td>8.547627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0078767</td>\n",
              "      <td>1</td>\n",
              "      <td>7.059906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0079302</td>\n",
              "      <td>1</td>\n",
              "      <td>8.755107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0079417</td>\n",
              "      <td>2</td>\n",
              "      <td>8.671958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5128</th>\n",
              "      <td>tt5778384</td>\n",
              "      <td>3</td>\n",
              "      <td>1.256404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5129</th>\n",
              "      <td>tt5909128</td>\n",
              "      <td>1</td>\n",
              "      <td>1.156079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5130</th>\n",
              "      <td>tt6003368</td>\n",
              "      <td>1</td>\n",
              "      <td>8.951005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5131</th>\n",
              "      <td>tt6113488</td>\n",
              "      <td>2</td>\n",
              "      <td>5.964918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5132</th>\n",
              "      <td>tt6265828</td>\n",
              "      <td>1</td>\n",
              "      <td>12.427831</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5133 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        imdb_id  actress_movie_count  actress_movie_pop\n",
              "0     tt0035423                    1           7.776708\n",
              "1     tt0077651                    1           8.547627\n",
              "2     tt0078767                    1           7.059906\n",
              "3     tt0079302                    1           8.755107\n",
              "4     tt0079417                    2           8.671958\n",
              "...         ...                  ...                ...\n",
              "5128  tt5778384                    3           1.256404\n",
              "5129  tt5909128                    1           1.156079\n",
              "5130  tt6003368                    1           8.951005\n",
              "5131  tt6113488                    2           5.964918\n",
              "5132  tt6265828                    1          12.427831\n",
              "\n",
              "[5133 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_1i_QapcW4W9"
      },
      "source": [
        "### director"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RsAlrj5VW4W-",
        "scrolled": true,
        "outputId": "4665f109-8981-496a-994e-6161fa9d1a50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "director = cast[cast['category'] == 'director'].loc[:,('imdb_name_id','imdb_id','popularity')].groupby('imdb_name_id').agg({'popularity':np.mean,'imdb_id':np.size}).reset_index().rename(columns={'popularity':'director_pop','imdb_id':'director_in_movie'})\n",
        "director = pd.merge(cast.loc[:,('imdb_id','imdb_name_id')],director,how='inner',on='imdb_name_id').rename(columns={'imdb_name_id':'director_id'})\n",
        "director.drop(director[director['director_in_movie'] == 1].index,inplace=True)\n",
        "director"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>imdb_id</th>\n",
              "      <th>director_id</th>\n",
              "      <th>director_pop</th>\n",
              "      <th>director_in_movie</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0162661</td>\n",
              "      <td>nm0000318</td>\n",
              "      <td>15.067531</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt1014759</td>\n",
              "      <td>nm0000318</td>\n",
              "      <td>15.067531</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0096895</td>\n",
              "      <td>nm0000318</td>\n",
              "      <td>15.067531</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0103776</td>\n",
              "      <td>nm0000318</td>\n",
              "      <td>15.067531</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0408236</td>\n",
              "      <td>nm0000318</td>\n",
              "      <td>15.067531</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10219</th>\n",
              "      <td>tt1899353</td>\n",
              "      <td>nm2153088</td>\n",
              "      <td>9.481859</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10220</th>\n",
              "      <td>tt2265171</td>\n",
              "      <td>nm2153088</td>\n",
              "      <td>9.481859</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10237</th>\n",
              "      <td>tt1129405</td>\n",
              "      <td>nm1322135</td>\n",
              "      <td>1.294602</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10238</th>\n",
              "      <td>tt0946998</td>\n",
              "      <td>nm1322135</td>\n",
              "      <td>1.294602</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10239</th>\n",
              "      <td>tt2182019</td>\n",
              "      <td>nm1322135</td>\n",
              "      <td>1.294602</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6700 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         imdb_id director_id  director_pop  director_in_movie\n",
              "0      tt0162661   nm0000318     15.067531                 18\n",
              "1      tt1014759   nm0000318     15.067531                 18\n",
              "2      tt0096895   nm0000318     15.067531                 18\n",
              "3      tt0103776   nm0000318     15.067531                 18\n",
              "4      tt0408236   nm0000318     15.067531                 18\n",
              "...          ...         ...           ...                ...\n",
              "10219  tt1899353   nm2153088      9.481859                  2\n",
              "10220  tt2265171   nm2153088      9.481859                  2\n",
              "10237  tt1129405   nm1322135      1.294602                  2\n",
              "10238  tt0946998   nm1322135      1.294602                  2\n",
              "10239  tt2182019   nm1322135      1.294602                  2\n",
              "\n",
              "[6700 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TIVrkySEW4XD",
        "scrolled": true,
        "outputId": "3d737d07-7370-4fa4-cd79-68a7eb60e7ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "director_pop = director.groupby('imdb_id',as_index=False).agg({'director_pop':sum,'director_id':np.size}).rename(columns={'director_id':'director_movie_count'})\n",
        "director_pop['director_movie_pop'] = director_pop['director_pop'] / director_pop['director_movie_count']\n",
        "director_pop.drop(['director_pop'],axis=1,inplace=True)\n",
        "director_pop"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>imdb_id</th>\n",
              "      <th>director_movie_count</th>\n",
              "      <th>director_movie_pop</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0035423</td>\n",
              "      <td>1</td>\n",
              "      <td>15.934599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0066921</td>\n",
              "      <td>1</td>\n",
              "      <td>15.983537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0077651</td>\n",
              "      <td>1</td>\n",
              "      <td>9.819353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0078767</td>\n",
              "      <td>1</td>\n",
              "      <td>11.053215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0079116</td>\n",
              "      <td>2</td>\n",
              "      <td>7.354229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5262</th>\n",
              "      <td>tt6083388</td>\n",
              "      <td>1</td>\n",
              "      <td>5.806312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5263</th>\n",
              "      <td>tt6113488</td>\n",
              "      <td>1</td>\n",
              "      <td>2.425265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5264</th>\n",
              "      <td>tt6265828</td>\n",
              "      <td>1</td>\n",
              "      <td>18.264861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5265</th>\n",
              "      <td>tt6389344</td>\n",
              "      <td>1</td>\n",
              "      <td>0.468888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5266</th>\n",
              "      <td>tt6690310</td>\n",
              "      <td>1</td>\n",
              "      <td>4.604850</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5267 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        imdb_id  director_movie_count  director_movie_pop\n",
              "0     tt0035423                     1           15.934599\n",
              "1     tt0066921                     1           15.983537\n",
              "2     tt0077651                     1            9.819353\n",
              "3     tt0078767                     1           11.053215\n",
              "4     tt0079116                     2            7.354229\n",
              "...         ...                   ...                 ...\n",
              "5262  tt6083388                     1            5.806312\n",
              "5263  tt6113488                     1            2.425265\n",
              "5264  tt6265828                     1           18.264861\n",
              "5265  tt6389344                     1            0.468888\n",
              "5266  tt6690310                     1            4.604850\n",
              "\n",
              "[5267 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bFm7jbS5loIr"
      },
      "source": [
        "### writer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bi4jWoi-lqoW",
        "outputId": "32979615-c054-4419-810c-f77ef1b8fcf0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "writer = cast[cast['category'] == 'writer'].loc[:,('imdb_name_id','imdb_id','popularity')].groupby('imdb_name_id').agg({'popularity':np.mean,'imdb_id':np.size}).reset_index().rename(columns={'popularity':'writer_pop','imdb_id':'writer_in_movie'})\n",
        "writer = pd.merge(cast.loc[:,('imdb_id','imdb_name_id')],writer,how='inner',on='imdb_name_id').rename(columns={'imdb_name_id':'writer_id'})\n",
        "writer.drop(writer[writer['writer_in_movie'] == 1].index,inplace=True)\n",
        "writer"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>imdb_id</th>\n",
              "      <th>writer_id</th>\n",
              "      <th>writer_pop</th>\n",
              "      <th>writer_in_movie</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>tt0162661</td>\n",
              "      <td>nm0001825</td>\n",
              "      <td>9.006363</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>tt0780653</td>\n",
              "      <td>nm0001825</td>\n",
              "      <td>9.006363</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>tt0114369</td>\n",
              "      <td>nm0001825</td>\n",
              "      <td>9.006363</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>tt0134273</td>\n",
              "      <td>nm0001825</td>\n",
              "      <td>9.006363</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>tt0113303</td>\n",
              "      <td>nm0001825</td>\n",
              "      <td>9.006363</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14525</th>\n",
              "      <td>tt2473510</td>\n",
              "      <td>nm2065670</td>\n",
              "      <td>9.274591</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14526</th>\n",
              "      <td>tt2436386</td>\n",
              "      <td>nm3091255</td>\n",
              "      <td>9.274591</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14527</th>\n",
              "      <td>tt2473510</td>\n",
              "      <td>nm3091255</td>\n",
              "      <td>9.274591</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14545</th>\n",
              "      <td>tt1190539</td>\n",
              "      <td>nm2974384</td>\n",
              "      <td>5.817782</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14546</th>\n",
              "      <td>tt1230385</td>\n",
              "      <td>nm2974384</td>\n",
              "      <td>5.817782</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8307 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         imdb_id  writer_id  writer_pop  writer_in_movie\n",
              "20     tt0162661  nm0001825    9.006363                4\n",
              "21     tt0780653  nm0001825    9.006363                4\n",
              "22     tt0114369  nm0001825    9.006363                4\n",
              "23     tt0134273  nm0001825    9.006363                4\n",
              "24     tt0113303  nm0001825    9.006363                4\n",
              "...          ...        ...         ...              ...\n",
              "14525  tt2473510  nm2065670    9.274591                2\n",
              "14526  tt2436386  nm3091255    9.274591                2\n",
              "14527  tt2473510  nm3091255    9.274591                2\n",
              "14545  tt1190539  nm2974384    5.817782                2\n",
              "14546  tt1230385  nm2974384    5.817782                2\n",
              "\n",
              "[8307 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_pIRvHimlzz9",
        "outputId": "ee18ca8e-9c53-4128-f27d-2e7904c2129b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "writer_pop = writer.groupby('imdb_id',as_index=False).agg({'writer_pop':sum,'writer_id':np.size}).rename(columns={'writer_id':'writer_movie_count'})\n",
        "writer_pop['writer_movie_pop'] = writer_pop['writer_pop'] / writer_pop['writer_movie_count']\n",
        "writer_pop.drop(['writer_pop'],axis=1,inplace=True)\n",
        "writer_pop"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>imdb_id</th>\n",
              "      <th>writer_movie_count</th>\n",
              "      <th>writer_movie_pop</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
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              "      <td>tt6083388</td>\n",
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              "    <tr>\n",
              "      <th>4609</th>\n",
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              "    <tr>\n",
              "      <th>4610</th>\n",
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              "      <td>1.474589</td>\n",
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              "    <tr>\n",
              "      <th>4611</th>\n",
              "      <td>tt6574272</td>\n",
              "      <td>1</td>\n",
              "      <td>43.574867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4612</th>\n",
              "      <td>tt6690310</td>\n",
              "      <td>1</td>\n",
              "      <td>8.940664</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4613 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        imdb_id  writer_movie_count  writer_movie_pop\n",
              "0     tt0035423                   1          7.377117\n",
              "1     tt0077651                   2          9.825820\n",
              "2     tt0078767                   4         13.545866\n",
              "3     tt0079470                   2         11.243368\n",
              "4     tt0079668                   1         11.071279\n",
              "...         ...                 ...               ...\n",
              "4608  tt6083388                   1          2.648138\n",
              "4609  tt6113488                   1          3.636398\n",
              "4610  tt6389344                   1          1.474589\n",
              "4611  tt6574272                   1         43.574867\n",
              "4612  tt6690310                   1          8.940664\n",
              "\n",
              "[4613 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UWHipbvBW4XH"
      },
      "source": [
        "### combo cast"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jlgkhE6kW4XJ",
        "scrolled": true,
        "outputId": "681a69b3-972f-47c7-ff33-c54ec73b3d31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        }
      },
      "source": [
        "combo_cast = pd.merge(actor_pop,actress_pop,how='outer',on='imdb_id').merge(director_pop,how='outer',on='imdb_id').merge(writer_pop,how='outer',on='imdb_id')\n",
        "combo_cast = combo_cast.fillna(0)\n",
        "combo_cast['crew_count'] = (combo_cast['actor_movie_count']+combo_cast['actress_movie_count'])\n",
        "combo_cast['crew_pop'] = (combo_cast['actor_movie_pop']+ combo_cast['actress_movie_pop']) / combo_cast['crew_count']\n",
        "combo_cast"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>imdb_id</th>\n",
              "      <th>actor_movie_count</th>\n",
              "      <th>actor_movie_pop</th>\n",
              "      <th>actress_movie_count</th>\n",
              "      <th>actress_movie_pop</th>\n",
              "      <th>director_movie_count</th>\n",
              "      <th>director_movie_pop</th>\n",
              "      <th>writer_movie_count</th>\n",
              "      <th>writer_movie_pop</th>\n",
              "      <th>crew_count</th>\n",
              "      <th>crew_pop</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>2.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>9.634168</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.547627</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.819353</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.825820</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.090897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0078767</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.030415</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.059906</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.053215</td>\n",
              "      <td>4.0</td>\n",
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              "      <th>4</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.203426</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6873</th>\n",
              "      <td>tt3819668</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.113339</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6874</th>\n",
              "      <td>tt4057376</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.013055</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6875</th>\n",
              "      <td>tt4080672</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>8.169884</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6876</th>\n",
              "      <td>tt5973626</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>16.272650</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6877</th>\n",
              "      <td>tt6574272</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>43.574867</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6878 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        imdb_id  actor_movie_count  ...  crew_count  crew_pop\n",
              "0     tt0035423                3.0  ...         4.0  4.131288\n",
              "1     tt0066921                2.0  ...         2.0  4.999333\n",
              "2     tt0077651                1.0  ...         2.0  9.090897\n",
              "3     tt0078767                2.0  ...         3.0  5.030107\n",
              "4     tt0079116                2.0  ...         2.0  6.203426\n",
              "...         ...                ...  ...         ...       ...\n",
              "6873  tt3819668                0.0  ...         0.0       NaN\n",
              "6874  tt4057376                0.0  ...         0.0       NaN\n",
              "6875  tt4080672                0.0  ...         0.0       NaN\n",
              "6876  tt5973626                0.0  ...         0.0       NaN\n",
              "6877  tt6574272                0.0  ...         0.0       NaN\n",
              "\n",
              "[6878 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "exnYbZ4N0VoW"
      },
      "source": [
        "## revenue/profit "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AvbcBGkt26rh"
      },
      "source": [
        "### actor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fT94EHrq24OB",
        "outputId": "391bee81-62e2-46f9-8e2a-62144204f719",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "actor_rev_pro = cast[cast['category'] == 'actor'].loc[:,('imdb_name_id','imdb_id','revenue','revenue1','profit')].groupby('imdb_name_id').agg({'imdb_id':np.size,'revenue':np.sum,'revenue1':np.mean,'profit':np.mean}).reset_index().rename(columns={'imdb_id':'actors_in_movie','revenue':'sum_revenue','revenue1':'mean_revenue','profit':'actor_avg_profit'})\n",
        "actor_rev_pro.drop(actor_rev_pro[actor_rev_pro['actors_in_movie'] < 5].index,inplace=True)\n",
        "actor_rev_pro.drop(columns=['actors_in_movie'],axis=1,inplace=True)\n",
        "actor_rev_pro = pd.merge(actor_rev_pro,imdb_cast.loc[:,('imdb_name_id','primaryName')],how='inner',on='imdb_name_id').drop_duplicates()\n",
        "actor_rev_pro"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>imdb_name_id</th>\n",
              "      <th>sum_revenue</th>\n",
              "      <th>mean_revenue</th>\n",
              "      <th>actor_avg_profit</th>\n",
              "      <th>primaryName</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>nm0000008</td>\n",
              "      <td>249145588</td>\n",
              "      <td>4.982912e+07</td>\n",
              "      <td>1.258912e+07</td>\n",
              "      <td>Marlon Brando</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>nm0000024</td>\n",
              "      <td>186117535</td>\n",
              "      <td>3.101959e+07</td>\n",
              "      <td>1.981959e+07</td>\n",
              "      <td>John Gielgud</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>nm0000056</td>\n",
              "      <td>800008996</td>\n",
              "      <td>7.272809e+07</td>\n",
              "      <td>3.483718e+07</td>\n",
              "      <td>Paul Newman</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>289</th>\n",
              "      <td>nm0000092</td>\n",
              "      <td>238545263</td>\n",
              "      <td>2.385453e+07</td>\n",
              "      <td>3.104526e+06</td>\n",
              "      <td>John Cleese</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>647</th>\n",
              "      <td>nm0000093</td>\n",
              "      <td>6787307497</td>\n",
              "      <td>1.834407e+08</td>\n",
              "      <td>1.209948e+08</td>\n",
              "      <td>Brad Pitt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123714</th>\n",
              "      <td>nm2851530</td>\n",
              "      <td>494736895</td>\n",
              "      <td>7.067670e+07</td>\n",
              "      <td>2.901956e+07</td>\n",
              "      <td>Dane DeHaan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123748</th>\n",
              "      <td>nm2955013</td>\n",
              "      <td>3778468435</td>\n",
              "      <td>3.778468e+08</td>\n",
              "      <td>2.944468e+08</td>\n",
              "      <td>Liam Hemsworth</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123769</th>\n",
              "      <td>nm3009232</td>\n",
              "      <td>697749081</td>\n",
              "      <td>1.395498e+08</td>\n",
              "      <td>7.414982e+07</td>\n",
              "      <td>Ezra Miller</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123787</th>\n",
              "      <td>nm3485845</td>\n",
              "      <td>100168882</td>\n",
              "      <td>2.003378e+07</td>\n",
              "      <td>-9.262236e+05</td>\n",
              "      <td>Adam Driver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123827</th>\n",
              "      <td>nm3528539</td>\n",
              "      <td>249277968</td>\n",
              "      <td>4.985559e+07</td>\n",
              "      <td>2.695559e+07</td>\n",
              "      <td>Jeremy Irvine</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>866 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       imdb_name_id  sum_revenue  ...  actor_avg_profit     primaryName\n",
              "0         nm0000008    249145588  ...      1.258912e+07   Marlon Brando\n",
              "51        nm0000024    186117535  ...      1.981959e+07    John Gielgud\n",
              "196       nm0000056    800008996  ...      3.483718e+07     Paul Newman\n",
              "289       nm0000092    238545263  ...      3.104526e+06     John Cleese\n",
              "647       nm0000093   6787307497  ...      1.209948e+08       Brad Pitt\n",
              "...             ...          ...  ...               ...             ...\n",
              "123714    nm2851530    494736895  ...      2.901956e+07     Dane DeHaan\n",
              "123748    nm2955013   3778468435  ...      2.944468e+08  Liam Hemsworth\n",
              "123769    nm3009232    697749081  ...      7.414982e+07     Ezra Miller\n",
              "123787    nm3485845    100168882  ...     -9.262236e+05     Adam Driver\n",
              "123827    nm3528539    249277968  ...      2.695559e+07   Jeremy Irvine\n",
              "\n",
              "[866 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PoCnm21k6hqQ"
      },
      "source": [
        "### actress"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qJRx5uk_6kP2",
        "outputId": "c8bfd6ed-3724-4ce2-9b8e-b3fd2e2897b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "actress_rev_pro = cast[cast['category'] == 'actress'].loc[:,('imdb_name_id','imdb_id','revenue','revenue1','profit')].groupby('imdb_name_id').agg({'imdb_id':np.size,'revenue':np.sum,'revenue1':np.mean,'profit':np.mean}).reset_index().rename(columns={'imdb_id':'actress_in_movie','revenue':'sum_revenue','revenue1':'mean_revenue','profit':'actress_avg_profit'})\n",
        "actress_rev_pro.drop(actress_rev_pro[actress_rev_pro['actress_in_movie'] < 5].index,inplace=True)\n",
        "actress_rev_pro.drop(columns=['actress_in_movie'],axis=1,inplace=True)\n",
        "actress_rev_pro = pd.merge(actress_rev_pro,imdb_cast.loc[:,('imdb_name_id','primaryName')],how='inner',on='imdb_name_id').drop_duplicates()\n",
        "actress_rev_pro"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>imdb_name_id</th>\n",
              "      <th>sum_revenue</th>\n",
              "      <th>mean_revenue</th>\n",
              "      <th>actress_avg_profit</th>\n",
              "      <th>primaryName</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>nm0000002</td>\n",
              "      <td>83205048</td>\n",
              "      <td>1.664101e+07</td>\n",
              "      <td>-9.589904e+05</td>\n",
              "      <td>Lauren Bacall</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>nm0000084</td>\n",
              "      <td>372665777</td>\n",
              "      <td>4.140731e+07</td>\n",
              "      <td>5.518420e+06</td>\n",
              "      <td>Li Gong</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>nm0000096</td>\n",
              "      <td>316034884</td>\n",
              "      <td>4.514784e+07</td>\n",
              "      <td>2.243355e+07</td>\n",
              "      <td>Gillian Anderson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>449</th>\n",
              "      <td>nm0000098</td>\n",
              "      <td>2804924267</td>\n",
              "      <td>1.038861e+08</td>\n",
              "      <td>6.896386e+07</td>\n",
              "      <td>Jennifer Aniston</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>803</th>\n",
              "      <td>nm0000099</td>\n",
              "      <td>303084359</td>\n",
              "      <td>2.020562e+07</td>\n",
              "      <td>3.056239e+05</td>\n",
              "      <td>Patricia Arquette</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58820</th>\n",
              "      <td>nm2539953</td>\n",
              "      <td>772300202</td>\n",
              "      <td>1.103286e+08</td>\n",
              "      <td>5.775717e+07</td>\n",
              "      <td>Alicia Vikander</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58863</th>\n",
              "      <td>nm2794962</td>\n",
              "      <td>800151506</td>\n",
              "      <td>1.000189e+08</td>\n",
              "      <td>6.958144e+07</td>\n",
              "      <td>Hailee Steinfeld</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58907</th>\n",
              "      <td>nm3053338</td>\n",
              "      <td>1322964529</td>\n",
              "      <td>2.645929e+08</td>\n",
              "      <td>1.910729e+08</td>\n",
              "      <td>Margot Robbie</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59041</th>\n",
              "      <td>nm3114649</td>\n",
              "      <td>177494927</td>\n",
              "      <td>3.549899e+07</td>\n",
              "      <td>6.989854e+05</td>\n",
              "      <td>Charlotte Le Bon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59062</th>\n",
              "      <td>nm3614913</td>\n",
              "      <td>248633570</td>\n",
              "      <td>3.551908e+07</td>\n",
              "      <td>1.929051e+07</td>\n",
              "      <td>Zoey Deutch</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>489 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      imdb_name_id  sum_revenue  ...  actress_avg_profit        primaryName\n",
              "0        nm0000002     83205048  ...       -9.589904e+05      Lauren Bacall\n",
              "69       nm0000084    372665777  ...        5.518420e+06            Li Gong\n",
              "105      nm0000096    316034884  ...        2.243355e+07   Gillian Anderson\n",
              "449      nm0000098   2804924267  ...        6.896386e+07   Jennifer Aniston\n",
              "803      nm0000099    303084359  ...        3.056239e+05  Patricia Arquette\n",
              "...            ...          ...  ...                 ...                ...\n",
              "58820    nm2539953    772300202  ...        5.775717e+07    Alicia Vikander\n",
              "58863    nm2794962    800151506  ...        6.958144e+07   Hailee Steinfeld\n",
              "58907    nm3053338   1322964529  ...        1.910729e+08      Margot Robbie\n",
              "59041    nm3114649    177494927  ...        6.989854e+05   Charlotte Le Bon\n",
              "59062    nm3614913    248633570  ...        1.929051e+07        Zoey Deutch\n",
              "\n",
              "[489 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LJsNXpgQ66sh"
      },
      "source": [
        "### director"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i5UJMzc868Bf",
        "outputId": "bc7a35c2-8744-432a-faee-c1039664d928",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "director_rev_pro = cast[cast['category'] == 'director'].loc[:,('imdb_name_id','imdb_id','revenue','revenue1','profit')].groupby('imdb_name_id').agg({'imdb_id':np.size,'revenue':np.sum,'revenue1':np.mean,'profit':np.mean}).reset_index().rename(columns={'imdb_id':'director_in_movie','revenue':'sum_revenue','revenue1':'mean_revenue','profit':'director_avg_profit'})\n",
        "director_rev_pro.drop(director_rev_pro[director_rev_pro['director_in_movie'] < 5].index,inplace=True)\n",
        "director_rev_pro.drop(columns=['director_in_movie'],axis=1,inplace=True)\n",
        "director_rev_pro = pd.merge(director_rev_pro,imdb_cast.loc[:,('imdb_name_id','primaryName')],how='inner',on='imdb_name_id').drop_duplicates()\n",
        "director_rev_pro"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>imdb_name_id</th>\n",
              "      <th>sum_revenue</th>\n",
              "      <th>mean_revenue</th>\n",
              "      <th>director_avg_profit</th>\n",
              "      <th>primaryName</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>nm0000095</td>\n",
              "      <td>754612838</td>\n",
              "      <td>4.192294e+07</td>\n",
              "      <td>2.716182e+07</td>\n",
              "      <td>Woody Allen</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>nm0000108</td>\n",
              "      <td>1202768531</td>\n",
              "      <td>2.004614e+08</td>\n",
              "      <td>1.152614e+08</td>\n",
              "      <td>Luc Besson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>382</th>\n",
              "      <td>nm0000116</td>\n",
              "      <td>6177010898</td>\n",
              "      <td>8.824301e+08</td>\n",
              "      <td>7.754444e+08</td>\n",
              "      <td>James Cameron</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>533</th>\n",
              "      <td>nm0000118</td>\n",
              "      <td>293927674</td>\n",
              "      <td>1.837048e+07</td>\n",
              "      <td>1.868917e+06</td>\n",
              "      <td>John Carpenter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>619</th>\n",
              "      <td>nm0000127</td>\n",
              "      <td>906341881</td>\n",
              "      <td>6.042279e+07</td>\n",
              "      <td>4.146946e+07</td>\n",
              "      <td>Wes Craven</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23923</th>\n",
              "      <td>nm1349376</td>\n",
              "      <td>3207125358</td>\n",
              "      <td>5.345209e+08</td>\n",
              "      <td>4.173542e+08</td>\n",
              "      <td>Francis Lawrence</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24014</th>\n",
              "      <td>nm1349522</td>\n",
              "      <td>274479318</td>\n",
              "      <td>4.574655e+07</td>\n",
              "      <td>2.628822e+07</td>\n",
              "      <td>Jonathan Levine</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24041</th>\n",
              "      <td>nm1429471</td>\n",
              "      <td>620889425</td>\n",
              "      <td>1.034816e+08</td>\n",
              "      <td>7.064824e+07</td>\n",
              "      <td>Jaume Collet-Serra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24057</th>\n",
              "      <td>nm1490123</td>\n",
              "      <td>2556967130</td>\n",
              "      <td>3.196209e+08</td>\n",
              "      <td>2.824084e+08</td>\n",
              "      <td>James Wan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24085</th>\n",
              "      <td>nm1921345</td>\n",
              "      <td>2866566</td>\n",
              "      <td>4.777610e+05</td>\n",
              "      <td>-9.022239e+06</td>\n",
              "      <td>Steven C. Miller</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>351 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      imdb_name_id  sum_revenue  ...  director_avg_profit         primaryName\n",
              "0        nm0000095    754612838  ...         2.716182e+07         Woody Allen\n",
              "93       nm0000108   1202768531  ...         1.152614e+08          Luc Besson\n",
              "382      nm0000116   6177010898  ...         7.754444e+08       James Cameron\n",
              "533      nm0000118    293927674  ...         1.868917e+06      John Carpenter\n",
              "619      nm0000127    906341881  ...         4.146946e+07          Wes Craven\n",
              "...            ...          ...  ...                  ...                 ...\n",
              "23923    nm1349376   3207125358  ...         4.173542e+08    Francis Lawrence\n",
              "24014    nm1349522    274479318  ...         2.628822e+07     Jonathan Levine\n",
              "24041    nm1429471    620889425  ...         7.064824e+07  Jaume Collet-Serra\n",
              "24057    nm1490123   2556967130  ...         2.824084e+08           James Wan\n",
              "24085    nm1921345      2866566  ...        -9.022239e+06    Steven C. Miller\n",
              "\n",
              "[351 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w2h_WNnRTws7"
      },
      "source": [
        "##### Movies directed by James Cameron"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fbm63g_yT1FB",
        "outputId": "db54b312-4fad-4c40-e205-2493e9bdb4da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        }
      },
      "source": [
        "james = cast[cast['primaryName']=='James Cameron'].drop(columns=['imdb_name_id','category','revenue1','primaryName'])\n",
        "james = pd.merge(james,new_df.loc[:,('title','year','budget','imdb_id')],how='inner',on='imdb_id').drop(columns=['imdb_id'])\n",
        "james = james[['title','year','budget','revenue','profit','popularity']]\n",
        "james.sort_values('profit',ascending=False).reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>year</th>\n",
              "      <th>budget</th>\n",
              "      <th>revenue</th>\n",
              "      <th>profit</th>\n",
              "      <th>popularity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Avatar</td>\n",
              "      <td>2009</td>\n",
              "      <td>237000000</td>\n",
              "      <td>2789968301</td>\n",
              "      <td>2552968301</td>\n",
              "      <td>185.070892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Titanic</td>\n",
              "      <td>1998</td>\n",
              "      <td>200000000</td>\n",
              "      <td>2187463944</td>\n",
              "      <td>1987463944</td>\n",
              "      <td>26.889070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Terminator 2: Judgment Day</td>\n",
              "      <td>1991</td>\n",
              "      <td>102000000</td>\n",
              "      <td>520884847</td>\n",
              "      <td>418884847</td>\n",
              "      <td>22.661695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Terminator Genisys</td>\n",
              "      <td>2015</td>\n",
              "      <td>155000000</td>\n",
              "      <td>440603537</td>\n",
              "      <td>285603537</td>\n",
              "      <td>30.188198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>True Lies</td>\n",
              "      <td>1994</td>\n",
              "      <td>115000000</td>\n",
              "      <td>378882411</td>\n",
              "      <td>263882411</td>\n",
              "      <td>11.396099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Rambo: First Blood Part II</td>\n",
              "      <td>1985</td>\n",
              "      <td>44000000</td>\n",
              "      <td>300400432</td>\n",
              "      <td>256400432</td>\n",
              "      <td>11.924462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Terminator 3: Rise of the Machines</td>\n",
              "      <td>2003</td>\n",
              "      <td>200000000</td>\n",
              "      <td>433371112</td>\n",
              "      <td>233371112</td>\n",
              "      <td>20.818907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Aliens</td>\n",
              "      <td>1986</td>\n",
              "      <td>18500000</td>\n",
              "      <td>131335326</td>\n",
              "      <td>112835326</td>\n",
              "      <td>21.761179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>The Terminator</td>\n",
              "      <td>1985</td>\n",
              "      <td>6400000</td>\n",
              "      <td>78475971</td>\n",
              "      <td>72075971</td>\n",
              "      <td>19.293562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>The Abyss</td>\n",
              "      <td>1989</td>\n",
              "      <td>70000000</td>\n",
              "      <td>90000098</td>\n",
              "      <td>20000098</td>\n",
              "      <td>9.809585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Strange Days</td>\n",
              "      <td>1995</td>\n",
              "      <td>42000000</td>\n",
              "      <td>7959291</td>\n",
              "      <td>-34040709</td>\n",
              "      <td>11.223055</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                 title  year  ...      profit  popularity\n",
              "0                               Avatar  2009  ...  2552968301  185.070892\n",
              "1                              Titanic  1998  ...  1987463944   26.889070\n",
              "2           Terminator 2: Judgment Day  1991  ...   418884847   22.661695\n",
              "3                   Terminator Genisys  2015  ...   285603537   30.188198\n",
              "4                            True Lies  1994  ...   263882411   11.396099\n",
              "5           Rambo: First Blood Part II  1985  ...   256400432   11.924462\n",
              "6   Terminator 3: Rise of the Machines  2003  ...   233371112   20.818907\n",
              "7                               Aliens  1986  ...   112835326   21.761179\n",
              "8                       The Terminator  1985  ...    72075971   19.293562\n",
              "9                            The Abyss  1989  ...    20000098    9.809585\n",
              "10                        Strange Days  1995  ...   -34040709   11.223055\n",
              "\n",
              "[11 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IVqYhn0imbnU"
      },
      "source": [
        "### writer\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QdZtsszLmh9v",
        "outputId": "c628f297-3784-4b4c-b2ac-24b0eff37bf7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "writer_rev_pro = cast[cast['category'] == 'writer'].loc[:,('imdb_name_id','imdb_id','revenue','revenue1','profit')].groupby('imdb_name_id').agg({'imdb_id':np.size,'revenue':np.sum,'revenue1':np.mean,'profit':np.mean}).reset_index().rename(columns={'imdb_id':'writer_in_movie','revenue':'sum_revenue','revenue1':'mean_revenue','profit':'writer_avg_profit'})\n",
        "writer_rev_pro.drop(writer_rev_pro[writer_rev_pro['writer_in_movie'] < 5].index,inplace=True)\n",
        "writer_rev_pro.drop(columns=['writer_in_movie'],axis=1,inplace=True)\n",
        "writer_rev_pro = pd.merge(writer_rev_pro,imdb_cast.loc[:,('imdb_name_id','primaryName')],how='inner',on='imdb_name_id').drop_duplicates()\n",
        "writer_rev_pro"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>imdb_name_id</th>\n",
              "      <th>sum_revenue</th>\n",
              "      <th>mean_revenue</th>\n",
              "      <th>writer_avg_profit</th>\n",
              "      <th>primaryName</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>nm0000108</td>\n",
              "      <td>1767187334</td>\n",
              "      <td>1.039522e+08</td>\n",
              "      <td>7.422867e+07</td>\n",
              "      <td>Luc Besson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>289</th>\n",
              "      <td>nm0000118</td>\n",
              "      <td>353353139</td>\n",
              "      <td>3.533531e+07</td>\n",
              "      <td>2.253531e+07</td>\n",
              "      <td>John Carpenter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>375</th>\n",
              "      <td>nm0000127</td>\n",
              "      <td>594765927</td>\n",
              "      <td>5.406963e+07</td>\n",
              "      <td>3.879690e+07</td>\n",
              "      <td>Wes Craven</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>501</th>\n",
              "      <td>nm0000175</td>\n",
              "      <td>1468339777</td>\n",
              "      <td>4.318646e+07</td>\n",
              "      <td>2.515999e+07</td>\n",
              "      <td>Stephen King</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1156</th>\n",
              "      <td>nm0000184</td>\n",
              "      <td>6258564437</td>\n",
              "      <td>5.689604e+08</td>\n",
              "      <td>4.932331e+08</td>\n",
              "      <td>George Lucas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40056</th>\n",
              "      <td>nm2354099</td>\n",
              "      <td>536267074</td>\n",
              "      <td>1.072534e+08</td>\n",
              "      <td>8.645341e+07</td>\n",
              "      <td>Scott Neustadter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40084</th>\n",
              "      <td>nm2769412</td>\n",
              "      <td>3408644888</td>\n",
              "      <td>5.681075e+08</td>\n",
              "      <td>4.972741e+08</td>\n",
              "      <td>Stephenie Meyer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40098</th>\n",
              "      <td>nm2898643</td>\n",
              "      <td>230368861</td>\n",
              "      <td>4.607377e+07</td>\n",
              "      <td>2.159377e+07</td>\n",
              "      <td>Junli Guo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40106</th>\n",
              "      <td>nm3123612</td>\n",
              "      <td>1858075409</td>\n",
              "      <td>3.716151e+08</td>\n",
              "      <td>2.596151e+08</td>\n",
              "      <td>Jon Spaihts</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40125</th>\n",
              "      <td>nm5022110</td>\n",
              "      <td>1926794523</td>\n",
              "      <td>3.853589e+08</td>\n",
              "      <td>3.095589e+08</td>\n",
              "      <td>Michael J. Wilson</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>396 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      imdb_name_id  sum_revenue  ...  writer_avg_profit        primaryName\n",
              "0        nm0000108   1767187334  ...       7.422867e+07         Luc Besson\n",
              "289      nm0000118    353353139  ...       2.253531e+07     John Carpenter\n",
              "375      nm0000127    594765927  ...       3.879690e+07         Wes Craven\n",
              "501      nm0000175   1468339777  ...       2.515999e+07       Stephen King\n",
              "1156     nm0000184   6258564437  ...       4.932331e+08       George Lucas\n",
              "...            ...          ...  ...                ...                ...\n",
              "40056    nm2354099    536267074  ...       8.645341e+07   Scott Neustadter\n",
              "40084    nm2769412   3408644888  ...       4.972741e+08    Stephenie Meyer\n",
              "40098    nm2898643    230368861  ...       2.159377e+07          Junli Guo\n",
              "40106    nm3123612   1858075409  ...       2.596151e+08        Jon Spaihts\n",
              "40125    nm5022110   1926794523  ...       3.095589e+08  Michael J. Wilson\n",
              "\n",
              "[396 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q7lLwctJH-0r",
        "outputId": "a3f1ecb1-baff-4597-a69f-8ef302584329",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        }
      },
      "source": [
        "fig, axes = plt.subplots(1, 4, figsize=(15, 5))\n",
        "fig.suptitle('Writer with highest grossing movies')\n",
        "actress_prof = actress_rev_pro.sort_values('actress_avg_profit',ascending=False).loc[:,('primaryName','actress_avg_profit')].head(10)\n",
        "director_prof = director_rev_pro.sort_values('director_avg_profit',ascending=False).loc[:,('primaryName','director_avg_profit')].head(10)\n",
        "writer_prof = writer_rev_pro.sort_values('writer_avg_profit',ascending=False).loc[:,('primaryName','writer_avg_profit')].head(10)\n",
        "actor_prof = actor_rev_pro.sort_values('actor_avg_profit',ascending=False).loc[:,('primaryName','actor_avg_profit')].head(10)\n",
        "ax= sns.barplot(x='primaryName',y='actor_avg_profit',data=actor_prof,palette='Set1',ax=axes[0])\n",
        "ax.set_xticklabels(ax.get_xticklabels(),rotation=90)\n",
        "ax= sns.barplot(x='primaryName',y='actress_avg_profit',data=actress_prof,palette='Paired_r',ax=axes[1])\n",
        "ax.set_xticklabels(ax.get_xticklabels(),rotation=90)\n",
        "ax= sns.barplot(x='primaryName',y='director_avg_profit',data=director_prof,ax=axes[2])\n",
        "ax.set_xticklabels(ax.get_xticklabels(),rotation=90)\n",
        "ax= sns.barplot(x='primaryName',y='writer_avg_profit',data=writer_prof,palette='icefire_r',ax=axes[3])\n",
        "ax.set_xticklabels(ax.get_xticklabels(),rotation=90)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0, 'J.R.R. Tolkien'),\n",
              " Text(0, 0, 'J.K. Rowling'),\n",
              " Text(0, 0, 'Lawrence Kasdan'),\n",
              " Text(0, 0, 'Linda Woolverton'),\n",
              " Text(0, 0, 'Philippa Boyens'),\n",
              " Text(0, 0, 'Michael Arndt'),\n",
              " Text(0, 0, 'Steve Kloves'),\n",
              " Text(0, 0, 'Steve Ditko'),\n",
              " Text(0, 0, 'Jay Wolpert'),\n",
              " Text(0, 0, 'Melissa Rosenberg')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tWoCah9TW4XO"
      },
      "source": [
        "### genre"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mxiA3yntW4XP",
        "scrolled": true,
        "outputId": "4aeedf4b-595a-4610-cbb3-530434ef9cd0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "genre = new_df['genre'].astype(str).str.split(',', expand = True).replace(regex=[r' ',r'\\n'], value='')\n",
        "genre"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Comedy</td>\n",
              "      <td>Fantasy</td>\n",
              "      <td>Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Crime</td>\n",
              "      <td>Drama</td>\n",
              "      <td>Sci-Fi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Horror</td>\n",
              "      <td>Thriller</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Horror</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Biography</td>\n",
              "      <td>Crime</td>\n",
              "      <td>Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8865</th>\n",
              "      <td>Biography</td>\n",
              "      <td>Drama</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8869</th>\n",
              "      <td>Drama</td>\n",
              "      <td>Fantasy</td>\n",
              "      <td>Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8870</th>\n",
              "      <td>Animation</td>\n",
              "      <td>Action</td>\n",
              "      <td>Adventure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8872</th>\n",
              "      <td>Horror</td>\n",
              "      <td>Mystery</td>\n",
              "      <td>Sci-Fi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8874</th>\n",
              "      <td>Action</td>\n",
              "      <td>Crime</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7098 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              0         1          2\n",
              "0        Comedy   Fantasy    Romance\n",
              "2         Crime     Drama     Sci-Fi\n",
              "5        Horror  Thriller       None\n",
              "6        Horror      None       None\n",
              "7     Biography     Crime      Drama\n",
              "...         ...       ...        ...\n",
              "8865  Biography     Drama       None\n",
              "8869      Drama   Fantasy    Romance\n",
              "8870  Animation    Action  Adventure\n",
              "8872     Horror   Mystery     Sci-Fi\n",
              "8874     Action     Crime       None\n",
              "\n",
              "[7098 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uJw9sMI9W4XU",
        "outputId": "ea01c5da-f52a-411e-cd55-14fd02d3ac2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "genre_arr = pd.unique(genre.values.ravel('K'))\n",
        "genre_arr = genre_arr[genre_arr != np.array(None)]\n",
        "genre_arr"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Comedy', 'Crime', 'Horror', 'Biography', 'Drama', 'Action',\n",
              "       'Adventure', 'Mystery', 'Fantasy', 'Animation', 'Western',\n",
              "       'Family', 'Sci-Fi', 'Romance', 'Thriller', 'Musical', 'War',\n",
              "       'Sport', 'Music', 'History'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o46DdF-ZW4XY"
      },
      "source": [
        "genre = new_df[['genre', 'imdb_id']].replace(regex=r' ', value='')\n",
        "genre['genre_list'] = new_df['genre'].astype(str).replace(regex=[r' ',r'\\n'], value='')\n",
        "genre['genre_count'] = genre['genre_list'].str.split(',').str.len()\n",
        "for g in genre_arr:\n",
        "    genre[f'{g}'] = np.where(genre['genre'].str.contains(g), 1, 0)\n",
        "genre.drop('genre', axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ev1-q2ttW4Xc",
        "scrolled": true,
        "outputId": "e57a826d-af9d-4502-a8ac-754b6fd8a467",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        }
      },
      "source": [
        "genre"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>imdb_id</th>\n",
              "      <th>genre_list</th>\n",
              "      <th>genre_count</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0066921</td>\n",
              "      <td>Crime,Drama,Sci-Fi</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>tt0077651</td>\n",
              "      <td>Horror,Thriller</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>tt0078767</td>\n",
              "      <td>Horror</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>tt0079116</td>\n",
              "      <td>Biography,Crime,Drama</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8865</th>\n",
              "      <td>tt6113488</td>\n",
              "      <td>Biography,Drama</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8869</th>\n",
              "      <td>tt6265828</td>\n",
              "      <td>Drama,Fantasy,Romance</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8870</th>\n",
              "      <td>tt6389344</td>\n",
              "      <td>Animation,Action,Adventure</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8872</th>\n",
              "      <td>tt6574272</td>\n",
              "      <td>Horror,Mystery,Sci-Fi</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8874</th>\n",
              "      <td>tt6690310</td>\n",
              "      <td>Action,Crime</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7098 rows × 23 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        imdb_id                  genre_list  genre_count  ...  Sport  Music  History\n",
              "0     tt0035423      Comedy,Fantasy,Romance            3  ...      0      0        0\n",
              "2     tt0066921          Crime,Drama,Sci-Fi            3  ...      0      0        0\n",
              "5     tt0077651             Horror,Thriller            2  ...      0      0        0\n",
              "6     tt0078767                      Horror            1  ...      0      0        0\n",
              "7     tt0079116       Biography,Crime,Drama            3  ...      0      0        0\n",
              "...         ...                         ...          ...  ...    ...    ...      ...\n",
              "8865  tt6113488             Biography,Drama            2  ...      0      0        0\n",
              "8869  tt6265828       Drama,Fantasy,Romance            3  ...      0      0        0\n",
              "8870  tt6389344  Animation,Action,Adventure            3  ...      0      0        0\n",
              "8872  tt6574272       Horror,Mystery,Sci-Fi            3  ...      0      0        0\n",
              "8874  tt6690310                Action,Crime            2  ...      0      0        0\n",
              "\n",
              "[7098 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vYBDOZ14W4Xf"
      },
      "source": [
        "# Create genre_point dataframe to calulate point based on popularity\n",
        "genre_point_df = pd.DataFrame(genre_arr, columns=['genre'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sp2rCHb7W4Xt",
        "scrolled": true,
        "outputId": "40f68ec1-c035-43af-baf8-455e54131a43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "new_df = new_df.drop(columns=['genre'])\n",
        "cast_genre = pd.merge(combo_cast,genre,on='imdb_id',how='inner')\n",
        "data = pd.merge(new_df,cast_genre,on='imdb_id',how='inner')\n",
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>imdb_id</th>\n",
              "      <th>title</th>\n",
              "      <th>date_published</th>\n",
              "      <th>duration</th>\n",
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              "      <th>revenue</th>\n",
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              "      <th>year</th>\n",
              "      <th>popularity</th>\n",
              "      <th>overview</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>day_of_week</th>\n",
              "      <th>month_name</th>\n",
              "      <th>profit</th>\n",
              "      <th>ROI</th>\n",
              "      <th>actor_movie_count</th>\n",
              "      <th>actor_movie_pop</th>\n",
              "      <th>actress_movie_count</th>\n",
              "      <th>actress_movie_pop</th>\n",
              "      <th>director_movie_count</th>\n",
              "      <th>director_movie_pop</th>\n",
              "      <th>writer_movie_count</th>\n",
              "      <th>writer_movie_pop</th>\n",
              "      <th>crew_count</th>\n",
              "      <th>crew_pop</th>\n",
              "      <th>genre_list</th>\n",
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              "      <th>Comedy</th>\n",
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              "      <th>Family</th>\n",
              "      <th>Sci-Fi</th>\n",
              "      <th>Romance</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>tt0035423</td>\n",
              "      <td>Kate &amp; Leopold</td>\n",
              "      <td>2002-04-05</td>\n",
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              "      <td>48000000</td>\n",
              "      <td>76019048</td>\n",
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              "      <td>7.196356</td>\n",
              "      <td>When her scientist ex-boyfriend discovers a po...</td>\n",
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              "      <td>April</td>\n",
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              "      <td>8.748445</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.776708</td>\n",
              "      <td>1.0</td>\n",
              "      <td>15.934599</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.377117</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.131288</td>\n",
              "      <td>Comedy,Fantasy,Romance</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0066921</td>\n",
              "      <td>A Clockwork Orange</td>\n",
              "      <td>2000-03-17</td>\n",
              "      <td>136</td>\n",
              "      <td>2200000</td>\n",
              "      <td>26903440</td>\n",
              "      <td>1481.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>2000</td>\n",
              "      <td>17.112594</td>\n",
              "      <td>Demonic gang-leader Alex goes on the spree of ...</td>\n",
              "      <td>3</td>\n",
              "      <td>17</td>\n",
              "      <td>Friday</td>\n",
              "      <td>March</td>\n",
              "      <td>24703440</td>\n",
              "      <td>1122.883636</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.998665</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>15.983537</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.999333</td>\n",
              "      <td>Crime,Drama,Sci-Fi</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0077651</td>\n",
              "      <td>Halloween</td>\n",
              "      <td>1980-03-07</td>\n",
              "      <td>91</td>\n",
              "      <td>325000</td>\n",
              "      <td>47000000</td>\n",
              "      <td>1399.0</td>\n",
              "      <td>406.0</td>\n",
              "      <td>1980</td>\n",
              "      <td>13.672759</td>\n",
              "      <td>In John Carpenter's horror classic, a psychoti...</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>Friday</td>\n",
              "      <td>March</td>\n",
              "      <td>46675000</td>\n",
              "      <td>14361.538462</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.634168</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.547627</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.819353</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.825820</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.090897</td>\n",
              "      <td>Horror,Thriller</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0078767</td>\n",
              "      <td>The Amityville Horror</td>\n",
              "      <td>1980-01-25</td>\n",
              "      <td>117</td>\n",
              "      <td>4700000</td>\n",
              "      <td>86432000</td>\n",
              "      <td>296.0</td>\n",
              "      <td>125.0</td>\n",
              "      <td>1980</td>\n",
              "      <td>14.003180</td>\n",
              "      <td>George Lutz and his wife Kathleen, move into t...</td>\n",
              "      <td>1</td>\n",
              "      <td>25</td>\n",
              "      <td>Friday</td>\n",
              "      <td>January</td>\n",
              "      <td>81732000</td>\n",
              "      <td>1738.978723</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.030415</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.059906</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.053215</td>\n",
              "      <td>4.0</td>\n",
              "      <td>13.545866</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.030107</td>\n",
              "      <td>Horror</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0079116</td>\n",
              "      <td>Escape from Alcatraz</td>\n",
              "      <td>1980-01-25</td>\n",
              "      <td>112</td>\n",
              "      <td>8000000</td>\n",
              "      <td>43004381</td>\n",
              "      <td>156.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>1980</td>\n",
              "      <td>11.837667</td>\n",
              "      <td>Escape from Alcatraz tells the story of the on...</td>\n",
              "      <td>1</td>\n",
              "      <td>25</td>\n",
              "      <td>Friday</td>\n",
              "      <td>January</td>\n",
              "      <td>35004381</td>\n",
              "      <td>437.554763</td>\n",
              "      <td>2.0</td>\n",
              "      <td>12.406851</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.354229</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.203426</td>\n",
              "      <td>Biography,Crime,Drama</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6891</th>\n",
              "      <td>tt6113488</td>\n",
              "      <td>The Case for Christ</td>\n",
              "      <td>2017-04-07</td>\n",
              "      <td>112</td>\n",
              "      <td>5000000</td>\n",
              "      <td>17644496</td>\n",
              "      <td>133.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>2017</td>\n",
              "      <td>3.674827</td>\n",
              "      <td>Based on the true story of an award-winning in...</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>Friday</td>\n",
              "      <td>April</td>\n",
              "      <td>12644496</td>\n",
              "      <td>252.889920</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.750526</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.964918</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.425265</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.636398</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.571815</td>\n",
              "      <td>Biography,Drama</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6892</th>\n",
              "      <td>tt6265828</td>\n",
              "      <td>A Ghost Story</td>\n",
              "      <td>2017-08-11</td>\n",
              "      <td>92</td>\n",
              "      <td>100000</td>\n",
              "      <td>1951683</td>\n",
              "      <td>478.0</td>\n",
              "      <td>308.0</td>\n",
              "      <td>2017</td>\n",
              "      <td>24.339781</td>\n",
              "      <td>Recently deceased, a white-sheeted ghost retur...</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "      <td>Friday</td>\n",
              "      <td>August</td>\n",
              "      <td>1851683</td>\n",
              "      <td>1851.683000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.920034</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.427831</td>\n",
              "      <td>1.0</td>\n",
              "      <td>18.264861</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.173932</td>\n",
              "      <td>Drama,Fantasy,Romance</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6893</th>\n",
              "      <td>tt6389344</td>\n",
              "      <td>Tri bogatyrya i Morskoy tsar</td>\n",
              "      <td>2017-01-01</td>\n",
              "      <td>75</td>\n",
              "      <td>4000000</td>\n",
              "      <td>13816357</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.468664</td>\n",
              "      <td>No plot overview available</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>January</td>\n",
              "      <td>9816357</td>\n",
              "      <td>245.408925</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.559049</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.468888</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.474589</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.639762</td>\n",
              "      <td>Animation,Action,Adventure</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6894</th>\n",
              "      <td>tt6574272</td>\n",
              "      <td>Phoenix Forgotten</td>\n",
              "      <td>2017-04-21</td>\n",
              "      <td>87</td>\n",
              "      <td>2800000</td>\n",
              "      <td>3697729</td>\n",
              "      <td>68.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>2017</td>\n",
              "      <td>12.560942</td>\n",
              "      <td>20 years after three teenagers disappeared in ...</td>\n",
              "      <td>4</td>\n",
              "      <td>21</td>\n",
              "      <td>Friday</td>\n",
              "      <td>April</td>\n",
              "      <td>897729</td>\n",
              "      <td>32.061750</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>43.574867</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Horror,Mystery,Sci-Fi</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6895</th>\n",
              "      <td>tt6690310</td>\n",
              "      <td>Fei fan ren wu</td>\n",
              "      <td>2017-04-06</td>\n",
              "      <td>117</td>\n",
              "      <td>23000000</td>\n",
              "      <td>22757764</td>\n",
              "      <td>8.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2017</td>\n",
              "      <td>2.552316</td>\n",
              "      <td>‘Extraordinary Mission’ is a new Chinese actio...</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>April</td>\n",
              "      <td>-242236</td>\n",
              "      <td>-1.053200</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.604850</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.940664</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Action,Crime</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6896 rows × 49 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        imdb_id                         title  ... Music  History\n",
              "0     tt0035423                Kate & Leopold  ...     0        0\n",
              "1     tt0066921            A Clockwork Orange  ...     0        0\n",
              "2     tt0077651                     Halloween  ...     0        0\n",
              "3     tt0078767         The Amityville Horror  ...     0        0\n",
              "4     tt0079116          Escape from Alcatraz  ...     0        0\n",
              "...         ...                           ...  ...   ...      ...\n",
              "6891  tt6113488           The Case for Christ  ...     0        0\n",
              "6892  tt6265828                 A Ghost Story  ...     0        0\n",
              "6893  tt6389344  Tri bogatyrya i Morskoy tsar  ...     0        0\n",
              "6894  tt6574272             Phoenix Forgotten  ...     0        0\n",
              "6895  tt6690310                Fei fan ren wu  ...     0        0\n",
              "\n",
              "[6896 rows x 49 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QJguEYySUd4b"
      },
      "source": [
        "data = data[(data['year'] >= 1980) & (data['year'] <= 2017)]\n",
        "data.reset_index(drop=True,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ko9rZyNuW4Yu"
      },
      "source": [
        "# Merge rating"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "broXARkarzRa",
        "outputId": "055035bf-37d1-4667-a715-fae163666422",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6896, 49)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cvT3ouzSW4Yv",
        "scrolled": true,
        "outputId": "990debb4-26ae-4fb2-bc9b-c5ca8a78c742",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        }
      },
      "source": [
        "imdb_rating = pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/Group Assignment/IMDb ratings.csv\", delimiter=\",\")\n",
        "imdb_rating = imdb_rating.drop(columns=['allgenders_0age_avg_vote','allgenders_0age_votes','males_0age_avg_vote','males_0age_votes','females_0age_avg_vote','females_0age_votes','votes_10','votes_9','votes_8','votes_7','votes_6','votes_5','votes_4','votes_3','votes_2','votes_1','weighted_average_vote','top1000_voters_votes', 'top1000_voters_rating'])\n",
        "imdb_rating = imdb_rating.rename(columns={'imdb_title_id':'imdb_id'})\n",
        "imdb_rating"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\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>imdb_id</th>\n",
              "      <th>total_votes</th>\n",
              "      <th>mean_vote</th>\n",
              "      <th>median_vote</th>\n",
              "      <th>allgenders_18age_avg_vote</th>\n",
              "      <th>allgenders_18age_votes</th>\n",
              "      <th>allgenders_30age_avg_vote</th>\n",
              "      <th>allgenders_30age_votes</th>\n",
              "      <th>allgenders_45age_avg_vote</th>\n",
              "      <th>allgenders_45age_votes</th>\n",
              "      <th>males_allages_avg_vote</th>\n",
              "      <th>males_allages_votes</th>\n",
              "      <th>males_18age_avg_vote</th>\n",
              "      <th>males_18age_votes</th>\n",
              "      <th>males_30age_avg_vote</th>\n",
              "      <th>males_30age_votes</th>\n",
              "      <th>males_45age_avg_vote</th>\n",
              "      <th>males_45age_votes</th>\n",
              "      <th>females_allages_avg_vote</th>\n",
              "      <th>females_allages_votes</th>\n",
              "      <th>females_18age_avg_vote</th>\n",
              "      <th>females_18age_votes</th>\n",
              "      <th>females_30age_avg_vote</th>\n",
              "      <th>females_30age_votes</th>\n",
              "      <th>females_45age_avg_vote</th>\n",
              "      <th>females_45age_votes</th>\n",
              "      <th>us_voters_rating</th>\n",
              "      <th>us_voters_votes</th>\n",
              "      <th>non_us_voters_rating</th>\n",
              "      <th>non_us_voters_votes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000574</td>\n",
              "      <td>537</td>\n",
              "      <td>6.3</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.2</td>\n",
              "      <td>126.0</td>\n",
              "      <td>5.9</td>\n",
              "      <td>209.0</td>\n",
              "      <td>6.4</td>\n",
              "      <td>100.0</td>\n",
              "      <td>6.1</td>\n",
              "      <td>393.0</td>\n",
              "      <td>6.2</td>\n",
              "      <td>112.0</td>\n",
              "      <td>5.9</td>\n",
              "      <td>186.0</td>\n",
              "      <td>6.4</td>\n",
              "      <td>85.0</td>\n",
              "      <td>6.1</td>\n",
              "      <td>48.0</td>\n",
              "      <td>5.7</td>\n",
              "      <td>14.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>6.6</td>\n",
              "      <td>14.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>6.2</td>\n",
              "      <td>309.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0001892</td>\n",
              "      <td>171</td>\n",
              "      <td>6.1</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.7</td>\n",
              "      <td>25.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>6.3</td>\n",
              "      <td>58.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>131.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>6.3</td>\n",
              "      <td>50.0</td>\n",
              "      <td>5.7</td>\n",
              "      <td>16.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>6.2</td>\n",
              "      <td>27.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>114.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0002101</td>\n",
              "      <td>420</td>\n",
              "      <td>5.2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.6</td>\n",
              "      <td>24.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>104.0</td>\n",
              "      <td>5.3</td>\n",
              "      <td>180.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>278.0</td>\n",
              "      <td>4.6</td>\n",
              "      <td>20.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>89.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>159.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>39.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>6.2</td>\n",
              "      <td>20.0</td>\n",
              "      <td>5.5</td>\n",
              "      <td>197.0</td>\n",
              "      <td>4.7</td>\n",
              "      <td>103.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0002130</td>\n",
              "      <td>2019</td>\n",
              "      <td>6.9</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>429.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>783.0</td>\n",
              "      <td>7.1</td>\n",
              "      <td>430.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1464.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>371.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>701.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>346.0</td>\n",
              "      <td>7.2</td>\n",
              "      <td>205.0</td>\n",
              "      <td>6.8</td>\n",
              "      <td>53.0</td>\n",
              "      <td>7.3</td>\n",
              "      <td>74.0</td>\n",
              "      <td>7.4</td>\n",
              "      <td>75.0</td>\n",
              "      <td>7.1</td>\n",
              "      <td>452.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1076.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0002199</td>\n",
              "      <td>438</td>\n",
              "      <td>5.8</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.7</td>\n",
              "      <td>38.0</td>\n",
              "      <td>5.6</td>\n",
              "      <td>156.0</td>\n",
              "      <td>5.9</td>\n",
              "      <td>147.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>318.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>34.0</td>\n",
              "      <td>5.7</td>\n",
              "      <td>142.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>131.0</td>\n",
              "      <td>5.4</td>\n",
              "      <td>29.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6.5</td>\n",
              "      <td>15.0</td>\n",
              "      <td>5.9</td>\n",
              "      <td>161.0</td>\n",
              "      <td>5.7</td>\n",
              "      <td>164.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>81268</th>\n",
              "      <td>tt9903716</td>\n",
              "      <td>219</td>\n",
              "      <td>8.1</td>\n",
              "      <td>9.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>4.6</td>\n",
              "      <td>9.0</td>\n",
              "      <td>7.1</td>\n",
              "      <td>97.0</td>\n",
              "      <td>7.1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>7.3</td>\n",
              "      <td>28.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>17.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.6</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>7.1</td>\n",
              "      <td>45.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81269</th>\n",
              "      <td>tt9905412</td>\n",
              "      <td>510</td>\n",
              "      <td>7.8</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81270</th>\n",
              "      <td>tt9905462</td>\n",
              "      <td>604</td>\n",
              "      <td>8.1</td>\n",
              "      <td>9.0</td>\n",
              "      <td>5.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81271</th>\n",
              "      <td>tt9911774</td>\n",
              "      <td>369</td>\n",
              "      <td>8.2</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81272</th>\n",
              "      <td>tt9914286</td>\n",
              "      <td>190</td>\n",
              "      <td>9.5</td>\n",
              "      <td>10.0</td>\n",
              "      <td>8.2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>11.0</td>\n",
              "      <td>8.2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.7</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.2</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>81273 rows × 30 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         imdb_id  total_votes  ...  non_us_voters_rating  non_us_voters_votes\n",
              "0      tt0000574          537  ...                   6.2                309.0\n",
              "1      tt0001892          171  ...                   6.0                114.0\n",
              "2      tt0002101          420  ...                   4.7                103.0\n",
              "3      tt0002130         2019  ...                   7.0               1076.0\n",
              "4      tt0002199          438  ...                   5.7                164.0\n",
              "...          ...          ...  ...                   ...                  ...\n",
              "81268  tt9903716          219  ...                   7.1                 45.0\n",
              "81269  tt9905412          510  ...                   1.0                  3.0\n",
              "81270  tt9905462          604  ...                   4.0                  3.0\n",
              "81271  tt9911774          369  ...                   1.0                  1.0\n",
              "81272  tt9914286          190  ...                   6.2                  5.0\n",
              "\n",
              "[81273 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X3SxnsfHW4Yz"
      },
      "source": [
        "final_df = pd.merge(data,imdb_rating,how='inner',on='imdb_id')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OQyl6s5OW4ZO"
      },
      "source": [
        "# Processing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "higoZDhkW4ZO"
      },
      "source": [
        "final_df['overview_count'] = final_df['overview'].str.split().str.len()\n",
        "final_df['overview_count'] = final_df['overview_count'].replace(np.nan,0,regex=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4hH_Su1XW4ZY"
      },
      "source": [
        "final_df['ROI_group'] = np.where(final_df['ROI'] > 0, 1, 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QIwhZyrDW4Zb",
        "scrolled": true
      },
      "source": [
        "total = final_df.isnull().sum().sort_values(ascending=False)\n",
        "zero = final_df.isin([0]).sum()\n",
        "percent_1 = final_df.isnull().sum()/final_df.isnull().count()*100\n",
        "percent_2 = (round(percent_1, 1)).sort_values(ascending=False)\n",
        "missing_data = pd.concat([total, zero,percent_2], axis=1, keys=['Total nan','Total zero', '%'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WWTv5qjCW4Ze",
        "outputId": "a6bbba00-190f-405c-f517-dceb4251a3e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        }
      },
      "source": [
        "sns.heatmap(final_df.isnull())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f729ef53160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lV7xCvO-W4Zj"
      },
      "source": [
        "final_df['females_45age_votes'].fillna(value=final_df['females_allages_votes'], inplace=True)\n",
        "final_df['females_18age_votes'].fillna(value=final_df['females_allages_votes'], inplace=True)\n",
        "final_df['females_30age_votes'].fillna(value=final_df['females_allages_votes'], inplace=True)\n",
        "final_df['females_45age_avg_vote'].fillna(value=final_df['females_allages_avg_vote'], inplace=True)\n",
        "final_df['females_18age_avg_vote'].fillna(value=final_df['females_allages_avg_vote'], inplace=True)\n",
        "final_df['females_30age_avg_vote'].fillna(value=final_df['females_allages_avg_vote'], inplace=True)\n",
        "final_df['males_18age_avg_vote'].fillna(value=final_df['males_allages_avg_vote'], inplace=True)\n",
        "final_df['males_45age_avg_vote'].fillna(value=final_df['males_allages_avg_vote'], inplace=True)\n",
        "final_df['males_18age_votes'].fillna(value=final_df['males_allages_votes'], inplace=True)\n",
        "final_df['males_45age_votes'].fillna(value=final_df['males_allages_votes'], inplace=True)\n",
        "final_df['allgenders_18age_avg_vote'].fillna(value=(final_df['males_18age_avg_vote']+final_df['females_18age_avg_vote'])/2, inplace=True)\n",
        "final_df['allgenders_18age_votes'].fillna(value=(final_df['males_18age_votes']+final_df['females_18age_votes'])/2, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "El2JprECW4Zl"
      },
      "source": [
        "final_df = final_df.fillna(0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hFpWgm1lW4Zo"
      },
      "source": [
        "### Create Users or Critics feature"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OiDkhcCwW4Zo"
      },
      "source": [
        "final_df['us_voters_votes'].fillna(value=final_df['total_votes']-final_df['non_us_voters_votes'], inplace=True)\n",
        "final_df['us_voters_rating'].fillna(value=final_df['mean_vote'], inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHkQT-L2W4Zq"
      },
      "source": [
        "final_df['reviews_from_critics'].fillna(value=0, inplace=True)\n",
        "final_df['reviews_from_users'].fillna(value=0, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "df8taTtWW4Zs"
      },
      "source": [
        "conditions = [(final_df['reviews_from_users'] == final_df['reviews_from_critics']), \n",
        "              (final_df['reviews_from_users'] > final_df['reviews_from_critics']), \n",
        "              (final_df['reviews_from_users'] < final_df['reviews_from_critics'])]\n",
        "choices = [0, 1, 2]\n",
        "final_df['users_or_critics'] = np.select(conditions, choices, default=0)\n",
        "final_df.drop(['reviews_from_users', 'reviews_from_critics'], axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iGfHoMl6b7r9"
      },
      "source": [
        "### Statistic about cleaning data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "URnH6TAYcEVQ",
        "outputId": "71a1d778-e555-4a06-b544-995b86a223e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        }
      },
      "source": [
        "stats = pd.DataFrame({'rows':[imdb_df.shape[0],meta_data.shape[0],merge_df.shape[0],dataframe.shape[0],year_df.shape[0],new_df.shape[0],imdb_cast.shape[0],cast.shape[0],data.shape[0],imdb_rating.shape[0],final_df.shape[0]],'columns':[imdb_df.shape[1],meta_data.shape[1],merge_df.shape[1],dataframe.shape[1],year_df.shape[1],new_df.shape[1],imdb_cast.shape[1],cast.shape[1],data.shape[1],imdb_rating.shape[1],final_df.shape[1]]},index=['imdb_df','meta_data','merge_df','dataframe','year_df','new_df','imdb_cast','cast','data','imdb_rating','final_df'])\n",
        "stats"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>rows</th>\n",
              "      <th>columns</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>imdb_df</th>\n",
              "      <td>81273</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>meta_data</th>\n",
              "      <td>45466</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>merge_df</th>\n",
              "      <td>34521</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dataframe</th>\n",
              "      <td>9377</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year_df</th>\n",
              "      <td>7098</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>new_df</th>\n",
              "      <td>7098</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>imdb_cast</th>\n",
              "      <td>23811321</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cast</th>\n",
              "      <td>47888</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>data</th>\n",
              "      <td>6896</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>imdb_rating</th>\n",
              "      <td>81273</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>final_df</th>\n",
              "      <td>6896</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 rows  columns\n",
              "imdb_df         81273       10\n",
              "meta_data       45466        3\n",
              "merge_df        34521       12\n",
              "dataframe        9377       16\n",
              "year_df          7098       18\n",
              "new_df           7098       17\n",
              "imdb_cast    23811321        4\n",
              "cast            47888        8\n",
              "data             6896       49\n",
              "imdb_rating     81273       30\n",
              "final_df         6896       79"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOAVGJodOKkO",
        "outputId": "51df4bdf-00f3-4946-f1c4-9ca1971dfcd8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "final_df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 6896 entries, 0 to 6895\n",
            "Data columns (total 79 columns):\n",
            " #   Column                     Non-Null Count  Dtype         \n",
            "---  ------                     --------------  -----         \n",
            " 0   imdb_id                    6896 non-null   object        \n",
            " 1   title                      6896 non-null   object        \n",
            " 2   date_published             6896 non-null   datetime64[ns]\n",
            " 3   duration                   6896 non-null   int64         \n",
            " 4   budget                     6896 non-null   int64         \n",
            " 5   revenue                    6896 non-null   int64         \n",
            " 6   year                       6896 non-null   int64         \n",
            " 7   popularity                 6896 non-null   float64       \n",
            " 8   overview                   6896 non-null   object        \n",
            " 9   Month                      6896 non-null   int64         \n",
            " 10  Day                        6896 non-null   int64         \n",
            " 11  day_of_week                6896 non-null   object        \n",
            " 12  month_name                 6896 non-null   object        \n",
            " 13  profit                     6896 non-null   int64         \n",
            " 14  ROI                        6896 non-null   float64       \n",
            " 15  actor_movie_count          6896 non-null   float64       \n",
            " 16  actor_movie_pop            6896 non-null   float64       \n",
            " 17  actress_movie_count        6896 non-null   float64       \n",
            " 18  actress_movie_pop          6896 non-null   float64       \n",
            " 19  director_movie_count       6896 non-null   float64       \n",
            " 20  director_movie_pop         6896 non-null   float64       \n",
            " 21  writer_movie_count         6896 non-null   float64       \n",
            " 22  writer_movie_pop           6896 non-null   float64       \n",
            " 23  crew_count                 6896 non-null   float64       \n",
            " 24  crew_pop                   6896 non-null   float64       \n",
            " 25  genre_list                 6896 non-null   object        \n",
            " 26  genre_count                6896 non-null   int64         \n",
            " 27  Comedy                     6896 non-null   int64         \n",
            " 28  Crime                      6896 non-null   int64         \n",
            " 29  Horror                     6896 non-null   int64         \n",
            " 30  Biography                  6896 non-null   int64         \n",
            " 31  Drama                      6896 non-null   int64         \n",
            " 32  Action                     6896 non-null   int64         \n",
            " 33  Adventure                  6896 non-null   int64         \n",
            " 34  Mystery                    6896 non-null   int64         \n",
            " 35  Fantasy                    6896 non-null   int64         \n",
            " 36  Animation                  6896 non-null   int64         \n",
            " 37  Western                    6896 non-null   int64         \n",
            " 38  Family                     6896 non-null   int64         \n",
            " 39  Sci-Fi                     6896 non-null   int64         \n",
            " 40  Romance                    6896 non-null   int64         \n",
            " 41  Thriller                   6896 non-null   int64         \n",
            " 42  Musical                    6896 non-null   int64         \n",
            " 43  War                        6896 non-null   int64         \n",
            " 44  Sport                      6896 non-null   int64         \n",
            " 45  Music                      6896 non-null   int64         \n",
            " 46  History                    6896 non-null   int64         \n",
            " 47  total_votes                6896 non-null   int64         \n",
            " 48  mean_vote                  6896 non-null   float64       \n",
            " 49  median_vote                6896 non-null   float64       \n",
            " 50  allgenders_18age_avg_vote  6896 non-null   float64       \n",
            " 51  allgenders_18age_votes     6896 non-null   float64       \n",
            " 52  allgenders_30age_avg_vote  6896 non-null   float64       \n",
            " 53  allgenders_30age_votes     6896 non-null   float64       \n",
            " 54  allgenders_45age_avg_vote  6896 non-null   float64       \n",
            " 55  allgenders_45age_votes     6896 non-null   float64       \n",
            " 56  males_allages_avg_vote     6896 non-null   float64       \n",
            " 57  males_allages_votes        6896 non-null   float64       \n",
            " 58  males_18age_avg_vote       6896 non-null   float64       \n",
            " 59  males_18age_votes          6896 non-null   float64       \n",
            " 60  males_30age_avg_vote       6896 non-null   float64       \n",
            " 61  males_30age_votes          6896 non-null   float64       \n",
            " 62  males_45age_avg_vote       6896 non-null   float64       \n",
            " 63  males_45age_votes          6896 non-null   float64       \n",
            " 64  females_allages_avg_vote   6896 non-null   float64       \n",
            " 65  females_allages_votes      6896 non-null   float64       \n",
            " 66  females_18age_avg_vote     6896 non-null   float64       \n",
            " 67  females_18age_votes        6896 non-null   float64       \n",
            " 68  females_30age_avg_vote     6896 non-null   float64       \n",
            " 69  females_30age_votes        6896 non-null   float64       \n",
            " 70  females_45age_avg_vote     6896 non-null   float64       \n",
            " 71  females_45age_votes        6896 non-null   float64       \n",
            " 72  us_voters_rating           6896 non-null   float64       \n",
            " 73  us_voters_votes            6896 non-null   float64       \n",
            " 74  non_us_voters_rating       6896 non-null   float64       \n",
            " 75  non_us_voters_votes        6896 non-null   float64       \n",
            " 76  overview_count             6896 non-null   float64       \n",
            " 77  ROI_group                  6896 non-null   int64         \n",
            " 78  users_or_critics           6896 non-null   int64         \n",
            "dtypes: datetime64[ns](1), float64(41), int64(31), object(6)\n",
            "memory usage: 4.2+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rhf6h_MWW4Zu"
      },
      "source": [
        "## Genre point"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LuL8_s3xW4Zv",
        "outputId": "f3b0faaa-3c22-41b5-8dd8-692b145fc38d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        }
      },
      "source": [
        "final_df.columns.values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['imdb_id', 'title', 'date_published', 'duration', 'budget',\n",
              "       'revenue', 'year', 'popularity', 'overview', 'Month', 'Day',\n",
              "       'day_of_week', 'month_name', 'profit', 'ROI', 'actor_movie_count',\n",
              "       'actor_movie_pop', 'actress_movie_count', 'actress_movie_pop',\n",
              "       'director_movie_count', 'director_movie_pop', 'writer_movie_count',\n",
              "       'writer_movie_pop', 'crew_count', 'crew_pop', 'genre_list',\n",
              "       'genre_count', 'Comedy', 'Crime', 'Horror', 'Biography', 'Drama',\n",
              "       'Action', 'Adventure', 'Mystery', 'Fantasy', 'Animation',\n",
              "       'Western', 'Family', 'Sci-Fi', 'Romance', 'Thriller', 'Musical',\n",
              "       'War', 'Sport', 'Music', 'History', 'total_votes', 'mean_vote',\n",
              "       'median_vote', 'allgenders_18age_avg_vote',\n",
              "       'allgenders_18age_votes', 'allgenders_30age_avg_vote',\n",
              "       'allgenders_30age_votes', 'allgenders_45age_avg_vote',\n",
              "       'allgenders_45age_votes', 'males_allages_avg_vote',\n",
              "       'males_allages_votes', 'males_18age_avg_vote', 'males_18age_votes',\n",
              "       'males_30age_avg_vote', 'males_30age_votes',\n",
              "       'males_45age_avg_vote', 'males_45age_votes',\n",
              "       'females_allages_avg_vote', 'females_allages_votes',\n",
              "       'females_18age_avg_vote', 'females_18age_votes',\n",
              "       'females_30age_avg_vote', 'females_30age_votes',\n",
              "       'females_45age_avg_vote', 'females_45age_votes',\n",
              "       'us_voters_rating', 'us_voters_votes', 'non_us_voters_rating',\n",
              "       'non_us_voters_votes', 'overview_count', 'ROI_group',\n",
              "       'users_or_critics'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8KgzNM4jW4Zy"
      },
      "source": [
        "genre_df = final_df[['imdb_id','genre_list']].copy()\n",
        "genre_df = pd.DataFrame(genre_df['genre_list'].str.split(',').tolist(), index=genre_df['imdb_id']).stack()\n",
        "genre_df = genre_df.reset_index([0, 'imdb_id'])\n",
        "genre_df.columns = ['imdb_id', 'genre']\n",
        "merge_df = final_df.drop(['title','year','day_of_week','month_name',\n",
        "                          'date_published', 'genre_list', \n",
        "                          'Drama', 'Adventure', 'Comedy','Crime', 'Western', \n",
        "                          'Biography', 'Action', 'Animation', 'Horror','Family', 'Mystery', \n",
        "                          'Musical', 'Sci-Fi', 'Fantasy', 'Romance','Thriller', 'Music', 'War', \n",
        "                          'Sport', 'History','users_or_critics'], axis=1)\n",
        "genre_df = pd.merge(genre_df, merge_df, on='imdb_id')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iJD60t32W4Z0"
      },
      "source": [
        "genre_df['point'] = genre_df['popularity']/genre_df['genre_count']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_5YZkGrcX9Np"
      },
      "source": [
        "genre_revenue_month = genre_df[['genre','Month','revenue']].copy().groupby(['genre','Month'])['revenue'].mean().to_frame()\n",
        "genre_revenue_month.reset_index(inplace=True)\n",
        "genre_revenue_month = genre_revenue_month.groupby('genre')['revenue'].apply(list).to_frame()\n",
        "genre_revenue_month.reset_index(inplace=True)\n",
        "genre_profit_month = genre_df[['genre','Month','profit']].copy().groupby(['genre','Month'])['profit'].mean().to_frame()\n",
        "genre_profit_month.reset_index(inplace=True)\n",
        "genre_profit_month = genre_profit_month.groupby('genre')['profit'].apply(list).to_frame()\n",
        "genre_profit_month.reset_index(inplace=True)\n",
        "genre_m_r_p = pd.merge(left=genre_revenue_month, right=genre_profit_month, on='genre')\n",
        "genre_m_r_p.rename(columns={'revenue':'revenue_by_month','profit':'profit_by_month'}, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XZeM22-9W4Z4",
        "scrolled": false,
        "outputId": "8c8cf9f4-bd45-4aa6-fe1f-afb4ff901ffc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 695
        }
      },
      "source": [
        "genre_month = genre_df[['genre','Month']].copy().groupby('genre')['Month'].apply(list).to_frame()\n",
        "genre_month.reset_index(inplace=True)\n",
        "for i, row in genre_month.iterrows():\n",
        "    count = [0] * 12\n",
        "    for u in row.values[1]:\n",
        "        count[u-1] += 1\n",
        "    genre_month['Month'][i]= count\n",
        "print(sum(genre_month['Month'].values[0]))\n",
        "genre_month['Total'] = [sum(a) for a in genre_month['Month']]\n",
        "genre_month = pd.merge(left=genre_month, right=genre_m_r_p, on='genre')\n",
        "genre_month"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1707\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>genre</th>\n",
              "      <th>Month</th>\n",
              "      <th>Total</th>\n",
              "      <th>revenue_by_month</th>\n",
              "      <th>profit_by_month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Action</td>\n",
              "      <td>[143, 111, 133, 148, 128, 132, 170, 190, 160, ...</td>\n",
              "      <td>1707</td>\n",
              "      <td>[76939542.91608392, 77970583.53153153, 1116974...</td>\n",
              "      <td>[40981102.35664336, 40364277.225225225, 634417...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adventure</td>\n",
              "      <td>[60, 90, 97, 104, 72, 82, 159, 126, 60, 122, 7...</td>\n",
              "      <td>1189</td>\n",
              "      <td>[123193462.81666666, 127067644.42222223, 16394...</td>\n",
              "      <td>[73345129.48333333, 75888241.06666666, 1022923...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Animation</td>\n",
              "      <td>[9, 27, 30, 20, 13, 18, 59, 31, 12, 43, 12, 47]</td>\n",
              "      <td>321</td>\n",
              "      <td>[218296810.44444445, 208651958.44444445, 21407...</td>\n",
              "      <td>[154705143.7777778, 125735428.74074075, 153303...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Biography</td>\n",
              "      <td>[62, 47, 64, 27, 25, 25, 16, 18, 36, 34, 48, 20]</td>\n",
              "      <td>422</td>\n",
              "      <td>[99901144.59677419, 42811978.34042553, 4593100...</td>\n",
              "      <td>[67607596.20967741, 20375808.127659574, 202067...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Comedy</td>\n",
              "      <td>[186, 230, 253, 243, 221, 205, 217, 270, 228, ...</td>\n",
              "      <td>2730</td>\n",
              "      <td>[57930886.55913979, 73633777.57391304, 5535296...</td>\n",
              "      <td>[34281623.11827957, 43877821.05217391, 3147464...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Crime</td>\n",
              "      <td>[149, 103, 147, 138, 116, 99, 71, 117, 155, 12...</td>\n",
              "      <td>1403</td>\n",
              "      <td>[44101914.37583893, 55312115.291262135, 421963...</td>\n",
              "      <td>[20930390.88590604, 27937552.18446602, 1838277...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Drama</td>\n",
              "      <td>[402, 329, 347, 345, 293, 281, 209, 258, 366, ...</td>\n",
              "      <td>3745</td>\n",
              "      <td>[64733468.49751244, 45200692.89361702, 5106129...</td>\n",
              "      <td>[40538045.6119403, 23739853.069908816, 2985575...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Family</td>\n",
              "      <td>[12, 32, 32, 26, 28, 22, 56, 52, 10, 52, 25, 68]</td>\n",
              "      <td>415</td>\n",
              "      <td>[111103196.58333333, 87399212.625, 163707413.3...</td>\n",
              "      <td>[89915363.25, 46742962.625, 119863663.375, 721...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Fantasy</td>\n",
              "      <td>[41, 44, 50, 41, 43, 34, 43, 58, 36, 55, 41, 78]</td>\n",
              "      <td>564</td>\n",
              "      <td>[49420466.56097561, 83348958.97727273, 1388053...</td>\n",
              "      <td>[18967100.707317073, 42184868.06818182, 902327...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>History</td>\n",
              "      <td>[32, 33, 19, 23, 11, 14, 10, 14, 20, 16, 11, 17]</td>\n",
              "      <td>220</td>\n",
              "      <td>[103274381.65625, 42940329.18181818, 52338489....</td>\n",
              "      <td>[68680631.65625, 15064571.606060605, 19622700....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Horror</td>\n",
              "      <td>[72, 56, 60, 72, 68, 57, 40, 65, 80, 93, 66, 24]</td>\n",
              "      <td>753</td>\n",
              "      <td>[36442529.375, 48929317.125, 34811183.91666666...</td>\n",
              "      <td>[20303293.263888888, 31947174.26785714, 220965...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Music</td>\n",
              "      <td>[22, 19, 15, 14, 15, 12, 9, 25, 17, 28, 17, 14]</td>\n",
              "      <td>207</td>\n",
              "      <td>[55603576.04545455, 57749509.0, 41977645.66666...</td>\n",
              "      <td>[40262666.95454545, 36078456.368421055, 287343...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Musical</td>\n",
              "      <td>[6, 4, 3, 5, 2, 4, 7, 1, 7, 5, 6, 10]</td>\n",
              "      <td>60</td>\n",
              "      <td>[154149076.0, 43509155.75, 421505681.3333333, ...</td>\n",
              "      <td>[119565742.66666667, 15384155.75, 363005681.33...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Mystery</td>\n",
              "      <td>[67, 58, 52, 62, 43, 38, 28, 61, 60, 74, 36, 27]</td>\n",
              "      <td>606</td>\n",
              "      <td>[35061012.70149254, 54161521.98275862, 5312013...</td>\n",
              "      <td>[15403923.149253732, 29430142.672413792, 30075...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Romance</td>\n",
              "      <td>[97, 115, 101, 118, 113, 105, 66, 107, 115, 10...</td>\n",
              "      <td>1220</td>\n",
              "      <td>[72040335.88659793, 56375225.54782609, 5501329...</td>\n",
              "      <td>[47561263.72164948, 33076095.11304348, 3661631...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Sci-Fi</td>\n",
              "      <td>[32, 34, 36, 51, 39, 40, 53, 47, 41, 36, 43, 33]</td>\n",
              "      <td>485</td>\n",
              "      <td>[50274333.78125, 62187909.941176474, 142455875...</td>\n",
              "      <td>[21021208.78125, 34251145.23529412, 88644208.9...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Sport</td>\n",
              "      <td>[18, 14, 16, 20, 20, 14, 14, 16, 24, 18, 11, 4]</td>\n",
              "      <td>189</td>\n",
              "      <td>[65246044.05555555, 55574620.85714286, 4959860...</td>\n",
              "      <td>[44041599.61111111, 21717478.0, 26204859.5, 20...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Thriller</td>\n",
              "      <td>[112, 102, 98, 116, 100, 86, 59, 117, 128, 109...</td>\n",
              "      <td>1168</td>\n",
              "      <td>[49139491.946428575, 64783738.813725494, 45658...</td>\n",
              "      <td>[26662420.51785714, 39357562.34313726, 2063812...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>War</td>\n",
              "      <td>[10, 13, 12, 19, 4, 5, 5, 10, 14, 12, 13, 4]</td>\n",
              "      <td>121</td>\n",
              "      <td>[80059213.9, 24887103.230769232, 51793153.3333...</td>\n",
              "      <td>[44259213.9, 9756334.0, 24728153.333333332, 95...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Western</td>\n",
              "      <td>[4, 7, 2, 3, 7, 4, 0, 2, 4, 4, 3, 7]</td>\n",
              "      <td>47</td>\n",
              "      <td>[106664899.5, 55543050.571428575, 214628770.5,...</td>\n",
              "      <td>[77989899.5, 33285907.714285713, 202628770.5, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        genre  ...                                    profit_by_month\n",
              "0      Action  ...  [40981102.35664336, 40364277.225225225, 634417...\n",
              "1   Adventure  ...  [73345129.48333333, 75888241.06666666, 1022923...\n",
              "2   Animation  ...  [154705143.7777778, 125735428.74074075, 153303...\n",
              "3   Biography  ...  [67607596.20967741, 20375808.127659574, 202067...\n",
              "4      Comedy  ...  [34281623.11827957, 43877821.05217391, 3147464...\n",
              "5       Crime  ...  [20930390.88590604, 27937552.18446602, 1838277...\n",
              "6       Drama  ...  [40538045.6119403, 23739853.069908816, 2985575...\n",
              "7      Family  ...  [89915363.25, 46742962.625, 119863663.375, 721...\n",
              "8     Fantasy  ...  [18967100.707317073, 42184868.06818182, 902327...\n",
              "9     History  ...  [68680631.65625, 15064571.606060605, 19622700....\n",
              "10     Horror  ...  [20303293.263888888, 31947174.26785714, 220965...\n",
              "11      Music  ...  [40262666.95454545, 36078456.368421055, 287343...\n",
              "12    Musical  ...  [119565742.66666667, 15384155.75, 363005681.33...\n",
              "13    Mystery  ...  [15403923.149253732, 29430142.672413792, 30075...\n",
              "14    Romance  ...  [47561263.72164948, 33076095.11304348, 3661631...\n",
              "15     Sci-Fi  ...  [21021208.78125, 34251145.23529412, 88644208.9...\n",
              "16      Sport  ...  [44041599.61111111, 21717478.0, 26204859.5, 20...\n",
              "17   Thriller  ...  [26662420.51785714, 39357562.34313726, 2063812...\n",
              "18        War  ...  [44259213.9, 9756334.0, 24728153.333333332, 95...\n",
              "19    Western  ...  [77989899.5, 33285907.714285713, 202628770.5, ...\n",
              "\n",
              "[20 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SFih5MfJW4Z9"
      },
      "source": [
        "genre_point = genre_df.drop(['Month','popularity','genre_count'],axis=1).groupby('genre', as_index=False).mean()\n",
        "genre_act_count = genre_df[['genre']].groupby('genre', as_index=False).sum()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gPp6y_HkW4aF"
      },
      "source": [
        "genre_point_df= pd.merge(genre_point_df, genre_point, on='genre')\n",
        "genre_point_df= pd.merge(genre_point_df, genre_act_count, on='genre')\n",
        "genre_point_df= pd.merge(genre_point_df, genre_month, on='genre')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "auzdwC0xW4aG",
        "scrolled": true,
        "outputId": "058de2ca-febd-4ea0-f35d-884c2a03a6ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "genre_point_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>genre</th>\n",
              "      <th>duration</th>\n",
              "      <th>budget</th>\n",
              "      <th>revenue</th>\n",
              "      <th>Day</th>\n",
              "      <th>profit</th>\n",
              "      <th>ROI</th>\n",
              "      <th>actor_movie_count</th>\n",
              "      <th>actor_movie_pop</th>\n",
              "      <th>actress_movie_count</th>\n",
              "      <th>actress_movie_pop</th>\n",
              "      <th>director_movie_count</th>\n",
              "      <th>director_movie_pop</th>\n",
              "      <th>writer_movie_count</th>\n",
              "      <th>writer_movie_pop</th>\n",
              "      <th>crew_count</th>\n",
              "      <th>crew_pop</th>\n",
              "      <th>total_votes</th>\n",
              "      <th>mean_vote</th>\n",
              "      <th>median_vote</th>\n",
              "      <th>allgenders_18age_avg_vote</th>\n",
              "      <th>allgenders_18age_votes</th>\n",
              "      <th>allgenders_30age_avg_vote</th>\n",
              "      <th>allgenders_30age_votes</th>\n",
              "      <th>allgenders_45age_avg_vote</th>\n",
              "      <th>allgenders_45age_votes</th>\n",
              "      <th>males_allages_avg_vote</th>\n",
              "      <th>males_allages_votes</th>\n",
              "      <th>males_18age_avg_vote</th>\n",
              "      <th>males_18age_votes</th>\n",
              "      <th>males_30age_avg_vote</th>\n",
              "      <th>males_30age_votes</th>\n",
              "      <th>males_45age_avg_vote</th>\n",
              "      <th>males_45age_votes</th>\n",
              "      <th>females_allages_avg_vote</th>\n",
              "      <th>females_allages_votes</th>\n",
              "      <th>females_18age_avg_vote</th>\n",
              "      <th>females_18age_votes</th>\n",
              "      <th>females_30age_avg_vote</th>\n",
              "      <th>females_30age_votes</th>\n",
              "      <th>females_45age_avg_vote</th>\n",
              "      <th>females_45age_votes</th>\n",
              "      <th>us_voters_rating</th>\n",
              "      <th>us_voters_votes</th>\n",
              "      <th>non_us_voters_rating</th>\n",
              "      <th>non_us_voters_votes</th>\n",
              "      <th>overview_count</th>\n",
              "      <th>ROI_group</th>\n",
              "      <th>point</th>\n",
              "      <th>Month</th>\n",
              "      <th>Total</th>\n",
              "      <th>revenue_by_month</th>\n",
              "      <th>profit_by_month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Comedy</td>\n",
              "      <td>100.274359</td>\n",
              "      <td>2.619015e+07</td>\n",
              "      <td>6.763872e+07</td>\n",
              "      <td>15.928205</td>\n",
              "      <td>4.144857e+07</td>\n",
              "      <td>321.005584</td>\n",
              "      <td>2.144322</td>\n",
              "      <td>8.223010</td>\n",
              "      <td>1.233333</td>\n",
              "      <td>6.495735</td>\n",
              "      <td>1.011722</td>\n",
              "      <td>6.798847</td>\n",
              "      <td>1.317582</td>\n",
              "      <td>6.423742</td>\n",
              "      <td>3.377656</td>\n",
              "      <td>4.528848</td>\n",
              "      <td>59802.282784</td>\n",
              "      <td>6.271355</td>\n",
              "      <td>6.283883</td>\n",
              "      <td>6.196300</td>\n",
              "      <td>14372.642491</td>\n",
              "      <td>6.003150</td>\n",
              "      <td>25074.879121</td>\n",
              "      <td>5.957179</td>\n",
              "      <td>6457.964835</td>\n",
              "      <td>5.991648</td>\n",
              "      <td>37065.587546</td>\n",
              "      <td>6.120220</td>\n",
              "      <td>10067.470696</td>\n",
              "      <td>5.951319</td>\n",
              "      <td>19946.657875</td>\n",
              "      <td>5.906850</td>\n",
              "      <td>5258.098168</td>\n",
              "      <td>6.213883</td>\n",
              "      <td>10665.976923</td>\n",
              "      <td>6.308571</td>\n",
              "      <td>4107.586447</td>\n",
              "      <td>6.142784</td>\n",
              "      <td>4843.578388</td>\n",
              "      <td>6.190916</td>\n",
              "      <td>1102.217949</td>\n",
              "      <td>6.076300</td>\n",
              "      <td>11796.693773</td>\n",
              "      <td>5.968828</td>\n",
              "      <td>27282.023077</td>\n",
              "      <td>51.636996</td>\n",
              "      <td>0.588278</td>\n",
              "      <td>3.349708</td>\n",
              "      <td>[186, 230, 253, 243, 221, 205, 217, 270, 228, ...</td>\n",
              "      <td>2730</td>\n",
              "      <td>[57930886.55913979, 73633777.57391304, 5535296...</td>\n",
              "      <td>[34281623.11827957, 43877821.05217391, 3147464...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Crime</td>\n",
              "      <td>106.382038</td>\n",
              "      <td>2.338307e+07</td>\n",
              "      <td>4.469702e+07</td>\n",
              "      <td>16.119743</td>\n",
              "      <td>2.131395e+07</td>\n",
              "      <td>108.943233</td>\n",
              "      <td>2.478974</td>\n",
              "      <td>8.834156</td>\n",
              "      <td>0.980756</td>\n",
              "      <td>6.180335</td>\n",
              "      <td>1.058446</td>\n",
              "      <td>7.359247</td>\n",
              "      <td>1.179615</td>\n",
              "      <td>6.673653</td>\n",
              "      <td>3.459729</td>\n",
              "      <td>4.518612</td>\n",
              "      <td>73939.825374</td>\n",
              "      <td>6.451818</td>\n",
              "      <td>6.498931</td>\n",
              "      <td>6.457662</td>\n",
              "      <td>15595.183892</td>\n",
              "      <td>6.257947</td>\n",
              "      <td>32727.159658</td>\n",
              "      <td>6.218247</td>\n",
              "      <td>8615.768354</td>\n",
              "      <td>6.281397</td>\n",
              "      <td>50553.824661</td>\n",
              "      <td>6.431148</td>\n",
              "      <td>12637.128297</td>\n",
              "      <td>6.244975</td>\n",
              "      <td>28142.582324</td>\n",
              "      <td>6.192516</td>\n",
              "      <td>7317.635068</td>\n",
              "      <td>6.386172</td>\n",
              "      <td>8623.366358</td>\n",
              "      <td>6.516322</td>\n",
              "      <td>2754.655738</td>\n",
              "      <td>6.307627</td>\n",
              "      <td>4216.608696</td>\n",
              "      <td>6.390663</td>\n",
              "      <td>1167.818247</td>\n",
              "      <td>6.322452</td>\n",
              "      <td>12976.335709</td>\n",
              "      <td>6.245830</td>\n",
              "      <td>35674.390592</td>\n",
              "      <td>49.337135</td>\n",
              "      <td>0.491803</td>\n",
              "      <td>2.973960</td>\n",
              "      <td>[149, 103, 147, 138, 116, 99, 71, 117, 155, 12...</td>\n",
              "      <td>1403</td>\n",
              "      <td>[44101914.37583893, 55312115.291262135, 421963...</td>\n",
              "      <td>[20930390.88590604, 27937552.18446602, 1838277...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Horror</td>\n",
              "      <td>97.527224</td>\n",
              "      <td>1.492569e+07</td>\n",
              "      <td>3.790863e+07</td>\n",
              "      <td>15.880478</td>\n",
              "      <td>2.298294e+07</td>\n",
              "      <td>514839.471161</td>\n",
              "      <td>1.703851</td>\n",
              "      <td>7.315871</td>\n",
              "      <td>1.047809</td>\n",
              "      <td>6.045655</td>\n",
              "      <td>0.938911</td>\n",
              "      <td>7.117669</td>\n",
              "      <td>1.235060</td>\n",
              "      <td>7.029108</td>\n",
              "      <td>2.751660</td>\n",
              "      <td>5.008731</td>\n",
              "      <td>55929.452855</td>\n",
              "      <td>5.889774</td>\n",
              "      <td>5.906375</td>\n",
              "      <td>5.772643</td>\n",
              "      <td>11805.977424</td>\n",
              "      <td>5.683001</td>\n",
              "      <td>25298.569721</td>\n",
              "      <td>5.686720</td>\n",
              "      <td>6420.918991</td>\n",
              "      <td>5.685126</td>\n",
              "      <td>37139.342629</td>\n",
              "      <td>5.748340</td>\n",
              "      <td>8922.185923</td>\n",
              "      <td>5.664542</td>\n",
              "      <td>21124.641434</td>\n",
              "      <td>5.652590</td>\n",
              "      <td>5419.406375</td>\n",
              "      <td>5.806375</td>\n",
              "      <td>7959.628154</td>\n",
              "      <td>5.814874</td>\n",
              "      <td>2725.314741</td>\n",
              "      <td>5.763878</td>\n",
              "      <td>3889.501992</td>\n",
              "      <td>5.892430</td>\n",
              "      <td>903.338645</td>\n",
              "      <td>5.793758</td>\n",
              "      <td>10928.796813</td>\n",
              "      <td>5.624170</td>\n",
              "      <td>26432.739708</td>\n",
              "      <td>50.261620</td>\n",
              "      <td>0.579017</td>\n",
              "      <td>3.675093</td>\n",
              "      <td>[72, 56, 60, 72, 68, 57, 40, 65, 80, 93, 66, 24]</td>\n",
              "      <td>753</td>\n",
              "      <td>[36442529.375, 48929317.125, 34811183.91666666...</td>\n",
              "      <td>[20303293.263888888, 31947174.26785714, 220965...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Biography</td>\n",
              "      <td>120.424171</td>\n",
              "      <td>2.429407e+07</td>\n",
              "      <td>4.844800e+07</td>\n",
              "      <td>15.263033</td>\n",
              "      <td>2.415393e+07</td>\n",
              "      <td>115.279801</td>\n",
              "      <td>2.229858</td>\n",
              "      <td>9.255471</td>\n",
              "      <td>1.116114</td>\n",
              "      <td>6.809116</td>\n",
              "      <td>0.988152</td>\n",
              "      <td>7.975777</td>\n",
              "      <td>1.028436</td>\n",
              "      <td>6.540456</td>\n",
              "      <td>3.345972</td>\n",
              "      <td>5.189593</td>\n",
              "      <td>86917.080569</td>\n",
              "      <td>7.124171</td>\n",
              "      <td>7.194313</td>\n",
              "      <td>7.098341</td>\n",
              "      <td>20143.182464</td>\n",
              "      <td>6.917299</td>\n",
              "      <td>35805.571090</td>\n",
              "      <td>6.910900</td>\n",
              "      <td>9958.144550</td>\n",
              "      <td>6.926540</td>\n",
              "      <td>55924.433649</td>\n",
              "      <td>7.045261</td>\n",
              "      <td>15380.059242</td>\n",
              "      <td>6.873697</td>\n",
              "      <td>29364.218009</td>\n",
              "      <td>6.857583</td>\n",
              "      <td>8105.296209</td>\n",
              "      <td>7.116825</td>\n",
              "      <td>12995.104265</td>\n",
              "      <td>7.145972</td>\n",
              "      <td>4486.037915</td>\n",
              "      <td>7.038863</td>\n",
              "      <td>6004.616114</td>\n",
              "      <td>7.154265</td>\n",
              "      <td>1695.872038</td>\n",
              "      <td>6.986019</td>\n",
              "      <td>14231.452607</td>\n",
              "      <td>6.914929</td>\n",
              "      <td>40847.938389</td>\n",
              "      <td>46.362559</td>\n",
              "      <td>0.561611</td>\n",
              "      <td>2.965986</td>\n",
              "      <td>[62, 47, 64, 27, 25, 25, 16, 18, 36, 34, 48, 20]</td>\n",
              "      <td>422</td>\n",
              "      <td>[99901144.59677419, 42811978.34042553, 4593100...</td>\n",
              "      <td>[67607596.20967741, 20375808.127659574, 202067...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Drama</td>\n",
              "      <td>110.801602</td>\n",
              "      <td>2.083200e+07</td>\n",
              "      <td>4.543689e+07</td>\n",
              "      <td>15.896662</td>\n",
              "      <td>2.460489e+07</td>\n",
              "      <td>245.385992</td>\n",
              "      <td>2.053672</td>\n",
              "      <td>8.253303</td>\n",
              "      <td>1.179172</td>\n",
              "      <td>6.556880</td>\n",
              "      <td>0.911615</td>\n",
              "      <td>6.563624</td>\n",
              "      <td>0.915621</td>\n",
              "      <td>5.693019</td>\n",
              "      <td>3.232844</td>\n",
              "      <td>4.769477</td>\n",
              "      <td>68786.381842</td>\n",
              "      <td>6.704513</td>\n",
              "      <td>6.776636</td>\n",
              "      <td>6.663097</td>\n",
              "      <td>15575.387984</td>\n",
              "      <td>6.468785</td>\n",
              "      <td>29095.846195</td>\n",
              "      <td>6.451108</td>\n",
              "      <td>7912.269693</td>\n",
              "      <td>6.466061</td>\n",
              "      <td>43645.680107</td>\n",
              "      <td>6.603818</td>\n",
              "      <td>11409.989052</td>\n",
              "      <td>6.421682</td>\n",
              "      <td>23548.707877</td>\n",
              "      <td>6.398425</td>\n",
              "      <td>6459.576769</td>\n",
              "      <td>6.665180</td>\n",
              "      <td>11125.801335</td>\n",
              "      <td>6.735461</td>\n",
              "      <td>3949.947664</td>\n",
              "      <td>6.590227</td>\n",
              "      <td>5199.121495</td>\n",
              "      <td>6.680374</td>\n",
              "      <td>1328.833912</td>\n",
              "      <td>6.542884</td>\n",
              "      <td>11906.571696</td>\n",
              "      <td>6.453111</td>\n",
              "      <td>32454.803471</td>\n",
              "      <td>50.757810</td>\n",
              "      <td>0.489453</td>\n",
              "      <td>3.120886</td>\n",
              "      <td>[402, 329, 347, 345, 293, 281, 209, 258, 366, ...</td>\n",
              "      <td>3745</td>\n",
              "      <td>[64733468.49751244, 45200692.89361702, 5106129...</td>\n",
              "      <td>[40538045.6119403, 23739853.069908816, 2985575...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       genre  ...                                    profit_by_month\n",
              "0     Comedy  ...  [34281623.11827957, 43877821.05217391, 3147464...\n",
              "1      Crime  ...  [20930390.88590604, 27937552.18446602, 1838277...\n",
              "2     Horror  ...  [20303293.263888888, 31947174.26785714, 220965...\n",
              "3  Biography  ...  [67607596.20967741, 20375808.127659574, 202067...\n",
              "4      Drama  ...  [40538045.6119403, 23739853.069908816, 2985575...\n",
              "\n",
              "[5 rows x 53 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aDrj2kabW4aJ"
      },
      "source": [
        "genre_point_df.to_csv('/content/drive/My Drive/Colab Notebooks/Group Assignment/genre_point.csv',index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7LEp0oLoW4aL"
      },
      "source": [
        "final_df.to_csv('/content/drive/My Drive/Colab Notebooks/Group Assignment/movies.csv',index=False)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}