Imdb Data.ipynb

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    "colab": {
      "name": "Data Wrangling2.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMrj1+UKTQZMakI5s1K0+5C",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
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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/aryaparasharmrt/Complete-Python-3-Bootcamp/blob/master/Imdb%20Data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kc5PZKjQaHFy"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oN6c_nPoaen8",
        "outputId": "ad96b24e-d8b0-4265-f2b5-bfb973069671"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\") "
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aPumi2abZq23"
      },
      "source": [
        "working_dir_path = '/content/drive/MyDrive/CSV'\n",
        "df=pd.read_csv(working_dir_path + '/imdb_data.csv')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dKs6RHEDQL3L",
        "outputId": "9dda10af-7536-49e9-d937-149438fe6d2f"
      },
      "source": [
        "df['crew'][0:3]"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    [{'credit_id': '59ac067c92514107af02c8c8', 'de...\n",
              "1    [{'credit_id': '52fe43fe9251416c7502563d', 'de...\n",
              "2    [{'credit_id': '54d5356ec3a3683ba0000039', 'de...\n",
              "Name: crew, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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          "height": 266
        },
        "id": "4oqLEK1AZsn_",
        "outputId": "8b672645-3fba-47ed-fb28-9844fdf910d5"
      },
      "source": [
        "df.head(1)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\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>id</th>\n",
              "      <th>belongs_to_collection</th>\n",
              "      <th>budget</th>\n",
              "      <th>genres</th>\n",
              "      <th>homepage</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>runtime</th>\n",
              "      <th>spoken_languages</th>\n",
              "      <th>status</th>\n",
              "      <th>tagline</th>\n",
              "      <th>title</th>\n",
              "      <th>Keywords</th>\n",
              "      <th>cast</th>\n",
              "      <th>crew</th>\n",
              "      <th>revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>[{'id': 313576, 'name': 'Hot Tub Time Machine ...</td>\n",
              "      <td>14000000</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>tt2637294</td>\n",
              "      <td>en</td>\n",
              "      <td>Hot Tub Time Machine 2</td>\n",
              "      <td>When Lou, who has become the \"father of the In...</td>\n",
              "      <td>6.575393</td>\n",
              "      <td>/tQtWuwvMf0hCc2QR2tkolwl7c3c.jpg</td>\n",
              "      <td>[{'name': 'Paramount Pictures', 'id': 4}, {'na...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>2/20/15</td>\n",
              "      <td>93.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>The Laws of Space and Time are About to be Vio...</td>\n",
              "      <td>Hot Tub Time Machine 2</td>\n",
              "      <td>[{'id': 4379, 'name': 'time travel'}, {'id': 9...</td>\n",
              "      <td>[{'cast_id': 4, 'character': 'Lou', 'credit_id...</td>\n",
              "      <td>[{'credit_id': '59ac067c92514107af02c8c8', 'de...</td>\n",
              "      <td>12314651</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            "text/plain": [
              "   id  ...   revenue\n",
              "0   1  ...  12314651\n",
              "\n",
              "[1 rows x 23 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oATtrBmAa52J"
      },
      "source": [
        "df2=df[(df['runtime']>100) & (df['runtime']<150)]\n",
        "df3=df[(df['runtime']<=100)]\n",
        "df1=df[(df['runtime']>=150)]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M13kHV5NbXv2",
        "outputId": "268f77a4-5061-4d68-b3c5-ab41ace31b11"
      },
      "source": [
        "print(df1.shape[1])\n",
        "print(df2.shape[0])\n",
        "print(df3.shape[0])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "23\n",
            "1586\n",
            "1277\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IsibqFU_cTni",
        "outputId": "aeb20547-d46e-4132-aee4-2b7b024bc092"
      },
      "source": [
        "df1.shape[0]+df2.shape[0]+df3.shape[0]"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2998"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
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      "metadata": {
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          "height": 571
        },
        "id": "spFS48LEcpcK",
        "outputId": "e2761883-0b27-485f-caca-e504b66206fb"
      },
      "source": [
        ""
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2897\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2898\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2899\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 1",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-15ad416f67f2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m#df2.head(4)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m#df3.head(41`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2904\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2905\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2906\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2907\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2908\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2898\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2899\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2900\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2902\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 1"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TLF9UhNic_Uz"
      },
      "source": [
        "new_df=pd.concat([df1,df2])"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Cwu3bYO5Nhp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "506937c3-494e-4ac8-e03b-9d1f0419ab90"
      },
      "source": [
        "new_df"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
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              "\n",
              "[1721 rows x 23 columns]"
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              "      <td>/6bpthoORc53b2SEG3JwFBRZQnT5.jpg</td>\n",
              "      <td>[{'name': 'Touchstone Pictures', 'id': 9195}]</td>\n",
              "      <td>[{'iso_3166_1': 'GB', 'name': 'United Kingdom'...</td>\n",
              "      <td>8/18/11</td>\n",
              "      <td>106.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>You can't run from evil when it lives next door.</td>\n",
              "      <td>Fright Night</td>\n",
              "      <td>[{'id': 813, 'name': 'sunrise'}, {'id': 3133, ...</td>\n",
              "      <td>[{'cast_id': 9, 'character': 'Charley Brewster...</td>\n",
              "      <td>[{'credit_id': '52fe4956c3a36847f8194f53', 'de...</td>\n",
              "      <td>24922237</td>\n",
              "    </tr>\n",
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              "      <th>76</th>\n",
              "      <td>77</td>\n",
              "      <td>NaN</td>\n",
              "      <td>500000</td>\n",
              "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>tt0242572</td>\n",
              "      <td>ta</td>\n",
              "      <td>Kandukondain Kandukondain</td>\n",
              "      <td>Two young women with differing views on love f...</td>\n",
              "      <td>0.660663</td>\n",
              "      <td>/AnhaX3iivU7aLc6fLuAuHXTsbgO.jpg</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[{'iso_3166_1': 'IN', 'name': 'India'}]</td>\n",
              "      <td>5/5/00</td>\n",
              "      <td>157.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
              "      <td>Released</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Kandukondain Kandukondain</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[{'cast_id': 1, 'character': 'Manohar', 'credi...</td>\n",
              "      <td>[{'credit_id': '52fe47c7c3a368484e0d8f41', 'de...</td>\n",
              "      <td>500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-42427fdf-c49a-40a5-8a84-e050894a6b43')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-42427fdf-c49a-40a5-8a84-e050894a6b43 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-42427fdf-c49a-40a5-8a84-e050894a6b43');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "    id  ...   revenue\n",
              "75  76  ...  24922237\n",
              "76  77  ...    500000\n",
              "\n",
              "[2 rows x 23 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "liiXFPifKVVC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "outputId": "58f63086-237f-403c-e2fb-3eb6501a7089"
      },
      "source": [
        "new_df['geners'][0]"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2897\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2898\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2899\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'geners'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-9b269b57d4a6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnew_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'geners'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2904\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2905\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2906\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2907\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2908\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2898\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2899\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2900\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2902\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'geners'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-VnIipaag5mA"
      },
      "source": [
        "df4=df1.iloc[1:5,2:4]\n",
        "df5=df2.iloc[1:5,2:4]\n",
        "new_df1=pd.concat([df4,df5],axis=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "BZZJHEY7uudN",
        "outputId": "0c79235d-df44-4c4d-9b43-4a6aa6fd1ea9"
      },
      "source": [
        "new_df1"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-a02ce131bfc3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnew_df1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'new_df1' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hJ9ejusRuxPd"
      },
      "source": [
        "df4"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iwQKJfsiu5P3"
      },
      "source": [
        "df5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8U0udNCZvUZ_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "98231b98-7cb7-4e1f-f107-2e42ff6aed51"
      },
      "source": [
        " df"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "      <td>10/10/14</td>\n",
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              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
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              "      <td>Whiplash</td>\n",
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              "      <td>tt1821480</td>\n",
              "      <td>hi</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>3/9/12</td>\n",
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              "      <td>16000000</td>\n",
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              "      <td>3923970</td>\n",
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              "    <tr>\n",
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              "      <th>2995</th>\n",
              "      <td>2996</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
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              "      <td>9.853270</td>\n",
              "      <td>/j8Q7pQ27hvH54wpxJzIuQgQCdro.jpg</td>\n",
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              "      <td>4/22/94</td>\n",
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              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
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              "      <td>Chasers</td>\n",
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              "      <td>[{'cast_id': 2, 'character': 'Rock Reilly', 'c...</td>\n",
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              "      <td>sv</td>\n",
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              "      <td>3.727996</td>\n",
              "      <td>/sS01LSy6KDrCZAhtkO18UdnWFT1.jpg</td>\n",
              "      <td>[{'name': 'Memfis Film', 'id': 321}, {'name': ...</td>\n",
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              "      <td>3/28/13</td>\n",
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              "      <td>[{'iso_639_1': 'sv', 'name': 'svenska'}]</td>\n",
              "      <td>Released</td>\n",
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              "      <td>We Are the Best!</td>\n",
              "      <td>[{'id': 1192, 'name': 'sweden'}, {'id': 4470, ...</td>\n",
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              "    <tr>\n",
              "      <th>2997</th>\n",
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              "      <td>14.482345</td>\n",
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              "      <td>[{'name': 'New Line Cinema', 'id': 12}, {'name...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>10/11/96</td>\n",
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              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>What's forgotten is not always gone.</td>\n",
              "      <td>The Long Kiss Goodnight</td>\n",
              "      <td>[{'id': 441, 'name': 'assassination'}, {'id': ...</td>\n",
              "      <td>[{'cast_id': 10, 'character': 'Samantha Caine ...</td>\n",
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              "    <tr>\n",
              "      <th>2998</th>\n",
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              "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 10749, '...</td>\n",
              "      <td>http://www.alongcamepolly.com/</td>\n",
              "      <td>tt0343135</td>\n",
              "      <td>en</td>\n",
              "      <td>Along Came Polly</td>\n",
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              "      <td>15.725542</td>\n",
              "      <td>/nIY4kvJTTnxoBR0wycrXng5MOYs.jpg</td>\n",
              "      <td>[{'name': 'Jersey Films', 'id': 216}, {'name':...</td>\n",
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              "      <td>1/16/04</td>\n",
              "      <td>90.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>For the most cautious man on Earth, life is ab...</td>\n",
              "      <td>Along Came Polly</td>\n",
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              "      <td>[{'cast_id': 8, 'character': 'Reuben Feffer', ...</td>\n",
              "      <td>[{'credit_id': '556f817b9251410866000a63', 'de...</td>\n",
              "      <td>171963386</td>\n",
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              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>3000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35000000</td>\n",
              "      <td>[{'id': 53, 'name': 'Thriller'}, {'id': 28, 'n...</td>\n",
              "      <td>http://www.abductionthefilm.com/</td>\n",
              "      <td>tt1600195</td>\n",
              "      <td>en</td>\n",
              "      <td>Abduction</td>\n",
              "      <td>A young man sets out to uncover the truth abou...</td>\n",
              "      <td>10.512109</td>\n",
              "      <td>/cUT6NQP5LAJpmUoStGtXmvNt4zA.jpg</td>\n",
              "      <td>[{'name': 'Lions Gate Films', 'id': 35}, {'nam...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>9/22/11</td>\n",
              "      <td>106.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>They stole his life. He's taking it back.</td>\n",
              "      <td>Abduction</td>\n",
              "      <td>[{'id': 591, 'name': 'cia'}, {'id': 822, 'name...</td>\n",
              "      <td>[{'cast_id': 2, 'character': 'Nathan Harper', ...</td>\n",
              "      <td>[{'credit_id': '5391990d0e0a260fb5001629', 'de...</td>\n",
              "      <td>82087155</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3000 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5230caa4-e147-4374-83a3-1ff1876dab56')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "          const element = document.querySelector('#df-5230caa4-e147-4374-83a3-1ff1876dab56');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "        id  ...    revenue\n",
              "0        1  ...   12314651\n",
              "1        2  ...   95149435\n",
              "2        3  ...   13092000\n",
              "3        4  ...   16000000\n",
              "4        5  ...    3923970\n",
              "...    ...  ...        ...\n",
              "2995  2996  ...    1596687\n",
              "2996  2997  ...     180590\n",
              "2997  2998  ...   89456761\n",
              "2998  2999  ...  171963386\n",
              "2999  3000  ...   82087155\n",
              "\n",
              "[3000 rows x 23 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JTFwJ7dXyHQR"
      },
      "source": [
        "english_movies=df[df['original_language']=='en']"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "aZeME6uz5-Mv",
        "outputId": "accc65f6-f0c2-4800-d452-1b23c1d6ab26"
      },
      "source": [
        "english_movies"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>10/10/14</td>\n",
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              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
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              "      <td>Whiplash</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>83.0</td>\n",
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              "      <td>Pinocchio and the Emperor of the Night</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>The Possession</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[{'cast_id': 23, 'character': 'Clyde', 'credit...</td>\n",
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              "      <td>85446075</td>\n",
              "    </tr>\n",
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              "    <tr>\n",
              "      <th>2994</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'id': 18, 'name': 'Drama'}]</td>\n",
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              "      <td>en</td>\n",
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              "      <td>When David Greene receives a football scholars...</td>\n",
              "      <td>7.438381</td>\n",
              "      <td>/poV3j71mcmQkmjezc2H35xJsAhD.jpg</td>\n",
              "      <td>[{'name': 'Paramount Pictures', 'id': 4}]</td>\n",
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              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>Just because you’re accepted doesn’t mean you ...</td>\n",
              "      <td>School Ties</td>\n",
              "      <td>[{'id': 6075, 'name': 'sport'}, {'id': 10144, ...</td>\n",
              "      <td>[{'cast_id': 2, 'character': 'David Greene', '...</td>\n",
              "      <td>[{'credit_id': '5637777ac3a3681b4d01f9f5', 'de...</td>\n",
              "      <td>14715067</td>\n",
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              "      <td>9.853270</td>\n",
              "      <td>/j8Q7pQ27hvH54wpxJzIuQgQCdro.jpg</td>\n",
              "      <td>[{'name': 'Warner Bros.', 'id': 6194}, {'name'...</td>\n",
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              "      <td>Released</td>\n",
              "      <td>It was supposed to be a routine prisoner trans...</td>\n",
              "      <td>Chasers</td>\n",
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              "      <td>[{'cast_id': 2, 'character': 'Rock Reilly', 'c...</td>\n",
              "      <td>[{'credit_id': '52fe4494c3a368484e02ac7d', 'de...</td>\n",
              "      <td>1596687</td>\n",
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              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>2998</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>tt0116908</td>\n",
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              "      <td>Samantha Caine, suburban homemaker, is the ide...</td>\n",
              "      <td>14.482345</td>\n",
              "      <td>/4MENR8x6mYqnZvp2hGjSaPJz64J.jpg</td>\n",
              "      <td>[{'name': 'New Line Cinema', 'id': 12}, {'name...</td>\n",
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              "      <td>10/11/96</td>\n",
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              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>What's forgotten is not always gone.</td>\n",
              "      <td>The Long Kiss Goodnight</td>\n",
              "      <td>[{'id': 441, 'name': 'assassination'}, {'id': ...</td>\n",
              "      <td>[{'cast_id': 10, 'character': 'Samantha Caine ...</td>\n",
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              "      <th>2998</th>\n",
              "      <td>2999</td>\n",
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              "      <td>http://www.alongcamepolly.com/</td>\n",
              "      <td>tt0343135</td>\n",
              "      <td>en</td>\n",
              "      <td>Along Came Polly</td>\n",
              "      <td>Reuben Feffer is a guy who's spent his entire ...</td>\n",
              "      <td>15.725542</td>\n",
              "      <td>/nIY4kvJTTnxoBR0wycrXng5MOYs.jpg</td>\n",
              "      <td>[{'name': 'Jersey Films', 'id': 216}, {'name':...</td>\n",
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              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
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              "      <td>For the most cautious man on Earth, life is ab...</td>\n",
              "      <td>Along Came Polly</td>\n",
              "      <td>[{'id': 966, 'name': 'beach'}, {'id': 2676, 'n...</td>\n",
              "      <td>[{'cast_id': 8, 'character': 'Reuben Feffer', ...</td>\n",
              "      <td>[{'credit_id': '556f817b9251410866000a63', 'de...</td>\n",
              "      <td>171963386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>3000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35000000</td>\n",
              "      <td>[{'id': 53, 'name': 'Thriller'}, {'id': 28, 'n...</td>\n",
              "      <td>http://www.abductionthefilm.com/</td>\n",
              "      <td>tt1600195</td>\n",
              "      <td>en</td>\n",
              "      <td>Abduction</td>\n",
              "      <td>A young man sets out to uncover the truth abou...</td>\n",
              "      <td>10.512109</td>\n",
              "      <td>/cUT6NQP5LAJpmUoStGtXmvNt4zA.jpg</td>\n",
              "      <td>[{'name': 'Lions Gate Films', 'id': 35}, {'nam...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>9/22/11</td>\n",
              "      <td>106.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>They stole his life. He's taking it back.</td>\n",
              "      <td>Abduction</td>\n",
              "      <td>[{'id': 591, 'name': 'cia'}, {'id': 822, 'name...</td>\n",
              "      <td>[{'cast_id': 2, 'character': 'Nathan Harper', ...</td>\n",
              "      <td>[{'credit_id': '5391990d0e0a260fb5001629', 'de...</td>\n",
              "      <td>82087155</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2575 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c7fb35ab-f59f-4680-87c4-cd29f8b1bf87')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ],
            "text/plain": [
              "        id  ...    revenue\n",
              "0        1  ...   12314651\n",
              "1        2  ...   95149435\n",
              "2        3  ...   13092000\n",
              "5        6  ...    3261638\n",
              "6        7  ...   85446075\n",
              "...    ...  ...        ...\n",
              "2994  2995  ...   14715067\n",
              "2995  2996  ...    1596687\n",
              "2997  2998  ...   89456761\n",
              "2998  2999  ...  171963386\n",
              "2999  3000  ...   82087155\n",
              "\n",
              "[2575 rows x 23 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z8SYkQtj6hhK"
      },
      "source": [
        "watching_time=df.loc[df['runtime']>=100,['imdb_id','overview']]"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWzAs-_06y_F"
      },
      "source": [
        "merged_df=pd.merge(english_movies,watching_time,how='inner',left_on='imdb_id',right_on='imdb_id')"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wecCeWv-7NmU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "outputId": "68f5fb98-9ac9-4092-facc-8fb723f5cd1c"
      },
      "source": [
        "merged_df.head(3)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff44670f-b8fb-417d-9018-3dc9fb3bf3c4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-ff44670f-b8fb-417d-9018-3dc9fb3bf3c4');\n",
              "          const dataTable =\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "   id  ...                                         overview_y\n",
              "0   2  ...  Mia Thermopolis is now a college graduate and ...\n",
              "1   3  ...  Under the direction of a ruthless instructor, ...\n",
              "2   9  ...  After telling the story of Flint's last journe...\n",
              "\n",
              "[3 rows x 24 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gPpfM-AS7Pg0",
        "outputId": "eada85e5-4d31-4b8e-da64-94fd222f7d7d"
      },
      "source": [
        "\n",
        "merged_df.shape"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1528, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wnx490ZE7Z5-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f00058d5-2171-4ae3-f332-939ab3f4c6db"
      },
      "source": [
        "merged_df.columns"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'belongs_to_collection', 'budget', 'genres', 'homepage',\n",
              "       'imdb_id', 'original_language', 'original_title', 'overview_x',\n",
              "       'popularity', 'poster_path', 'production_companies',\n",
              "       'production_countries', 'release_date', 'runtime', 'spoken_languages',\n",
              "       'status', 'tagline', 'title', 'Keywords', 'cast', 'crew', 'revenue',\n",
              "       'overview_y'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QabEtcBs7itJ"
      },
      "source": [
        "temp=df[(df['original_language']=='en') & (df['runtime']>100)]"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "InFYhnc48PMB",
        "outputId": "4232fc48-d0dc-4755-ce51-40a995ddccb8"
      },
      "source": [
        "temp.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1436, 23)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5pZ1jsZM8bS5",
        "outputId": "b2d1b422-8e0f-424f-c3f3-79d69f4498a3"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 23)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UsVqEXov84tQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "7b94064e-bff5-49b1-84e5-9a6b0b243d42"
      },
      "source": [
        "merged_df1=pd.merge(english_movies,watching_time,how='right',on='imdb_id')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-21-0242bda56c78>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmerged_df1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menglish_movies\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwatching_time\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'right'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'imdb_id'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'english_movies' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ELuttFQ2Aypl"
      },
      "source": [
        "merged_df1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G06x_LMGA1ge"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5HTqSUm5QAXT"
      },
      "source": [
        "from datetime import datetime\n",
        "from datetime import date\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c2xWadYIBbTg"
      },
      "source": [
        "df['new_release_date']=df['release_date'].apply(lambda x: datetime.strptime(x,'%m/%d/%y'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FTIukRtiBZiU",
        "outputId": "ecdfec78-011f-48c8-a3ed-51bf5dbfcedc"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>[{'id': 2505, 'name': 'coronation'}, {'id': 42...</td>\n",
              "      <td>[{'cast_id': 1, 'character': 'Mia Thermopolis'...</td>\n",
              "      <td>[{'credit_id': '52fe43fe9251416c7502563d', 'de...</td>\n",
              "      <td>95149435</td>\n",
              "      <td>2004-08-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3300000</td>\n",
              "      <td>[{'id': 18, 'name': 'Drama'}]</td>\n",
              "      <td>http://sonyclassics.com/whiplash/</td>\n",
              "      <td>tt2582802</td>\n",
              "      <td>en</td>\n",
              "      <td>Whiplash</td>\n",
              "      <td>Under the direction of a ruthless instructor, ...</td>\n",
              "      <td>64.299990</td>\n",
              "      <td>/lIv1QinFqz4dlp5U4lQ6HaiskOZ.jpg</td>\n",
              "      <td>[{'name': 'Bold Films', 'id': 2266}, {'name': ...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>10/10/14</td>\n",
              "      <td>105.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>The road to greatness can take you to the edge.</td>\n",
              "      <td>Whiplash</td>\n",
              "      <td>[{'id': 1416, 'name': 'jazz'}, {'id': 1523, 'n...</td>\n",
              "      <td>[{'cast_id': 5, 'character': 'Andrew Neimann',...</td>\n",
              "      <td>[{'credit_id': '54d5356ec3a3683ba0000039', 'de...</td>\n",
              "      <td>13092000</td>\n",
              "      <td>2014-10-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1200000</td>\n",
              "      <td>[{'id': 53, 'name': 'Thriller'}, {'id': 18, 'n...</td>\n",
              "      <td>http://kahaanithefilm.com/</td>\n",
              "      <td>tt1821480</td>\n",
              "      <td>hi</td>\n",
              "      <td>Kahaani</td>\n",
              "      <td>Vidya Bagchi (Vidya Balan) arrives in Kolkata ...</td>\n",
              "      <td>3.174936</td>\n",
              "      <td>/aTXRaPrWSinhcmCrcfJK17urp3F.jpg</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[{'iso_3166_1': 'IN', 'name': 'India'}]</td>\n",
              "      <td>3/9/12</td>\n",
              "      <td>122.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
              "      <td>Released</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Kahaani</td>\n",
              "      <td>[{'id': 10092, 'name': 'mystery'}, {'id': 1054...</td>\n",
              "      <td>[{'cast_id': 1, 'character': 'Vidya Bagchi', '...</td>\n",
              "      <td>[{'credit_id': '52fe48779251416c9108d6eb', 'de...</td>\n",
              "      <td>16000000</td>\n",
              "      <td>2012-03-09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'id': 28, 'name': 'Action'}, {'id': 53, 'nam...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>tt1380152</td>\n",
              "      <td>ko</td>\n",
              "      <td>마린보이</td>\n",
              "      <td>Marine Boy is the story of a former national s...</td>\n",
              "      <td>1.148070</td>\n",
              "      <td>/m22s7zvkVFDU9ir56PiiqIEWFdT.jpg</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[{'iso_3166_1': 'KR', 'name': 'South Korea'}]</td>\n",
              "      <td>2/5/09</td>\n",
              "      <td>118.0</td>\n",
              "      <td>[{'iso_639_1': 'ko', 'name': '한국어/조선말'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Marine Boy</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[{'cast_id': 3, 'character': 'Chun-soo', 'cred...</td>\n",
              "      <td>[{'credit_id': '52fe464b9251416c75073b43', 'de...</td>\n",
              "      <td>3923970</td>\n",
              "      <td>2009-02-05</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2995</th>\n",
              "      <td>2996</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 10749, '...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>tt0109403</td>\n",
              "      <td>en</td>\n",
              "      <td>Chasers</td>\n",
              "      <td>Military men Rock Reilly and Eddie Devane are ...</td>\n",
              "      <td>9.853270</td>\n",
              "      <td>/j8Q7pQ27hvH54wpxJzIuQgQCdro.jpg</td>\n",
              "      <td>[{'name': 'Warner Bros.', 'id': 6194}, {'name'...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>4/22/94</td>\n",
              "      <td>102.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>It was supposed to be a routine prisoner trans...</td>\n",
              "      <td>Chasers</td>\n",
              "      <td>[{'id': 378, 'name': 'prison'}, {'id': 572, 'n...</td>\n",
              "      <td>[{'cast_id': 2, 'character': 'Rock Reilly', 'c...</td>\n",
              "      <td>[{'credit_id': '52fe4494c3a368484e02ac7d', 'de...</td>\n",
              "      <td>1596687</td>\n",
              "      <td>1994-04-22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2996</th>\n",
              "      <td>2997</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 10402, 'n...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>tt2364975</td>\n",
              "      <td>sv</td>\n",
              "      <td>Vi är bäst!</td>\n",
              "      <td>Three girls in 1980s Stockholm decide to form ...</td>\n",
              "      <td>3.727996</td>\n",
              "      <td>/sS01LSy6KDrCZAhtkO18UdnWFT1.jpg</td>\n",
              "      <td>[{'name': 'Memfis Film', 'id': 321}, {'name': ...</td>\n",
              "      <td>[{'iso_3166_1': 'DK', 'name': 'Denmark'}, {'is...</td>\n",
              "      <td>3/28/13</td>\n",
              "      <td>102.0</td>\n",
              "      <td>[{'iso_639_1': 'sv', 'name': 'svenska'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>NaN</td>\n",
              "      <td>We Are the Best!</td>\n",
              "      <td>[{'id': 1192, 'name': 'sweden'}, {'id': 4470, ...</td>\n",
              "      <td>[{'cast_id': 5, 'character': 'Bobo', 'credit_i...</td>\n",
              "      <td>[{'credit_id': '5716b72ac3a3686678012c84', 'de...</td>\n",
              "      <td>180590</td>\n",
              "      <td>2013-03-28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>2998</td>\n",
              "      <td>NaN</td>\n",
              "      <td>65000000</td>\n",
              "      <td>[{'id': 80, 'name': 'Crime'}, {'id': 28, 'name...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>tt0116908</td>\n",
              "      <td>en</td>\n",
              "      <td>The Long Kiss Goodnight</td>\n",
              "      <td>Samantha Caine, suburban homemaker, is the ide...</td>\n",
              "      <td>14.482345</td>\n",
              "      <td>/4MENR8x6mYqnZvp2hGjSaPJz64J.jpg</td>\n",
              "      <td>[{'name': 'New Line Cinema', 'id': 12}, {'name...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>10/11/96</td>\n",
              "      <td>120.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>What's forgotten is not always gone.</td>\n",
              "      <td>The Long Kiss Goodnight</td>\n",
              "      <td>[{'id': 441, 'name': 'assassination'}, {'id': ...</td>\n",
              "      <td>[{'cast_id': 10, 'character': 'Samantha Caine ...</td>\n",
              "      <td>[{'credit_id': '52fe443a9251416c7502d579', 'de...</td>\n",
              "      <td>89456761</td>\n",
              "      <td>1996-10-11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>2999</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42000000</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 10749, '...</td>\n",
              "      <td>http://www.alongcamepolly.com/</td>\n",
              "      <td>tt0343135</td>\n",
              "      <td>en</td>\n",
              "      <td>Along Came Polly</td>\n",
              "      <td>Reuben Feffer is a guy who's spent his entire ...</td>\n",
              "      <td>15.725542</td>\n",
              "      <td>/nIY4kvJTTnxoBR0wycrXng5MOYs.jpg</td>\n",
              "      <td>[{'name': 'Jersey Films', 'id': 216}, {'name':...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1/16/04</td>\n",
              "      <td>90.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>For the most cautious man on Earth, life is ab...</td>\n",
              "      <td>Along Came Polly</td>\n",
              "      <td>[{'id': 966, 'name': 'beach'}, {'id': 2676, 'n...</td>\n",
              "      <td>[{'cast_id': 8, 'character': 'Reuben Feffer', ...</td>\n",
              "      <td>[{'credit_id': '556f817b9251410866000a63', 'de...</td>\n",
              "      <td>171963386</td>\n",
              "      <td>2004-01-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>3000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35000000</td>\n",
              "      <td>[{'id': 53, 'name': 'Thriller'}, {'id': 28, 'n...</td>\n",
              "      <td>http://www.abductionthefilm.com/</td>\n",
              "      <td>tt1600195</td>\n",
              "      <td>en</td>\n",
              "      <td>Abduction</td>\n",
              "      <td>A young man sets out to uncover the truth abou...</td>\n",
              "      <td>10.512109</td>\n",
              "      <td>/cUT6NQP5LAJpmUoStGtXmvNt4zA.jpg</td>\n",
              "      <td>[{'name': 'Lions Gate Films', 'id': 35}, {'nam...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>9/22/11</td>\n",
              "      <td>106.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>They stole his life. He's taking it back.</td>\n",
              "      <td>Abduction</td>\n",
              "      <td>[{'id': 591, 'name': 'cia'}, {'id': 822, 'name...</td>\n",
              "      <td>[{'cast_id': 2, 'character': 'Nathan Harper', ...</td>\n",
              "      <td>[{'credit_id': '5391990d0e0a260fb5001629', 'de...</td>\n",
              "      <td>82087155</td>\n",
              "      <td>2011-09-22</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3000 rows × 24 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        id  ... new_release_date\n",
              "0        1  ...       2015-02-20\n",
              "1        2  ...       2004-08-06\n",
              "2        3  ...       2014-10-10\n",
              "3        4  ...       2012-03-09\n",
              "4        5  ...       2009-02-05\n",
              "...    ...  ...              ...\n",
              "2995  2996  ...       1994-04-22\n",
              "2996  2997  ...       2013-03-28\n",
              "2997  2998  ...       1996-10-11\n",
              "2998  2999  ...       2004-01-16\n",
              "2999  3000  ...       2011-09-22\n",
              "\n",
              "[3000 rows x 24 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X-E_b25hK4BE"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4-RYOK4yQ2ZM"
      },
      "source": [
        "def convert_year(date):\n",
        "  return date.year"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "soh4byJQRl7K"
      },
      "source": [
        "df['year_of_release']=df[\"new_release_date\"].apply(convert_year)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EXEROu4qTTP-",
        "outputId": "9e25888c-26b8-4f11-e0b7-72faad15002b"
      },
      "source": [
        "df['year_of_release']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       2015\n",
              "1       2004\n",
              "2       2014\n",
              "3       2012\n",
              "4       2009\n",
              "        ... \n",
              "2995    1994\n",
              "2996    2013\n",
              "2997    1996\n",
              "2998    2004\n",
              "2999    2011\n",
              "Name: year_of_release, Length: 3000, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c2p7iKIQTYXS"
      },
      "source": [
        "new_object=df.groupby('year_of_release').agg({'runtime':'max'})\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "D58jDKKcT7Te",
        "outputId": "e313fe85-5e8c-4177-a309-b5dacd2c2863"
      },
      "source": [
        "new_object"
      ],
      "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>runtime</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year_of_release</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1969</th>\n",
              "      <td>149.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1970</th>\n",
              "      <td>137.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1971</th>\n",
              "      <td>136.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1972</th>\n",
              "      <td>175.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1973</th>\n",
              "      <td>151.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2064</th>\n",
              "      <td>188.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2065</th>\n",
              "      <td>160.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2066</th>\n",
              "      <td>189.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2067</th>\n",
              "      <td>152.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2068</th>\n",
              "      <td>176.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>90 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 runtime\n",
              "year_of_release         \n",
              "1969               149.0\n",
              "1970               137.0\n",
              "1971               136.0\n",
              "1972               175.0\n",
              "1973               151.0\n",
              "...                  ...\n",
              "2064               188.0\n",
              "2065               160.0\n",
              "2066               189.0\n",
              "2067               152.0\n",
              "2068               176.0\n",
              "\n",
              "[90 rows x 1 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TNGr14PgWRUZ"
      },
      "source": [
        "new_object2=df.groupby(['year_of_release','original_language']).agg({'runtime':'max'} )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
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        "id": "jGtoCspjbRnt",
        "outputId": "9cb68fe6-f3a0-466b-db43-8425d8b67fde"
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      "source": [
        "new_object2"
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>runtime</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year_of_release</th>\n",
              "      <th>original_language</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th rowspan=\"2\" valign=\"top\">1969</th>\n",
              "      <th>en</th>\n",
              "      <td>149.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fr</th>\n",
              "      <td>145.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">1970</th>\n",
              "      <th>en</th>\n",
              "      <td>137.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fr</th>\n",
              "      <td>126.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1971</th>\n",
              "      <th>en</th>\n",
              "      <td>136.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">2066</th>\n",
              "      <th>en</th>\n",
              "      <td>189.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>it</th>\n",
              "      <td>121.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2067</th>\n",
              "      <th>en</th>\n",
              "      <td>152.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">2068</th>\n",
              "      <th>en</th>\n",
              "      <td>176.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fr</th>\n",
              "      <td>107.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>348 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   runtime\n",
              "year_of_release original_language         \n",
              "1969            en                   149.0\n",
              "                fr                   145.0\n",
              "1970            en                   137.0\n",
              "                fr                   126.0\n",
              "1971            en                   136.0\n",
              "...                                    ...\n",
              "2066            en                   189.0\n",
              "                it                   121.0\n",
              "2067            en                   152.0\n",
              "2068            en                   176.0\n",
              "                fr                   107.0\n",
              "\n",
              "[348 rows x 1 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-zPYMf_zbUpm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        },
        "outputId": "43f2e2f9-b7bc-4756-8517-d384489e8dc2"
      },
      "source": [
        "df.loc[]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-32-1008ffe8599e>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    df.loc[]\u001b[0m\n\u001b[0m           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l2wdDw6wMvVw"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}