netflix_data_analysis.ipynb

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  "metadata": {
    "colab": {
      "name": "sdi1700082",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/NikosMav/DataAnalysis-Netflix/blob/main/netflix_data_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OqTPq7XkYtSr"
      },
      "source": [
        "**Τεχνικές Εξόρυξης Δεδομένων (ΥΣ11)**\n",
        "\n",
        "**1η Προγραμματιστική Άσκηση**\n",
        "\n",
        "> **Φοιτητής**: Νικόλαος Μαυραπίδης(sdi1700082)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-jjPXI3t0Aji",
        "outputId": "65676073-ab57-410e-f4b3-fc831b8c6946"
      },
      "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": "ZHMbEBtqMLqB"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "from collections import Counter\n",
        "import plotly.graph_objects as go\n",
        "from plotly.offline import init_notebook_mode, iplot\n",
        "from country_list import countries_for_language\n",
        "from plotly.subplots import make_subplots\n",
        "import plotly.express as px"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UGRKYKQXyzIZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3892df13-9cb3-4aa7-ddbb-71770a397172"
      },
      "source": [
        "# Leaving the csv path as it is\n",
        "netflix_dataframe = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Τεδε/Ergasia1/netflix_titles.csv')\n",
        "\n",
        "# Checking null values\n",
        "netflix_dataframe.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "show_id            0\n",
              "type               0\n",
              "title              0\n",
              "director        2389\n",
              "cast             718\n",
              "country          507\n",
              "date_added        10\n",
              "release_year       0\n",
              "rating             7\n",
              "duration           0\n",
              "listed_in          0\n",
              "description        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3raqVitj0Gx_"
      },
      "source": [
        "# ***Data Cleaning***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cRz2BI_m4Icn"
      },
      "source": [
        "As we can see our data frame has some missing data. We can manage that by replacing the missing values.\n",
        "\n",
        "First of all, we will fill every non existing Director and Cast cell with the word \"Unknown\". This will make our data more manageable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SZhiQ5U24J5k",
        "outputId": "f8e243f8-1043-46f7-e11c-2f24cbff7678"
      },
      "source": [
        "netflix_dataframe['director'].replace(np.nan,\"Unknown\",inplace=True)\n",
        "netflix_dataframe['cast'].replace(np.nan,\"Unknown\",inplace=True)\n",
        "netflix_dataframe.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "show_id           0\n",
              "type              0\n",
              "title             0\n",
              "director          0\n",
              "cast              0\n",
              "country         507\n",
              "date_added       10\n",
              "release_year      0\n",
              "rating            7\n",
              "duration          0\n",
              "listed_in         0\n",
              "description       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xdMm_wiUze_c"
      },
      "source": [
        "Country column is important for our visualization & since it has some null values we can replace them by United States, since United States has the largest nummber of shows."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N0Z0oyF1zdnl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "5dd621ae-3a47-4b7f-a515-e80c867018c8"
      },
      "source": [
        "netflix_dataframe['country'].value_counts().to_frame().head(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>United States</th>\n",
              "      <td>2555</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               country\n",
              "United States     2555"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ncta-Z7lCM3-"
      },
      "source": [
        "netflix_dataframe['country'].replace(np.nan,\"United States\",inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ngcHoVe8BwSo"
      },
      "source": [
        "We follow the same approach for 'date_added'."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "DHWEd5pvByGV",
        "outputId": "e2bddd14-78a7-47a9-895a-f25dfad6ad9b"
      },
      "source": [
        "netflix_dataframe['date_added'].value_counts().to_frame().head(1)"
      ],
      "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 thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date_added</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>January 1, 2020</th>\n",
              "      <td>118</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 date_added\n",
              "January 1, 2020         118"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QT4DkYUMCU2C"
      },
      "source": [
        "netflix_dataframe['date_added'].replace(np.nan,\"January 1,2020\",inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ERX9Xdkq0buU"
      },
      "source": [
        "Release month is important for visualizing our data, so let's separate the month from the date_added column, create a new column for release months only and concat it to our dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FufL_WAz0bJd"
      },
      "source": [
        "netflix_date = netflix_dataframe['date_added'].apply(lambda x: x.lstrip().split(\" \")[0]).to_frame()\n",
        "netflix_date.rename(columns = {'date_added' : 'release_month'}, inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "8tZ3ETj-1ZDZ",
        "outputId": "b31b150b-b4e1-42ea-fc60-5a2c114607c7"
      },
      "source": [
        "netflix_df_new = pd.concat([netflix_dataframe,netflix_date],axis=1)\n",
        "netflix_df_new.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>show_id</th>\n",
              "      <th>type</th>\n",
              "      <th>title</th>\n",
              "      <th>director</th>\n",
              "      <th>cast</th>\n",
              "      <th>country</th>\n",
              "      <th>date_added</th>\n",
              "      <th>release_year</th>\n",
              "      <th>rating</th>\n",
              "      <th>duration</th>\n",
              "      <th>listed_in</th>\n",
              "      <th>description</th>\n",
              "      <th>release_month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>s1</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>3%</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>João Miguel, Bianca Comparato, Michel Gomes, R...</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>August 14, 2020</td>\n",
              "      <td>2020</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td>4 Seasons</td>\n",
              "      <td>International TV Shows, TV Dramas, TV Sci-Fi &amp;...</td>\n",
              "      <td>In a future where the elite inhabit an island ...</td>\n",
              "      <td>August</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>s2</td>\n",
              "      <td>Movie</td>\n",
              "      <td>7:19</td>\n",
              "      <td>Jorge Michel Grau</td>\n",
              "      <td>Demián Bichir, Héctor Bonilla, Oscar Serrano, ...</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>December 23, 2016</td>\n",
              "      <td>2016</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td>93 min</td>\n",
              "      <td>Dramas, International Movies</td>\n",
              "      <td>After a devastating earthquake hits Mexico Cit...</td>\n",
              "      <td>December</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>s3</td>\n",
              "      <td>Movie</td>\n",
              "      <td>23:59</td>\n",
              "      <td>Gilbert Chan</td>\n",
              "      <td>Tedd Chan, Stella Chung, Henley Hii, Lawrence ...</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>December 20, 2018</td>\n",
              "      <td>2011</td>\n",
              "      <td>R</td>\n",
              "      <td>78 min</td>\n",
              "      <td>Horror Movies, International Movies</td>\n",
              "      <td>When an army recruit is found dead, his fellow...</td>\n",
              "      <td>December</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>s4</td>\n",
              "      <td>Movie</td>\n",
              "      <td>9</td>\n",
              "      <td>Shane Acker</td>\n",
              "      <td>Elijah Wood, John C. Reilly, Jennifer Connelly...</td>\n",
              "      <td>United States</td>\n",
              "      <td>November 16, 2017</td>\n",
              "      <td>2009</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>80 min</td>\n",
              "      <td>Action &amp; Adventure, Independent Movies, Sci-Fi...</td>\n",
              "      <td>In a postapocalyptic world, rag-doll robots hi...</td>\n",
              "      <td>November</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>s5</td>\n",
              "      <td>Movie</td>\n",
              "      <td>21</td>\n",
              "      <td>Robert Luketic</td>\n",
              "      <td>Jim Sturgess, Kevin Spacey, Kate Bosworth, Aar...</td>\n",
              "      <td>United States</td>\n",
              "      <td>January 1, 2020</td>\n",
              "      <td>2008</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>123 min</td>\n",
              "      <td>Dramas</td>\n",
              "      <td>A brilliant group of students become card-coun...</td>\n",
              "      <td>January</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  show_id  ... release_month\n",
              "0      s1  ...        August\n",
              "1      s2  ...      December\n",
              "2      s3  ...      December\n",
              "3      s4  ...      November\n",
              "4      s5  ...       January\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uOAvpJji3DRL"
      },
      "source": [
        "Lets clean the rating column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YufCvfez3C38",
        "outputId": "655f100c-4210-471f-c432-619a52d3895a"
      },
      "source": [
        "netflix_df_new[\"rating\"].isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "E0R8XLanb1ln",
        "outputId": "eb015822-9e55-4f7f-e8e0-a2908e25fb0a"
      },
      "source": [
        "netflix_df_new['rating'].value_counts().to_frame().head(1)"
      ],
      "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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>TV-MA</th>\n",
              "      <td>2863</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       rating\n",
              "TV-MA    2863"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Acn4jbQ3Zp0"
      },
      "source": [
        "Rating column has only 7 null values let's replace the null values with TV-MA since they make up the most amount of Ratings."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mbMbUTzs3nQ9",
        "outputId": "b49434e0-a63f-4c44-d057-850c3b12b663"
      },
      "source": [
        "netflix_df_new[\"rating\"].replace(np.nan,\"TV-MA\",inplace=True)\n",
        "netflix_df_new.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "show_id          0\n",
              "type             0\n",
              "title            0\n",
              "director         0\n",
              "cast             0\n",
              "country          0\n",
              "date_added       0\n",
              "release_year     0\n",
              "rating           0\n",
              "duration         0\n",
              "listed_in        0\n",
              "description      0\n",
              "release_month    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qbkj9uLrbNJU"
      },
      "source": [
        "We eliminated all Null values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c2G2XQjqTgel"
      },
      "source": [
        "Add season_count and duration features since they are important for visualizing our data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lCxW00XpT7cf"
      },
      "source": [
        "# Count the seasons and create a new column.\n",
        "netflix_df_new['season_count'] = netflix_df_new.apply(lambda x : x['duration'].split(\" \")[0] if \"Season\" in x['duration'] else \"\", axis = 1)\n",
        "netflix_df_new['season_count'] = pd.to_numeric(netflix_df_new['season_count'],errors='coerce')\n",
        "netflix_df_new = netflix_df_new.replace(np.nan, 0, regex=True)\n",
        "netflix_df_new['season_count'] = pd.to_numeric(netflix_df_new['season_count'], downcast ='signed')\n",
        "# Replace duration column with just the number of minutes.\n",
        "netflix_df_new['duration'] = netflix_df_new.apply(lambda x : x['duration'].split(\" \")[0] if \"Season\" not in x['duration'] else \"\", axis = 1)\n",
        "netflix_df_new['duration'] = pd.to_numeric(netflix_df_new['duration'],errors='coerce')\n",
        "netflix_df_new = netflix_df_new.replace(np.nan, 0, regex=True)\n",
        "netflix_df_new['duration'] = pd.to_numeric(netflix_df_new['duration'], downcast ='signed')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "4POgPIGjUKvS",
        "outputId": "01557872-c2fb-4e0c-d3da-255a00f87aa3"
      },
      "source": [
        "netflix_df_new.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>show_id</th>\n",
              "      <th>type</th>\n",
              "      <th>title</th>\n",
              "      <th>director</th>\n",
              "      <th>cast</th>\n",
              "      <th>country</th>\n",
              "      <th>date_added</th>\n",
              "      <th>release_year</th>\n",
              "      <th>rating</th>\n",
              "      <th>duration</th>\n",
              "      <th>listed_in</th>\n",
              "      <th>description</th>\n",
              "      <th>release_month</th>\n",
              "      <th>season_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>s1</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>3%</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>João Miguel, Bianca Comparato, Michel Gomes, R...</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>August 14, 2020</td>\n",
              "      <td>2020</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td>0</td>\n",
              "      <td>International TV Shows, TV Dramas, TV Sci-Fi &amp;...</td>\n",
              "      <td>In a future where the elite inhabit an island ...</td>\n",
              "      <td>August</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>s2</td>\n",
              "      <td>Movie</td>\n",
              "      <td>7:19</td>\n",
              "      <td>Jorge Michel Grau</td>\n",
              "      <td>Demián Bichir, Héctor Bonilla, Oscar Serrano, ...</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>December 23, 2016</td>\n",
              "      <td>2016</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td>93</td>\n",
              "      <td>Dramas, International Movies</td>\n",
              "      <td>After a devastating earthquake hits Mexico Cit...</td>\n",
              "      <td>December</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>s3</td>\n",
              "      <td>Movie</td>\n",
              "      <td>23:59</td>\n",
              "      <td>Gilbert Chan</td>\n",
              "      <td>Tedd Chan, Stella Chung, Henley Hii, Lawrence ...</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>December 20, 2018</td>\n",
              "      <td>2011</td>\n",
              "      <td>R</td>\n",
              "      <td>78</td>\n",
              "      <td>Horror Movies, International Movies</td>\n",
              "      <td>When an army recruit is found dead, his fellow...</td>\n",
              "      <td>December</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>s4</td>\n",
              "      <td>Movie</td>\n",
              "      <td>9</td>\n",
              "      <td>Shane Acker</td>\n",
              "      <td>Elijah Wood, John C. Reilly, Jennifer Connelly...</td>\n",
              "      <td>United States</td>\n",
              "      <td>November 16, 2017</td>\n",
              "      <td>2009</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>80</td>\n",
              "      <td>Action &amp; Adventure, Independent Movies, Sci-Fi...</td>\n",
              "      <td>In a postapocalyptic world, rag-doll robots hi...</td>\n",
              "      <td>November</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>s5</td>\n",
              "      <td>Movie</td>\n",
              "      <td>21</td>\n",
              "      <td>Robert Luketic</td>\n",
              "      <td>Jim Sturgess, Kevin Spacey, Kate Bosworth, Aar...</td>\n",
              "      <td>United States</td>\n",
              "      <td>January 1, 2020</td>\n",
              "      <td>2008</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>123</td>\n",
              "      <td>Dramas</td>\n",
              "      <td>A brilliant group of students become card-coun...</td>\n",
              "      <td>January</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>s6</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>46</td>\n",
              "      <td>Serdar Akar</td>\n",
              "      <td>Erdal Beşikçioğlu, Yasemin Allen, Melis Birkan...</td>\n",
              "      <td>Turkey</td>\n",
              "      <td>July 1, 2017</td>\n",
              "      <td>2016</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td>0</td>\n",
              "      <td>International TV Shows, TV Dramas, TV Mysteries</td>\n",
              "      <td>A genetics professor experiments with a treatm...</td>\n",
              "      <td>July</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>s7</td>\n",
              "      <td>Movie</td>\n",
              "      <td>122</td>\n",
              "      <td>Yasir Al Yasiri</td>\n",
              "      <td>Amina Khalil, Ahmed Dawood, Tarek Lotfy, Ahmed...</td>\n",
              "      <td>Egypt</td>\n",
              "      <td>June 1, 2020</td>\n",
              "      <td>2019</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td>95</td>\n",
              "      <td>Horror Movies, International Movies</td>\n",
              "      <td>After an awful accident, a couple admitted to ...</td>\n",
              "      <td>June</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>s8</td>\n",
              "      <td>Movie</td>\n",
              "      <td>187</td>\n",
              "      <td>Kevin Reynolds</td>\n",
              "      <td>Samuel L. Jackson, John Heard, Kelly Rowan, Cl...</td>\n",
              "      <td>United States</td>\n",
              "      <td>November 1, 2019</td>\n",
              "      <td>1997</td>\n",
              "      <td>R</td>\n",
              "      <td>119</td>\n",
              "      <td>Dramas</td>\n",
              "      <td>After one of his high school students attacks ...</td>\n",
              "      <td>November</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>s9</td>\n",
              "      <td>Movie</td>\n",
              "      <td>706</td>\n",
              "      <td>Shravan Kumar</td>\n",
              "      <td>Divya Dutta, Atul Kulkarni, Mohan Agashe, Anup...</td>\n",
              "      <td>India</td>\n",
              "      <td>April 1, 2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>TV-14</td>\n",
              "      <td>118</td>\n",
              "      <td>Horror Movies, International Movies</td>\n",
              "      <td>When a doctor goes missing, his psychiatrist w...</td>\n",
              "      <td>April</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>s10</td>\n",
              "      <td>Movie</td>\n",
              "      <td>1920</td>\n",
              "      <td>Vikram Bhatt</td>\n",
              "      <td>Rajneesh Duggal, Adah Sharma, Indraneil Sengup...</td>\n",
              "      <td>India</td>\n",
              "      <td>December 15, 2017</td>\n",
              "      <td>2008</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td>143</td>\n",
              "      <td>Horror Movies, International Movies, Thrillers</td>\n",
              "      <td>An architect and his wife move into a castle t...</td>\n",
              "      <td>December</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  show_id     type  ... release_month season_count\n",
              "0      s1  TV Show  ...        August            4\n",
              "1      s2    Movie  ...      December            0\n",
              "2      s3    Movie  ...      December            0\n",
              "3      s4    Movie  ...      November            0\n",
              "4      s5    Movie  ...       January            0\n",
              "5      s6  TV Show  ...          July            1\n",
              "6      s7    Movie  ...          June            0\n",
              "7      s8    Movie  ...      November            0\n",
              "8      s9    Movie  ...         April            0\n",
              "9     s10    Movie  ...      December            0\n",
              "\n",
              "[10 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VorE2B8_358f"
      },
      "source": [
        "So we successfully removed all the Null Values and preprocessed all our Data. Now we can visualize our Data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Ob1ebha2iBe"
      },
      "source": [
        "# ***Visualization***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E_1Tr80ZWtpS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "outputId": "4ccc89e6-490e-4ee3-c92a-1c97bf5c6070"
      },
      "source": [
        "## 1. Ποιό είδος υπερτερεί, οι ταινίες ή οι σειρές ;\n",
        "print(netflix_dataframe['type'].value_counts())\n",
        "\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.countplot(x='type', data=netflix_dataframe, order=netflix_dataframe.type.value_counts().index[0:2])\n",
        "plt.title('Movies or TV Shows', size=18);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Movie      5377\n",
            "TV Show    2410\n",
            "Name: type, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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n74wnSVLHDHpJkjpm0EuS1DGDXpKkjhn0kiR1zKCXJKljBr0kSR0z6CVJ6phBL0lSxwx6SZI6ZtBLktQxg16SpI4Z9JIkdcyglySpYwa9JEkdM+glSeqYQS9JUscMekmSOmbQS5LUMYNekqSOGfSSJHXMoJckqWMGvSRJHTPoJUnqmEEvSVLHDHpJkjpm0EuS1DGDXpKkjhn0kiR1zKCXJKljBr0kSR0z6CVJ6phBL0lSxwx6SZI6ZtBLktQxg16SpI4Z9JIkdcyglySpYwa9JEkdM+glSerYrII+ybmzKZs0/75JLkzyhSSXJ3ltK39okguSrEvyviT3buX3adPr2vylI+t6dSv/cpJDNmcHJUnalm0y6FtY7wbsnmTXJLu1x1JgzxnWfQfwtKp6FLAfcGiSA4E3ASdV1cOBm4FjW/1jgZtb+UmtHkn2AY4EHgkcCrwjyfabv6uSJG17Zjqj/z3gYuAX2/PE4yzg7ZtasAa3tcl7tUcBTwM+0MpXAoe314e1adr8g5KklZ9RVXdU1deAdcABs9o7SZK2cZsM+qr666p6KPCKqvq5qnpoezyqqjYZ9ABJtk9yCXAjsBr4KvDdqtrYqlzHj3sG9gS+0ba7EbgFeMBo+RTLjG7ruCRrk6xdv379TE2TJGmbsMNsKlXV25I8Hlg6ukxVrZphuTuB/ZLsAnyYoWdgLKrqFOAUgOXLl9e4tiNJ0mIyq6BP8i7gYcAlwJ2tuIBNBv2Eqvpukk8CjwN2SbJDO2vfC7i+Vbse2Bu4LskOwP2Bm0bKJ4wuI0mSNmFWQQ8sB/apqlmfKSdZAvyohfyOwDMYBth9EjgCOANYwXC9H+DsNv3ZNn9NVVWSs4H3JHkL8GBgGXDhbNshSdK2bLZB/0XgZ4Bvbca6HwSsbCPktwPOrKqPJrkCOCPJG4DPA6e2+qcC70qyDtjAMNKeqro8yZnAFcBG4Ph2SUCSJM1gtkG/O3BFkgsZvjYHQFX9xnQLVNWlwKOnKL+aKUbNV9UPgOdNs643Am+cZVslSVIz26A/cZyNkCRJ4zHbUfefGndDJEnS3JvtqPvvMYyyB7g3w81vbq+q+42rYZIk6Z6b7Rn9zhOvR+5Wd+C4GiVJkubGZv96Xbu17UcAf1xGkqSt3Gy77p87Mrkdw/fqfzCWFkmSpDkz21H3vz7yeiNwDUP3vSRJ2orN9hr9MeNuiCRJmnuzukafZK8kH05yY3t8MMle426cJEm6Z2Y7GO+dDPeif3B7/FMrkyRJW7HZBv2SqnpnVW1sj9OBJWNslyRJmgOzDfqbkrwoyfbt8SKGn5CVJElbsdkG/e8CzwduYPgFuyOAo8fUJkmSNEdm+/W61wErqupmgCS7AW9mOACQJElbqdme0e87EfIAVbWBKX6CVpIkbV1mG/TbJdl1YqKd0c+2N0CSJC2Q2Yb1XwGfTfL+Nv084I3jaZIkSZors70z3qoka4GntaLnVtUV42uWJEmaC7Pufm/BbrhLkrSIbPbP1EqSpMXDoJckqWMGvSRJHTPoJUnqmN+Fl7QofP11v7zQTZDusYf82WXzvk3P6CVJ6phBL0lSxwx6SZI6ZtBLktQxg16SpI4Z9JIkdcyglySpYwa9JEkdM+glSeqYQS9JUscMekmSOmbQS5LUMYNekqSOGfSSJHXMoJckqWNjC/okeyf5ZJIrklye5GWtfLckq5Nc1Z53beVJ8tYk65JcmmT/kXWtaPWvSrJiXG2WJKk34zyj3wj8UVXtAxwIHJ9kH+BVwLlVtQw4t00DPBNY1h7HASfDcGAAnAA8FjgAOGHi4ECSJG3a2IK+qr5VVf/RXn8PuBLYEzgMWNmqrQQOb68PA1bV4HPALkkeBBwCrK6qDVV1M7AaOHRc7ZYkqSfzco0+yVLg0cAFwB5V9a026wZgj/Z6T+AbI4td18qmK5ckSTMYe9An2Qn4IPDyqrp1dF5VFVBztJ3jkqxNsnb9+vVzsUpJkha9sQZ9knsxhPw/VtWHWvG3W5c87fnGVn49sPfI4nu1sunKf0JVnVJVy6tq+ZIlS+Z2RyRJWqTGOeo+wKnAlVX1lpFZZwMTI+dXAGeNlB/VRt8fCNzSuvg/DhycZNc2CO/gViZJkmawwxjX/QTgd4DLklzSyl4D/CVwZpJjgWuB57d55wDPAtYB3weOAaiqDUleD1zU6r2uqjaMsd2SJHVjbEFfVf8GZJrZB01Rv4Djp1nXacBpc9c6SZK2Dd4ZT5Kkjhn0kiR1zKCXJKljBr0kSR0z6CVJ6phBL0lSxwx6SZI6ZtBLktQxg16SpI4Z9JIkdcyglySpYwa9JEkdM+glSeqYQS9JUscMekmSOmbQS5LUMYNekqSOGfSSJHXMoJckqWMGvSRJHTPoJUnqmEEvSVLHDHpJkjpm0EuS1DGDXpKkjhn0kiR1zKCXJKljBr0kSR0z6CVJ6phBL0lSxwx6SZI6ZtBLktQxg16SpI4Z9JIkdcyglySpYwa9JEkdM+glSeqYQS9JUscMekmSOja2oE9yWpIbk3xxpGy3JKuTXNWed23lSfLWJOuSXJpk/5FlVrT6VyVZMa72SpLUo3Ge0Z8OHDqp7FXAuVW1DDi3TQM8E1jWHscBJ8NwYACcADwWOAA4YeLgQJIkzWxsQV9VnwY2TCo+DFjZXq8EDh8pX1WDzwG7JHkQcAiwuqo2VNXNwGrufvAgSZKmMd/X6Peoqm+11zcAe7TXewLfGKl3XSubrlySJM3Cgg3Gq6oCaq7Wl+S4JGuTrF2/fv1crVaSpEVtvoP+261LnvZ8Yyu/Hth7pN5erWy68rupqlOqanlVLV+yZMmcN1ySpMVovoP+bGBi5PwK4KyR8qPa6PsDgVtaF//HgYOT7NoG4R3cyiRJ0izsMK4VJ3kv8BRg9yTXMYye/0vgzCTHAtcCz2/VzwGeBawDvg8cA1BVG5K8Hrio1XtdVU0e4CdJkqYxtqCvqhdMM+ugKeoWcPw06zkNOG0OmyZJ0jbDO+NJktQxg16SpI4Z9JIkdcyglySpYwa9JEkdM+glSeqYQS9JUscMekmSOmbQS5LUMYNekqSOGfSSJHXMoJckqWMGvSRJHTPoJUnqmEEvSVLHDHpJkjpm0EuS1DGDXpKkjhn0kiR1zKCXJKljBr0kSR0z6CVJ6phBL0lSxwx6SZI6ZtBLktQxg16SpI4Z9JIkdcyglySpYwa9JEkdM+glSeqYQS9JUscMekmSOmbQS5LUMYNekqSOGfSSJHXMoJckqWMGvSRJHTPoJUnqmEEvSVLHFk3QJzk0yZeTrEvyqoVujyRJi8GiCPok2wN/AzwT2Ad4QZJ9FrZVkiRt/RZF0AMHAOuq6uqq+iFwBnDYArdJkqSt3mIJ+j2Bb4xMX9fKJEnSJuyw0A2YK0mOA45rk7cl+fJCtkf3yO7Adxa6ET3Lm1csdBO0dfKzN24nZJxr/9mpChdL0F8P7D0yvVcru0tVnQKcMp+N0ngkWVtVyxe6HdK2xs9enxZL1/1FwLIkD01yb+BI4OwFbpMkSVu9RXFGX1Ubk7wE+DiwPXBaVV2+wM2SJGmrtyiCHqCqzgHOWeh2aF54CUZaGH72OpSqWug2SJKkMVks1+glSdIWMOg1NkkqybtHpndIsj7JR7dwfb+f5Ki5a6G0eCV5QJJL2uOGJNe315XkkEl1X57k5CnW8b+SXJ7k0rbsY1v5NUl2n6990Xgtmmv0WpRuB34pyY5V9Z/AM5j0tcjNUVV/O2ctkxa5qroJ2A8gyYnAbVX15nZPkSMZBi9POBJ45ejySR4H/Bqwf1Xd0YL93vPRds0vz+g1bucAz26vXwC8d2JGkt2SfKSdTXwuyb5JtmtnE7uM1LsqyR5JTkzyilb2sCT/kuTiJJ9J8ovzulfS1usDwLPbV5FJshR4MPCZSfUeBHynqu4AqKrvVNU3R+a/NMl/JLls4vM11We2lV+WZJcMbproeUuyKskzxrmzmplBr3E7AzgyyX2BfYELRua9Fvh8Ve0LvAZYVVX/BZwFPAegdSVeW1XfnrTeU4CXVtVjgFcA7xjvbkiLQ1VtAC5k+BEwGM7mz6y7j7z+BLB3kq8keUeSJ0+a/52q2h84meEzBlN8Zlv5+cATgEcCVwNPauWPA/59bvZMW8qg11hV1aXAUoaz+clfj3wi8K5Wbw3wgCT3A94H/Farc2SbvkuSnYDHA+9PcgnwdwxnJ5IG72X47NCe3zu5QlXdBjyG4dbh64H3JTl6pMqH2vPFDJ9hmP4z+xngV9vjZOCXk+wJ3FxVt8/ZXmmLGPSaD2cDb2aK/2ym8Vng4UmWAIfz4/9wJmwHfLeq9ht5PGLumistemcBByXZH/ipqrp4qkpVdWdVnVdVJwAvAX5zZPYd7flOZh7P9WmGs/gnAecxHDgcwd0vF2gBGPSaD6cBr62qyyaVfwZ4IUCSpzB0Fd7auhg/DLwFuLINOrpLVd0KfC3J89qySfKoMe+DtGi0s/VPMnz2pjzATvILSZaNFO0HXDvDqqf7zH6D4QdxllXV1cC/MXT3f/qe7IfmhqPuNXZVdR3w1ilmnQicluRS4PvA6E+qvY/hNw6Onma1LwROTvKnwL0YxgJ8YY6aLPXgvQwHzEdOM38n4G1t4OtGYB0//gXQ6ZzI9J/ZCxhuUQ7DAcFfMAS+Fph3xpMkqWN23UuS1DGDXpKkjhn0kiR1zKCXJKljBr0kSR0z6CXNqN3H/MUL3Q5Jm8+glzQbuwAGvbQIGfSSZuMvgYe13yx/f5LDJ2Yk+cckhyU5OslZSc5rvzh4wkidFyW5sC3/d0m2n3IrkuacQS9pNl4FfLWq9gPeTrtjYZL7M/zA0D+3egcw3C99X+B5SZYneQTDjxQ9oS1/J+02qpLGz1vgStosVfWp9rOmSxhC/YNVtTEJwOqJ3yZI8iGGXzvbyPAraRe1OjsCNy5I46VtkEEvaUusAl7EcB/1Y0bKJ99Tu4AAK6vq1fPUNkkj7LqXNBvfA3YemT4deDlAVV0xUv6MJLsl2ZHhJ4bPB84FjkjyQIA2/2fnpdWSPKOXNLOquinJ+Um+CHysqv44yZXARyZVvRD4ILAX8O6qWgvQfmXwE0m2A34EHM/MP4kqaQ7463WSNluSnwIuA/avqlta2dHA8qp6yUK2TdJPsute0mZJ8nTgSuBtEyEvaevlGb0kSR3zjF6SpI4Z9JIkdcyglySpYwa9JEkdM+glSeqYQS9JUsf+Pz91oZxMGNcKAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bdlJQOi5vFuD"
      },
      "source": [
        "As can be seen, the number of **Movies** available on Netflix outnumbers **TV** **Shows** by a ration of 2:1."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "PS3zS6wULeYB",
        "outputId": "7a5af04c-e981-47a7-d049-ec860fe08d45"
      },
      "source": [
        "## 2. Τα τελευταία χρόνια το netflix επενδύει περισσότερο σε ταινίες ή σε σειρές;\n",
        "\n",
        "fig = px.pie(netflix_df_new, values = netflix_df_new['release_year'].value_counts()[:10], names = netflix_df_new['release_year'].value_counts()[:10].index,labels= netflix_df_new['release_year'].value_counts()[:10].index)\n",
        "fig.update_layout(title=\"Pie Chart for Distribution of Content based on Year Released\")\n",
        "fig.update_traces(textposition ='inside',textinfo='percent+label')\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
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          },
          "metadata": {
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "luQ6pB_oO-Uo"
      },
      "source": [
        "**2018** was the most productive year for Netflix."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YFPX_YeYZj9C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "outputId": "16f9aa7a-99c3-4035-934a-95a5b5626d21"
      },
      "source": [
        "plt.figure(figsize=(20,12))\n",
        "sns.countplot(x='release_year',hue='type', data=netflix_dataframe.sort_values(by='release_year')[-1:200:-1]);\n",
        "plt.title('Content released per year', size=18);"
      ],
      "execution_count": null,
      "outputs": [
        {
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\n",
            "text/plain": [
              "<Figure size 1440x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IwHxJunnKQRf"
      },
      "source": [
        "So, **2017** was the year when most of the movies were released. We can also observe that Netflix after the year **2018** is mostly concerned in producing more **TV** **Shows**. This is very clear in the year **2020** in which we see that **TV** **Show** production surmounted the **Movie** production. It is clear that Netflix is lately investing in **producing more TV Shows** than older years."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ckgE5VEK839B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 751
        },
        "outputId": "131ddef4-a0ea-446c-cbcb-d2fa31e315e8"
      },
      "source": [
        "## 3. Ποιά χώρα έχει το περισσότερο περιεχόμενο;\n",
        "def return_counter(data_frame, column_name, limit):\n",
        "  print('Top ', limit, ' country/countries with most content:', dict(Counter(data_frame[column_name].values).most_common(limit)))\n",
        "\n",
        "return_counter(netflix_dataframe, 'country', 1)\n",
        "\n",
        "## Frequency graph by country\n",
        "plt.figure(figsize=(20,12))\n",
        "sns.countplot(x='country', data=netflix_dataframe, order=netflix_dataframe.country.value_counts().index[0:10])\n",
        "plt.title('Top 10 most film productive countries', size=18);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Top  1  country/countries with most content: {'United States': 3062}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "faYogtwiLX0g"
      },
      "source": [
        "**United** **States** clearly is producing the most content on Netflix."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "Q_pHeODDvc-M",
        "outputId": "db6ea6f1-dc28-49e2-ad98-5b6d7584c693"
      },
      "source": [
        "## 4. Τι είδους περιεχόμενο έχει κάθε χώρα;\n",
        "\n",
        "# We will look at which Genres are the most popular for Movies...\n",
        "plt.figure(figsize=(10,6))\n",
        "netflix_df_new[netflix_df_new[\"type\"]==\"Movie\"][\"listed_in\"].value_counts()[:10].plot(kind=\"barh\",color=\"blue\")\n",
        "plt.title(\"Top 10 Categories of Movies - Graph 1\",size=18);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W7wFftCzv19H"
      },
      "source": [
        "The data shows that **Documentaries** is the largest category of movies."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "l7zPs9wPv6gE",
        "outputId": "fc2e2b5b-7b91-4232-cc22-35b036027b79"
      },
      "source": [
        "# and for TV Shows...\n",
        "plt.figure(figsize=(10,6))\n",
        "netflix_df_new[netflix_df_new[\"type\"]==\"TV Show\"][\"listed_in\"].value_counts()[:10].plot(kind=\"barh\",color=\"green\")\n",
        "plt.title(\"Top 10 Categories of TV Shows - Graph 2\",size=18);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j3OwoqfIwDYy"
      },
      "source": [
        "The data shows that **Kids' Shows** is by far the largest category of shows. The very high number of **Kids TV** shows may also be attributed to the fact that this type of content is cheaper to licence."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VSXQwEQob1yM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        },
        "outputId": "43e930fe-05d9-4a1f-ca85-ea0cff8e3a71"
      },
      "source": [
        "# And then calculate what content does each country provide.\n",
        "df_new = netflix_df_new.groupby(netflix_df_new[\"listed_in\"])[\"country\"].value_counts()\n",
        "# Show only the top 20 most produced genres per counrty.\n",
        "df_new.sort_values(ascending=False).head(20).to_frame()"
      ],
      "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></th>\n",
              "      <th>country</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>listed_in</th>\n",
              "      <th>country</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Documentaries</th>\n",
              "      <th>United States</th>\n",
              "      <td>244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stand-Up Comedy</th>\n",
              "      <th>United States</th>\n",
              "      <td>234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Children &amp; Family Movies</th>\n",
              "      <th>United States</th>\n",
              "      <td>117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dramas, International Movies</th>\n",
              "      <th>India</th>\n",
              "      <td>116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Comedies, Dramas, International Movies</th>\n",
              "      <th>India</th>\n",
              "      <td>114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kids' TV</th>\n",
              "      <th>United States</th>\n",
              "      <td>108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dramas, Independent Movies, International Movies</th>\n",
              "      <th>India</th>\n",
              "      <td>97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Children &amp; Family Movies, Comedies</th>\n",
              "      <th>United States</th>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dramas</th>\n",
              "      <th>United States</th>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Comedies</th>\n",
              "      <th>United States</th>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Reality TV</th>\n",
              "      <th>United States</th>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dramas, Independent Movies</th>\n",
              "      <th>United States</th>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Docuseries</th>\n",
              "      <th>United States</th>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Anime Series, International TV Shows</th>\n",
              "      <th>Japan</th>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>International TV Shows, Korean TV Shows, Romantic TV Shows</th>\n",
              "      <th>South Korea</th>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Documentaries, Music &amp; Musicals</th>\n",
              "      <th>United States</th>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Comedies, Dramas, Independent Movies</th>\n",
              "      <th>United States</th>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dramas, International Movies, Romantic Movies</th>\n",
              "      <th>India</th>\n",
              "      <td>57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Action &amp; Adventure, Dramas, International Movies</th>\n",
              "      <th>India</th>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TV Comedies</th>\n",
              "      <th>United States</th>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                  country\n",
              "listed_in                                          country               \n",
              "Documentaries                                      United States      244\n",
              "Stand-Up Comedy                                    United States      234\n",
              "Children & Family Movies                           United States      117\n",
              "Dramas, International Movies                       India              116\n",
              "Comedies, Dramas, International Movies             India              114\n",
              "Kids' TV                                           United States      108\n",
              "Dramas, Independent Movies, International Movies   India               97\n",
              "Children & Family Movies, Comedies                 United States       90\n",
              "Dramas                                             United States       78\n",
              "Comedies                                           United States       75\n",
              "Reality TV                                         United States       74\n",
              "Dramas, Independent Movies                         United States       73\n",
              "Docuseries                                         United States       67\n",
              "Anime Series, International TV Shows               Japan               67\n",
              "International TV Shows, Korean TV Shows, Romant... South Korea         63\n",
              "Documentaries, Music & Musicals                    United States       59\n",
              "Comedies, Dramas, Independent Movies               United States       58\n",
              "Dramas, International Movies, Romantic Movies      India               57\n",
              "Action & Adventure, Dramas, International Movies   India               55\n",
              "TV Comedies                                        United States       54"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "td_Vikgvw-g9"
      },
      "source": [
        "As expected due to **Graph 1** the most produced gerne are **documentaries** by the **United** **States**. Next up are **Stand-Up Comedies and Children & Family Shows** also by the **US**. Then comes **India** with **Drama, International Movies and Comedies, Dramas, International Movies**. As expected again due to **Graph 2** next up is **Kids' TV** from **US**. The list goes on and shows all the content that each country provides. The data is so much that we had to present it in a list and not in a graph form for readability reasons."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Nr_08vldRgfs",
        "outputId": "1fd25a28-9292-4299-abb2-1f4242f8b42b"
      },
      "source": [
        "## 5. Ετοιμάστε γραφήματα που δειχνουν τους ηθοποιούς με τις περισσότερες ταινίες σε κάθε χώρα.\n",
        "\n",
        "# creating a trace for plotting movies/tv shows\n",
        "def country_trace(country, flag = \"movie\"):\n",
        "  netflix_df_new[\"from_us\"] = netflix_df_new['country'].fillna(\"\").apply(lambda x : 1 if country.lower() in x.lower() else 0)\n",
        "  small = netflix_df_new[netflix_df_new[\"from_us\"] == 1]\n",
        "  if flag == \"movie\":\n",
        "      small = small[small[\"duration\"] != \"\"]\n",
        "  else:\n",
        "      small = small[small[\"season_count\"] != \"\"]\n",
        "  cast = \", \".join(small['cast'].fillna(\"\")).split(\", \")\n",
        "\n",
        "  if cast[0] == '':\n",
        "    return -1\n",
        "\n",
        "  tags = Counter(cast).most_common(25)\n",
        "  tags = [_ for _ in tags if \"\" != _[0]]\n",
        "\n",
        "  labels, values = [_[0]+\"  \" for _ in tags], [_[1] for _ in tags]\n",
        "  trace = go.Bar(y=labels[::-1], x=values[::-1], orientation=\"h\", name=\"\", marker=dict(color=\"#a678de\"))\n",
        "\n",
        "  # print(cast)\n",
        "  return trace\n",
        "\n",
        "\n",
        "# getting a country list for ease of use\n",
        "countries = dict(countries_for_language('en'))\n",
        "traces = []\n",
        "titles = []\n",
        "\n",
        "newline = 0\n",
        "\n",
        "# creating traces for NON empty countries and creating titles for the graphs\n",
        "for title in countries:\n",
        "  if country_trace(countries[title], flag=\"movie\") != -1:\n",
        "    traces.append(country_trace(countries[title], flag=\"movie\"))\n",
        "    titles.append(countries[title])\n",
        "    newline+=1\n",
        "    if(newline !=2):\n",
        "      titles.append(\"\")\n",
        "    else:\n",
        "      newline=0\n",
        "\n",
        "\n",
        "titles.pop()\n",
        "# geting 20 countries, change this number to see more or less countries\n",
        "countries_num = 20\n",
        "\n",
        "fig = make_subplots(rows=countries_num//2, cols=3, subplot_titles=titles)\n",
        "\n",
        "# row of plot\n",
        "j = 1\n",
        "\n",
        "# creating the plot\n",
        "for i in range(0,countries_num,2):\n",
        "  fig.add_trace(traces[i], j, 1)\n",
        "  fig.add_trace(traces[i+1], j, 3)\n",
        "  j += 1\n",
        "\n",
        "\n",
        "fig.update_layout(height=2400, showlegend=False)\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        "id": "xFn8hDhyI0Ji"
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      "source": [
        "We can get the information for the **cast** of the movies of a country by creating a suitable dataframe for each country. Also a dataframe of **Movies** is different from a dataframe of **TV Shows** and this is differentiated in the **country_trace** function"
      ]
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        "id": "4RE5l0PPUUFr",
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      "source": [
        "## 5. Το ίδιο και για τις σειρές.\n",
        "\n",
        "traces = []\n",
        "titles = []\n",
        "\n",
        "# geting 30 countries, change this number to see more or less countries\n",
        "countries_num = 30\n",
        "newline = 0\n",
        "# creating traces for NON empty countries and creating titles for the graphs\n",
        "for title in countries:\n",
        "  if country_trace(countries[title], flag=\"tv_shows\") != -1:\n",
        "    traces.append(country_trace(countries[title], flag=\"tv_shows\"))\n",
        "    titles.append(countries[title])\n",
        "    newline+=1\n",
        "    if(newline !=2):\n",
        "      titles.append(\"\")\n",
        "    else:\n",
        "      newline=0\n",
        "\n",
        "titles.pop()\n",
        "\n",
        "\n",
        "fig = make_subplots(rows=countries_num//2, cols=3, subplot_titles=titles)\n",
        "\n",
        "# row of plot\n",
        "j = 1\n",
        "\n",
        "# creating the plot\n",
        "for i in range(0,countries_num,2):\n",
        "  fig.add_trace(traces[i], j, 1)\n",
        "  fig.add_trace(traces[i+1], j, 3)\n",
        "  j += 1\n",
        "\n",
        "fig.update_layout(height=2600, showlegend=False)\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "        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": "llcM8kAIJM8R"
      },
      "source": [
        "Doing the same thing for the **TV Shows**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "02uczSArvrZM",
        "outputId": "ded3e08c-b1e4-426d-97c5-e3b25db0ab9d"
      },
      "source": [
        "## 6. Το netflix υποστηρίζει ότι παρέχει πλούσιο περιεχόμενο για όλες τις ηλικίες. Αληθεύει αυτό;\n",
        "\n",
        "plt.figure(figsize = (10,8))\n",
        "# First plot every rating for movies and tv shows\n",
        "sns.countplot(x=\"rating\",data= netflix_df_new, hue= \"type\", order = netflix_df_new['rating'].value_counts().index[0:15])\n",
        "plt.title('Ratings for Movies and TV Shows', size=18);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nrGjyk7Yp_SH"
      },
      "source": [
        "As shown above the Movies & TV Shows with a rating of **TV-MA** are in the clear majority. Next up is **TV-14**, **TV-PG** and **R**, which consists mostly of Movies."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "cYTbZI1guoHc",
        "outputId": "4375b0bc-7a48-4f06-99ef-aa9a6b36f95a"
      },
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "# Then differentiate the ratings for Movies and TV Shows\n",
        "sns.countplot(x='type',hue='rating',data=netflix_df_new)\n",
        "plt.title(\"Movies vs Tv shows Rating\", size=18);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s8Xr6Jdg0iEQ"
      },
      "source": [
        "Differentiating the ratings between Movies and TV shows shows a fairly 2:1 ratio **across the categories**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "FjmM4EAkmsnd",
        "outputId": "393919ca-08ac-4c0e-d680-c975dac3cc85"
      },
      "source": [
        "# Creating a dictionary for ratings.\n",
        "ratings_ages = {\n",
        "    'TV-PG': 'Older Kids',\n",
        "    'TV-MA': 'Mature',\n",
        "    'TV-Y7-FV': 'Older Kids',\n",
        "    'TV-Y7': 'Older Kids',\n",
        "    'TV-14': 'Teens',\n",
        "    'R': 'Mature',\n",
        "    'TV-Y': 'Little Kids',\n",
        "    'NR': 'Mature',\n",
        "    'PG-13': 'Teens',\n",
        "    'TV-G': 'Little Kids',\n",
        "    'PG': 'Older Kids',\n",
        "    'G': 'Little Kids',\n",
        "    'UR': 'Mature',\n",
        "    'NC-17': 'Mature'\n",
        "}\n",
        "\n",
        "# Add a new column 'rating_ages' to our dataframe based on the above dictionary\n",
        "netflix_df_new[\"ratings_ages\"] = netflix_df_new[\"rating\"].replace(ratings_ages)\n",
        "\n",
        "pd.crosstab(netflix_df_new[\"type\"],netflix_df_new[\"ratings_ages\"]).plot(kind=\"bar\", figsize=(10,8))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9e77706ad0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TViyp01h8lyp"
      },
      "source": [
        "It is clear that Netflix produces more **mature content** both in **Movies** and in **TV** **Shows**. Next up is content that is dedicated for **Teenagers**. **Older** **Kids** and **Little** **Kids** come in last place. This graph shows that Netflix does **not** provide a wide **variety of movies** for all ages and it tends to release more mature content. When it comes to the **TV** **Shows**, however, Netflix provides **content available for all ages** and tries to keep a balance between the different age groups."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "7xTE76REyBBn",
        "outputId": "ea06d6fe-93d8-48d6-f4e5-23a5b02ffd3b"
      },
      "source": [
        "## 7. Κάντε ένα γράφημα με το περιεχόμενο που προστίθεται ανά μήνα.\n",
        "\n",
        "plt.figure(figsize=(20,12))\n",
        "sns.countplot(x='release_month', data=netflix_df_new, order=netflix_df_new['release_month'].value_counts().index[0:12])\n",
        "plt.title('Released content per month', size=18);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QBHVtNQr10le"
      },
      "source": [
        "As we can observe the most content is being released during the month of **December**. That could be due to the fact that during the month of December is the holiday season."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 720
        },
        "id": "sPTTItBFOX3c",
        "outputId": "a0a65725-92f4-49da-8848-d9764b155804"
      },
      "source": [
        "## 8. Ετοιμάστε ένα γράφημα που παρουσιάζει συγκεντρωτικά τα είδη του περιεχομένου.\n",
        "\n",
        "categories = \", \".join(netflix_df_new['listed_in']).split(\", \")\n",
        "counter_list = Counter(categories).most_common(50)\n",
        "labels = [_[0] for _ in counter_list][::-1]\n",
        "values = [_[1] for _ in counter_list][::-1]\n",
        "\n",
        "plt.figure(figsize=(20,12))\n",
        "sns.barplot(y=labels, x=values)\n",
        "plt.title('Top Genres overall', size=18);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RO7AuvBHPIzY"
      },
      "source": [
        "It is clear that **International Movies** is the most common genre used in Netflix content. This actually makes sense given the fact that Netflix has many countries producing content and cooperating with each other."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "x7QL-E5hUjbm",
        "outputId": "b6e983a2-078a-4df3-cc49-ae96ff888bd2"
      },
      "source": [
        "## 9. Μελετηστε τους σκηνοθέτες ανά χώρα και παρουσιάστε σχετικά γραφήματα.\n",
        "\n",
        "# showing the top directors of a country\n",
        "def directors_most_content(country):\n",
        "  small = netflix_df_new[netflix_df_new[\"country\"] == country]\n",
        "  col = \"director\"\n",
        "  categories = \", \".join(small[col].fillna(\"\")).split(\", \")\n",
        "  counter_list = Counter(categories).most_common(12)\n",
        "  counter_list = [_ for _ in counter_list if _[0] != \"\"]\n",
        "  # if there are none then just return\n",
        "  if(len(counter_list) == 0):\n",
        "    return -1\n",
        "  labels = [_[0] for _ in counter_list][::-1]\n",
        "  values = [_[1] for _ in counter_list][::-1]\n",
        "  trace1 = go.Bar(y=labels, x=values, orientation=\"h\", name=\"TV Shows\", marker=dict(color=\"orange\"))\n",
        "  data = [trace1]\n",
        "  layout = go.Layout(title=\"Movie Directors from \" + country + \" with most content\", legend=dict(x=0.1, y=1.1, orientation=\"h\"))\n",
        "\n",
        "  fig = go.Figure(data, layout=layout)\n",
        "  fig.show()\n",
        "\n",
        "  # everything's good\n",
        "  return 0\n",
        "\n",
        "# iterating through the countries\n",
        "counter = 0\n",
        "for index in countries:\n",
        "  # checking the directors of this country, if the function returned -1 just go to the next country\n",
        "  if directors_most_content(countries[index]) == -1:\n",
        "    continue\n",
        "  counter+=1\n",
        "  # CHANGE THIS NUMBER IF YOU WANT TO SEE MORE OR LESS COUNTRIES\n",
        "  if counter == 20:\n",
        "    break"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "source": [
        "In order to study the **directors** of each country, we need to create a new dataframe for each country. Having a dataframe with each movie of a country we can easily get the information for the **directors** of that country."
      ]
    },
    {
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      "source": [
        "## 10. Μελετήστε τις σειρές και παρουσιάστε ένα γράφημα που τις δείχνει ανάλογα με τον αριθμό των seasons.\n",
        "\n",
        "# Create a new dataframe for TV Shows and their seasons.\n",
        "season_df = netflix_df_new[netflix_df_new[\"type\"]==\"TV Show\"]\n",
        "season_df = season_df[['type', 'title', 'season_count']]\n",
        "sorted = season_df.sort_values(by='season_count', ascending=False).reset_index(drop=True)\n",
        "\n",
        "plt.figure(figsize = (20,12))\n",
        "# Lets print the top 20 TV Shows by number of seasons.\n",
        "top = 20\n",
        "sns.barplot(x=\"title\", y=\"season_count\", data=sorted[0:top])\n",
        "plt.xticks(rotation=90);\n",
        "plt.title('Top 20 TV Shows sorted by the number of seasons', size=18);"
      ],
      "execution_count": null,
      "outputs": [
        {
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\n",
            "text/plain": [
              "<Figure size 1440x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KhGvGCddJbAh"
      },
      "source": [
        "We created previously a column called \"**season_count**\". By creating a new dataframe using the needed columns (\"season_count\", \"title\", \"type\"), we can easily find the **TV Shows with the most seasons**!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "KuOjvspb-rqw",
        "outputId": "ddf443c1-0105-44fd-afb7-8212ec5d8243"
      },
      "source": [
        "## 11. Ποιές είναι οι ταινίες με την πιο υψηλή βαθμολογία;\n",
        "\n",
        "# CHANGE THIS IF YOU WANT TO SEE MORE OR LESS TOP RATED MOVIES\n",
        "top_rated_movies = 30\n",
        "\n",
        "# reading IMDB Ratings\n",
        "imdb_ratings = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Τεδε/Ergasia1/IMDb ratings.csv',usecols=['weighted_average_vote'],low_memory=False)\n",
        "# reading IMDB Titles\n",
        "imdb_titles = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Τεδε/Ergasia1/IMDb movies.csv', usecols=['title','year','genre'],low_memory=False)\n",
        "\n",
        "# creating a new dataframe\n",
        "ratings = pd.DataFrame({'Title':imdb_titles.title,\n",
        "                    'Release Year':imdb_titles.year,\n",
        "                    'Rating': imdb_ratings.weighted_average_vote,\n",
        "                    'Genre':imdb_titles.genre})\n",
        "ratings.drop_duplicates(subset=['Title','Release Year','Rating'], inplace=True)\n",
        "\n",
        "ratings.dropna()\n",
        "\n",
        "# merging dataframes\n",
        "joint_data=ratings.merge(netflix_df_new,left_on='Title',right_on='title',how='inner')\n",
        "joint_data=joint_data.sort_values(by='Rating', ascending=False)\n",
        "\n",
        "# Getting the N top rated movies\n",
        "top_rated=joint_data[0:top_rated_movies]\n",
        "\n",
        "# plotting\n",
        "plt.figure(figsize=(20,10))\n",
        "sns.barplot(y='title', x='Rating', data=top_rated)\n",
        "plt.title('Best rated Movies', size=18);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AOuXHZuoJfT5"
      },
      "source": [
        "We need to find the highest rated movies. In order to do that we need to use 2 new files, specifically **IMDb ratings.csv** & **IMDb movies.csv**.\n",
        "\n",
        "By creating a new dataframe using a combination of columns by both files gives us the ability to mine the information that we want. Specificaly using **weighted_average_vote** & **title** columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UecYLqoUpupK"
      },
      "source": [
        "# ***Recommendation System***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_q-yamG3o2ph"
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "import nltk\n",
        "import re\n",
        "import numpy as np\n",
        "import heapq\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "from sklearn.metrics import jaccard_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "id": "Z8_57-Xt_fDx",
        "outputId": "79569336-0e3a-4eba-cb32-a99f07fc8e04"
      },
      "source": [
        "# Create the new dataframe for this part.\n",
        "recommendation_df = netflix_df_new[['show_id', 'title', 'description']]\n",
        "\n",
        "# Concat titles and descriptions.\n",
        "recommendation_df['text'] = recommendation_df['title'] + ' ' + recommendation_df['description']\n",
        "recommendation_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: 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"
        },
        {
          "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>show_id</th>\n",
              "      <th>title</th>\n",
              "      <th>description</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>s1</td>\n",
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              "      <td>In a future where the elite inhabit an island ...</td>\n",
              "      <td>3% In a future where the elite inhabit an isla...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>s2</td>\n",
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              "      <td>After a devastating earthquake hits Mexico Cit...</td>\n",
              "      <td>7:19 After a devastating earthquake hits Mexic...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>s3</td>\n",
              "      <td>23:59</td>\n",
              "      <td>When an army recruit is found dead, his fellow...</td>\n",
              "      <td>23:59 When an army recruit is found dead, his ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>s4</td>\n",
              "      <td>9</td>\n",
              "      <td>In a postapocalyptic world, rag-doll robots hi...</td>\n",
              "      <td>9 In a postapocalyptic world, rag-doll robots ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>s5</td>\n",
              "      <td>21</td>\n",
              "      <td>A brilliant group of students become card-coun...</td>\n",
              "      <td>21 A brilliant group of students become card-c...</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>7782</th>\n",
              "      <td>s7783</td>\n",
              "      <td>Zozo</td>\n",
              "      <td>When Lebanon's Civil War deprives Zozo of his ...</td>\n",
              "      <td>Zozo When Lebanon's Civil War deprives Zozo of...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7783</th>\n",
              "      <td>s7784</td>\n",
              "      <td>Zubaan</td>\n",
              "      <td>A scrappy but poor boy worms his way into a ty...</td>\n",
              "      <td>Zubaan A scrappy but poor boy worms his way in...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7784</th>\n",
              "      <td>s7785</td>\n",
              "      <td>Zulu Man in Japan</td>\n",
              "      <td>In this documentary, South African rapper Nast...</td>\n",
              "      <td>Zulu Man in Japan In this documentary, South A...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7785</th>\n",
              "      <td>s7786</td>\n",
              "      <td>Zumbo's Just Desserts</td>\n",
              "      <td>Dessert wizard Adriano Zumbo looks for the nex...</td>\n",
              "      <td>Zumbo's Just Desserts Dessert wizard Adriano Z...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7786</th>\n",
              "      <td>s7787</td>\n",
              "      <td>ZZ TOP: THAT LITTLE OL' BAND FROM TEXAS</td>\n",
              "      <td>This documentary delves into the mystique behi...</td>\n",
              "      <td>ZZ TOP: THAT LITTLE OL' BAND FROM TEXAS This d...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7787 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     show_id  ...                                               text\n",
              "0         s1  ...  3% In a future where the elite inhabit an isla...\n",
              "1         s2  ...  7:19 After a devastating earthquake hits Mexic...\n",
              "2         s3  ...  23:59 When an army recruit is found dead, his ...\n",
              "3         s4  ...  9 In a postapocalyptic world, rag-doll robots ...\n",
              "4         s5  ...  21 A brilliant group of students become card-c...\n",
              "...      ...  ...                                                ...\n",
              "7782   s7783  ...  Zozo When Lebanon's Civil War deprives Zozo of...\n",
              "7783   s7784  ...  Zubaan A scrappy but poor boy worms his way in...\n",
              "7784   s7785  ...  Zulu Man in Japan In this documentary, South A...\n",
              "7785   s7786  ...  Zumbo's Just Desserts Dessert wizard Adriano Z...\n",
              "7786   s7787  ...  ZZ TOP: THAT LITTLE OL' BAND FROM TEXAS This d...\n",
              "\n",
              "[7787 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "tn4iWay7ejhm",
        "outputId": "bf62250c-4f43-455c-f5dd-67754292691d"
      },
      "source": [
        "# define BOW\n",
        "CountVec = CountVectorizer(\n",
        "                          ngram_range=(1,2), # (1, 1) means only unigrams, (1, 2) means unigrams and bigrams, and (2, 2) means only bigrams\n",
        "                          stop_words='english',\n",
        "                          max_df=0.4,\n",
        "                          min_df=1,\n",
        "                          max_features=10000,\n",
        "                          lowercase=True\n",
        "                          )\n",
        "#transform\n",
        "bow_data = CountVec.fit_transform(recommendation_df['text'])\n",
        "\n",
        "#create dataframe\n",
        "bow_dataframe=pd.DataFrame(bow_data.toarray(),columns=CountVec.get_feature_names())\n",
        "bow_dataframe"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7787 rows × 10000 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      000  000 euros  000 prize  ...  zombies  zone  zulu\n",
              "0       0          0          0  ...        0     0     0\n",
              "1       0          0          0  ...        0     0     0\n",
              "2       0          0          0  ...        0     0     0\n",
              "3       0          0          0  ...        0     0     0\n",
              "4       0          0          0  ...        0     0     0\n",
              "...   ...        ...        ...  ...      ...   ...   ...\n",
              "7782    0          0          0  ...        0     0     0\n",
              "7783    0          0          0  ...        0     0     0\n",
              "7784    0          0          0  ...        0     0     1\n",
              "7785    1          0          1  ...        0     0     0\n",
              "7786    0          0          0  ...        0     0     0\n",
              "\n",
              "[7787 rows x 10000 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "289mlFYoe3F2",
        "outputId": "b7a6c733-31ac-45fe-cf08-f6086625017d"
      },
      "source": [
        "#define tf-idf\n",
        "tf_idf_vec = TfidfVectorizer(\n",
        "                        use_idf=True,\n",
        "                        ngram_range=(1,2), # (1, 1) means only unigrams, (1, 2) means unigrams and bigrams, and (2, 2) means only bigrams\n",
        "                        max_features=10000,\n",
        "                        max_df=0.4,    # drop words that occur in more than X percent of documents\n",
        "                        min_df=1,      # only use words that appear at least X times\n",
        "                        smooth_idf=True, # Prevents divide-by-zero errors\n",
        "                        lowercase=True, # Convert everything to lower case\n",
        "                        stop_words='english'\n",
        "                        )\n",
        "#transform\n",
        "tf_idf_data = tf_idf_vec.fit_transform(recommendation_df['text'])\n",
        "\n",
        "#create dataframe\n",
        "tf_idf_dataframe=pd.DataFrame(tf_idf_data.toarray(),columns=tf_idf_vec.get_feature_names())\n",
        "tf_idf_dataframe"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7786</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7787 rows × 10000 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           000  000 euros  000 prize  ...  zombies  zone      zulu\n",
              "0     0.000000        0.0   0.000000  ...      0.0   0.0  0.000000\n",
              "1     0.000000        0.0   0.000000  ...      0.0   0.0  0.000000\n",
              "2     0.000000        0.0   0.000000  ...      0.0   0.0  0.000000\n",
              "3     0.000000        0.0   0.000000  ...      0.0   0.0  0.000000\n",
              "4     0.000000        0.0   0.000000  ...      0.0   0.0  0.000000\n",
              "...        ...        ...        ...  ...      ...   ...       ...\n",
              "7782  0.000000        0.0   0.000000  ...      0.0   0.0  0.000000\n",
              "7783  0.000000        0.0   0.000000  ...      0.0   0.0  0.000000\n",
              "7784  0.000000        0.0   0.000000  ...      0.0   0.0  0.309619\n",
              "7785  0.221366        0.0   0.283566  ...      0.0   0.0  0.000000\n",
              "7786  0.000000        0.0   0.000000  ...      0.0   0.0  0.000000\n",
              "\n",
              "[7787 rows x 10000 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CUZWgt2GATYB"
      },
      "source": [
        "Calculating the cosine similarity matrix for all movies."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u5Nb_Z2MfSjO"
      },
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "cos_sim = cosine_similarity(tf_idf_dataframe)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YI7C7o9XpHe6"
      },
      "source": [
        "There is no pairwise version of the jaccard_similarity_score but there are pairwise versions of **other** **distances**.\n",
        "SciPy defines Jaccard distance as follows:\n",
        "\n",
        "*Given two vectors, u and v, the Jaccard distance is the proportion of those elements u[i] and v[i] that disagree where at least one of them is non-zero.*\n",
        "\n",
        "So it excludes the rows where both columns have $0$ values. jaccard_similarity_score doesn't. **Hamming** distance, on the other hand, is inline with the similarity definition:\n",
        "\n",
        "*The proportion of those vector elements between two n-vectors u and v which disagree.*\n",
        "\n",
        "So in order to calculate jaccard_similarity_score faster, we can use $1 - hamming$:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GxPLog01A_bQ"
      },
      "source": [
        "Calculating the Jaccard/Tanimoto similarity matrix for all movies."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rAFTX9PKmNvq"
      },
      "source": [
        "from sklearn.metrics.pairwise import pairwise_distances\n",
        "\n",
        "jac_sim = 1 - pairwise_distances(bow_dataframe, metric = \"hamming\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8DVOWfdvAqvQ"
      },
      "source": [
        "**Enumeration** and **indexing** of all Movies in the Dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jKBUXDFafhtv",
        "outputId": "5ef0b24f-1b18-40bb-836b-a62988243167"
      },
      "source": [
        "indices = pd.Series(recommendation_df.index, index=recommendation_df['title']).drop_duplicates()\n",
        "indices"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "title\n",
              "3%                                            0\n",
              "7:19                                          1\n",
              "23:59                                         2\n",
              "9                                             3\n",
              "21                                            4\n",
              "                                           ... \n",
              "Zozo                                       7782\n",
              "Zubaan                                     7783\n",
              "Zulu Man in Japan                          7784\n",
              "Zumbo's Just Desserts                      7785\n",
              "ZZ TOP: THAT LITTLE OL' BAND FROM TEXAS    7786\n",
              "Length: 7787, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4gXkrfV0Ybo4"
      },
      "source": [
        "Creation of **two** **dictionaries** for storing for each movie the **top** **100** most similar movies. One dictionary for the **BOW** method and the other for the **cosin** method. This will provide easy access to our programm for future predicitons."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Ex41Au2Qjpo"
      },
      "source": [
        "# Dictionaries for both cosine_similarity matrix and jaccard_similarity matrix.\n",
        "# For each movie store the top 100 most similar movies\n",
        "jac_dict = {}\n",
        "cos_dict = {}\n",
        "\n",
        "for idx in indices:\n",
        "\n",
        "  # Get the pairwise similarity scores of all movies with that movie\n",
        "  score_array_jac = list(enumerate(jac_sim[idx]))\n",
        "  score_array_cos = list(enumerate(cos_sim[idx]))\n",
        "\n",
        "  # Sort the movies based on the similarity scores\n",
        "  score_array_jac.sort(key=lambda x: x[1], reverse=True)\n",
        "  score_array_cos.sort(key=lambda x: x[1], reverse=True)\n",
        "\n",
        "  # Get the scores of the 100 most similar movies\n",
        "  score_array_jac = score_array_jac[1:100+1]\n",
        "  score_array_cos = score_array_cos[1:100+1]\n",
        "\n",
        "  # Get the movie indices\n",
        "  movie_indices_jac = [i[0] for i in score_array_jac]\n",
        "  movie_indices_cos = [i[0] for i in score_array_cos]\n",
        "\n",
        "  # Store the top 100 most similar movies\n",
        "  jac_dict[idx] = recommendation_df['title'].iloc[movie_indices_jac]\n",
        "  cos_dict[idx] = recommendation_df['title'].iloc[movie_indices_cos]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGPDYF5ZCHBq"
      },
      "source": [
        "Recommendation funtion **get_similar_movies1()**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1RzRkzZHfsUH"
      },
      "source": [
        "def get_similar_movies1(title, N, method='boolean'):\n",
        "  idx = indices[title]\n",
        "\n",
        "  if(method == 'boolean'):\n",
        "    movie_array = jac_dict[idx]\n",
        "    return movie_array.head(N)\n",
        "  elif(method == 'tf-idf'):\n",
        "    movie_array = cos_dict[idx]\n",
        "    return movie_array.head(N)\n",
        "  else:\n",
        "    print('wrong method')\n",
        "    return -1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hUV4sIHlZjcP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "208ceb40-24d5-475b-e192-47df08fd4fac"
      },
      "source": [
        "get_similar_movies1(\"Mortel\", 10, 'boolean')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6023                 Teresa\n",
              "1267              Chamatkar\n",
              "5741              Spartacus\n",
              "7549      WHAT DID JACK DO?\n",
              "821          Being Napoleon\n",
              "891         Bhool Bhulaiyaa\n",
              "2263                Frasier\n",
              "2403         Ghost Whispers\n",
              "4142    Miss Representation\n",
              "5602                 Signal\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lcUY-hw1TIYi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea3a2d51-1ad9-48b7-9847-e74e8729ddeb"
      },
      "source": [
        "get_similar_movies1(\"Mortel\", 10, 'tf-idf')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "979                                Blind Detective\n",
              "608                                 At First Light\n",
              "2159                             Figures of Speech\n",
              "3864                              Maharakshak Devi\n",
              "1267                                     Chamatkar\n",
              "4314    My Entire High School Sinking Into the Sea\n",
              "4033                              Men on a Mission\n",
              "6958                          The Umbrella Academy\n",
              "5602                                        Signal\n",
              "6429                                     The Guest\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cu8lz8eXTFrt"
      },
      "source": [
        "We observe that there's no time difference between these two because the most similar movies for each movie have been precalculated, but each method yields different results. As you can see the \"tf-idf\" method yields more relatable results than the \"BoW\" method!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_vCZZI31Da5H"
      },
      "source": [
        "Here for the next recommendation function we will split the **recommendation_df** in **text_df** which only has the 'text' column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "baVDKF5Wht7o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be7f293b-85e7-4edb-b3d5-563732d7a251"
      },
      "source": [
        "text_df = recommendation_df['text']\n",
        "text_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       3% In a future where the elite inhabit an isla...\n",
              "1       7:19 After a devastating earthquake hits Mexic...\n",
              "2       23:59 When an army recruit is found dead, his ...\n",
              "3       9 In a postapocalyptic world, rag-doll robots ...\n",
              "4       21 A brilliant group of students become card-c...\n",
              "                              ...                        \n",
              "7782    Zozo When Lebanon's Civil War deprives Zozo of...\n",
              "7783    Zubaan A scrappy but poor boy worms his way in...\n",
              "7784    Zulu Man in Japan In this documentary, South A...\n",
              "7785    Zumbo's Just Desserts Dessert wizard Adriano Z...\n",
              "7786    ZZ TOP: THAT LITTLE OL' BAND FROM TEXAS This d...\n",
              "Name: text, Length: 7787, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W7mS2x-6EpbK"
      },
      "source": [
        "Function for calculating the **jaccard** **similarity** between a certain description that the user gives and all other movies and tv shows in our dataframe. We use this **instead** of calculating again the **pairwise** **matrix** which saves us a lot of **time**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J2V9xbmd8Yeu"
      },
      "source": [
        "def compute_jaccard_similarity(index, cv_dataframe=bow_dataframe):\n",
        "  jaccard_sim = []\n",
        "  for j in range(len(cv_dataframe.index)):\n",
        "    jaccard_sim.append(jaccard_score(cv_dataframe.iloc[index], cv_dataframe.iloc[j], average='macro'))\n",
        "\n",
        "  return jaccard_sim"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZgtGhYeQFMSc"
      },
      "source": [
        "Recommendation function **get_similar_movies2()**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IXmjjKqVOzUP"
      },
      "source": [
        "def get_similar_movies2(text, N, method='boolean', text_df=text_df):\n",
        "\n",
        "  # Create a new Series with the only item the description the user gave.\n",
        "  d = {'text': text}\n",
        "  text = pd.Series(data=d, index=['text'])\n",
        "  # Concat the newly made df with the old one.\n",
        "  new_text_df = text_df.append(text, ignore_index=True)\n",
        "\n",
        "  if(method == 'boolean'):\n",
        "    # Transform\n",
        "    bow_data = CountVec.fit_transform(new_text_df)\n",
        "    # Create Dataframe\n",
        "    bow_dataframe=pd.DataFrame(bow_data.toarray(),columns=CountVec.get_feature_names())\n",
        "    # Compute Jaccard Similarity for the last index (the description that the user gave)\n",
        "    jac_sim = compute_jaccard_similarity(-1, bow_dataframe)\n",
        "    score_array = list(enumerate(jac_sim))\n",
        "\n",
        "  elif(method == 'tf-idf'):\n",
        "    #transform\n",
        "    tf_idf_data = tf_idf_vec.fit_transform(new_text_df)\n",
        "    #create dataframe\n",
        "    tf_idf_dataframe=pd.DataFrame(tf_idf_data.toarray(),columns=tf_idf_vec.get_feature_names())\n",
        "    # Get the pairwise similarity scores of all movies with that movie\n",
        "    cosine_sim = cosine_similarity(tf_idf_dataframe)\n",
        "    score_array = list(enumerate(cosine_sim[-1]))\n",
        "\n",
        "  else:\n",
        "    print('wrong method')\n",
        "    return -1\n",
        "\n",
        "  # Sort the movies based on the similarity scores\n",
        "  score_array.sort(key=lambda x: x[1], reverse=True)\n",
        "  # Get the scores of the 10 most similar movies\n",
        "  score_array = score_array[1:N+1]\n",
        "  # Get the movie indices\n",
        "  movie_indices = [i[0] for i in score_array]\n",
        "\n",
        "  # Return the top N most similar movies\n",
        "  return recommendation_df['title'].iloc[movie_indices]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bohTZ-_1MdTH"
      },
      "source": [
        "**Results** for **get_similar_movies2()**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-TXr4eCMz03",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c0448c8-983b-477c-91dc-6b0dcfea4bb4"
      },
      "source": [
        "get_similar_movies2(\"war between vietnam and usa\", 10, 'boolean')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4879                       Platoon\n",
              "1569                   Da 5 Bloods\n",
              "3462    Kulipari: An Army of Frogs\n",
              "3524                       Lakshya\n",
              "6651                      The Maus\n",
              "1736                 Dharmakshetra\n",
              "3646                       Lincoln\n",
              "3761                   Love Is War\n",
              "264                         Aadu 2\n",
              "1580                     Daemonium\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vzn1_mbBM03v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8075a38f-c26d-4711-b973-40615f33eca9"
      },
      "source": [
        "get_similar_movies2(\"war between vietnam and usa\", 10, 'tf-idf')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6980    The Vietnam War: A Film by Ken Burns and Lynn ...\n",
              "4879                                              Platoon\n",
              "611                                             Athlete A\n",
              "1569                                          Da 5 Bloods\n",
              "2589                                       Hamburger Hill\n",
              "6848                                        The Sapphires\n",
              "7175              Trailer Park Boys: Out of the Park: USA\n",
              "4359                                     My Stupid Boss 2\n",
              "7043                                              Thi Mai\n",
              "7479                                          War Machine\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zz2ca9s8M2vI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8853f84-58b4-4f11-ab5f-8807bd729d25"
      },
      "source": [
        "get_similar_movies2(\"Mickey Mouse\", 10, 'boolean')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2380    Geronimo Stilton\n",
              "6499       The Interview\n",
              "4446              Neruda\n",
              "485     An American Tail\n",
              "3739                Love\n",
              "5090    Raman Raghav 2.0\n",
              "1499             Cop Car\n",
              "2123        Fatal Affair\n",
              "4174               Money\n",
              "4165              Mojave\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gEvlhFv4M68B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3631eeef-ba8c-48c7-efc0-6275e69db620"
      },
      "source": [
        "get_similar_movies2(\"Mickey Mouse\", 10, 'tf-idf')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1597                     Danger Mouse: Classic Collection\n",
              "7108                                        Tip the Mouse\n",
              "7470    Walt Disney Animation Studios Short Films Coll...\n",
              "2380                                     Geronimo Stilton\n",
              "3739                                                 Love\n",
              "6499                                        The Interview\n",
              "5857                                        Stuart Little\n",
              "485                                      An American Tail\n",
              "5090                                     Raman Raghav 2.0\n",
              "1596                                         Danger Mouse\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "80gp6GNKNDGH"
      },
      "source": [
        "As we can see **boolean** method does give us some relatable content based on the text we give as input. However the recommendations are not so accurate. On the other hand, **tf-idf** method recommends many **accurate** movies and **tv** **shows** and seems to work better. Moreover, jaccard similarity is extremely time consuming versus cosine similarity which seems to be blazingly faster. So tf-idf is for sure the best method to use, given the fact that is more accurate and less time consuming.\n",
        "\n",
        "In general, we can see in this situation the superiority of **tf-idf**. We see that even with so little information it yields many relatable results. On the other hand the **\"BoW\"** method struggles to keep up, as it takes much more time to calculate the jaccard similarity than the cosine similarity and yields not so good results in the end. We have to keep in mind though the **curse of dimensionality** so it's natural that a **small** **description** makes our data **sparse** and does no good in **BoW**!"
      ]
    }
  ]
}