Best_Streaming_Service_Analysis.ipynb

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  "metadata": {
    "colab": {
      "name": "Best Streaming Service Analysis.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP1KGgHxcpFRsQ/VsMCbH9f",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/amankharwal/Website-data/blob/master/Best_Streaming_Service_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "xYRyaPI2L0pS",
        "outputId": "4fc8ca5a-0c96-4b6f-9b02-65060738b77d"
      },
      "source": [
        "from google.colab import files\r\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-91820239-ef10-4b84-94d0-232cfc58c446\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-91820239-ef10-4b84-94d0-232cfc58c446\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving tv_shows.csv to tv_shows.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d_j_Sr14L_or"
      },
      "source": [
        "import numpy as np # linear algebra\r\n",
        "import pandas as pd # data processing\r\n",
        "\r\n",
        "import plotly\r\n",
        "import plotly.express as px\r\n",
        "from plotly.subplots import make_subplots\r\n",
        "import seaborn as sns\r\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "xGjUSyzdMKkg",
        "outputId": "adbd10a6-29a9-48e6-a86f-202041ae5973"
      },
      "source": [
        "tv_shows = pd.read_csv('tv_shows.csv')\r\n",
        "tv_shows.head()"
      ],
      "execution_count": 3,
      "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>Unnamed: 0</th>\n",
              "      <th>Title</th>\n",
              "      <th>Year</th>\n",
              "      <th>Age</th>\n",
              "      <th>IMDb</th>\n",
              "      <th>Rotten Tomatoes</th>\n",
              "      <th>Netflix</th>\n",
              "      <th>Hulu</th>\n",
              "      <th>Prime Video</th>\n",
              "      <th>Disney+</th>\n",
              "      <th>type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Breaking Bad</td>\n",
              "      <td>2008</td>\n",
              "      <td>18+</td>\n",
              "      <td>9.5</td>\n",
              "      <td>96%</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Stranger Things</td>\n",
              "      <td>2016</td>\n",
              "      <td>16+</td>\n",
              "      <td>8.8</td>\n",
              "      <td>93%</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Money Heist</td>\n",
              "      <td>2017</td>\n",
              "      <td>18+</td>\n",
              "      <td>8.4</td>\n",
              "      <td>91%</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Sherlock</td>\n",
              "      <td>2010</td>\n",
              "      <td>16+</td>\n",
              "      <td>9.1</td>\n",
              "      <td>78%</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Better Call Saul</td>\n",
              "      <td>2015</td>\n",
              "      <td>18+</td>\n",
              "      <td>8.7</td>\n",
              "      <td>97%</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0             Title  Year  Age  ...  Hulu Prime Video  Disney+  type\n",
              "0           0      Breaking Bad  2008  18+  ...     0           0        0     1\n",
              "1           1   Stranger Things  2016  16+  ...     0           0        0     1\n",
              "2           2       Money Heist  2017  18+  ...     0           0        0     1\n",
              "3           3          Sherlock  2010  16+  ...     0           0        0     1\n",
              "4           4  Better Call Saul  2015  18+  ...     0           0        0     1\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KKWBnxjGMWNr",
        "outputId": "7176b9a6-644d-4caf-d9b3-091b7047187e"
      },
      "source": [
        "tv_shows.info()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5611 entries, 0 to 5610\n",
            "Data columns (total 11 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   Unnamed: 0       5611 non-null   int64  \n",
            " 1   Title            5611 non-null   object \n",
            " 2   Year             5611 non-null   int64  \n",
            " 3   Age              3165 non-null   object \n",
            " 4   IMDb             4450 non-null   float64\n",
            " 5   Rotten Tomatoes  1011 non-null   object \n",
            " 6   Netflix          5611 non-null   int64  \n",
            " 7   Hulu             5611 non-null   int64  \n",
            " 8   Prime Video      5611 non-null   int64  \n",
            " 9   Disney+          5611 non-null   int64  \n",
            " 10  type             5611 non-null   int64  \n",
            "dtypes: float64(1), int64(7), object(3)\n",
            "memory usage: 482.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "-LQxNgmNNIeJ",
        "outputId": "e801ee47-3616-4980-f211-e514e33be6c2"
      },
      "source": [
        "tv_shows.describe()"
      ],
      "execution_count": 5,
      "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>Unnamed: 0</th>\n",
              "      <th>Year</th>\n",
              "      <th>IMDb</th>\n",
              "      <th>Netflix</th>\n",
              "      <th>Hulu</th>\n",
              "      <th>Prime Video</th>\n",
              "      <th>Disney+</th>\n",
              "      <th>type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5611.000000</td>\n",
              "      <td>5611.000000</td>\n",
              "      <td>4450.000000</td>\n",
              "      <td>5611.000000</td>\n",
              "      <td>5611.000000</td>\n",
              "      <td>5611.000000</td>\n",
              "      <td>5611.000000</td>\n",
              "      <td>5611.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2805.000000</td>\n",
              "      <td>2011.021030</td>\n",
              "      <td>7.113258</td>\n",
              "      <td>0.344145</td>\n",
              "      <td>0.312600</td>\n",
              "      <td>0.382107</td>\n",
              "      <td>0.032080</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1619.900511</td>\n",
              "      <td>11.005116</td>\n",
              "      <td>1.132060</td>\n",
              "      <td>0.475131</td>\n",
              "      <td>0.463594</td>\n",
              "      <td>0.485946</td>\n",
              "      <td>0.176228</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1901.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1402.500000</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>6.600000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2805.000000</td>\n",
              "      <td>2015.000000</td>\n",
              "      <td>7.300000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4207.500000</td>\n",
              "      <td>2017.000000</td>\n",
              "      <td>7.900000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5610.000000</td>\n",
              "      <td>2020.000000</td>\n",
              "      <td>9.600000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Unnamed: 0         Year         IMDb  ...  Prime Video      Disney+    type\n",
              "count  5611.000000  5611.000000  4450.000000  ...  5611.000000  5611.000000  5611.0\n",
              "mean   2805.000000  2011.021030     7.113258  ...     0.382107     0.032080     1.0\n",
              "std    1619.900511    11.005116     1.132060  ...     0.485946     0.176228     0.0\n",
              "min       0.000000  1901.000000     1.000000  ...     0.000000     0.000000     1.0\n",
              "25%    1402.500000  2010.000000     6.600000  ...     0.000000     0.000000     1.0\n",
              "50%    2805.000000  2015.000000     7.300000  ...     0.000000     0.000000     1.0\n",
              "75%    4207.500000  2017.000000     7.900000  ...     1.000000     0.000000     1.0\n",
              "max    5610.000000  2020.000000     9.600000  ...     1.000000     1.000000     1.0\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LOuXrB9KNOL2"
      },
      "source": [
        "tv_shows.drop_duplicates(subset='Title',keep='first',inplace=True)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yEzeHjixNYSh"
      },
      "source": [
        "tv_shows['Rotten Tomatoes'] = tv_shows['Rotten Tomatoes'].fillna('0%')\r\n",
        "tv_shows['Rotten Tomatoes'] = tv_shows['Rotten Tomatoes'].apply(lambda x : x.rstrip('%'))\r\n",
        "tv_shows['Rotten Tomatoes'] = pd.to_numeric(tv_shows['Rotten Tomatoes'])\r\n",
        "\r\n",
        "tv_shows['IMDb'] = tv_shows['IMDb'].fillna(0)\r\n",
        "tv_shows['IMDb'] = tv_shows['IMDb']*10\r\n",
        "tv_shows['IMDb'] = tv_shows['IMDb'].astype('int')"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2I1G9nM6Nb5Y"
      },
      "source": [
        "tv_shows_long=pd.melt(tv_shows[['Title','Netflix','Hulu','Disney+','Prime Video']],id_vars=['Title'],var_name='StreamingOn', value_name='Present')\r\n",
        "tv_shows_long = tv_shows_long[tv_shows_long['Present'] == 1]\r\n",
        "tv_shows_long.drop(columns=['Present'],inplace=True)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GB0PrnozNflQ"
      },
      "source": [
        "tv_shows_combined = tv_shows_long.merge(tv_shows, on='Title', how='inner')"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ro_SpjuFNic3"
      },
      "source": [
        "tv_shows_combined.drop(columns = ['Unnamed: 0','Netflix','Hulu', 'Prime Video', 'Disney+', 'type'], inplace=True)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b1r6-PwSNkxm"
      },
      "source": [
        "tv_shows_both_ratings = tv_shows_combined[(tv_shows_combined.IMDb > 0) & tv_shows_combined['Rotten Tomatoes'] > 0]"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "FmZHreIHNno1",
        "outputId": "7c1bd127-8811-4bea-c21f-c5a784d526c2"
      },
      "source": [
        "tv_shows_combined.groupby('StreamingOn').Title.count().plot(kind='bar')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f16b0085c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tJnB/7Wmr2PQviYiI6LGuQ1/StsDFwIm2HwUWAS8D5gNrgDM6eM2FkkYkjaxdu7bbEiMiougq9CU9lyrwv2T7EgDbD9jeYPtJ4As8NYSzGphVe/oupe0ZbJ9te9j28NDQUDclRkRETTezdwScA/zI9qdr7TvVDnsbsLxsLwGOkvQ8SXOAucANnb5/RERMXjezd/YD3gvcKmlZafsrYIGk+YCBe4EPANheIeki4DaqmT/HZ+ZORER/dRz6tq8DNMauyzbxnNOB0zt9z4iI6E6uyI2IaJGEfkREi3Qzpj+wZp/8jaZL2Kx7P/6HTZcQEVNQevoRES2S0I+IaJGEfkREiyT0IyJaJKEfEdEiCf2IiBZJ6EdEtEhCPyKiRRL6EREtktCPiGiRhH5ERIsk9CMiWiShHxHRIn0PfUmHSLpD0kpJJ/f7/SMi2qyvoS9pGvAPwKHAPKqlFef1s4aIiDbrd09/H2Cl7btt/xq4EDi8zzVERLSWbPfvzaQjgUNs/+fy+L3Aa2yfsNFxC4GF5eEewB19K7IzM4CHmi5iCsnn2Vv5PHtrED7Pl9oeGmvHs3LlLNtnA2c3XcdESRqxPdx0HVNFPs/eyufZW4P+efZ7eGc1MKv2eJfSFhERfdDv0L8RmCtpjqStgaOAJX2uISKitfo6vGP7CUknAFcA04DFtlf0s4YtZGCGogZEPs/eyufZWwP9efb1RG5ERDQrV+RGRLRIQj8iokWelVM2IyKeTcrEk93Lwzts/6bJerqRnn7EFCTpJWO07dFELYNO0gHAnVS3kDkL+LGkNzRaVBdyIrdLkk6x/XdN1zHoxvtPZPvaftcyFUi6A/gb2xeVxycBx9rOva4mSdJS4F227yiPdwcusL13s5V1JqHfJUk32d6r6ToGnaR/rT18PtV9mpbafnNDJQ00STtRTS38FbAj8CPgJNs/b7SwASTpFtuv2lzboMiYfjwr2P6P9ceSZgGfbaicgWd7jaTLgVOAJ4GTE/gdG5H0T8D/Lo/fDYw0WE9X0tPvgKR7AAMCdgJ+UrZte7cma5sqJAlYkeGIzkj6FtW/yz+luvXJOcC1tv+i0cIGkKTnAccD+5em7wFn2V7fXFWdS+h3SdLNtl/ddB2DTtLnqX6RQjXBYD5wr+33NFfV4JJ0hO2v1R5vBZxi+2MNljWwJL0A2HV0XH+QJfS7lNDvDUnH1B4+QRX4/9ZUPRGjJL0V+CSwte05kuYDH7X91oZL60jG9LuXYOoB2+c1XcNUIOk62/tLeoyn/nKCp4Yft2uotEF2KtXEgu8A2F4maU6jFXUhod+ljReAicmRdCtPD6ff7qIKqYGcIdEU2/uX7y9qupYp5De2H6lOM/3WwA6RJPQ7VObuLga+bPtnTdczwN7SdAFTiaQdNrXf9rp+1TKFrJD0LmCapLlUJ8e/33BNHcuYfock/Q7wfuCdVNO3/hm40vlAo0EbzSzbWGaXdUDSC4EPAwdRfa5XAB+z/atGC+tQQr9Lkp5D1VtdBGygCv/PpUc1ORuNQW8NPBf4RcagJ0fS/ravk/T8QQ2l2LIyvNMFSa+i6u0fBlwMfIlqLu+3qaYcxgTVx6DLHP3DgX2bq2hgfQ7Ym2r4IVeKd6FcJT5ur3hQZ++kp9+hMqb//6guerm4fqGGpEtsv72x4qaITIedPEnXA7cARwAXbrzf9p/2vagBJemNZfPtwH/gqStyFwAP2P6zRgrrUnr6nfsj23ePtSOBP3mS6p/Zc4BhqvvGxOS8Bfh94GBgacO1DDTb3wWQdIbt4dquf5U0sLdhSOh37heSzgF2tn2opHnAa22f03RhA6p+750ngHuphnhiEmw/BFwoaZXt6+r7JO3XUFmDbhtJu4128soc/W0arqljGd7pkKRvUp20/bDtPctl7jfbfmXDpUWMeffX3BG2M5IOobpj6d1Us3deCnzA9hWNFtah9PQ7N8P2RZJOAbD9hKQNTRc1aDa6584zZAx6ciS9FngdMCTpz2u7tgOmNVPVYLN9eZmf/7ul6fZBvdkaJPS78QtJL6YElqR9gUeaLWkg1cdGP0J1yXt0bmtgW6r/2/Wrch8FjmykogEl6c22v73R+SaAl0nC9iWNFNalDO90SNJewOeBVwDLgSHgSNu3NFrYAMtsnd6R9FLb90l6oe3Hm65nEEk6zfZpkv55jN22/cd9L6oH0tPvkO2bypSuPajG+QZ6seRnifRAemfnct5pW2BXSXtSjUN/sOG6BsmKcpHb+5supJeyMHp39gH2pLoIZoGkoxuuJ2LUZ6mmbT4MYPuHwMAu5t2QdwH/LumLkg6VNCXOiaSn3yFJXwReBiyjuv0CVD3V8xsragBtdPuFF0p6dHQXuRVwV2zfv9GdITPRYBJsv03SdsDbqG6ytljS16kWRf9us9V1LqHfuWFgXm6w1p3cAniLuV/S6wBLei7wIarF0WMSbD8KnAecVyZuHAmcKWkH27Oara4zGd7p3HKqS7Mjno2Oo1rXdSawmupeUMc3WtEAkzSd6nYM7wR2AL7abEWdy+ydDkm6huo/0g3Ab+fsDupNmCLi6SRtSzW0swB4NbCE6n5G3xnkv/AT+h2q3YzpaQZ5rC8Gn6T/vondzsLoEyfpIeByqqC/YqrMzkvod0jSNsAvbT8paXeqq/W+OVX+YcRgknTSGM3bAMcCL7a9bZ9LGliSXmD7l03X0WsJ/Q6VWyu/HphOtTj6jcCvbb+70cIiCkkvojqBeyxwEXCG7QebrSqalhO5nVO50vHtwFm2/4jq6tyIRknaQdLfUt1XfytgL9t/mcAPSOh3Q+XmVu8GvlHa8nlGoyR9kuqvzseAV9o+zfbPGi5rSihr5Q68hFTnTgROAS61vULSbsA1DdcUcRKwM/DXwE8kPVq+Hqtd+BaTIOl1km4Dbi+P95R0VsNldSxj+hERmyDpB1QXZS0ZvSGgpOW2B3I4N1fkTpKkz9o+cbxFkzNPP2LqmUq3tEjoT94Xy/dPNVpFRPTLlLqlRYZ3uiBpCMD22qZriYgtQ9IM4HNUC84LuBL4kO2HGy2sQwn9Dkg6DTiB6kS4qBby/rztjzZZV0TE5mR4Z5LKuqP7Ab9n+57SthuwSNKf2f5MowVGRE9JmgP8F2A2tcwc1PN36elPkqSbgT+w/dBG7UPAlVnuL2JqkfRD4BzgVuDJ0fZBvc9WevqT99yNAx+qcf1ykicippZf2T6z6SJ6JaE/eb/ucF9EDKbPSTqV6gRu/TbqNzVXUucS+pO35zhXNgp4fr+LiYgt7pXAe4E389TwjsvjgZMx/YiITZC0kmpp1Cnxl3zuvRMRsWnLge2bLqJXMrwTEbFp2wO3S7qRKbA0akI/ImLTTm26gF7KmH5ERIukpx8RMQZJ19neX9JjPP2OuqJaZH67hkrrSnr6EREtktk7ERHjkDRN0u1N19FLCf2IiHHY3gDcIWnXpmvplYzpR0Rs2nRghaQbgF+MNmbKZkTE1PQ3TRfQSwn9iIgxSHo+cBzwO1S3VT7H9hPNVtW9zN6JiBiDpK8AvwG+BxwK3Gf7Q81W1b2EfkTEGCTdavuVZXsr4AbbezVcVtcyeyciYmy/Gd2YCsM6o9LTj4gYg6QNPDVbR8ALgMfJFbkRETEoMrwTEdEiCf2IiBZJ6EdEtEhCPwaapA9LWiHpFknLJL1G0omSXtjnOnaW9NUuX2OhpNvL1w2S9u9VfRGjciI3Bpak1wKfBg6wvV7SDGBr4PvAsO2HxnjOtHITrWcVSW8BPgIcbPshSXsBXwP2sf3TZquLqSQ9/RhkOwEP2V4PUEL+SGBn4BpJ1wBI+rmkMyT9EHitpPeUnvQySf9L0rRy3CJJI+Uvh4+MvomkeyX9XTl+RNJekq6QdJek48oxsyUtL9vvk3SJpMsl3SnpE7XXOlbSj8v7f0HS35ddfwn819FfVLZvAs4Djq/V8BFJN0m6VdLvbsHPNaawhH4MsiuBWSVEz5L0RttnAj8B3mT7TeW4bYAf2N4TeBh4J7Cf7fnABuDd5bgP2x4GXgW8UdKrau/17+X47wHnUv1y2Zeqdz6W+eV9Xgm8U9IsSTtT3bxrX2A/oB7cLweWbvQaI6V91EPlitBFwF9s5rOJGFNuuBYDy/bPJe0NvB54E/AVSSePcegG4OKyfSCwN3CjJKguuHmw7HuHpIVU/y92AuYBt5R9S8r3W4FtbT8GPCZpvaTtx3jPq20/AiDpNuClwAzgu7bXlfZ/AXafxI98Sfm+FHj7JJ4X8VsJ/RhoZXz+O8B3JN0KHDPGYb+qjeMLOM/2KfUDJM2h6j3/nu2fSToXeH7tkPXl+5O17dHHY/0/qh+zYZxj6m6j+mX07Vrb3sCKMV5zIq8XMaYM78TAkrSHpLm1pvnAfcBjwIvGedrVwJGSXlJeYwdJLwW2o7rk/hFJO1LdVbHXbqQaNppebuD1n2r7PgH8T0kvLnXNB94HnLUF6ogWS28hBtm2wOfL8MoTwEpgIbAAuFzST2rj+gDYvk3SXwNXSnoO1U21jrd9vaSbgduB+4F/63WxtldL+h/ADcC68l6PlH1LJM0Evi/JVL+43mN7Ta/riHbLlM2IPpK0bTkXsRVwKbDY9qVN1xXtkeGdiP46TdIyYDlwD9Vc/Ii+SU8/IqJF0tOPiGiRhH5ERIsk9CMiWiShHxHRIgn9iIgWSehHRLTI/wesW5KFdevIvQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "id": "HEV4U9PFNqry",
        "outputId": "1483fd51-655c-406e-ac65-05183acc6495"
      },
      "source": [
        "figure = []\r\n",
        "figure.append(px.violin(tv_shows_both_ratings, x = 'StreamingOn', y = 'IMDb', color='StreamingOn'))\r\n",
        "figure.append(px.violin(tv_shows_both_ratings, x = 'StreamingOn', y = 'Rotten Tomatoes', color='StreamingOn'))\r\n",
        "fig = make_subplots(rows=2, cols=4, shared_yaxes=True)\r\n",
        "\r\n",
        "for i in range(2):\r\n",
        "    for j in range(4):\r\n",
        "        fig.add_trace(figure[i]['data'][j], row=i+1, col=j+1)\r\n",
        "\r\n",
        "fig.update_layout(autosize=False, width=800, height=800)        \r\n",
        "fig.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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          "metadata": {
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    {
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        "id": "RrtgmUv3NuqA",
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      "source": [
        "px.scatter(tv_shows_both_ratings, x='IMDb',y='Rotten Tomatoes',color='StreamingOn')"
      ],
      "execution_count": 14,
      "outputs": [
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      "execution_count": null,
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