_1_Netflix_Movies_&_TV_Shows.ipynb

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      "name": "#1 Netflix Movies & TV Shows.ipynb",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/MathankumarSelvaraj/Intial-Training-of-Python/blob/master/_1_Netflix_Movies_%26_TV_Shows.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZadHKQ3anKRU",
        "colab_type": "text"
      },
      "source": [
        "## Importing the necessory library to perfrom the required task"
      ]
    },
    {
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      "source": [
        "\n",
        "#Importing the necessory library to perfrom the required task\n",
        "import pandas as pd\n",
        "from plotly.offline import iplot\n",
        "import plotly.graph_objs as go\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.figure_factory as ff\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "# the dataset is stored in variable name 'df'\n",
        "df = pd.read_csv(\"netflix_titles.csv\")\n",
        "df.head(5)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning:\n",
            "\n",
            "pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "\n"
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          "name": "stderr"
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              "<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",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>81145628</td>\n",
              "      <td>Movie</td>\n",
              "      <td>Norm of the North: King Sized Adventure</td>\n",
              "      <td>Richard Finn, Tim Maltby</td>\n",
              "      <td>Alan Marriott, Andrew Toth, Brian Dobson, Cole...</td>\n",
              "      <td>United States, India, South Korea, China</td>\n",
              "      <td>September 9, 2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>TV-PG</td>\n",
              "      <td>90 min</td>\n",
              "      <td>Children &amp; Family Movies, Comedies</td>\n",
              "      <td>Before planning an awesome wedding for his gra...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>80117401</td>\n",
              "      <td>Movie</td>\n",
              "      <td>Jandino: Whatever it Takes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Jandino Asporaat</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>September 9, 2016</td>\n",
              "      <td>2016</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td>94 min</td>\n",
              "      <td>Stand-Up Comedy</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>70234439</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Transformers Prime</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Peter Cullen, Sumalee Montano, Frank Welker, J...</td>\n",
              "      <td>United States</td>\n",
              "      <td>September 8, 2018</td>\n",
              "      <td>2013</td>\n",
              "      <td>TV-Y7-FV</td>\n",
              "      <td>1 Season</td>\n",
              "      <td>Kids' TV</td>\n",
              "      <td>With the help of three human allies, the Autob...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>80058654</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Transformers: Robots in Disguise</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Will Friedle, Darren Criss, Constance Zimmer, ...</td>\n",
              "      <td>United States</td>\n",
              "      <td>September 8, 2018</td>\n",
              "      <td>2016</td>\n",
              "      <td>TV-Y7</td>\n",
              "      <td>1 Season</td>\n",
              "      <td>Kids' TV</td>\n",
              "      <td>When a prison ship crash unleashes hundreds of...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>80125979</td>\n",
              "      <td>Movie</td>\n",
              "      <td>#realityhigh</td>\n",
              "      <td>Fernando Lebrija</td>\n",
              "      <td>Nesta Cooper, Kate Walsh, John Michael Higgins...</td>\n",
              "      <td>United States</td>\n",
              "      <td>September 8, 2017</td>\n",
              "      <td>2017</td>\n",
              "      <td>TV-14</td>\n",
              "      <td>99 min</td>\n",
              "      <td>Comedies</td>\n",
              "      <td>When nerdy high schooler Dani finally attracts...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    show_id  ...                                        description\n",
              "0  81145628  ...  Before planning an awesome wedding for his gra...\n",
              "1  80117401  ...  Jandino Asporaat riffs on the challenges of ra...\n",
              "2  70234439  ...  With the help of three human allies, the Autob...\n",
              "3  80058654  ...  When a prison ship crash unleashes hundreds of...\n",
              "4  80125979  ...  When nerdy high schooler Dani finally attracts...\n",
              "\n",
              "[5 rows x 12 columns]"
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      "cell_type": "markdown",
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      "source": [
        "## Spliting the 'season' from 'duration' and creating a column called 'season_count'\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
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        "outputId": "0474e5cf-5693-4aba-abf3-624bc4be15dc"
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      "source": [
        "#creating a function to split the season to get the counts (number of seasons) \n",
        "f = lambda df: df[\"duration\"].split(\" \") [0] if 'Season' in df['duration'] else \"\"\n",
        "df['season_count']= df.apply(f,axis=1)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "      <td>United States</td>\n",
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              "      <td>2013</td>\n",
              "      <td>TV-Y7-FV</td>\n",
              "      <td>1 Season</td>\n",
              "      <td>Kids' TV</td>\n",
              "      <td>With the help of three human allies, the Autob...</td>\n",
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              "      <td>80125979</td>\n",
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              "      <td>Fernando Lebrija</td>\n",
              "      <td>Nesta Cooper, Kate Walsh, John Michael Higgins...</td>\n",
              "      <td>United States</td>\n",
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              "      <td>2017</td>\n",
              "      <td>TV-14</td>\n",
              "      <td>99 min</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>6229</th>\n",
              "      <td>80000063</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Red vs. Blue</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Burnie Burns, Jason Saldaña, Gustavo Sorola, G...</td>\n",
              "      <td>United States</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015</td>\n",
              "      <td>NR</td>\n",
              "      <td>13 Seasons</td>\n",
              "      <td>TV Action &amp; Adventure, TV Comedies, TV Sci-Fi ...</td>\n",
              "      <td>This parody of first-person shooter games, mil...</td>\n",
              "      <td>13</td>\n",
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              "    <tr>\n",
              "      <th>6230</th>\n",
              "      <td>70286564</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Maron</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Marc Maron, Judd Hirsch, Josh Brener, Nora Zeh...</td>\n",
              "      <td>United States</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td>4 Seasons</td>\n",
              "      <td>TV Comedies</td>\n",
              "      <td>Marc Maron stars as Marc Maron, who interviews...</td>\n",
              "      <td>4</td>\n",
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              "      <th>6231</th>\n",
              "      <td>80116008</td>\n",
              "      <td>Movie</td>\n",
              "      <td>Little Baby Bum: Nursery Rhyme Friends</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016</td>\n",
              "      <td>NaN</td>\n",
              "      <td>60 min</td>\n",
              "      <td>Movies</td>\n",
              "      <td>Nursery rhymes and original music for children...</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6232</th>\n",
              "      <td>70281022</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>A Young Doctor's Notebook and Other Stories</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Daniel Radcliffe, Jon Hamm, Adam Godley, Chris...</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2013</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td>2 Seasons</td>\n",
              "      <td>British TV Shows, TV Comedies, TV Dramas</td>\n",
              "      <td>Set during the Russian Revolution, this comic ...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6233</th>\n",
              "      <td>70153404</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Friends</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Jennifer Aniston, Courteney Cox, Lisa Kudrow, ...</td>\n",
              "      <td>United States</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2003</td>\n",
              "      <td>TV-14</td>\n",
              "      <td>10 Seasons</td>\n",
              "      <td>Classic &amp; Cult TV, TV Comedies</td>\n",
              "      <td>This hit sitcom follows the merry misadventure...</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6234 rows × 13 columns</p>\n",
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            ],
            "text/plain": [
              "       show_id  ... season_count\n",
              "0     81145628  ...             \n",
              "1     80117401  ...             \n",
              "2     70234439  ...            1\n",
              "3     80058654  ...            1\n",
              "4     80125979  ...             \n",
              "...        ...  ...          ...\n",
              "6229  80000063  ...           13\n",
              "6230  70286564  ...            4\n",
              "6231  80116008  ...             \n",
              "6232  70281022  ...            2\n",
              "6233  70153404  ...           10\n",
              "\n",
              "[6234 rows x 13 columns]"
            ]
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          "metadata": {
            "tags": []
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y3kzXt_JnHn0",
        "colab_type": "text"
      },
      "source": [
        "## The 'date_added' variable is splited into 'year_added' and 'month_added' to dive deep into the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4jgEv61FkqUb",
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        "colab": {}
      },
      "source": [
        "#The 'date_added' variable is splited into 'year_added' and 'month_added' to dive deep into the dataset\n",
        "df[\"date_added\"] = pd.to_datetime(df['date_added'])\n",
        "df['year_added'] = df['date_added'].dt.year\n",
        "df['month_added'] = df['date_added'].dt.month"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "US90ggRwnQqu",
        "colab_type": "text"
      },
      "source": [
        "## Removing the string 'min' from the variable 'duration'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "69k1neXyPtGQ",
        "colab_type": "code",
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          "height": 666
        },
        "outputId": "5ccd63c3-c52c-48f9-db36-f31ec853a232"
      },
      "source": [
        "#Removing the string 'min' from the variable 'duration'\n",
        "min = lambda df: df[\"duration\"].split(\" \") [0] if 'Season' not in df['duration'] else \"\"\n",
        "df['duration']= df.apply(min,axis=1)\n",
        "df.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>season_count</th>\n",
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              "      <th>0</th>\n",
              "      <td>81145628</td>\n",
              "      <td>Movie</td>\n",
              "      <td>Norm of the North: King Sized Adventure</td>\n",
              "      <td>Richard Finn, Tim Maltby</td>\n",
              "      <td>Alan Marriott, Andrew Toth, Brian Dobson, Cole...</td>\n",
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              "      <td>2019.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>80117401</td>\n",
              "      <td>Movie</td>\n",
              "      <td>Jandino: Whatever it Takes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Jandino Asporaat</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2016-09-09</td>\n",
              "      <td>2016</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td>94</td>\n",
              "      <td>Stand-Up Comedy</td>\n",
              "      <td>Jandino Asporaat riffs on the challenges of ra...</td>\n",
              "      <td></td>\n",
              "      <td>2016.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>70234439</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Transformers Prime</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Peter Cullen, Sumalee Montano, Frank Welker, J...</td>\n",
              "      <td>United States</td>\n",
              "      <td>2018-09-08</td>\n",
              "      <td>2013</td>\n",
              "      <td>TV-Y7-FV</td>\n",
              "      <td></td>\n",
              "      <td>Kids' TV</td>\n",
              "      <td>With the help of three human allies, the Autob...</td>\n",
              "      <td>1</td>\n",
              "      <td>2018.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>80058654</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Transformers: Robots in Disguise</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Will Friedle, Darren Criss, Constance Zimmer, ...</td>\n",
              "      <td>United States</td>\n",
              "      <td>2018-09-08</td>\n",
              "      <td>2016</td>\n",
              "      <td>TV-Y7</td>\n",
              "      <td></td>\n",
              "      <td>Kids' TV</td>\n",
              "      <td>When a prison ship crash unleashes hundreds of...</td>\n",
              "      <td>1</td>\n",
              "      <td>2018.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>80125979</td>\n",
              "      <td>Movie</td>\n",
              "      <td>#realityhigh</td>\n",
              "      <td>Fernando Lebrija</td>\n",
              "      <td>Nesta Cooper, Kate Walsh, John Michael Higgins...</td>\n",
              "      <td>United States</td>\n",
              "      <td>2017-09-08</td>\n",
              "      <td>2017</td>\n",
              "      <td>TV-14</td>\n",
              "      <td>99</td>\n",
              "      <td>Comedies</td>\n",
              "      <td>When nerdy high schooler Dani finally attracts...</td>\n",
              "      <td></td>\n",
              "      <td>2017.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    show_id     type  ... year_added month_added\n",
              "0  81145628    Movie  ...     2019.0         9.0\n",
              "1  80117401    Movie  ...     2016.0         9.0\n",
              "2  70234439  TV Show  ...     2018.0         9.0\n",
              "3  80058654  TV Show  ...     2018.0         9.0\n",
              "4  80125979    Movie  ...     2017.0         9.0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v0ERE_fPncQb",
        "colab_type": "text"
      },
      "source": [
        "## Counting the number of Movies and TV shows in the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jeGVyM4qWLtz",
        "colab_type": "code",
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          "height": 542
        },
        "outputId": "c8e7d237-1df0-4f66-df70-fab856ee853b"
      },
      "source": [
        "#created new variables to get the counts of movie and tv_shows\n",
        "c1=sum(df['type'].str.count('Movie')) \n",
        "c2 =sum(df['type'].str.count('TV Show'))\n",
        "#created a dictionary\n",
        "count = {'Movie': [c1],\n",
        "        'TV_Show': [c2]\n",
        "        }\n",
        "#coverting into dataframe 'df_1' which has the total counts of movies and tv_shows\n",
        "df_1 = pd.DataFrame(count, columns = ['Movie', 'TV_Show'])\n",
        "#df1_Title = ['No of Movie','No of TV Show']\n",
        "table1_df1 = go.Figure(data=[go.Table(\n",
        "    header=dict(values=list(['No of Movie','No of TV Show']),\n",
        "                fill_color='#fca103',\n",
        "                align='center',font=dict(color='Black', size=16),height=40),\n",
        "    cells=dict(values=[df_1.Movie, df_1.TV_Show],\n",
        "               fill_color='Black',\n",
        "               align='center',font=dict(color='White', size=12)))\n",
        "])\n",
        "table1_df1.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kz3qR5gxniF8",
        "colab_type": "text"
      },
      "source": [
        "## Inital plot of the movies and TV_shows to see the distribution of each type respectively"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b1hRfIcBWmN9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "1a4b6b0f-d242-4680-9521-c22f1339a85e"
      },
      "source": [
        "## plot\n",
        "df1_Title = ['movie','tv_show']\n",
        "trace = go.Pie(labels=df1_Title, values=df_1.iloc[0], pull=[0.07, 0], marker=dict(colors=[\"#fca103\", \"#000000\"]))\n",
        "layout = go.Layout(title=\"Propotion of the content\", height=600, legend=dict(x=0, y=0))\n",
        "fig1_pie = go.Figure(data = [trace], layout = layout)\n",
        "iplot(fig1_pie)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
            "tags": []
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SlXu2BVtnrgC",
        "colab_type": "text"
      },
      "source": [
        "## Spliting the dataset according to the variable 'Type' such as 'Movies' and 'Tv_shows'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n4RDjI2ni8GC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 649
        },
        "outputId": "cb6a10ba-a66e-425b-bd95-67a7713e58ef"
      },
      "source": [
        "#using groupby in 'type' variable to group all the movies\n",
        "Movie = df.groupby(df['type']) \n",
        "movie_df = Movie.get_group(\"Movie\")\n",
        "#coverting it into a dataframe, 'movie_df' has the movie data\n",
        "movie_df = pd.DataFrame(movie_df)\n",
        "movie_df.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "    .dataframe tbody tr th {\n",
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              "<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>season_count</th>\n",
              "      <th>year_added</th>\n",
              "      <th>month_added</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>81145628</td>\n",
              "      <td>Movie</td>\n",
              "      <td>Norm of the North: King Sized Adventure</td>\n",
              "      <td>Richard Finn, Tim Maltby</td>\n",
              "      <td>Alan Marriott, Andrew Toth, Brian Dobson, Cole...</td>\n",
              "      <td>United States, India, South Korea, China</td>\n",
              "      <td>2019-09-09</td>\n",
              "      <td>2019</td>\n",
              "      <td>TV-PG</td>\n",
              "      <td>90</td>\n",
              "      <td>Children &amp; Family Movies, Comedies</td>\n",
              "      <td>Before planning an awesome wedding for his gra...</td>\n",
              "      <td></td>\n",
              "      <td>2019.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>80117401</td>\n",
              "      <td>Movie</td>\n",
              "      <td>Jandino: Whatever it Takes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Jandino Asporaat</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2016-09-09</td>\n",
              "      <td>2016</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td>94</td>\n",
              "      <td>Stand-Up Comedy</td>\n",
              "      <td>Jandino Asporaat riffs on the challenges of ra...</td>\n",
              "      <td></td>\n",
              "      <td>2016.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>80125979</td>\n",
              "      <td>Movie</td>\n",
              "      <td>#realityhigh</td>\n",
              "      <td>Fernando Lebrija</td>\n",
              "      <td>Nesta Cooper, Kate Walsh, John Michael Higgins...</td>\n",
              "      <td>United States</td>\n",
              "      <td>2017-09-08</td>\n",
              "      <td>2017</td>\n",
              "      <td>TV-14</td>\n",
              "      <td>99</td>\n",
              "      <td>Comedies</td>\n",
              "      <td>When nerdy high schooler Dani finally attracts...</td>\n",
              "      <td></td>\n",
              "      <td>2017.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>70304989</td>\n",
              "      <td>Movie</td>\n",
              "      <td>Automata</td>\n",
              "      <td>Gabe Ibáñez</td>\n",
              "      <td>Antonio Banderas, Dylan McDermott, Melanie Gri...</td>\n",
              "      <td>Bulgaria, United States, Spain, Canada</td>\n",
              "      <td>2017-09-08</td>\n",
              "      <td>2014</td>\n",
              "      <td>R</td>\n",
              "      <td>110</td>\n",
              "      <td>International Movies, Sci-Fi &amp; Fantasy, Thrillers</td>\n",
              "      <td>In a dystopian future, an insurance adjuster f...</td>\n",
              "      <td></td>\n",
              "      <td>2017.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>80164077</td>\n",
              "      <td>Movie</td>\n",
              "      <td>Fabrizio Copano: Solo pienso en mi</td>\n",
              "      <td>Rodrigo Toro, Francisco Schultz</td>\n",
              "      <td>Fabrizio Copano</td>\n",
              "      <td>Chile</td>\n",
              "      <td>2017-09-08</td>\n",
              "      <td>2017</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td>60</td>\n",
              "      <td>Stand-Up Comedy</td>\n",
              "      <td>Fabrizio Copano takes audience participation t...</td>\n",
              "      <td></td>\n",
              "      <td>2017.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "    show_id   type  ... year_added month_added\n",
              "0  81145628  Movie  ...     2019.0         9.0\n",
              "1  80117401  Movie  ...     2016.0         9.0\n",
              "4  80125979  Movie  ...     2017.0         9.0\n",
              "6  70304989  Movie  ...     2017.0         9.0\n",
              "7  80164077  Movie  ...     2017.0         9.0\n",
              "\n",
              "[5 rows x 15 columns]"
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          "metadata": {
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          "execution_count": 7
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    {
      "cell_type": "code",
      "metadata": {
        "id": "XeugqhiKjrW0",
        "colab_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "511c028d-fe99-4ddf-cbad-eb02494b9b2f"
      },
      "source": [
        "#Using string matching in variable 'Type' to get all the Tv_Shows\n",
        "_is_TVShow = df['type']=='TV Show'\n",
        "tvshow_df = df[_is_TVShow]\n",
        "#tvshow_df has the Tv Shows data\n",
        "tvshow_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>2</th>\n",
              "      <td>70234439</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Transformers Prime</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Peter Cullen, Sumalee Montano, Frank Welker, J...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>80058654</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Transformers: Robots in Disguise</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>2018.0</td>\n",
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              "      <td>2017.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>80117902</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Fire Chasers</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United States</td>\n",
              "      <td>2017-09-08</td>\n",
              "      <td>2017</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td></td>\n",
              "      <td>Docuseries, Science &amp; Nature TV</td>\n",
              "      <td>As California's 2016 fire season rages, brave ...</td>\n",
              "      <td>1</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>80244601</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Castle of Stars</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Chaiyapol Pupart, Jintanutda Lummakanon, Worra...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2018-09-07</td>\n",
              "      <td>2015</td>\n",
              "      <td>TV-14</td>\n",
              "      <td></td>\n",
              "      <td>International TV Shows, Romantic TV Shows, TV ...</td>\n",
              "      <td>As four couples with different lifestyles go t...</td>\n",
              "      <td>1</td>\n",
              "      <td>2018.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6228</th>\n",
              "      <td>80159925</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Kikoriki</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Igor Dmitriev</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2010</td>\n",
              "      <td>TV-Y</td>\n",
              "      <td></td>\n",
              "      <td>Kids' TV</td>\n",
              "      <td>A wacky rabbit and his gang of animal pals hav...</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6229</th>\n",
              "      <td>80000063</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Red vs. Blue</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Burnie Burns, Jason Saldaña, Gustavo Sorola, G...</td>\n",
              "      <td>United States</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2015</td>\n",
              "      <td>NR</td>\n",
              "      <td></td>\n",
              "      <td>TV Action &amp; Adventure, TV Comedies, TV Sci-Fi ...</td>\n",
              "      <td>This parody of first-person shooter games, mil...</td>\n",
              "      <td>13</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6230</th>\n",
              "      <td>70286564</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Maron</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Marc Maron, Judd Hirsch, Josh Brener, Nora Zeh...</td>\n",
              "      <td>United States</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2016</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td></td>\n",
              "      <td>TV Comedies</td>\n",
              "      <td>Marc Maron stars as Marc Maron, who interviews...</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6232</th>\n",
              "      <td>70281022</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>A Young Doctor's Notebook and Other Stories</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Daniel Radcliffe, Jon Hamm, Adam Godley, Chris...</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2013</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td></td>\n",
              "      <td>British TV Shows, TV Comedies, TV Dramas</td>\n",
              "      <td>Set during the Russian Revolution, this comic ...</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6233</th>\n",
              "      <td>70153404</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Friends</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Jennifer Aniston, Courteney Cox, Lisa Kudrow, ...</td>\n",
              "      <td>United States</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2003</td>\n",
              "      <td>TV-14</td>\n",
              "      <td></td>\n",
              "      <td>Classic &amp; Cult TV, TV Comedies</td>\n",
              "      <td>This hit sitcom follows the merry misadventure...</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1969 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       show_id     type  ... year_added month_added\n",
              "2     70234439  TV Show  ...     2018.0         9.0\n",
              "3     80058654  TV Show  ...     2018.0         9.0\n",
              "5     80163890  TV Show  ...     2017.0         9.0\n",
              "8     80117902  TV Show  ...     2017.0         9.0\n",
              "26    80244601  TV Show  ...     2018.0         9.0\n",
              "...        ...      ...  ...        ...         ...\n",
              "6228  80159925  TV Show  ...        NaN         NaN\n",
              "6229  80000063  TV Show  ...        NaN         NaN\n",
              "6230  70286564  TV Show  ...        NaN         NaN\n",
              "6232  70281022  TV Show  ...        NaN         NaN\n",
              "6233  70153404  TV Show  ...        NaN         NaN\n",
              "\n",
              "[1969 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OZdGWnCWoD4p",
        "colab_type": "text"
      },
      "source": [
        "## Removing the missing values in variable 'year_added' from both Movie and Tv Shows dataset "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DkxuLvF7_di3",
        "colab_type": "code",
        "colab": {
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          "height": 34
        },
        "outputId": "1c3d8052-54c9-4b7f-8253-3014eb9c41b6"
      },
      "source": [
        "#Removing the missing values in variable 'year_added' from both Movie and Tv Shows dataset\n",
        "tvshow_df = tvshow_df.dropna(subset=['year_added'])\n",
        "movie_df = movie_df.dropna(subset = ['year_added'] )\n",
        "tvshow_df['year_added'].isna().sum()\n",
        "movie_df['year_added'].isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0PTvU45pofDq",
        "colab_type": "text"
      },
      "source": [
        "## Using pivot to get the total number of Tv_Shows added each year"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r1_JSLqpAsAy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "c073e10c-3e31-48fe-f221-e78cb1525ec8"
      },
      "source": [
        "#Removing the missing values in variable 'year_added' from both Movie and Tv Shows dataset\n",
        "tvshow_pivot = pd.pivot_table(tvshow_df,index=['year_added'],values=[\"show_id\"],aggfunc='count')\n",
        "tvshow_pivot.sort_values(by=['year_added'], inplace=True)\n",
        "tvshow_pivot['TvShows'] = tvshow_pivot['show_id']\n",
        "del tvshow_pivot['show_id']\n",
        "tvshow_pivot = pd.DataFrame(tvshow_pivot.to_records())\n",
        "tvshow_pivot = tvshow_pivot.drop(index=9)\n",
        "tvshow_pivot\n",
        "\n",
        "\n",
        "table2_Tvshow= go.Figure(data=[go.Table(\n",
        "    header=dict(values=list(['year','No of TV Shows']),\n",
        "                fill_color='#fca103',\n",
        "                align='center',font=dict(color='Black', size=16),height=40),\n",
        "    cells=dict(values=[tvshow_pivot.year_added, tvshow_pivot.TvShows],\n",
        "               fill_color='Black',\n",
        "               align='center',font=dict(color='White', size=12)))\n",
        "])\n",
        "table2_Tvshow.show()\n"
      ],
      "execution_count": null,
      "outputs": [
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              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gj1qUxobonM6",
        "colab_type": "text"
      },
      "source": [
        "## Using pivot to get the total number of movies added each year"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2VGiX02sGEq5",
        "colab_type": "code",
        "colab": {
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          "height": 542
        },
        "outputId": "da83177e-1a1a-4b08-aae1-6450538fbbda"
      },
      "source": [
        "movie_pivot = pd.pivot_table(movie_df,index=['year_added'],values=[\"show_id\"],aggfunc='count')\n",
        "movie_pivot.sort_values(by=['year_added'], inplace=True)\n",
        "movie_pivot.rename(columns = {'show_id': 'Movies'}, inplace= True)\n",
        "movie_pivot = pd.DataFrame(movie_pivot.to_records())\n",
        "movie_pivot= movie_pivot.drop(index=12) # Removing 2020 year's count, because that data not complete\n",
        "movie_pivot.tail(5)\n",
        "\n",
        "table3_movie= go.Figure(data=[go.Table(\n",
        "    header=dict(values=list(['Year','No of Movies']),\n",
        "                fill_color='#fca103',\n",
        "                align='center',font=dict(color='Black', size=16),height=40),\n",
        "    cells=dict(values=[movie_pivot.year_added, movie_pivot.Movies],\n",
        "               fill_color='Black',\n",
        "               align='center',font=dict(color='White', size=12)))\n",
        "])\n",
        "table3_movie.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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          "metadata": {
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    },
    {
      "cell_type": "code",
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        "colab": {}
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      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XsQpxVSf8BLm",
        "colab_type": "text"
      },
      "source": [
        "## Distribution of TV Shows and Movies added over the years"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wkk9u6QTIfO9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "2960deda-bfd1-4fb5-d26b-96abf3115828"
      },
      "source": [
        "#Distribution of TV Shows and Movies added over the years\n",
        "layout = go.Layout(title='Number of TV Shows and Movies added vs Year',showlegend =True,legend=dict(x=0.3, y=1))\n",
        "fig2_contentByYear = go.Figure(layout=layout)\n",
        "fig2_contentByYear.add_trace(go.Scatter(x =movie_pivot['year_added'], y=movie_pivot['Movies'],\n",
        "                   name='Movies Over the years',fill='tozeroy',marker=dict(color=\"#fca103\"))) \n",
        "fig2_contentByYear.add_trace(go.Scatter(x =tvshow_pivot['year_added'], y=tvshow_pivot['TvShows'],\n",
        "                   name='TV show Over the years',fill='tozeroy',marker=dict(color=\"#000000\"))) \n",
        "\n",
        "fig2_contentByYear.show()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
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\"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Number of TV Shows and Movies added vs Year\"}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('3c534e94-6340-4d32-8b02-403b5cff632b');\n",
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              "        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": "feZVUgkK8Qjq",
        "colab_type": "text"
      },
      "source": [
        "## Using String matching fucntion to get the movies added in specific years"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CMOQbs9jss5K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Getting the movies added over the recent years\n",
        "movie_df_2019 = movie_df[movie_df['year_added']==2019]\n",
        "movie_df_2018 = movie_df[movie_df['year_added']==2018]\n",
        "movie_df_2017 = movie_df[movie_df['year_added']==2017]\n",
        "#movie_df_2017"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j0aMPCkQOccy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "d45254df-a51c-4250-acc9-780175d7449e"
      },
      "source": [
        "#columns = ['Children_and_Family_Movies','Dramas','Comedies','Documentaries','Horror Movies','Stand-Up Comedy','International Movies','Anime Features','Action & Adventure'\n",
        "#'Movies','Thrillers','Classic Movies','Cult Movies','Sci-Fi & Fantasy','Independent Movies','Romantic Movies','Faith & Spirituality','LGBTQ Movies','Music & Musicals',\n",
        "#'Sports Movies']\n",
        "# Aggregating the genres \n",
        "genres_2019 = pd.DataFrame({ 'movie_genres' : movie_df_2019['listed_in']})\n",
        "genres_2018 = pd.DataFrame({ 'movie_genres' : movie_df_2018['listed_in']})\n",
        "genres_2017 = pd.DataFrame({ 'movie_genres' : movie_df_2017['listed_in']})\n",
        "genres_2017.tail(5)"
      ],
      "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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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movie_genres</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5571</th>\n",
              "      <td>Dramas, International Movies</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5572</th>\n",
              "      <td>Documentaries</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5573</th>\n",
              "      <td>Documentaries, International Movies, Music &amp; M...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5574</th>\n",
              "      <td>Dramas, International Movies</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5575</th>\n",
              "      <td>Dramas, International Movies, Thrillers</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                           movie_genres\n",
              "5571                       Dramas, International Movies\n",
              "5572                                      Documentaries\n",
              "5573  Documentaries, International Movies, Music & M...\n",
              "5574                       Dramas, International Movies\n",
              "5575            Dramas, International Movies, Thrillers"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9M5zjRUq9ofv",
        "colab_type": "text"
      },
      "source": [
        "## Creating new columns with unique genres to get the total counts of genres present in the variable 'listed_in' for the year 2019"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8bsz4Iz95PCE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "outputId": "59d3e073-c179-46d3-86e3-a99bc823a7cd"
      },
      "source": [
        "#Creating new columns with unique genres to get the total counts of genres present in the variable 'listed_in' for the year 2019\n",
        "genres_2019.loc[genres_2019[\"movie_genres\"].str.find(\"Comedies\") >= 0, 'Comedies'] = 1\n",
        "genres_2019.loc[genres_2019[\"movie_genres\"].str.find(\"Children & Family Movies\") >= 0, 'children_family_movies'] = 1\n",
        "genres_2019.loc[genres_2019[\"movie_genres\"].str.find(\"International Movies\") >= 0, 'International_Movies'] = 1\n",
        "genres_2019.loc[genres_2019[\"movie_genres\"].str.find(\"Dramas\") >= 0, 'Dramas'] = 1\n",
        "genres_2019.loc[genres_2019[\"movie_genres\"].str.find(\"Documentaries\") >= 0, 'Documentaries'] = 1\n",
        "genres_2019.loc[genres_2019[\"movie_genres\"].str.find(\"Horror Movies\") >= 0, 'Horror_Movies'] = 1\n",
        "genres_2019.loc[genres_2019[\"movie_genres\"].str.find(\"Stand-Up Comedy\") >= 0, 'Stand-Up_Comedy'] = 1\n",
        "genres_2019.loc[genres_2019[\"movie_genres\"].str.find(\"Anime Features\") >= 0, 'Anime_Features'] = 1\n",
        "genres_2019.loc[genres_2019[\"movie_genres\"].str.find(\"Action & Adventure\") >= 0, 'Action_Adventure'] = 1\n",
        "genres_2019.loc[genres_2019[\"movie_genres\"].str.find(\"Movies\") >= 0, 'Movies'] = 1\n",
        "genres_2019.loc[genres_2019[\"movie_genres\"].str.find(\"Thrillers\") >= 0, 'Thrillers'] = 1\n",
        "genres_2019.loc[genres_2019[\"movie_genres\"].str.find(\"Classic Movies\") >= 0, 'Classic_Movies'] = 1\n",
        "genres_2019.loc[genres_2019[\"movie_genres\"].str.find(\"Sci-Fi & Fantasy\") >= 0, 'Sci-Fi_Fantasy'] = 1\n",
        "genres_2019.loc[genres_2019[\"movie_genres\"].str.find(\"Independent Movies\") >= 0, 'Independent_Movies'] = 1\n",
        "genres_2019.loc[genres_2019[\"movie_genres\"].str.find(\"Faith & Spirituality\") >= 0, 'Faith_Spirituality'] = 1\n",
        "genres_2019.loc[genres_2019[\"movie_genres\"].str.find(\"LGBTQ Movies\") >= 0, 'LGBTQ_Movies'] = 1\n",
        "genres_2019.loc[genres_2019[\"movie_genres\"].str.find(\"Sports Movies\") >= 0, 'Sports_Movies'] = 1\n",
        "genres_2019.loc[genres_2019[\"movie_genres\"].str.find(\"Music & Musicals\") >= 0, 'Music_Musicals'] = 1\n",
        "\n"
      ],
      "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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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movie_genres</th>\n",
              "      <th>Comedies</th>\n",
              "      <th>children_family_movies</th>\n",
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              "      <th>Horror_Movies</th>\n",
              "      <th>Stand-Up_Comedy</th>\n",
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              "      <th>Action_Adventure</th>\n",
              "      <th>Movies</th>\n",
              "      <th>Thrillers</th>\n",
              "      <th>Classic_Movies</th>\n",
              "      <th>Sci-Fi_Fantasy</th>\n",
              "      <th>Independent_Movies</th>\n",
              "      <th>Faith_Spirituality</th>\n",
              "      <th>LGBTQ_Movies</th>\n",
              "      <th>Sports_Movies</th>\n",
              "      <th>Music_Musicals</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Dramas, International Movies, Thrillers</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Comedies, Dramas, Independent Movies</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Comedies, International Movies</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Comedies, International Movies, Romantic Movies</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "                                       movie_genres  ...  Music_Musicals\n",
              "0                Children & Family Movies, Comedies  ...             NaN\n",
              "35          Dramas, International Movies, Thrillers  ...             NaN\n",
              "36             Comedies, Dramas, Independent Movies  ...             NaN\n",
              "37                   Comedies, International Movies  ...             NaN\n",
              "38  Comedies, International Movies, Romantic Movies  ...             NaN\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "## Creating new columns with unique genres to get the total counts of genres present in the variable 'listed_in' for the year 2018"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KTzsCGMouY4E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "2c3455bd-f147-44c3-978e-6b39734789b3"
      },
      "source": [
        "#Creating new columns with unique genres to get the total counts of genres present in the variable 'listed_in' for the year 2018\n",
        "genres_2018.loc[genres_2018[\"movie_genres\"].str.find(\"Comedies\") >= 0, 'Comedies'] = 1\n",
        "genres_2018.loc[genres_2018[\"movie_genres\"].str.find(\"Children & Family Movies\") >= 0, 'children_family_movies'] = 1\n",
        "genres_2018.loc[genres_2018[\"movie_genres\"].str.find(\"International Movies\") >= 0, 'International_Movies'] = 1\n",
        "genres_2018.loc[genres_2018[\"movie_genres\"].str.find(\"Dramas\") >= 0, 'Dramas'] = 1\n",
        "genres_2018.loc[genres_2018[\"movie_genres\"].str.find(\"Documentaries\") >= 0, 'Documentaries'] = 1\n",
        "genres_2018.loc[genres_2018[\"movie_genres\"].str.find(\"Horror Movies\") >= 0, 'Horror_Movies'] = 1\n",
        "genres_2018.loc[genres_2018[\"movie_genres\"].str.find(\"Stand-Up Comedy\") >= 0, 'Stand-Up_Comedy'] = 1\n",
        "genres_2018.loc[genres_2018[\"movie_genres\"].str.find(\"Anime Features\") >= 0, 'Anime_Features'] = 1\n",
        "genres_2018.loc[genres_2018[\"movie_genres\"].str.find(\"Action & Adventure\") >= 0, 'Action_Adventure'] = 1\n",
        "genres_2018.loc[genres_2018[\"movie_genres\"].str.find(\"Movies\") >= 0, 'Movies'] = 1\n",
        "genres_2018.loc[genres_2018[\"movie_genres\"].str.find(\"Thrillers\") >= 0, 'Thrillers'] = 1\n",
        "genres_2018.loc[genres_2018[\"movie_genres\"].str.find(\"Classic Movies\") >= 0, 'Classic_Movies'] = 1\n",
        "genres_2018.loc[genres_2018[\"movie_genres\"].str.find(\"Sci-Fi & Fantasy\") >= 0, 'Sci-Fi_Fantasy'] = 1\n",
        "genres_2018.loc[genres_2018[\"movie_genres\"].str.find(\"Independent Movies\") >= 0, 'Independent_Movies'] = 1\n",
        "genres_2018.loc[genres_2018[\"movie_genres\"].str.find(\"Faith & Spirituality\") >= 0, 'Faith_Spirituality'] = 1\n",
        "genres_2018.loc[genres_2018[\"movie_genres\"].str.find(\"LGBTQ Movies\") >= 0, 'LGBTQ_Movies'] = 1\n",
        "genres_2018.loc[genres_2018[\"movie_genres\"].str.find(\"Sports Movies\") >= 0, 'Sports_Movies'] = 1\n",
        "genres_2018.loc[genres_2018[\"movie_genres\"].str.find(\"Music & Musicals\") >= 0, 'Music_Musicals'] = 1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movie_genres</th>\n",
              "      <th>Comedies</th>\n",
              "      <th>children_family_movies</th>\n",
              "      <th>International_Movies</th>\n",
              "      <th>Dramas</th>\n",
              "      <th>Documentaries</th>\n",
              "      <th>Horror_Movies</th>\n",
              "      <th>Stand-Up_Comedy</th>\n",
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              "      <th>Action_Adventure</th>\n",
              "      <th>Movies</th>\n",
              "      <th>Thrillers</th>\n",
              "      <th>Classic_Movies</th>\n",
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              "      <th>Independent_Movies</th>\n",
              "      <th>Faith_Spirituality</th>\n",
              "      <th>LGBTQ_Movies</th>\n",
              "      <th>Sports_Movies</th>\n",
              "      <th>Music_Musicals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Documentaries</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Horror Movies, International Movies</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Children &amp; Family Movies, Comedies, Sci-Fi &amp; F...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Comedies, Romantic Movies</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Dramas, International Movies, Thrillers</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>NaN</td>\n",
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              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         movie_genres  ...  Music_Musicals\n",
              "27                                      Documentaries  ...             NaN\n",
              "29                Horror Movies, International Movies  ...             NaN\n",
              "30  Children & Family Movies, Comedies, Sci-Fi & F...  ...             NaN\n",
              "31                          Comedies, Romantic Movies  ...             NaN\n",
              "32            Dramas, International Movies, Thrillers  ...             NaN\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kyhb0fJb-Xfe",
        "colab_type": "text"
      },
      "source": [
        "## Summing up to get the total counts of each genres being added in year 2019 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "we_QUG8titjs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "#Summing up to get the total counts of each genres being added in year 2019\n",
        "data_genres_2019 =[['children_family_movies',genres_2019['children_family_movies'].sum()],\n",
        "                   ['Comedies',genres_2019['Comedies'].sum()],\n",
        "                   ['International_Movies',genres_2019['International_Movies'].sum()],\n",
        "                   ['Dramas',genres_2019['Dramas'].sum()],\n",
        "                   ['Documentaries',genres_2019['Documentaries'].sum()],\n",
        "                   ['Horror_Movies',genres_2019['Horror_Movies'].sum()],\n",
        "                   ['Stand-Up_Comedy',genres_2019['Stand-Up_Comedy'].sum()],\n",
        "                   ['Anime_Features',genres_2019['Anime_Features'].sum()],\n",
        "                   ['Action_Adventure',genres_2019['Action_Adventure'].sum()],\n",
        "                   ['Thrillers',genres_2019['Thrillers'].sum()],\n",
        "                   ['Classic_Movies',genres_2019['Classic_Movies'].sum()],\n",
        "                   ['Sci-Fi_Fantasy',genres_2019['Sci-Fi_Fantasy'].sum()],\n",
        "                   ['Independent_Movies',genres_2019['Independent_Movies'].sum()],\n",
        "                   ['LGBTQ_Movies',genres_2019['LGBTQ_Movies'].sum()],\n",
        "                   ['Sports_Movies',genres_2019['Sports_Movies'].sum()],\n",
        "                   ['Music_Musicals',genres_2019['Music_Musicals'].sum()],\n",
        "                  ]\n",
        "no_movieGenres = pd.DataFrame(data_genres_2019, columns = ['Genres', '2019']) \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2t0ZnthF_LiR",
        "colab_type": "text"
      },
      "source": [
        "## Summing up to get the total counts of each genres being added in year 2018"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_pb6CMM6vHlT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "576353be-f0ad-456e-cae5-b6172ead9fd6"
      },
      "source": [
        "#Summing up to get the total counts of each genres being added in year 2018\n",
        "data_genres_2018 =[genres_2018['children_family_movies'].sum(),\n",
        "                   genres_2018['Comedies'].sum(),\n",
        "                   genres_2018['International_Movies'].sum(),\n",
        "                   genres_2018['Dramas'].sum(),\n",
        "                   genres_2018['Documentaries'].sum(),\n",
        "                   genres_2018['Horror_Movies'].sum(),\n",
        "                   genres_2018['Stand-Up_Comedy'].sum(),\n",
        "                   genres_2018['Anime_Features'].sum(),\n",
        "                   genres_2018['Action_Adventure'].sum(),\n",
        "                   genres_2018['Thrillers'].sum(),\n",
        "                   genres_2018['Classic_Movies'].sum(),\n",
        "                   genres_2018['Sci-Fi_Fantasy'].sum(),\n",
        "                   genres_2018['Independent_Movies'].sum(),\n",
        "                   genres_2018['LGBTQ_Movies'].sum(),\n",
        "                   genres_2018['Sports_Movies'].sum(),\n",
        "                   genres_2018['Music_Musicals'].sum(),\n",
        "                  ]\n",
        "data_genres_2018\n",
        "no_movieGenres['2018'] =data_genres_2018\n",
        "#Converting the float values to integers\n",
        "no_movieGenres['2018'] = no_movieGenres['2018'].astype(int)\n",
        "no_movieGenres['2019'] = no_movieGenres['2019'].astype(int)\n",
        "no_movieGenres\n",
        "\n",
        "#Formatting the result to a table\n",
        "table4_movieByYear= go.Figure(data=[go.Table(\n",
        "    header=dict(values=list(['Genres','2019','2018']),\n",
        "                fill_color='#fca103',\n",
        "                align='center',font=dict(color='Black', size=16),height=40),\n",
        "    cells=dict(values=[no_movieGenres.Genres, no_movieGenres['2019'], no_movieGenres['2018']],\n",
        "               fill_color='Black',\n",
        "               align='center',font=dict(color='White', size=12)))\n",
        "])\n",
        "table4_movieByYear.update_layout(width=1000, height=1000)\n",
        "table4_movieByYear.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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          "metadata": {
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iLTvHfeJCN7Y",
        "colab_type": "text"
      },
      "source": [
        "## Bar chart showing the counts of genres for the years 2018 and 2019"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9UbTX036dXgy",
        "colab_type": "code",
        "colab": {
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          "height": 542
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        "outputId": "cc404633-e36c-42db-e74e-1bdc9f51926f"
      },
      "source": [
        "#Bar chart showing the counts of genres for the years 2018 and 2019\n",
        "layout_bar = go.Layout(title='Distribution of genres for the years 2018 and 2019',showlegend =True,legend=dict(x=0.9, y=1))\n",
        "fig3_movieGenresByYears = go.Figure(layout=layout_bar)\n",
        "fig3_movieGenresByYears.add_trace(go.Bar(x =no_movieGenres['2019'], y=no_movieGenres['Genres'],name='2019',marker=dict(color=\"#fca103\"),orientation='h')) \n",
        "fig3_movieGenresByYears.add_trace(go.Bar(x =no_movieGenres['2018'], y=no_movieGenres['Genres'],name='2018',marker=dict(color=\"#000000\"),orientation='h')) \n",
        "\n",
        "fig3_movieGenresByYears.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
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    {
      "cell_type": "markdown",
      "metadata": {
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        "colab_type": "text"
      },
      "source": [
        "## Doing the same as above for the TV Shows"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vvRfsmVTcBi1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Doing the same as above for the TV Shows\n",
        "tvshow_df_2019 = tvshow_df[tvshow_df['year_added']==2019]\n",
        "tvshow_df_2018 = tvshow_df[tvshow_df['year_added']==2018]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tyDToZ4DiTrA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tvshow_genres_2019 = pd.DataFrame({ 'tvshow_genres' : tvshow_df_2019['listed_in']})\n",
        "tvshow_genres_2018 = pd.DataFrame({ 'tvshow_genres' : tvshow_df_2018['listed_in']})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hhOLvp4gjnkX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tvshow_genres_2019.loc[tvshow_genres_2019[\"tvshow_genres\"].str.find(\"International TV Shows\") >= 0, 'international_tv_shows'] = 1\n",
        "tvshow_genres_2019.loc[tvshow_genres_2019[\"tvshow_genres\"].str.find(\"Science & Nature TV\") >= 0, 'science_nature_tv'] = 1\n",
        "tvshow_genres_2019.loc[tvshow_genres_2019[\"tvshow_genres\"].str.find(\"Romantic TV Shows\") >= 0, 'romantic_tv_shows'] = 1\n",
        "tvshow_genres_2019.loc[tvshow_genres_2019[\"tvshow_genres\"].str.find(\"TV Comedies\") >= 0, 'tv_comedies'] = 1\n",
        "tvshow_genres_2019.loc[tvshow_genres_2019[\"tvshow_genres\"].str.find(\"TV Dramas\") >= 0, 'tv_dramas'] = 1\n",
        "tvshow_genres_2019.loc[tvshow_genres_2019[\"tvshow_genres\"].str.find(\"Docuseries\") >= 0, 'docuseries'] = 1\n",
        "tvshow_genres_2019.loc[tvshow_genres_2019[\"tvshow_genres\"].str.find(\"Reality TV\") >= 0, 'reality_tv'] = 1\n",
        "tvshow_genres_2019.loc[tvshow_genres_2019[\"tvshow_genres\"].str.find(\"TV Action & Adventure\") >= 0, 'tv_action_adventure'] = 1\n",
        "tvshow_genres_2019.loc[tvshow_genres_2019[\"tvshow_genres\"].str.find(\"Korean TV Shows\") >= 0, 'korean_tv_shows'] = 1\n",
        "tvshow_genres_2019.loc[tvshow_genres_2019[\"tvshow_genres\"].str.find(\"Crime TV Shows\") >= 0, 'crime_tv_shows'] = 1\n",
        "tvshow_genres_2019.loc[tvshow_genres_2019[\"tvshow_genres\"].str.find(\"Teen TV Shows\") >= 0, 'teen_tv_shows'] = 1\n",
        "tvshow_genres_2019.loc[tvshow_genres_2019[\"tvshow_genres\"].str.find(\"Kids TV\") >= 0, 'kids_tv'] = 1\n",
        "tvshow_genres_2019.loc[tvshow_genres_2019[\"tvshow_genres\"].str.find(\"Spanish-Language TV Shows\") >= 0, 'spanish_language_tv_shows'] = 1\n",
        "tvshow_genres_2019.loc[tvshow_genres_2019[\"tvshow_genres\"].str.find(\"TV Horror\") >= 0, 'tv_horror'] = 1\n",
        "tvshow_genres_2019.loc[tvshow_genres_2019[\"tvshow_genres\"].str.find(\"TV Mysteries\") >= 0, 'tv_mysteries'] = 1\n",
        "tvshow_genres_2019.loc[tvshow_genres_2019[\"tvshow_genres\"].str.find(\"TV Thrillers\") >= 0, 'tv_thrillers'] = 1\n",
        "tvshow_genres_2019.loc[tvshow_genres_2019[\"tvshow_genres\"].str.find(\"Stand-Up Comedy & Talk Shows\") >= 0, 'stand-up_comedy_talk_shows'] = 1\n",
        "tvshow_genres_2019.loc[tvshow_genres_2019[\"tvshow_genres\"].str.find(\"TV Sci-Fi & Fantasy\") >= 0, 'tv_sci-fi_fantasy'] = 1\n",
        "tvshow_genres_2019.loc[tvshow_genres_2019[\"tvshow_genres\"].str.find(\"Classic & Cult TV\") >= 0, 'classic_cult_tv'] = 1\n",
        "tvshow_genres_2019.loc[tvshow_genres_2019[\"tvshow_genres\"].str.find(\"British TV Shows\") >= 0, 'british_tv_shows'] = 1\n",
        "tvshow_genres_2019.loc[tvshow_genres_2019[\"tvshow_genres\"].str.find(\"Anime Series\") >= 0, 'anime_series'] = 1\n",
        "#tvshow_genres_2019"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RLGArg_dqGKF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tvshow_genres_2018.loc[tvshow_genres_2018[\"tvshow_genres\"].str.find(\"International TV Shows\") >= 0, 'international_tv_shows'] = 1\n",
        "tvshow_genres_2018.loc[tvshow_genres_2018[\"tvshow_genres\"].str.find(\"Science & Nature TV\") >= 0, 'science_nature_tv'] = 1\n",
        "tvshow_genres_2018.loc[tvshow_genres_2018[\"tvshow_genres\"].str.find(\"Romantic TV Shows\") >= 0, 'romantic_tv_shows'] = 1\n",
        "tvshow_genres_2018.loc[tvshow_genres_2018[\"tvshow_genres\"].str.find(\"TV Comedies\") >= 0, 'tv_comedies'] = 1\n",
        "tvshow_genres_2018.loc[tvshow_genres_2018[\"tvshow_genres\"].str.find(\"TV Dramas\") >= 0, 'tv_dramas'] = 1\n",
        "tvshow_genres_2018.loc[tvshow_genres_2018[\"tvshow_genres\"].str.find(\"Docuseries\") >= 0, 'docuseries'] = 1\n",
        "tvshow_genres_2018.loc[tvshow_genres_2018[\"tvshow_genres\"].str.find(\"Reality TV\") >= 0, 'reality_tv'] = 1\n",
        "tvshow_genres_2018.loc[tvshow_genres_2018[\"tvshow_genres\"].str.find(\"TV Action & Adventure\") >= 0, 'tv_action_adventure'] = 1\n",
        "tvshow_genres_2018.loc[tvshow_genres_2018[\"tvshow_genres\"].str.find(\"Korean TV Shows\") >= 0, 'korean_tv_shows'] = 1\n",
        "tvshow_genres_2018.loc[tvshow_genres_2018[\"tvshow_genres\"].str.find(\"Crime TV Shows\") >= 0, 'crime_tv_shows'] = 1\n",
        "tvshow_genres_2018.loc[tvshow_genres_2018[\"tvshow_genres\"].str.find(\"Teen TV Shows\") >= 0, 'teen_tv_shows'] = 1\n",
        "tvshow_genres_2018.loc[tvshow_genres_2018[\"tvshow_genres\"].str.find(\"Kids TV\") >= 0, 'kids_tv'] = 1\n",
        "tvshow_genres_2018.loc[tvshow_genres_2018[\"tvshow_genres\"].str.find(\"Spanish-Language TV Shows\") >= 0, 'spanish_language_tv_shows'] = 1\n",
        "tvshow_genres_2018.loc[tvshow_genres_2018[\"tvshow_genres\"].str.find(\"TV Horror\") >= 0, 'tv_horror'] = 1\n",
        "tvshow_genres_2018.loc[tvshow_genres_2018[\"tvshow_genres\"].str.find(\"TV Mysteries\") >= 0, 'tv_mysteries'] = 1\n",
        "tvshow_genres_2018.loc[tvshow_genres_2018[\"tvshow_genres\"].str.find(\"TV Thrillers\") >= 0, 'tv_thrillers'] = 1\n",
        "tvshow_genres_2018.loc[tvshow_genres_2018[\"tvshow_genres\"].str.find(\"Stand-Up Comedy & Talk Shows\") >= 0, 'stand-up_comedy_talk_shows'] = 1\n",
        "tvshow_genres_2018.loc[tvshow_genres_2018[\"tvshow_genres\"].str.find(\"TV Sci-Fi & Fantasy\") >= 0, 'tv_sci-fi_fantasy'] = 1\n",
        "tvshow_genres_2018.loc[tvshow_genres_2018[\"tvshow_genres\"].str.find(\"Classic & Cult TV\") >= 0, 'classic_cult_tv'] = 1\n",
        "tvshow_genres_2018.loc[tvshow_genres_2018[\"tvshow_genres\"].str.find(\"British TV Shows\") >= 0, 'british_tv_shows'] = 1\n",
        "tvshow_genres_2018.loc[tvshow_genres_2018[\"tvshow_genres\"].str.find(\"Anime Series\") >= 0, 'anime_series'] = 1\n",
        "#tvshow_genres_2018.head(10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oot21DslrCXc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tvshow_data_genres_2019 = [['international_tv_shows',tvshow_genres_2019['international_tv_shows'].sum()],\n",
        "                          ['science_nature_tv',tvshow_genres_2019['science_nature_tv'].sum()],\n",
        "                          ['romantic_tv_shows',tvshow_genres_2019['romantic_tv_shows'].sum()],\n",
        "                          ['tv_comedies',tvshow_genres_2019['tv_comedies'].sum()],\n",
        "                          ['tv_dramas',tvshow_genres_2019['tv_dramas'].sum()],\n",
        "                          ['docuseries',tvshow_genres_2019['docuseries'].sum()],\n",
        "                          ['reality_tv',tvshow_genres_2019['reality_tv'].sum()],\n",
        "                          ['tv_action_adventure',tvshow_genres_2019['tv_action_adventure'].sum()],\n",
        "                          ['korean_tv_shows',tvshow_genres_2019['korean_tv_shows'].sum()],\n",
        "                          ['crime_tv_shows',tvshow_genres_2019['crime_tv_shows'].sum()],\n",
        "                          ['teen_tv_shows',tvshow_genres_2019['teen_tv_shows'].sum()],\n",
        "                          ['kids_tv',tvshow_genres_2019['kids_tv'].sum()],\n",
        "                          ['spanish_language_tv_shows',tvshow_genres_2019['spanish_language_tv_shows'].sum()],\n",
        "                          ['tv_horror',tvshow_genres_2019['tv_horror'].sum()],\n",
        "                          ['tv_mysteries',tvshow_genres_2019['tv_mysteries'].sum()],\n",
        "                          ['tv_thrillers',tvshow_genres_2019['tv_thrillers'].sum()],\n",
        "                          ['stand-up_comedy_talk_shows',tvshow_genres_2019['stand-up_comedy_talk_shows'].sum()],\n",
        "                          ['tv_sci-fi_fantasy',tvshow_genres_2019['tv_sci-fi_fantasy'].sum()],\n",
        "                          ['classic_cult_tv',tvshow_genres_2019['classic_cult_tv'].sum()],\n",
        "                          ['british_tv_shows',tvshow_genres_2019['british_tv_shows'].sum()],\n",
        "                          ['anime_series',tvshow_genres_2019['anime_series'].sum()]]\n",
        "#tvshow_data_genres_2019\n",
        "no_tvshow_genres = pd.DataFrame(tvshow_data_genres_2019, columns = ['Genres', '2019'])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OHyyReQ7wnOs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "outputId": "b51968ed-1047-4d74-fb6b-9ad32c6e265e"
      },
      "source": [
        "tvshow_data_genres_2018 = [tvshow_genres_2018['international_tv_shows'].sum(),\n",
        "                          tvshow_genres_2018['science_nature_tv'].sum(),\n",
        "                          tvshow_genres_2018['romantic_tv_shows'].sum(),\n",
        "                          tvshow_genres_2018['tv_comedies'].sum(),\n",
        "                          tvshow_genres_2018['tv_dramas'].sum(),\n",
        "                          tvshow_genres_2018['docuseries'].sum(),\n",
        "                          tvshow_genres_2018['reality_tv'].sum(),\n",
        "                          tvshow_genres_2018['tv_action_adventure'].sum(),\n",
        "                          tvshow_genres_2018['korean_tv_shows'].sum(),\n",
        "                          tvshow_genres_2018['crime_tv_shows'].sum(),\n",
        "                          tvshow_genres_2018['teen_tv_shows'].sum(),\n",
        "                          tvshow_genres_2018['kids_tv'].sum(),\n",
        "                          tvshow_genres_2018['spanish_language_tv_shows'].sum(),\n",
        "                          tvshow_genres_2018['tv_horror'].sum(),\n",
        "                          tvshow_genres_2018['tv_mysteries'].sum(),\n",
        "                          tvshow_genres_2018['tv_thrillers'].sum(),\n",
        "                          tvshow_genres_2018['stand-up_comedy_talk_shows'].sum(),\n",
        "                          tvshow_genres_2018['tv_sci-fi_fantasy'].sum(),\n",
        "                          tvshow_genres_2018['classic_cult_tv'].sum(),\n",
        "                          tvshow_genres_2018['british_tv_shows'].sum(),\n",
        "                          tvshow_genres_2018['anime_series'].sum()]\n",
        "#tvshow_data_genres_2018\n",
        "no_tvshow_genres['2018'] =tvshow_data_genres_2018\n",
        "no_tvshow_genres['2018'] = no_tvshow_genres['2018'].astype(int)\n",
        "no_tvshow_genres['2019'] = no_tvshow_genres['2019'].astype(int)\n",
        "no_tvshow_genres\n",
        "\n",
        "\n",
        "#Formatting the result to a table\n",
        "table5_tvshowByYear= go.Figure(data=[go.Table(\n",
        "    header=dict(values=list(['Genres','2019','2018']),\n",
        "                fill_color='#fca103',\n",
        "                align='center',font=dict(color='Black', size=16),height=40),\n",
        "    cells=dict(values=[no_tvshow_genres.Genres, no_tvshow_genres['2019'], no_tvshow_genres['2018']],\n",
        "               fill_color='Black',\n",
        "               align='center',font=dict(color='White', size=12)))\n",
        "])\n",
        "table5_tvshowByYear.update_layout(width=1000, height=800)\n",
        "table5_tvshowByYear.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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              "                        {\"responsive\": true}\n",
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              "                            \n",
              "var gd = document.getElementById('f73e84a9-66dd-4743-82b4-6051257959a2');\n",
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              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
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              "}});\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",
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              "                \n",
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              "        </div>\n",
              "</body>\n",
              "</html>"
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          },
          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RtFRr_oZxVj-",
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          "height": 542
        },
        "outputId": "b41ca917-c20d-40cd-c0a0-4a481458aebd"
      },
      "source": [
        "fig4_TvShowsByYear = go.Figure(layout=layout_bar)\n",
        "fig4_TvShowsByYear.add_trace(go.Bar(x =no_tvshow_genres['Genres'], y=no_tvshow_genres['2019'],name='2019',marker=dict(color=\"#fca103\"))) \n",
        "fig4_TvShowsByYear.add_trace(go.Bar(x =no_tvshow_genres['Genres'], y=no_tvshow_genres['2018'],name='2018',marker=dict(color=\"#000000\"))) \n",
        "\n",
        "fig4_TvShowsByYear.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Distribution of genres for the years 2018 and 2019\"}},\n",
              "                        {\"responsive\": true}\n",
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              "                            \n",
              "var gd = document.getElementById('b4de428c-c42a-44e9-b12e-9f17560b53d1');\n",
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              "            Plotly.purge(gd);\n",
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              "\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",
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              "</body>\n",
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u-6Hr-UW73Hc",
        "colab_type": "text"
      },
      "source": [
        "## Removing the missing values in the variable 'Country'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SYWyibG672rt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ad296453-c768-487b-ce9d-b55839ef83da"
      },
      "source": [
        "#Removing the missing values in the variable 'Country'\n",
        "country_df = df.dropna(subset=['country'])\n",
        "country_df['country'].isna().sum()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>81145628</td>\n",
              "      <td>Movie</td>\n",
              "      <td>Norm of the North: King Sized Adventure</td>\n",
              "      <td>Richard Finn, Tim Maltby</td>\n",
              "      <td>Alan Marriott, Andrew Toth, Brian Dobson, Cole...</td>\n",
              "      <td>United States, India, South Korea, China</td>\n",
              "      <td>2019-09-09</td>\n",
              "      <td>2019</td>\n",
              "      <td>TV-PG</td>\n",
              "      <td>90</td>\n",
              "      <td>Children &amp; Family Movies, Comedies</td>\n",
              "      <td>Before planning an awesome wedding for his gra...</td>\n",
              "      <td></td>\n",
              "      <td>2019.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>80117401</td>\n",
              "      <td>Movie</td>\n",
              "      <td>Jandino: Whatever it Takes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Jandino Asporaat</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2016-09-09</td>\n",
              "      <td>2016</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td>94</td>\n",
              "      <td>Stand-Up Comedy</td>\n",
              "      <td>Jandino Asporaat riffs on the challenges of ra...</td>\n",
              "      <td></td>\n",
              "      <td>2016.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>70234439</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Transformers Prime</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Peter Cullen, Sumalee Montano, Frank Welker, J...</td>\n",
              "      <td>United States</td>\n",
              "      <td>2018-09-08</td>\n",
              "      <td>2013</td>\n",
              "      <td>TV-Y7-FV</td>\n",
              "      <td></td>\n",
              "      <td>Kids' TV</td>\n",
              "      <td>With the help of three human allies, the Autob...</td>\n",
              "      <td>1</td>\n",
              "      <td>2018.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>80058654</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Transformers: Robots in Disguise</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Will Friedle, Darren Criss, Constance Zimmer, ...</td>\n",
              "      <td>United States</td>\n",
              "      <td>2018-09-08</td>\n",
              "      <td>2016</td>\n",
              "      <td>TV-Y7</td>\n",
              "      <td></td>\n",
              "      <td>Kids' TV</td>\n",
              "      <td>When a prison ship crash unleashes hundreds of...</td>\n",
              "      <td>1</td>\n",
              "      <td>2018.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>80125979</td>\n",
              "      <td>Movie</td>\n",
              "      <td>#realityhigh</td>\n",
              "      <td>Fernando Lebrija</td>\n",
              "      <td>Nesta Cooper, Kate Walsh, John Michael Higgins...</td>\n",
              "      <td>United States</td>\n",
              "      <td>2017-09-08</td>\n",
              "      <td>2017</td>\n",
              "      <td>TV-14</td>\n",
              "      <td>99</td>\n",
              "      <td>Comedies</td>\n",
              "      <td>When nerdy high schooler Dani finally attracts...</td>\n",
              "      <td></td>\n",
              "      <td>2017.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>80163890</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Apaches</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Alberto Ammann, Eloy Azorín, Verónica Echegui,...</td>\n",
              "      <td>Spain</td>\n",
              "      <td>2017-09-08</td>\n",
              "      <td>2016</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td></td>\n",
              "      <td>Crime TV Shows, International TV Shows, Spanis...</td>\n",
              "      <td>A young journalist is forced into a life of cr...</td>\n",
              "      <td>1</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>70304989</td>\n",
              "      <td>Movie</td>\n",
              "      <td>Automata</td>\n",
              "      <td>Gabe Ibáñez</td>\n",
              "      <td>Antonio Banderas, Dylan McDermott, Melanie Gri...</td>\n",
              "      <td>Bulgaria, United States, Spain, Canada</td>\n",
              "      <td>2017-09-08</td>\n",
              "      <td>2014</td>\n",
              "      <td>R</td>\n",
              "      <td>110</td>\n",
              "      <td>International Movies, Sci-Fi &amp; Fantasy, Thrillers</td>\n",
              "      <td>In a dystopian future, an insurance adjuster f...</td>\n",
              "      <td></td>\n",
              "      <td>2017.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>80164077</td>\n",
              "      <td>Movie</td>\n",
              "      <td>Fabrizio Copano: Solo pienso en mi</td>\n",
              "      <td>Rodrigo Toro, Francisco Schultz</td>\n",
              "      <td>Fabrizio Copano</td>\n",
              "      <td>Chile</td>\n",
              "      <td>2017-09-08</td>\n",
              "      <td>2017</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td>60</td>\n",
              "      <td>Stand-Up Comedy</td>\n",
              "      <td>Fabrizio Copano takes audience participation t...</td>\n",
              "      <td></td>\n",
              "      <td>2017.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>80117902</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Fire Chasers</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United States</td>\n",
              "      <td>2017-09-08</td>\n",
              "      <td>2017</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td></td>\n",
              "      <td>Docuseries, Science &amp; Nature TV</td>\n",
              "      <td>As California's 2016 fire season rages, brave ...</td>\n",
              "      <td>1</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>70304990</td>\n",
              "      <td>Movie</td>\n",
              "      <td>Good People</td>\n",
              "      <td>Henrik Ruben Genz</td>\n",
              "      <td>James Franco, Kate Hudson, Tom Wilkinson, Omar...</td>\n",
              "      <td>United States, United Kingdom, Denmark, Sweden</td>\n",
              "      <td>2017-09-08</td>\n",
              "      <td>2014</td>\n",
              "      <td>R</td>\n",
              "      <td>90</td>\n",
              "      <td>Action &amp; Adventure, Thrillers</td>\n",
              "      <td>A struggling couple can't believe their luck w...</td>\n",
              "      <td></td>\n",
              "      <td>2017.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    show_id     type  ... year_added month_added\n",
              "0  81145628    Movie  ...     2019.0         9.0\n",
              "1  80117401    Movie  ...     2016.0         9.0\n",
              "2  70234439  TV Show  ...     2018.0         9.0\n",
              "3  80058654  TV Show  ...     2018.0         9.0\n",
              "4  80125979    Movie  ...     2017.0         9.0\n",
              "5  80163890  TV Show  ...     2017.0         9.0\n",
              "6  70304989    Movie  ...     2017.0         9.0\n",
              "7  80164077    Movie  ...     2017.0         9.0\n",
              "8  80117902  TV Show  ...     2017.0         9.0\n",
              "9  70304990    Movie  ...     2017.0         9.0\n",
              "\n",
              "[10 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CUmqchDTUR1f",
        "colab_type": "text"
      },
      "source": [
        "## Removed the rows which had more than one country listed in the 'country' variable "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "56544gZABP0w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ced2860a-40f9-4485-b84e-c6b52fb280e4"
      },
      "source": [
        "#Removed the rows which had more than one country listed in the 'country' variable\n",
        "solo_countries = lambda country_df: country_df[\"country\"].split(\",\") [0]\n",
        "country_df['country']= country_df.apply(solo_countries,axis=1)\n",
        "country_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>season_count</th>\n",
              "      <th>year_added</th>\n",
              "      <th>month_added</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>81145628</td>\n",
              "      <td>Movie</td>\n",
              "      <td>Norm of the North: King Sized Adventure</td>\n",
              "      <td>Richard Finn, Tim Maltby</td>\n",
              "      <td>Alan Marriott, Andrew Toth, Brian Dobson, Cole...</td>\n",
              "      <td>United States</td>\n",
              "      <td>2019-09-09</td>\n",
              "      <td>2019</td>\n",
              "      <td>TV-PG</td>\n",
              "      <td>90</td>\n",
              "      <td>Children &amp; Family Movies, Comedies</td>\n",
              "      <td>Before planning an awesome wedding for his gra...</td>\n",
              "      <td></td>\n",
              "      <td>2019.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>80117401</td>\n",
              "      <td>Movie</td>\n",
              "      <td>Jandino: Whatever it Takes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Jandino Asporaat</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2016-09-09</td>\n",
              "      <td>2016</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td>94</td>\n",
              "      <td>Stand-Up Comedy</td>\n",
              "      <td>Jandino Asporaat riffs on the challenges of ra...</td>\n",
              "      <td></td>\n",
              "      <td>2016.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>70234439</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Transformers Prime</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Peter Cullen, Sumalee Montano, Frank Welker, J...</td>\n",
              "      <td>United States</td>\n",
              "      <td>2018-09-08</td>\n",
              "      <td>2013</td>\n",
              "      <td>TV-Y7-FV</td>\n",
              "      <td></td>\n",
              "      <td>Kids' TV</td>\n",
              "      <td>With the help of three human allies, the Autob...</td>\n",
              "      <td>1</td>\n",
              "      <td>2018.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>80058654</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Transformers: Robots in Disguise</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Will Friedle, Darren Criss, Constance Zimmer, ...</td>\n",
              "      <td>United States</td>\n",
              "      <td>2018-09-08</td>\n",
              "      <td>2016</td>\n",
              "      <td>TV-Y7</td>\n",
              "      <td></td>\n",
              "      <td>Kids' TV</td>\n",
              "      <td>When a prison ship crash unleashes hundreds of...</td>\n",
              "      <td>1</td>\n",
              "      <td>2018.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>80125979</td>\n",
              "      <td>Movie</td>\n",
              "      <td>#realityhigh</td>\n",
              "      <td>Fernando Lebrija</td>\n",
              "      <td>Nesta Cooper, Kate Walsh, John Michael Higgins...</td>\n",
              "      <td>United States</td>\n",
              "      <td>2017-09-08</td>\n",
              "      <td>2017</td>\n",
              "      <td>TV-14</td>\n",
              "      <td>99</td>\n",
              "      <td>Comedies</td>\n",
              "      <td>When nerdy high schooler Dani finally attracts...</td>\n",
              "      <td></td>\n",
              "      <td>2017.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6227</th>\n",
              "      <td>80005756</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>The Adventures of Figaro Pho</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Luke Jurevicius, Craig Behenna, Charlotte Haml...</td>\n",
              "      <td>Australia</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2015</td>\n",
              "      <td>TV-Y7</td>\n",
              "      <td></td>\n",
              "      <td>Kids' TV, TV Comedies</td>\n",
              "      <td>Imagine your worst fears, then multiply them: ...</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6229</th>\n",
              "      <td>80000063</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Red vs. Blue</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Burnie Burns, Jason Saldaña, Gustavo Sorola, G...</td>\n",
              "      <td>United States</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2015</td>\n",
              "      <td>NR</td>\n",
              "      <td></td>\n",
              "      <td>TV Action &amp; Adventure, TV Comedies, TV Sci-Fi ...</td>\n",
              "      <td>This parody of first-person shooter games, mil...</td>\n",
              "      <td>13</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6230</th>\n",
              "      <td>70286564</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Maron</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Marc Maron, Judd Hirsch, Josh Brener, Nora Zeh...</td>\n",
              "      <td>United States</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2016</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td></td>\n",
              "      <td>TV Comedies</td>\n",
              "      <td>Marc Maron stars as Marc Maron, who interviews...</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6232</th>\n",
              "      <td>70281022</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>A Young Doctor's Notebook and Other Stories</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Daniel Radcliffe, Jon Hamm, Adam Godley, Chris...</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2013</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td></td>\n",
              "      <td>British TV Shows, TV Comedies, TV Dramas</td>\n",
              "      <td>Set during the Russian Revolution, this comic ...</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6233</th>\n",
              "      <td>70153404</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Friends</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Jennifer Aniston, Courteney Cox, Lisa Kudrow, ...</td>\n",
              "      <td>United States</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2003</td>\n",
              "      <td>TV-14</td>\n",
              "      <td></td>\n",
              "      <td>Classic &amp; Cult TV, TV Comedies</td>\n",
              "      <td>This hit sitcom follows the merry misadventure...</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5758 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       show_id     type  ... year_added month_added\n",
              "0     81145628    Movie  ...     2019.0         9.0\n",
              "1     80117401    Movie  ...     2016.0         9.0\n",
              "2     70234439  TV Show  ...     2018.0         9.0\n",
              "3     80058654  TV Show  ...     2018.0         9.0\n",
              "4     80125979    Movie  ...     2017.0         9.0\n",
              "...        ...      ...  ...        ...         ...\n",
              "6227  80005756  TV Show  ...        NaN         NaN\n",
              "6229  80000063  TV Show  ...        NaN         NaN\n",
              "6230  70286564  TV Show  ...        NaN         NaN\n",
              "6232  70281022  TV Show  ...        NaN         NaN\n",
              "6233  70153404  TV Show  ...        NaN         NaN\n",
              "\n",
              "[5758 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7TXsdVhxUaT6",
        "colab_type": "text"
      },
      "source": [
        "## Selected few variables in particular to dive further into the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YpzpsAVQB95k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "outputId": "a63c172e-5ba3-46ee-dd4e-66b5d661bee8"
      },
      "source": [
        "selected_columns = country_df[[\"country\",\"type\",\"year_added\"]]\n",
        "temp_country_table = selected_columns.copy()\n",
        "#Formatting the result to a table\n",
        "temp_country_table= go.Figure(data=[go.Table(\n",
        "    header=dict(values=list(['Country','Type','Year_added']),\n",
        "                fill_color='#fca103',\n",
        "                align='center',font=dict(color='Black', size=16),height=40),\n",
        "    cells=dict(values=[temp_country_table.country, temp_country_table['type'], temp_country_table['year_added']],\n",
        "               fill_color='Black',\n",
        "               align='center',font=dict(color='White', size=12)))\n",
        "])\n",
        "temp_country_table.update_layout(width=1000, height=800)\n",
        "temp_country_table.show()"
      ],
      "execution_count": null,
      "outputs": [
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              "                        {\"responsive\": true}\n",
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              "                            \n",
              "var gd = document.getElementById('14066ff5-e79f-4383-b257-a577a605e1b9');\n",
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              "        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": "dqJaq_08UhCS",
        "colab_type": "text"
      },
      "source": [
        "## Getting the total counts of TV Shows and Movies from the variable 'type' using 'aggregate count'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gbpb5y4pKAMx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Getting the total counts of TV Shows and Movies from the variable 'type' using 'aggregate count'\n",
        "movieby_country = pd.pivot_table(temp_country_table[temp_country_table.type == 'Movie'], index='country', values='type', aggfunc='count')\n",
        "tvshowby_country = pd.pivot_table(temp_country_table[temp_country_table.type == 'TV Show'], index='country', values='type', aggfunc='count')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8LIuddRRUo_7",
        "colab_type": "text"
      },
      "source": [
        "## Converting the Pivot table to Data Frame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTc9NtebKKak",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "580d2c2c-fbe5-495d-d4cc-5afd65c433d3"
      },
      "source": [
        "#Converting the Pivot table to Data Frame\n",
        "movieby_country_df = pd.DataFrame(movieby_country.to_records())\n",
        "movieby_country_df\n",
        "tvshowby_country_df = pd.DataFrame(tvshowby_country.to_records())\n"
      ],
      "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>country</th>\n",
              "      <th>type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Australia</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Belgium</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Canada</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     country  type\n",
              "0  Argentina    14\n",
              "1  Australia    44\n",
              "2    Belgium     6\n",
              "3     Brazil    15\n",
              "4     Canada    76"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wSkaKT3WUxKT",
        "colab_type": "text"
      },
      "source": [
        "## Sorting the result by the number of content corresponding to the country"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4yTbgjVXKepf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Sorting the result by the number of content corresponding to the country\n",
        "movieby_country_df.sort_values('type',ascending=False,inplace=True)\n",
        "tvshowby_country_df.sort_values('type',ascending=False,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JgQIrOsFTGUx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tvshowby_country_df.head(10)\n",
        "top10_tvshow = tvshowby_country_df.head(10)\n",
        "movieby_country_df.head(10)\n",
        "top10_movies = movieby_country_df.head(10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DQ2AGJFs-ZE0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "008982b9-ef57-422b-8338-de7ef5864116"
      },
      "source": [
        "top10_movies\n",
        "table5_Top10Movies= go.Figure(data=[go.Table(\n",
        "    header=dict(values=list(['Country','Movie(s)_count']),\n",
        "                fill_color='#fca103',\n",
        "                align='center',font=dict(color='Black', size=16),height=40),\n",
        "    cells=dict(values=[top10_movies.country, top10_movies.type],\n",
        "               fill_color='Black',\n",
        "               align='center',font=dict(color='White', size=12)))\n",
        "])\n",
        "table5_Top10Movies.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "<html>\n",
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              "                        {\"responsive\": true}\n",
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              "                            \n",
              "var gd = document.getElementById('e8047879-d21a-4028-8b5d-fa007991d647');\n",
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              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
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              "\n",
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              "if (outputEl) {{\n",
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              "                        })\n",
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              "        </div>\n",
              "</body>\n",
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          },
          "metadata": {
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      ]
    },
    {
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        "id": "_5DwjBfh_OR_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
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        "outputId": "cb3a49a1-b6a8-4cfc-82db-6c8b39890704"
      },
      "source": [
        "top10_tvshow\n",
        "table6_Top10TvShow= go.Figure(data=[go.Table(\n",
        "    header=dict(values=list(['Country','TvShows_count']),\n",
        "                fill_color='#fca103',\n",
        "                align='center',font=dict(color='Black', size=16),height=40),\n",
        "    cells=dict(values=[top10_tvshow.country, top10_tvshow.type],\n",
        "               fill_color='Black',\n",
        "               align='center',font=dict(color='White', size=12)))\n",
        "])\n",
        "table6_Top10TvShow.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "            observer.disconnect();\n",
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              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
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              "    x.observe(notebookContainer, {childList: true});\n",
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              "                        })\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L4vLGXHvSa7V",
        "colab_type": "text"
      },
      "source": [
        "## Number of TV shows and Movies across different countries "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EkgFGCS3Tg_W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "d602d6b3-1413-44f9-d247-1ca65560a1b5"
      },
      "source": [
        "#Number of TV shows and Movies across different countries\n",
        "layout_country = go.Layout(title='Distribution of TV Shows and Movies in different countries ',showlegend =True,legend=dict(x=0.7, y=0.9))\n",
        "fig_3 = go.Figure(layout=layout_country)\n",
        "fig_3.add_trace(go.Bar(x =top10_movies['country'], y=top10_movies['type'],name='Movie',marker=dict(color=\"#fca103\")))\n",
        "fig_3.add_trace(go.Bar(x =top10_tvshow['country'], y=top10_tvshow['type'],name='TV Show',marker=dict(color=\"#000000\")))\n",
        "fig_3.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Distribution of TV Shows and Movies in different countries \"}},\n",
              "                        {\"responsive\": true}\n",
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              "\n",
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              "        </div>\n",
              "</body>\n",
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SAd09JJshE5V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "b9fa678c-c68d-445e-b1f8-5e1481c349aa"
      },
      "source": [
        "country_year = pd.crosstab(temp_country_table.country, temp_country_table.year_added, values=temp_country_table.type, aggfunc='count').round(0)\n",
        "country_year.fillna(0).round(0)\n",
        "fig = go.Figure(data=go.Heatmap(\n",
        "                    z=country_year,colorscale='blackbody'))\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AMcTrZ9bSrYX",
        "colab_type": "text"
      },
      "source": [
        "## Distribution on different types of Rating"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PskGGh5Tf5gg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "ae70e644-9d03-4975-873c-da7d8f53d112"
      },
      "source": [
        "#Distribution on different types of Rating\n",
        "sns.set(rc={'figure.figsize':(15,6)})\n",
        "sns.countplot(x=df['rating'],color='#fca103')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb17520cf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uSWXlhk0rCdA",
        "colab_type": "text"
      },
      "source": [
        "## Top 10 oldest TV_Shows on Netflix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8bgBqlNB6wa6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "2a2bd1ca-c612-473e-8f2b-49525e086a7e"
      },
      "source": [
        "#Sorting the release_year to get the oldest TV_Shows\n",
        "tvshow_sort = tvshow_df.sort_values(\"release_year\", ascending = True)\n",
        "tv_show_oldest = tvshow_sort[['title', \"release_year\"]][:10] #Filtering the rows and columns at the same time\n",
        "tv_show_oldest\n",
        "\n",
        "\n",
        "fig = go.Figure(data=[go.Table(\n",
        "    header=dict(values=list(['Title of the TV SHOW','Year of Release']),\n",
        "                fill_color='#fca103',\n",
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        "    cells=dict(values=[tv_show_oldest.title, tv_show_oldest.release_year],\n",
        "               fill_color='Black',\n",
        "               align='center',font=dict(color='White', size=12)))\n",
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        "\n",
        "fig.show()\n"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5XHTpnZyDBLR",
        "colab_type": "text"
      },
      "source": [
        "## Top 10 oldest Movies on Netflix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LNrwR5xjBeRc",
        "colab_type": "code",
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      "source": [
        "movie_sort = movie_df.sort_values(\"release_year\", ascending = True)\n",
        "movie_oldest = movie_sort[['title', \"release_year\"]][:10] #Filtering the rows and columns at the same time\n",
        "movie_oldest\n",
        "#movie_oldest_table = ff.create_table(movie_oldest,annotation_offset=0.45)\n",
        "#movie_oldest_table.show()\n",
        "list_1 = ['Title of the Movie','Year of Release']\n",
        "fig = go.Figure(data=[go.Table(\n",
        "    header=dict(values=list(list_1),\n",
        "                fill_color='#fca103',\n",
        "                align='center',font=dict(color='Black', size=16),height=40),\n",
        "    cells=dict(values=[movie_oldest.title, movie_oldest.release_year],\n",
        "               fill_color='Black',\n",
        "               align='center',font=dict(color='White', size=12)))\n",
        "])\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
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          "metadata": {
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    {
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        "id": "TkCHPy2-nblG",
        "colab_type": "code",
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          "height": 221
        },
        "outputId": "ae84fb2a-4908-4bdc-d479-1608e96794fd"
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      "source": [
        "duration_int = movie_df['duration']\n",
        "type(duration_int)\n",
        "tv_show_duration = tvshow_df['season_count']\n",
        "type(tv_show_duration)\n",
        "duration_int.astype(int)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        90\n",
              "1        94\n",
              "4        99\n",
              "6       110\n",
              "7        60\n",
              "       ... \n",
              "5575    106\n",
              "5577     70\n",
              "5578    102\n",
              "5579     88\n",
              "5580    109\n",
              "Name: duration, Length: 4264, dtype: int64"
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          "metadata": {
            "tags": []
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          "execution_count": 41
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      "cell_type": "markdown",
      "metadata": {
        "id": "nVn2uf3dDZjG",
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      "source": [
        "## Distribution of movie 'Duration'"
      ]
    },
    {
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        "colab_type": "code",
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        "hist_data = [duration_int.astype(int)]\n",
        "group_labels = ['Movie Duration'] # name of the dataset\n",
        "\n",
        "fig_duration = ff.create_distplot(hist_data, group_labels,bin_size=0.7, curve_type= 'normal', colors=[\"#fca103\",'#000000'],rug_text=None,show_rug=False)\n",
        "fig_duration.show()"
      ],
      "execution_count": null,
      "outputs": [
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-tysCoPm1-eg",
        "colab_type": "text"
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      "source": [
        "## Distribution of TV_Show Seasons"
      ]
    },
    {
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      "metadata": {
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        "colab_type": "code",
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      "source": [
        "#distplot with normal distribution\n",
        "hist_TV_data = [tv_show_duration.astype(int)]\n",
        "tv_group_labels = ['Number of Seasons'] # name of the dataset\n",
        "\n",
        "TV_fig_duration = ff.create_distplot(hist_TV_data, tv_group_labels,bin_size=0.5, curve_type= 'normal', colors=[\"#fca103\",'#000000'],show_rug=False)\n",
        "TV_fig_duration.show()"
      ],
      "execution_count": null,
      "outputs": [
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      "source": [
        "More Analysis in progress. Coming Soon!!\n",
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        "\n"
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}