NetflixContentAnalysis.ipynb

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        "import pandas as pd\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "import plotly.io as pio\n",
        "pio.templates.default = \"plotly_white\"\n",
        "\n",
        "netflix_data = pd.read_csv(\"/content/netflix_content_2023.csv\")\n",
        "\n",
        "netflix_data.head()"
      ],
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              "                                 Title Available Globally? Release Date  \\\n",
              "0            The Night Agent: Season 1                 Yes   2023-03-23   \n",
              "1            Ginny & Georgia: Season 2                 Yes   2023-01-05   \n",
              "2   The Glory: Season 1 // 더 글로리: 시즌 1                 Yes   2022-12-30   \n",
              "3                  Wednesday: Season 1                 Yes   2022-11-23   \n",
              "4  Queen Charlotte: A Bridgerton Story                 Yes   2023-05-04   \n",
              "\n",
              "   Hours Viewed Language Indicator Content Type  \n",
              "0  81,21,00,000            English         Show  \n",
              "1  66,51,00,000            English         Show  \n",
              "2  62,28,00,000             Korean         Show  \n",
              "3  50,77,00,000            English         Show  \n",
              "4  50,30,00,000            English        Movie  "
            ],
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              "variable_name": "netflix_data",
              "summary": "{\n  \"name\": \"netflix_data\",\n  \"rows\": 24812,\n  \"fields\": [\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19158,\n        \"samples\": [\n          \"Life Isn't All Ha Ha Hee Hee: Season 1\",\n          \"Part-Time Spy // \\ube44\\uc815\\uaddc\\uc9c1 \\ud2b9\\uc218\\uc694\\uc6d0\",\n          \"The Flash (2014): Season 2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Available Globally?\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Release Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1783,\n        \"samples\": [\n          \"2023-04-17\",\n          \"2019-05-01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hours Viewed\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 889,\n        \"samples\": [\n          \"4,54,00,000\",\n          \"2,79,00,000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Language Indicator\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"English\",\n          \"Korean\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Content Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Movie\",\n          \"Show\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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      "source": [
        "netflix_data['Hours Viewed'] = netflix_data['Hours Viewed'].replace(',', '', regex=True).astype(float)\n",
        "\n",
        "netflix_data[['Title', 'Hours Viewed']].head()"
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              "                                 Title  Hours Viewed\n",
              "0            The Night Agent: Season 1   812100000.0\n",
              "1            Ginny & Georgia: Season 2   665100000.0\n",
              "2   The Glory: Season 1 // 더 글로리: 시즌 1   622800000.0\n",
              "3                  Wednesday: Season 1   507700000.0\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
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              "summary": "{\n  \"name\": \"netflix_data[['Title', 'Hours Viewed']]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Ginny & Georgia: Season 2\",\n          \"Queen Charlotte: A Bridgerton Story\",\n          \"The Glory: Season 1 // \\ub354 \\uae00\\ub85c\\ub9ac: \\uc2dc\\uc98c 1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hours Viewed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 127692650.53244059,\n        \"min\": 503000000.0,\n        \"max\": 812100000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          665100000.0,\n          503000000.0,\n          622800000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# aggregate viewership hours by content type\n",
        "content_type_viewership = netflix_data.groupby('Content Type')['Hours Viewed'].sum()\n",
        "\n",
        "fig = go.Figure(data=[\n",
        "    go.Bar(\n",
        "        x=content_type_viewership.index,\n",
        "        y=content_type_viewership.values,\n",
        "        marker_color=['skyblue', 'salmon']\n",
        "    )\n",
        "])\n",
        "\n",
        "fig.update_layout(\n",
        "    title='Total Viewership Hours by Content Type (2023)',\n",
        "    xaxis_title='Content Type',\n",
        "    yaxis_title='Total Hours Viewed (in billions)',\n",
        "    xaxis_tickangle=0,\n",
        "    height=500,\n",
        "    width=800\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
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        "outputId": "0f84968c-c208-4458-fbb2-df709c27ccb6"
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      "execution_count": 3,
      "outputs": [
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    {
      "cell_type": "code",
      "source": [
        "# aggregate viewership hours by language\n",
        "language_viewership = netflix_data.groupby('Language Indicator')['Hours Viewed'].sum().sort_values(ascending=False)\n",
        "\n",
        "fig = go.Figure(data=[\n",
        "    go.Bar(\n",
        "        x=language_viewership.index,\n",
        "        y=language_viewership.values,\n",
        "        marker_color='lightcoral'\n",
        "    )\n",
        "])\n",
        "\n",
        "fig.update_layout(\n",
        "    title='Total Viewership Hours by Language (2023)',\n",
        "    xaxis_title='Language',\n",
        "    yaxis_title='Total Hours Viewed (in billions)',\n",
        "    xaxis_tickangle=45,\n",
        "    height=600,\n",
        "    width=1000\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "Wyu3Cljtgxec",
        "outputId": "016ea8b5-7c55-4e71-ab78-0feb49868ef1"
      },
      "execution_count": 4,
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      "source": [
        "# convert the \"Release Date\" to a datetime format and extract the month\n",
        "netflix_data['Release Date'] = pd.to_datetime(netflix_data['Release Date'])\n",
        "netflix_data['Release Month'] = netflix_data['Release Date'].dt.month\n",
        "\n",
        "# aggregate viewership hours by release month\n",
        "monthly_viewership = netflix_data.groupby('Release Month')['Hours Viewed'].sum()\n",
        "\n",
        "fig = go.Figure(data=[\n",
        "    go.Scatter(\n",
        "        x=monthly_viewership.index,\n",
        "        y=monthly_viewership.values,\n",
        "        mode='lines+markers',\n",
        "        marker=dict(color='blue'),\n",
        "        line=dict(color='blue')\n",
        "    )\n",
        "])\n",
        "\n",
        "fig.update_layout(\n",
        "    title='Total Viewership Hours by Release Month (2023)',\n",
        "    xaxis_title='Month',\n",
        "    yaxis_title='Total Hours Viewed (in billions)',\n",
        "    xaxis=dict(\n",
        "        tickmode='array',\n",
        "        tickvals=list(range(1, 13)),\n",
        "        ticktext=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
        "    ),\n",
        "    height=600,\n",
        "    width=1000\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
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        "id": "EF4IFwVsg-tR",
        "outputId": "d01e6458-ac82-4dc5-c44b-0fa163457243"
      },
      "execution_count": 5,
      "outputs": [
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    {
      "cell_type": "code",
      "source": [
        "# extract the top 5 titles based on viewership hours\n",
        "top_5_titles = netflix_data.nlargest(5, 'Hours Viewed')\n",
        "top_5_titles[['Title', 'Hours Viewed', 'Language Indicator', 'Content Type', 'Release Date']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "JkJEHFoUhFv6",
        "outputId": "8f00ecaf-c931-462d-e24f-fcfd5cf7b73b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 Title  Hours Viewed  \\\n",
              "0                            The Night Agent: Season 1   812100000.0   \n",
              "1                            Ginny & Georgia: Season 2   665100000.0   \n",
              "18227  King the Land: Limited Series // 킹더랜드: 리미티드 시리즈   630200000.0   \n",
              "2                   The Glory: Season 1 // 더 글로리: 시즌 1   622800000.0   \n",
              "18214                              ONE PIECE: Season 1   541900000.0   \n",
              "\n",
              "      Language Indicator Content Type Release Date  \n",
              "0                English         Show   2023-03-23  \n",
              "1                English         Show   2023-01-05  \n",
              "18227             Korean        Movie   2023-06-17  \n",
              "2                 Korean         Show   2022-12-30  \n",
              "18214            English         Show   2023-08-31  "
            ],
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              "summary": "{\n  \"name\": \"top_5_titles[['Title', 'Hours Viewed', 'Language Indicator', 'Content Type', 'Release Date']]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Ginny & Georgia: Season 2\",\n          \"ONE PIECE: Season 1\",\n          \"King the Land: Limited Series // \\ud0b9\\ub354\\ub79c\\ub4dc: \\ub9ac\\ubbf8\\ud2f0\\ub4dc \\uc2dc\\ub9ac\\uc988\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hours Viewed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 99025537.11038381,\n        \"min\": 541900000.0,\n        \"max\": 812100000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          665100000.0,\n          541900000.0,\n          630200000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Language Indicator\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Korean\",\n          \"English\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Content Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Movie\",\n          \"Show\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Release Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2022-12-30 00:00:00\",\n        \"max\": \"2023-08-31 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2023-01-05 00:00:00\",\n          \"2023-08-31 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# aggregate viewership hours by content type and release month\n",
        "monthly_viewership_by_type = netflix_data.pivot_table(index='Release Month',\n",
        "                                                      columns='Content Type',\n",
        "                                                      values='Hours Viewed',\n",
        "                                                      aggfunc='sum')\n",
        "\n",
        "fig = go.Figure()\n",
        "\n",
        "for content_type in monthly_viewership_by_type.columns:\n",
        "    fig.add_trace(\n",
        "        go.Scatter(\n",
        "            x=monthly_viewership_by_type.index,\n",
        "            y=monthly_viewership_by_type[content_type],\n",
        "            mode='lines+markers',\n",
        "            name=content_type\n",
        "        )\n",
        "    )\n",
        "\n",
        "fig.update_layout(\n",
        "    title='Viewership Trends by Content Type and Release Month (2023)',\n",
        "    xaxis_title='Month',\n",
        "    yaxis_title='Total Hours Viewed (in billions)',\n",
        "    xaxis=dict(\n",
        "        tickmode='array',\n",
        "        tickvals=list(range(1, 13)),\n",
        "        ticktext=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
        "    ),\n",
        "    height=600,\n",
        "    width=1000,\n",
        "    legend_title='Content Type'\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
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    {
      "cell_type": "code",
      "source": [
        "# define seasons based on release months\n",
        "def get_season(month):\n",
        "    if month in [12, 1, 2]:\n",
        "        return 'Winter'\n",
        "    elif month in [3, 4, 5]:\n",
        "        return 'Spring'\n",
        "    elif month in [6, 7, 8]:\n",
        "        return 'Summer'\n",
        "    else:\n",
        "        return 'Fall'\n",
        "\n",
        "# apply the season categorization to the dataset\n",
        "netflix_data['Release Season'] = netflix_data['Release Month'].apply(get_season)\n",
        "\n",
        "# aggregate viewership hours by release season\n",
        "seasonal_viewership = netflix_data.groupby('Release Season')['Hours Viewed'].sum()\n",
        "\n",
        "# order the seasons as 'Winter', 'Spring', 'Summer', 'Fall'\n",
        "seasons_order = ['Winter', 'Spring', 'Summer', 'Fall']\n",
        "seasonal_viewership = seasonal_viewership.reindex(seasons_order)\n",
        "\n",
        "fig = go.Figure(data=[\n",
        "    go.Bar(\n",
        "        x=seasonal_viewership.index,\n",
        "        y=seasonal_viewership.values,\n",
        "        marker_color='orange'\n",
        "    )\n",
        "])\n",
        "\n",
        "fig.update_layout(\n",
        "    title='Total Viewership Hours by Release Season (2023)',\n",
        "    xaxis_title='Season',\n",
        "    yaxis_title='Total Hours Viewed (in billions)',\n",
        "    xaxis_tickangle=0,\n",
        "    height=500,\n",
        "    width=800,\n",
        "    xaxis=dict(\n",
        "        categoryorder='array',\n",
        "        categoryarray=seasons_order\n",
        "    )\n",
        ")\n",
        "\n",
        "fig.show()"
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "monthly_releases = netflix_data['Release Month'].value_counts().sort_index()\n",
        "\n",
        "monthly_viewership = netflix_data.groupby('Release Month')['Hours Viewed'].sum()\n",
        "\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(\n",
        "    go.Bar(\n",
        "        x=monthly_releases.index,\n",
        "        y=monthly_releases.values,\n",
        "        name='Number of Releases',\n",
        "        marker_color='goldenrod',\n",
        "        opacity=0.7,\n",
        "        yaxis='y1'\n",
        "    )\n",
        ")\n",
        "\n",
        "fig.add_trace(\n",
        "    go.Scatter(\n",
        "        x=monthly_viewership.index,\n",
        "        y=monthly_viewership.values,\n",
        "        name='Viewership Hours',\n",
        "        mode='lines+markers',\n",
        "        marker=dict(color='red'),\n",
        "        line=dict(color='red'),\n",
        "        yaxis='y2'\n",
        "    )\n",
        ")\n",
        "\n",
        "fig.update_layout(\n",
        "    title='Monthly Release Patterns and Viewership Hours (2023)',\n",
        "    xaxis=dict(\n",
        "        title='Month',\n",
        "        tickmode='array',\n",
        "        tickvals=list(range(1, 13)),\n",
        "        ticktext=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
        "    ),\n",
        "    yaxis=dict(\n",
        "        title='Number of Releases',\n",
        "        showgrid=False,\n",
        "        side='left'\n",
        "    ),\n",
        "    yaxis2=dict(\n",
        "        title='Total Hours Viewed (in billions)',\n",
        "        overlaying='y',\n",
        "        side='right',\n",
        "        showgrid=False\n",
        "    ),\n",
        "    legend=dict(\n",
        "        x=1.05,\n",
        "        y=1,\n",
        "        orientation='v',\n",
        "        xanchor='left'\n",
        "    ),\n",
        "    height=600,\n",
        "    width=1000\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "Hm9dtDkjhvOL",
        "outputId": "c8467e47-5376-4c93-a601-4c7cf63df988"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "netflix_data['Release Day'] = netflix_data['Release Date'].dt.day_name()\n",
        "\n",
        "weekday_releases = netflix_data['Release Day'].value_counts().reindex(\n",
        "    ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
        ")\n",
        "\n",
        "# aggregate viewership hours by day of the week\n",
        "weekday_viewership = netflix_data.groupby('Release Day')['Hours Viewed'].sum().reindex(\n",
        "    ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
        ")\n",
        "\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(\n",
        "    go.Bar(\n",
        "        x=weekday_releases.index,\n",
        "        y=weekday_releases.values,\n",
        "        name='Number of Releases',\n",
        "        marker_color='blue',\n",
        "        opacity=0.6,\n",
        "        yaxis='y1'\n",
        "    )\n",
        ")\n",
        "\n",
        "fig.add_trace(\n",
        "    go.Scatter(\n",
        "        x=weekday_viewership.index,\n",
        "        y=weekday_viewership.values,\n",
        "        name='Viewership Hours',\n",
        "        mode='lines+markers',\n",
        "        marker=dict(color='red'),\n",
        "        line=dict(color='red'),\n",
        "        yaxis='y2'\n",
        "    )\n",
        ")\n",
        "\n",
        "fig.update_layout(\n",
        "    title='Weekly Release Patterns and Viewership Hours (2023)',\n",
        "    xaxis=dict(\n",
        "        title='Day of the Week',\n",
        "        categoryorder='array',\n",
        "        categoryarray=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
        "    ),\n",
        "    yaxis=dict(\n",
        "        title='Number of Releases',\n",
        "        showgrid=False,\n",
        "        side='left'\n",
        "    ),\n",
        "    yaxis2=dict(\n",
        "        title='Total Hours Viewed (in billions)',\n",
        "        overlaying='y',\n",
        "        side='right',\n",
        "        showgrid=False\n",
        "    ),\n",
        "    legend=dict(\n",
        "        x=1.05,\n",
        "        y=1,\n",
        "        orientation='v',\n",
        "        xanchor='left'\n",
        "    ),\n",
        "    height=600,\n",
        "    width=1000\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "KUZ-d9VPh4t0",
        "outputId": "890f2249-8d11-4657-f97d-10111184345b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
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      "source": [
        "# define significant holidays and events in 2023\n",
        "important_dates = [\n",
        "    '2023-01-01',  # new year's day\n",
        "    '2023-02-14',  # valentine's ay\n",
        "    '2023-07-04',  # independence day (US)\n",
        "    '2023-10-31',  # halloween\n",
        "    '2023-12-25'   # christmas day\n",
        "]\n",
        "\n",
        "# convert to datetime\n",
        "important_dates = pd.to_datetime(important_dates)\n",
        "\n",
        "# check for content releases close to these significant holidays (within a 3-day window)\n",
        "holiday_releases = netflix_data[netflix_data['Release Date'].apply(\n",
        "    lambda x: any((x - date).days in range(-3, 4) for date in important_dates)\n",
        ")]\n",
        "\n",
        "# aggregate viewership hours for releases near significant holidays\n",
        "holiday_viewership = holiday_releases.groupby('Release Date')['Hours Viewed'].sum()\n",
        "\n",
        "holiday_releases[['Title', 'Release Date', 'Hours Viewed']]"
      ],
      "metadata": {
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        "id": "8ff0HnSxh_zW",
        "outputId": "e98a02f5-6045-43d9-a813-9a71028454fe"
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      "execution_count": 13,
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              "                                                   Title Release Date  \\\n",
              "2                     The Glory: Season 1 // 더 글로리: 시즌 1   2022-12-30   \n",
              "6                             La Reina del Sur: Season 3   2022-12-30   \n",
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              "124    Lady Voyeur: Limited Series // Olhar Indiscret...   2022-12-31   \n",
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              "       Hours Viewed  \n",
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              "11      252500000.0  \n",
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              "      <td>The Glory: Season 1 // 더 글로리: 시즌 1</td>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-096319db-f749-48a7-8d7d-b64aec2704f4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-096319db-f749-48a7-8d7d-b64aec2704f4')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-096319db-f749-48a7-8d7d-b64aec2704f4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"holiday_releases[['Title', 'Release Date', 'Hours Viewed']]\",\n  \"rows\": 98,\n  \"fields\": [\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 68,\n        \"samples\": [\n          \"The Tailor: Season 3 // Terzi: 3. Sezon\",\n          \"Rise of Empires: Ottoman: Mehmed vs. Vlad\",\n          \"Lady Voyeur: Limited Series // Olhar Indiscreto: Miniss\\u00e9rie\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Release Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2022-12-29 00:00:00\",\n        \"max\": \"2023-12-28 00:00:00\",\n        \"num_unique_values\": 23,\n        \"samples\": [\n          \"2023-11-03 00:00:00\",\n          \"2023-02-16 00:00:00\",\n          \"2022-12-30 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hours Viewed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 86161773.01920351,\n        \"min\": 400000.0,\n        \"max\": 622800000.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          4200000.0,\n          622800000.0,\n          9500000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UMwsQ1AGiJFA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}