GeeksforGeeks_Project2_GFGDataAnalysis.ipynb

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      "mount_file_id": "1qIW7kGHFuKEkKDegV7qkqZxAlt2J1L9f",
      "authorship_tag": "ABX9TyPZNCRkG0fFocA56KN6zQb7",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/anayjain/GFG-DataAnalysis/blob/main/GeeksforGeeks_Project2_GFGDataAnalysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Project Name: GeeksforGeeks Data Analysis\n",
        "Scrape the Geeksforgeeks youtube channel videos of the past 6 months' dataset\n",
        "\n",
        "## Tasks & Questions:\n",
        "1. Number of videos in the past 6 months from the start date. Must mention the dates in the solution.\n",
        "2. Create a pandas data frame with columns name as videos title, views, Length of videos, and videos link\n",
        "3. Name the most viewed topics in the past 6 months.\n",
        "4. Name the topics with the highest video length.\n",
        "5. Make a comparison between the number of views and video length using a Graph.\n",
        "\n",
        "Dataset: https://www.youtube.com/@GeeksforGeeksVideos/videos"
      ],
      "metadata": {
        "id": "6mnYVka9FHLv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Enabling YouTube API\n",
        "To enable YouTube Data API, you should follow below steps:"
      ],
      "metadata": {
        "id": "B0AFtiXPFMx9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Go to Google's API Console and create a project, or use an existing one. https://console.cloud.google.com/\n",
        "2. In the library panel, search for YouTube Data API v3, click on it and click Enable.\n",
        "3. In the credentials panel, click on Create Credentials, and choose API key.\n",
        "4. You'll see a window with the API key. Make sure to copy and save the API key generated, we will use it later."
      ],
      "metadata": {
        "id": "SaAT9Bh0FRTi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Install required libraries"
      ],
      "metadata": {
        "id": "Fihrn_1IFUlr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip3 install --upgrade google-api-python-client #install google api client for api access\n",
        "!pip3 install utcnow\n",
        "!pip3 install itables\n",
        "!pip3 install isoduration"
      ],
      "metadata": {
        "id": "rIMIJGNhFboy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Download Channel Data and Id"
      ],
      "metadata": {
        "id": "Wx-oeLM3FnDK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from googleapiclient.discovery import build\n",
        "\n",
        "from utcnow import utcnow\n",
        "from datetime import datetime, timedelta\n",
        "from dateutil.relativedelta import *\n",
        "import math\n",
        "\n",
        "import json\n",
        "import pandas as pd\n",
        "import config\n",
        "\n",
        "from itables import show\n",
        "from tabulate import tabulate\n",
        "\n",
        "import plotly.express as px\n",
        "\n",
        "from isoduration import parse_duration\n"
      ],
      "metadata": {
        "id": "CFaD11CZFp57"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_channel_videos(youtube, **kwargs):\n",
        "    return youtube.search().list(\n",
        "        **kwargs\n",
        "    ).execute()\n",
        "\n",
        "def search(youtube, **kwargs):\n",
        "    return youtube.search().list(\n",
        "        part=\"snippet\",\n",
        "        **kwargs\n",
        "    ).execute()\n",
        "\n",
        "def get_video_details(youtube, **kwargs):\n",
        "    return youtube.videos().list(\n",
        "        part=\"snippet,contentDetails,statistics\",\n",
        "        **kwargs\n",
        "    ).execute()"
      ],
      "metadata": {
        "id": "pfTQQXwyQmoC"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# API information\n",
        "api_service_name = \"youtube\"\n",
        "api_version = \"v3\"\n",
        "# API key\n",
        "DEVELOPER_KEY = config.DEVELOPER_KEY\n",
        "# API client\n",
        "youtube = build(\n",
        "    api_service_name, api_version, developerKey = DEVELOPER_KEY)"
      ],
      "metadata": {
        "id": "2zQabk-oGyLz"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "channel_url = \"https://www.youtube.com/@GeeksforGeeksVideos\"\n",
        "# get the channel name from the URL\n",
        "name = channel_url.split(\"/\")[-1]\n",
        "response_id = search(youtube, q=name, maxResults=1)\n",
        "items = response_id.get(\"items\")\n",
        "if items:\n",
        "    channel_id = items[0][\"snippet\"][\"channelId\"]\n",
        "print(channel_id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hLrXzQbEQ2wB",
        "outputId": "ac448aa4-a2a5-4c2d-e1de-21f93db1821b"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "UC0RhatS1pyxInC00YKjjBqQ\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Extract All Video data based on Channel Id"
      ],
      "metadata": {
        "id": "CZkYlBB78l_e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# First get current datatime timsetamp\n",
        "current_time = datetime.now()\n",
        "\n",
        "# Subtract 6 months from current datatime timestamp\n",
        "current_time_RFC3339 = utcnow.get(current_time)\n",
        "after = current_time - relativedelta(months=+6)\n",
        "after_RFC3339 = utcnow.get(after)\n",
        "\n",
        "#Run search for all channel video published after datetime provided\n",
        "params = {\n",
        "        'part': 'snippet',\n",
        "        'q': '',\n",
        "        'channelId': channel_id,\n",
        "        'publishedAfter': after_RFC3339,\n",
        "        'type': 'video',\n",
        "    }\n",
        "res = get_channel_videos(youtube, **params)\n",
        "# get total results and check how many pages of videos are listed\n",
        "tot = res[\"pageInfo\"]['totalResults']\n",
        "n_pages = math.ceil(tot / 50)\n",
        "n_videos = 0\n",
        "video_data = []\n",
        "video_url = []\n",
        "next_page_token = None\n",
        "# Get all results through pagination\n",
        "for i in range(n_pages):\n",
        "    params = {\n",
        "        'part': 'snippet',\n",
        "        'q': '',\n",
        "        'channelId': channel_id,\n",
        "        'publishedAfter': after_RFC3339,\n",
        "        'maxResults': 50, # maximum permitted result per page\n",
        "        'type': 'video',\n",
        "    }\n",
        "    # this is to get the next page and save the token\n",
        "    if next_page_token:\n",
        "        params['pageToken'] = next_page_token\n",
        "    res = get_channel_videos(youtube, **params)\n",
        "    channel_videos = res.get(\"items\")\n",
        "    for v in channel_videos:\n",
        "      n_videos += 1\n",
        "      video_id = v[\"id\"][\"videoId\"]\n",
        "      # easily construct video URL by its ID\n",
        "      url = f\"https://www.youtube.com/watch?v={video_id}\"\n",
        "      # check for unique videos\n",
        "      if url not in video_url:\n",
        "        video_url.append(url)\n",
        "        video_response = get_video_details(youtube, id=video_id)\n",
        "        video_data.append(video_response.get(\"items\")[0])\n",
        "    if \"nextPageToken\" in res:\n",
        "        next_page_token = res[\"nextPageToken\"]"
      ],
      "metadata": {
        "id": "4No6YunQ8TaP"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Number of videos in the past 6 months from the start date.\n",
        "(Must mention the dates in the solution.)"
      ],
      "metadata": {
        "id": "d3GgBlhxF3lI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "totalResults = n_videos\n",
        "print(\"The total number of videos in past 6 months from {} is {}.\".format(after_RFC3339,totalResults))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e1URWOKVF28L",
        "outputId": "34d85326-0b48-43cb-dee4-dad4a756c57c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The total number of videos in past 6 months from 2023-02-07T17:42:17.567678Z is 202.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create a pandas data frame with columns name as videos title, views, Length of videos, and videos link"
      ],
      "metadata": {
        "id": "sFiWH68uRzDR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save entire video data in dataframe\n",
        "df = pd.DataFrame()\n",
        "for i in range(len(video_data)):\n",
        "  snippet = video_data[i]['snippet']\n",
        "  content = video_data[i]['contentDetails']\n",
        "  stats = video_data[i]['statistics']\n",
        "  snippet.update(content)\n",
        "  snippet.update(stats)\n",
        "  df = pd.concat([df, pd.DataFrame.from_records([snippet])], ignore_index=True)\n",
        "df['video_link'] = video_url\n",
        "# Save a copy of raw data\n",
        "raw_table = df\n",
        "# Remove unnecessary columns\n",
        "df = df.drop(['publishedAt','channelId','description','thumbnails','tags','categoryId'],axis=1)\n",
        "df = df.drop(['liveBroadcastContent','defaultLanguage','caption','licensedContent','contentRating','projection'],axis=1)\n",
        "df = df.drop(['localized','defaultAudioLanguage','dimension','definition'],axis=1)\n",
        "# show(df.head(5))\n",
        "tabulate(df.head(5), headers = 'keys', tablefmt = 'html')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "ePTTdGmyR4T5",
        "outputId": "e4924f0f-8c91-45f8-eebf-14df201acbfc"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<table>\\n<thead>\\n<tr><th style=\"text-align: right;\">  </th><th>title                                                          </th><th>channelTitle  </th><th>duration  </th><th style=\"text-align: right;\">  viewCount</th><th style=\"text-align: right;\">  likeCount</th><th style=\"text-align: right;\">  favoriteCount</th><th style=\"text-align: right;\">  commentCount</th><th style=\"text-align: right;\">  regionRestriction</th><th>video_link                                 </th></tr>\\n</thead>\\n<tbody>\\n<tr><td style=\"text-align: right;\"> 0</td><td>Create Your Own Apps Today | GeeksforGeeks                     </td><td>GeeksforGeeks </td><td>PT38S     </td><td style=\"text-align: right;\">      34923</td><td style=\"text-align: right;\">         89</td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">             1</td><td style=\"text-align: right;\">                nan</td><td>https://www.youtube.com/watch?v=LycNCWC3g18</td></tr>\\n<tr><td style=\"text-align: right;\"> 1</td><td>Can you solve this puzzle? | Give your answers in comments 👇🏻</td><td>GeeksforGeeks </td><td>PT33S     </td><td style=\"text-align: right;\">       2411</td><td style=\"text-align: right;\">        119</td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">            11</td><td style=\"text-align: right;\">                nan</td><td>https://www.youtube.com/watch?v=CGZkoNtQl_U</td></tr>\\n<tr><td style=\"text-align: right;\"> 2</td><td>First Ever KBC at our Offline Classes | GeeksforGeeks          </td><td>GeeksforGeeks </td><td>PT37S     </td><td style=\"text-align: right;\">        598</td><td style=\"text-align: right;\">         19</td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">             0</td><td style=\"text-align: right;\">                nan</td><td>https://www.youtube.com/watch?v=chYg2EAKW2A</td></tr>\\n<tr><td style=\"text-align: right;\"> 3</td><td>Caution : 100% Relatable                                       </td><td>GeeksforGeeks </td><td>PT25S     </td><td style=\"text-align: right;\">       5863</td><td style=\"text-align: right;\">        277</td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">             2</td><td style=\"text-align: right;\">                nan</td><td>https://www.youtube.com/watch?v=0NoD0Y_mAy4</td></tr>\\n<tr><td style=\"text-align: right;\"> 4</td><td>😡😤                                                           </td><td>GeeksforGeeks </td><td>PT11S     </td><td style=\"text-align: right;\">       4155</td><td style=\"text-align: right;\">        164</td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">             2</td><td style=\"text-align: right;\">                nan</td><td>https://www.youtube.com/watch?v=24G2og7huNY</td></tr>\\n</tbody>\\n</table>'"
            ],
            "text/html": [
              "<table>\n",
              "<thead>\n",
              "<tr><th style=\"text-align: right;\">  </th><th>title                                                          </th><th>channelTitle  </th><th>duration  </th><th style=\"text-align: right;\">  viewCount</th><th style=\"text-align: right;\">  likeCount</th><th style=\"text-align: right;\">  favoriteCount</th><th style=\"text-align: right;\">  commentCount</th><th style=\"text-align: right;\">  regionRestriction</th><th>video_link                                 </th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td style=\"text-align: right;\"> 0</td><td>Create Your Own Apps Today | GeeksforGeeks                     </td><td>GeeksforGeeks </td><td>PT38S     </td><td style=\"text-align: right;\">      34923</td><td style=\"text-align: right;\">         89</td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">             1</td><td style=\"text-align: right;\">                nan</td><td>https://www.youtube.com/watch?v=LycNCWC3g18</td></tr>\n",
              "<tr><td style=\"text-align: right;\"> 1</td><td>Can you solve this puzzle? | Give your answers in comments 👇🏻</td><td>GeeksforGeeks </td><td>PT33S     </td><td style=\"text-align: right;\">       2411</td><td style=\"text-align: right;\">        119</td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">            11</td><td style=\"text-align: right;\">                nan</td><td>https://www.youtube.com/watch?v=CGZkoNtQl_U</td></tr>\n",
              "<tr><td style=\"text-align: right;\"> 2</td><td>First Ever KBC at our Offline Classes | GeeksforGeeks          </td><td>GeeksforGeeks </td><td>PT37S     </td><td style=\"text-align: right;\">        598</td><td style=\"text-align: right;\">         19</td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">             0</td><td style=\"text-align: right;\">                nan</td><td>https://www.youtube.com/watch?v=chYg2EAKW2A</td></tr>\n",
              "<tr><td style=\"text-align: right;\"> 3</td><td>Caution : 100% Relatable                                       </td><td>GeeksforGeeks </td><td>PT25S     </td><td style=\"text-align: right;\">       5863</td><td style=\"text-align: right;\">        277</td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">             2</td><td style=\"text-align: right;\">                nan</td><td>https://www.youtube.com/watch?v=0NoD0Y_mAy4</td></tr>\n",
              "<tr><td style=\"text-align: right;\"> 4</td><td>😡😤                                                           </td><td>GeeksforGeeks </td><td>PT11S     </td><td style=\"text-align: right;\">       4155</td><td style=\"text-align: right;\">        164</td><td style=\"text-align: right;\">              0</td><td style=\"text-align: right;\">             2</td><td style=\"text-align: right;\">                nan</td><td>https://www.youtube.com/watch?v=24G2og7huNY</td></tr>\n",
              "</tbody>\n",
              "</table>"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
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          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Most viewed topics in the past 6 months."
      ],
      "metadata": {
        "id": "IcEM1DCAhwND"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert viewCount from string to integer\n",
        "df['viewCount'] = df['viewCount'].astype('int')\n",
        "# Sort topics by number of views\n",
        "most_viewed = df.sort_values(by=['viewCount'],ascending=False)\n",
        "print(\"Most viewed topics in the past 6 months ->\")\n",
        "# show(most_viewed[['title', 'viewCount', 'video_link']].head(5))\n",
        "tabulate(most_viewed[['title', 'viewCount', 'video_link']].head(5), headers = 'keys', tablefmt = 'html')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "wEpPHzyHXWTj",
        "outputId": "34deecc2-8859-4b7c-d6f5-7b39b0d9638f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Most viewed topics in the past 6 months ->\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<table>\\n<thead>\\n<tr><th style=\"text-align: right;\">  </th><th>title                                                                                     </th><th style=\"text-align: right;\">  viewCount</th><th>video_link                                 </th></tr>\\n</thead>\\n<tbody>\\n<tr><td style=\"text-align: right;\">28</td><td>Learn System Design with GeeksforGeeks                                                    </td><td style=\"text-align: right;\">     118339</td><td>https://www.youtube.com/watch?v=XQEZ07JhVuA</td></tr>\\n<tr><td style=\"text-align: right;\">20</td><td>GeeksforGeeks Classroom Program | Now in Noida and Gurugram!                              </td><td style=\"text-align: right;\">     111314</td><td>https://www.youtube.com/watch?v=16D2cuRy5JY</td></tr>\\n<tr><td style=\"text-align: right;\">42</td><td>Free Summer Offline Classes on Python Programing | For Students Aged 14-21 | GeeksforGeeks</td><td style=\"text-align: right;\">     106937</td><td>https://www.youtube.com/watch?v=OOLXHwZzHfM</td></tr>\\n<tr><td style=\"text-align: right;\">39</td><td>Full Stack Development | LIVE Classes | GeeksforGeeks                                     </td><td style=\"text-align: right;\">      91939</td><td>https://www.youtube.com/watch?v=cBfC9HLR9Qk</td></tr>\\n<tr><td style=\"text-align: right;\">35</td><td>Job Fair for Students | Till 25th May Only | GeeksforGeeks                                </td><td style=\"text-align: right;\">      82854</td><td>https://www.youtube.com/watch?v=1GEegOb3fHE</td></tr>\\n</tbody>\\n</table>'"
            ],
            "text/html": [
              "<table>\n",
              "<thead>\n",
              "<tr><th style=\"text-align: right;\">  </th><th>title                                                                                     </th><th style=\"text-align: right;\">  viewCount</th><th>video_link                                 </th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td style=\"text-align: right;\">28</td><td>Learn System Design with GeeksforGeeks                                                    </td><td style=\"text-align: right;\">     118339</td><td>https://www.youtube.com/watch?v=XQEZ07JhVuA</td></tr>\n",
              "<tr><td style=\"text-align: right;\">20</td><td>GeeksforGeeks Classroom Program | Now in Noida and Gurugram!                              </td><td style=\"text-align: right;\">     111314</td><td>https://www.youtube.com/watch?v=16D2cuRy5JY</td></tr>\n",
              "<tr><td style=\"text-align: right;\">42</td><td>Free Summer Offline Classes on Python Programing | For Students Aged 14-21 | GeeksforGeeks</td><td style=\"text-align: right;\">     106937</td><td>https://www.youtube.com/watch?v=OOLXHwZzHfM</td></tr>\n",
              "<tr><td style=\"text-align: right;\">39</td><td>Full Stack Development | LIVE Classes | GeeksforGeeks                                     </td><td style=\"text-align: right;\">      91939</td><td>https://www.youtube.com/watch?v=cBfC9HLR9Qk</td></tr>\n",
              "<tr><td style=\"text-align: right;\">35</td><td>Job Fair for Students | Till 25th May Only | GeeksforGeeks                                </td><td style=\"text-align: right;\">      82854</td><td>https://www.youtube.com/watch?v=1GEegOb3fHE</td></tr>\n",
              "</tbody>\n",
              "</table>"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"The most viewed topic in past 6 months is {} with {} views.\".format(list(most_viewed['title'])[0],list(most_viewed['viewCount'])[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5beDPYZrDUZw",
        "outputId": "26663b5f-28af-4b19-cd60-6b590bd39930"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The most viewed topic in past 6 months is Learn System Design with GeeksforGeeks with 118339 views.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Topics with the highest video length"
      ],
      "metadata": {
        "id": "OmpbvkxWmg_H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create function to parse ISO-8601 duration and calculate the total seconds\n",
        "def calculate_duration_sec(s):\n",
        "  duration = parse_duration(s)\n",
        "  tot_sec = (int(duration.time.hours) * 60 * 60) + (int(duration.time.minutes) * 60) + int(duration.time.seconds)\n",
        "  return tot_sec\n",
        "\n",
        "#Append a new column duration in seconds to table\n",
        "duration = list(df['duration'])\n",
        "duration_sec = []\n",
        "for i in duration:\n",
        "  duration_sec.append(calculate_duration_sec(i))\n",
        "df.insert(3,'duration_in_seconds', duration_sec)\n",
        "\n",
        "most_duration = df.sort_values(by=['duration_in_seconds'],ascending=False)\n",
        "print(\"Longest duration topics in the past 6 months ->\")\n",
        "# show(most_duration[['title', 'duration', 'duration_in_seconds', 'video_link']].head(5))\n",
        "tabulate(most_duration[['title', 'duration', 'duration_in_seconds', 'video_link']].head(5), headers = 'keys', tablefmt = 'html')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "m4Ww6hxsnB9M",
        "outputId": "74dcf05a-cc08-45c6-d10b-d7e8b7481805"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Longest duration topics in the past 6 months ->\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<table>\\n<thead>\\n<tr><th style=\"text-align: right;\">   </th><th>title                                                   </th><th>duration  </th><th style=\"text-align: right;\">  duration_in_seconds</th><th>video_link                                 </th></tr>\\n</thead>\\n<tbody>\\n<tr><td style=\"text-align: right;\">138</td><td>SDE Preparation in 3 hours                              </td><td>PT2H55M27S</td><td style=\"text-align: right;\">                10527</td><td>https://www.youtube.com/watch?v=ftDoBLp-OfU</td></tr>\\n<tr><td style=\"text-align: right;\"> 98</td><td>CodeCamp Day 2 | Exploring Arrays and Problem Solving   </td><td>PT2H29M20S</td><td style=\"text-align: right;\">                 8960</td><td>https://www.youtube.com/watch?v=OMHeYpQCCPE</td></tr>\\n<tr><td style=\"text-align: right;\">187</td><td>CodeCamp Day 13 | Discovering Graph Traversal Algorithms</td><td>PT2H14M24S</td><td style=\"text-align: right;\">                 8064</td><td>https://www.youtube.com/watch?v=hgkjJD5hb5g</td></tr>\\n<tr><td style=\"text-align: right;\">197</td><td>CodeCamp Day 19 | Exploring Advanced Topics in DSA      </td><td>PT2H13M39S</td><td style=\"text-align: right;\">                 8019</td><td>https://www.youtube.com/watch?v=kLZGFHK_bIc</td></tr>\\n<tr><td style=\"text-align: right;\">134</td><td>CodeCamp Day 11 | Journey into Binary Trees             </td><td>PT2H10M41S</td><td style=\"text-align: right;\">                 7841</td><td>https://www.youtube.com/watch?v=U1UKjsA4jNg</td></tr>\\n</tbody>\\n</table>'"
            ],
            "text/html": [
              "<table>\n",
              "<thead>\n",
              "<tr><th style=\"text-align: right;\">   </th><th>title                                                   </th><th>duration  </th><th style=\"text-align: right;\">  duration_in_seconds</th><th>video_link                                 </th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td style=\"text-align: right;\">138</td><td>SDE Preparation in 3 hours                              </td><td>PT2H55M27S</td><td style=\"text-align: right;\">                10527</td><td>https://www.youtube.com/watch?v=ftDoBLp-OfU</td></tr>\n",
              "<tr><td style=\"text-align: right;\"> 98</td><td>CodeCamp Day 2 | Exploring Arrays and Problem Solving   </td><td>PT2H29M20S</td><td style=\"text-align: right;\">                 8960</td><td>https://www.youtube.com/watch?v=OMHeYpQCCPE</td></tr>\n",
              "<tr><td style=\"text-align: right;\">187</td><td>CodeCamp Day 13 | Discovering Graph Traversal Algorithms</td><td>PT2H14M24S</td><td style=\"text-align: right;\">                 8064</td><td>https://www.youtube.com/watch?v=hgkjJD5hb5g</td></tr>\n",
              "<tr><td style=\"text-align: right;\">197</td><td>CodeCamp Day 19 | Exploring Advanced Topics in DSA      </td><td>PT2H13M39S</td><td style=\"text-align: right;\">                 8019</td><td>https://www.youtube.com/watch?v=kLZGFHK_bIc</td></tr>\n",
              "<tr><td style=\"text-align: right;\">134</td><td>CodeCamp Day 11 | Journey into Binary Trees             </td><td>PT2H10M41S</td><td style=\"text-align: right;\">                 7841</td><td>https://www.youtube.com/watch?v=U1UKjsA4jNg</td></tr>\n",
              "</tbody>\n",
              "</table>"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Scatter Plot Graph of number of views v/s video length"
      ],
      "metadata": {
        "id": "eNdocbh31K0e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = df\n",
        "\n",
        "df.to_csv('youtube_video_6months.csv', index=False)\n",
        "\n",
        "data['duration_in_millisec'] = data['duration_in_seconds'] * 1000"
      ],
      "metadata": {
        "id": "1LoGKZDi1Q3r"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.scatter(data, x=\"duration_in_millisec\", y=\"viewCount\",\n",
        "                 trendline=\"rolling\", trendline_options=dict(window=5, win_type=\"gaussian\", function_args=dict(std=2)), trendline_color_override=\"mediumpurple\",\n",
        "                  title=\"No. of Views V/S Length of Videos\", hover_data=['title'], labels={})\n",
        "fig.update_traces(marker={\n",
        "    'size': 7,\n",
        "    'color': 'royalblue',\n",
        "    'symbol': 'circle-open'})\n",
        "fig.update_layout(showlegend=True)\n",
        "\n",
        "fig.update_layout(\n",
        "    legend=dict(\n",
        "        x=0.01,\n",
        "        y=.98,\n",
        "        traceorder=\"normal\",\n",
        "        font=dict(\n",
        "            family=\"sans-serif\",\n",
        "            size=12,\n",
        "            color=\"Black\"\n",
        "        ),\n",
        "        bgcolor=\"LightSteelBlue\",\n",
        "        bordercolor=\"dimgray\",\n",
        "        borderwidth=2\n",
        "    ))\n",
        "\n",
        "# fig.show()"
      ],
      "metadata": {
        "id": "ln0h3b4iMwim"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "![newplot.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "3BmKNj6-Fd-r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the graph above you can see that no. of views is extremely high when the duration of videos is less. As the duration increases there is steep decline in viewer count until it reaches a stable value wherein even 10million millisec duration videos get a low viewer count."
      ],
      "metadata": {
        "id": "c9pei2KKLL8u"
      }
    }
  ]
}