Youtube_Video_Ranker.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Youtube_Video_Ranker.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "AL-iTwNQ4CT0"
      },
      "source": [
        "from googleapiclient.discovery import build\r\n",
        "from textblob import TextBlob\r\n",
        "from tqdm import tqdm\r\n",
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import sys\r\n",
        "import re"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zDE13P3j5cy7"
      },
      "source": [
        "# cd  \"/content/drive/MyDrive/Colab_Work/IR_Youtube_Comments\""
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "abZoJyg76C26"
      },
      "source": [
        "## Setup Youtube API"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AsAQP3ml5in-"
      },
      "source": [
        "SCOPES = ['https://www.googleapis.com/auth/youtube']\r\n",
        "API_SERVICE_NAME = 'youtube'\r\n",
        "API_VERSION = 'v3'"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H0bqrZnp5qT4"
      },
      "source": [
        "API_KEY = 'api_key'"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWCHMpVd5wtf"
      },
      "source": [
        "def build_service():\r\n",
        "  return build(API_SERVICE_NAME, API_VERSION, developerKey = API_KEY)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IBQDlCxP6KAb"
      },
      "source": [
        "## Fetch YouTube Videos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0MUsrYCpcRq7"
      },
      "source": [
        "Get youtube videos based on the query"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TmUny0QN6cON"
      },
      "source": [
        "def search_yt_videos(query, service):\r\n",
        "  request = service.search().list(\r\n",
        "    part=\"snippet\",\r\n",
        "    maxResults=25,\r\n",
        "    order=\"relevance\",\r\n",
        "    q=query,\r\n",
        "    relevanceLanguage=\"en\",\r\n",
        "    type=\"video\"\r\n",
        "  )\r\n",
        "  videos_list = request.execute()\r\n",
        "  return videos_list"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D5u5scyjccys"
      },
      "source": [
        "Get the stats of the youtube video (likes, dislikes, count of comments)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBV0HmIu8pwD"
      },
      "source": [
        "def get_video_stats(video_id, service):\r\n",
        "  video_stats = service.videos().list(\r\n",
        "      part=\"snippet,statistics\",\r\n",
        "      id=video_id,\r\n",
        "  ).execute()\r\n",
        "  return video_stats"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HR7xKKa0clwo"
      },
      "source": [
        "Get the comments on the video using the videoID"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "23_Rk0E7-fDs"
      },
      "source": [
        "def get_video_comments(videoID, service):\r\n",
        "  video_comments = service.commentThreads().list(\r\n",
        "      part = 'snippet',\r\n",
        "      videoId = videoID,\r\n",
        "      maxResults = 100, \r\n",
        "      order = 'relevance', \r\n",
        "      textFormat = 'plainText',\r\n",
        "      ).execute()\r\n",
        "  return video_comments"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RHXlnE0Jc31U"
      },
      "source": [
        "Create a datafram from the retreived videos list"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2WRVIlpvJDst"
      },
      "source": [
        "def get_videos_list_df(videos_list):\r\n",
        "  video_id = []\r\n",
        "  channel = []\r\n",
        "  video_title = []\r\n",
        "  for item in videos_list['items']:\r\n",
        "    video_id.append(item['id']['videoId'])\r\n",
        "    channel.append(item['snippet']['channelTitle'])\r\n",
        "    video_title.append(item['snippet']['title'])\r\n",
        "\r\n",
        "  output_dict = {\r\n",
        "        'Channel': channel,\r\n",
        "        'Video Title': video_title,\r\n",
        "        'Video ID': video_id,\r\n",
        "        }\r\n",
        "\r\n",
        "  output_df = pd.DataFrame(output_dict, columns = output_dict.keys())\r\n",
        "  return output_df"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IcmtyvlVc-XH"
      },
      "source": [
        "Generate the dataset\r\n",
        "Contains the video stats and all the comments on the video in a dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3hS3O3x47zHQ"
      },
      "source": [
        "def gen_dataset(videos_list, service):\r\n",
        "  video_id = []\r\n",
        "  channel = []\r\n",
        "  video_title = []\r\n",
        "  for item in videos_list['items']:\r\n",
        "      video_id.append(item['id']['videoId'])\r\n",
        "      channel.append(item['snippet']['channelTitle'])\r\n",
        "      video_title.append(item['snippet']['title'])\r\n",
        "\r\n",
        "  video_id_pop = []\r\n",
        "  channel_pop = []\r\n",
        "  video_title_pop = []\r\n",
        "  video_views_pop = []\r\n",
        "  video_likes_pop = []\r\n",
        "  video_dislikes_pop = []\r\n",
        "  video_comments_cnt_pop = []\r\n",
        "  comments_pop = []\r\n",
        "  comment_id_pop = []\r\n",
        "\r\n",
        "  for i, videoID in enumerate(tqdm(video_id, ncols = 100)):\r\n",
        "\r\n",
        "    video_stats = get_video_stats(videoID, service)\r\n",
        "\r\n",
        "    try:\r\n",
        "      video_views = (video_stats['items'][0]['statistics']['viewCount'])\r\n",
        "    except KeyError as l:\r\n",
        "      video_views = 0\r\n",
        "    try:\r\n",
        "      video_likes = (video_stats['items'][0]['statistics']['likeCount'])\r\n",
        "    except KeyError as l:\r\n",
        "      video_likes = 0\r\n",
        "    try:\r\n",
        "      video_dislikes = (video_stats['items'][0]['statistics']['dislikeCount'])\r\n",
        "    except KeyError as l:\r\n",
        "      video_dislikes = 0\r\n",
        "    try:\r\n",
        "      video_comments_cnt = (video_stats['items'][0]['statistics']['commentCount'])\r\n",
        "    except KeyError as l:\r\n",
        "      video_comments_cnt = 0\r\n",
        "\r\n",
        "    if video_comments_cnt == 0:\r\n",
        "      continue\r\n",
        "\r\n",
        "    try:\r\n",
        "      video_comments = get_video_comments(videoID, service)\r\n",
        "    except:\r\n",
        "      continue\r\n",
        "    comments_temp = []\r\n",
        "    comment_id_temp = []\r\n",
        "    \r\n",
        "    \r\n",
        "    for item in video_comments['items']:\r\n",
        "        comments_temp.append(item['snippet']['topLevelComment']['snippet']['textDisplay'])\r\n",
        "        comment_id_temp.append(item['snippet']['topLevelComment']['id'])\r\n",
        "\r\n",
        "    comments_pop.extend(comments_temp)\r\n",
        "    comment_id_pop.extend(comment_id_temp)\r\n",
        "\r\n",
        "    video_views_pop.extend([video_views]*len(comments_temp))\r\n",
        "    video_likes_pop.extend([video_likes]*len(comments_temp))\r\n",
        "    video_dislikes_pop.extend([video_dislikes]*len(comments_temp))\r\n",
        "    video_comments_cnt_pop.extend([video_comments_cnt]*len(comments_temp))\r\n",
        "\r\n",
        "    video_id_pop.extend([video_id[i]]*len(comments_temp))\r\n",
        "    channel_pop.extend([channel[i]]*len(comments_temp))\r\n",
        "    video_title_pop.extend([video_title[i]]*len(comments_temp))\r\n",
        "    \r\n",
        "  query_pop = [query] * len(video_id_pop)\r\n",
        "\r\n",
        "  output_dict = {\r\n",
        "          'Query': query_pop,\r\n",
        "          'Channel': channel_pop,\r\n",
        "          'Video Title': video_title_pop,\r\n",
        "          'Video ID': video_id_pop,\r\n",
        "          'Video Views':video_views_pop,\r\n",
        "          'Video Likes':video_likes_pop,\r\n",
        "          'Video Dislikes': video_dislikes_pop,\r\n",
        "          'Video Comments':video_comments_cnt_pop,\r\n",
        "          'Comment': comments_pop,\r\n",
        "          'Comment ID': comment_id_pop,\r\n",
        "          }\r\n",
        "\r\n",
        "  output_df = pd.DataFrame(output_dict, columns = output_dict.keys())\r\n",
        "  return output_df"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BhZc0h9MDgUb"
      },
      "source": [
        "## Clean Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qBdSldDSezbT"
      },
      "source": [
        "Clean up the comments in the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G7j6zoqLDfFo"
      },
      "source": [
        "def clean_dataset(df):\r\n",
        "  # Remove emojis\r\n",
        "  df = df.apply(lambda x: x.str.encode('ascii', 'ignore').str.decode('ascii'))\r\n",
        "  regex = r\"[^0-9A-Za-z'\\t]\"\r\n",
        "  df['cleaned_comments'] = df['Comment'].apply(lambda x:re.sub(regex,\"  \",x))\r\n",
        "\r\n",
        "  df['cleaned_comments'] = df['cleaned_comments'].str.lower()\r\n",
        "\r\n",
        "  dataset = df[['Query', 'Video Title', 'Video ID', 'Video Views', 'Video Likes', 'Video Dislikes', 'Video Comments', 'Comment ID','cleaned_comments']].copy()\r\n",
        "  dataset = dataset.replace({' +':' '},regex=True)\r\n",
        "\r\n",
        "  # take care of garbage/irrelevant values\r\n",
        "  dataset = df.replace(to_replace='None', value=np.nan).dropna()\r\n",
        "  \r\n",
        "  dataset['Video Views'] = pd.to_numeric(dataset['Video Views'])\r\n",
        "  dataset['Video Views'] = dataset['Video Views'].astype(int)\r\n",
        "  dataset['Video Likes'] = pd.to_numeric(dataset['Video Likes'])\r\n",
        "  dataset['Video Likes'] = dataset['Video Likes'].astype(int)\r\n",
        "  dataset['Video Dislikes'] = pd.to_numeric(dataset['Video Dislikes'])\r\n",
        "  dataset['Video Dislikes'] = dataset['Video Dislikes'].astype(int)\r\n",
        "  dataset['Video Comments'] = pd.to_numeric(dataset['Video Comments'])\r\n",
        "  dataset['Video Comments'] = dataset['Video Comments'].astype(int)\r\n",
        "\r\n",
        "  return dataset"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kau7XbljfJyJ"
      },
      "source": [
        "Calculate the mean positive value of all the comments on a video"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TzRoOIeIE_1n"
      },
      "source": [
        "def get_comment_polarity(dataset):\r\n",
        "  dataset['polarity'] = dataset['cleaned_comments'].apply(lambda x: TextBlob(x).sentiment.polarity)\r\n",
        "  dataset['subjectivity'] = dataset['cleaned_comments'].apply(lambda x: TextBlob(x).sentiment.subjectivity)\r\n",
        "\r\n",
        "  dataset_grp = dataset[['Query', 'Video ID', 'Video Views', 'Video Likes', 'Video Dislikes', 'Video Comments', 'polarity']].copy()\r\n",
        "\r\n",
        "  dataset_grp = dataset_grp.groupby(['Video ID', 'Query'], as_index=False).mean()\r\n",
        "  dataset_grp = dataset_grp.sort_values(['Query', 'polarity'])\r\n",
        "  return dataset_grp"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IeDLeiQxfSVx"
      },
      "source": [
        "Ranking factor that takes care of the likes, dislikes and count of comments in the video along with the mean positive value of all the comments on a video"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "32H-WYoL8Xs1"
      },
      "source": [
        "def get_rank_fact(vid_data):\r\n",
        "  if vid_data['Video Dislikes'] == 0:\r\n",
        "    rank_fact = ((vid_data['Video Likes']/1) + (vid_data['polarity']*vid_data['Video Comments'])) * vid_data['Video Views']\r\n",
        "  else:\r\n",
        "    rank_fact = ((vid_data['Video Likes']/vid_data['Video Dislikes']) + (vid_data['polarity']*vid_data['Video Comments'])) * vid_data['Video Views']\r\n",
        "  return rank_fact"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YXcaZ121f0eV"
      },
      "source": [
        "Apply the ranking factor to all the videos and sort them"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XWCFfEm2UqLO"
      },
      "source": [
        "def apply_rank_fact(dataset):\r\n",
        "  dataset['Rank Fact'] = dataset.apply(get_rank_fact, axis=1)\r\n",
        "  dataset = dataset.sort_values(['Query', 'Rank Fact'], ascending=False)\r\n",
        "  return dataset"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Gt_H1Hmf-8b"
      },
      "source": [
        "Returns the top videos as per the new ranking factor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SY95AccPhWvY"
      },
      "source": [
        "def get_top_videos(comments_df, dataset, query):\r\n",
        "  df = comments_df.copy() \r\n",
        "  df = df.drop_duplicates(subset=['Video ID', 'Video Title'])\r\n",
        "  dataset = pd.merge(dataset, df[['Video Title', 'Channel', 'Video ID']], on='Video ID', how='inner')\r\n",
        "  op = dataset.loc[dataset['Query'] == query].sort_values('Rank Fact', ascending=False)\r\n",
        "  return op"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L0Fdw8F6R6r7"
      },
      "source": [
        "## Driver Functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-JYJhLsqgOZ9"
      },
      "source": [
        "Set up the YouTube API"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YE40O0iG-Qr6"
      },
      "source": [
        "service = build_service()"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ynEg2Xpz502o"
      },
      "source": [
        "query = \"Java Tutorials\"\r\n",
        "# query = sys.argv[1]"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VrbsMCMHIBv-"
      },
      "source": [
        "yt_videos = search_yt_videos(query, service)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i_BbdyyL-Rwx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0010b61-21bd-4e46-88bb-070fcbb2121a"
      },
      "source": [
        "videos_list_df = get_videos_list_df(yt_videos)\r\n",
        "dataset = gen_dataset(yt_videos, service)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|███████████████████████████████████████████████████████████████| 25/25 [00:12<00:00,  2.03it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RG-DIHuNnDT5"
      },
      "source": [
        "clean_ds = clean_dataset(dataset)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g5RzbPdmnM5m"
      },
      "source": [
        "rank_ds = apply_rank_fact(get_comment_polarity(clean_ds))"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RmK1QF5-nZTY"
      },
      "source": [
        "output = get_top_videos(clean_ds, rank_ds, query)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4b_det4bRuAg"
      },
      "source": [
        "ranked_videos = output[['Channel', 'Video Title', 'Video ID']]"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "-Ifw6MTSKTEu",
        "outputId": "11b09520-aba0-44f8-a0c7-f270fbf3d6e2"
      },
      "source": [
        "ranked_videos.head(10)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
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              "      <th>Channel</th>\n",
              "      <th>Video Title</th>\n",
              "      <th>Video ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Derek Banas</td>\n",
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              "      <td>Programming with Mosh</td>\n",
              "      <td>Java Tutorial for Beginners [2020]</td>\n",
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              "      <th>2</th>\n",
              "      <td>freeCodeCamp.org</td>\n",
              "      <td>Learn Java 8 - Full Tutorial for Beginners</td>\n",
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              "    <tr>\n",
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              "      <td>CodeWithHarry</td>\n",
              "      <td>Java tutorial in hindi</td>\n",
              "      <td>rV_3Lewxx6o</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Telusko</td>\n",
              "      <td>Java Tutorial for Beginners | Full Course</td>\n",
              "      <td>8cm1x4bC610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Alex Lee</td>\n",
              "      <td>Learn Java in 14 Minutes (seriously)</td>\n",
              "      <td>RRubcjpTkks</td>\n",
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              "      <td>Duckademy IT courses</td>\n",
              "      <td>Java tutorial for complete beginners with inte...</td>\n",
              "      <td>JPOzWljLYuU</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>edureka!</td>\n",
              "      <td>Java Full Course | Java Tutorial for Beginners...</td>\n",
              "      <td>hBh_CC5y8-s</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Caleb Curry</td>\n",
              "      <td>Java Programming All-in-One Tutorial Series (6...</td>\n",
              "      <td>r3GGV2TG_vw</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Channel  ...     Video ID\n",
              "0            Derek Banas  ...  WPvGqX-TXP0\n",
              "1  Programming with Mosh  ...  eIrMbAQSU34\n",
              "2       freeCodeCamp.org  ...  grEKMHGYyns\n",
              "3          CodeWithHarry  ...  rV_3Lewxx6o\n",
              "4                Telusko  ...  8cm1x4bC610\n",
              "5               Alex Lee  ...  RRubcjpTkks\n",
              "6        Tamil Hacks 2.0  ...  XLnimroGCIg\n",
              "7   Duckademy IT courses  ...  JPOzWljLYuU\n",
              "8               edureka!  ...  hBh_CC5y8-s\n",
              "9            Caleb Curry  ...  r3GGV2TG_vw\n",
              "\n",
              "[10 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0hD307QpRu59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "44c5fb1b-7d0e-49f2-eca4-0d288c700510"
      },
      "source": [
        "videos_list_df.head(10)"
      ],
      "execution_count": 25,
      "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>Channel</th>\n",
              "      <th>Video Title</th>\n",
              "      <th>Video ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Programming with Mosh</td>\n",
              "      <td>Java Tutorial for Beginners [2020]</td>\n",
              "      <td>eIrMbAQSU34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>edureka!</td>\n",
              "      <td>Java Full Course | Java Tutorial for Beginners...</td>\n",
              "      <td>hBh_CC5y8-s</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Alex Lee</td>\n",
              "      <td>Learn Java in 14 Minutes (seriously)</td>\n",
              "      <td>RRubcjpTkks</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Amigoscode</td>\n",
              "      <td>Java Full Course | Java Tutorial for Beginners...</td>\n",
              "      <td>Qgl81fPcLc8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Telusko</td>\n",
              "      <td>Java Tutorial for Beginners | Full Course</td>\n",
              "      <td>8cm1x4bC610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>freeCodeCamp.org</td>\n",
              "      <td>Learn Java 8 - Full Tutorial for Beginners</td>\n",
              "      <td>grEKMHGYyns</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>blondiebytes</td>\n",
              "      <td>Learn Java in 25 minutes | Java Tutorial for B...</td>\n",
              "      <td>RLi1rOgTRbA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Derek Banas</td>\n",
              "      <td>Java Tutorial</td>\n",
              "      <td>n-xAqcBCws4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Caleb Curry</td>\n",
              "      <td>Java Programming Tutorial 1 - Introduction to ...</td>\n",
              "      <td>2dZiMBwX_5Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>freeCodeCamp.org</td>\n",
              "      <td>Intro to Java Programming - Course for Absolut...</td>\n",
              "      <td>GoXwIVyNvX0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Channel  ...     Video ID\n",
              "0  Programming with Mosh  ...  eIrMbAQSU34\n",
              "1               edureka!  ...  hBh_CC5y8-s\n",
              "2               Alex Lee  ...  RRubcjpTkks\n",
              "3             Amigoscode  ...  Qgl81fPcLc8\n",
              "4                Telusko  ...  8cm1x4bC610\n",
              "5       freeCodeCamp.org  ...  grEKMHGYyns\n",
              "6           blondiebytes  ...  RLi1rOgTRbA\n",
              "7            Derek Banas  ...  n-xAqcBCws4\n",
              "8            Caleb Curry  ...  2dZiMBwX_5Q\n",
              "9       freeCodeCamp.org  ...  GoXwIVyNvX0\n",
              "\n",
              "[10 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    }
  ]
}