YT-Data-Analysis-Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4949d501",
   "metadata": {},
   "outputs": [],
   "source": [
    "from googleapiclient.discovery import build\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cc931c2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "api_key = 'AIzaSyD18M1V1s_l5JTS4VHKQUDO60eUdbeKFys'\n",
    "channel_ids = ['UCBJycsmduvYEL83R_U4JriQ', 'UC-lHJZR3Gqxm24_Vd_AJ5Yw', 'UCtinbF-Q-fVthA0qrFQTgXQ', \n",
    "               'UCIEv3lZ_tNXHzL3ox-_uUGQ', 'UCX6OQ3DkcsbYNE6H8uQQuVA']\n",
    "\n",
    "youtube = build('youtube', 'v3', developerKey=api_key)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad60fd97",
   "metadata": {},
   "source": [
    "## Get Channel Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1ef25f65",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_channel_stats(youtube, channel_ids):\n",
    "    all_data = []\n",
    "    \n",
    "    request = youtube.channels().list(\n",
    "                part='snippet,contentDetails,statistics',\n",
    "                id=','.join(channel_ids))\n",
    "    response = request.execute()\n",
    "    \n",
    "    for i in range(len(response['items'])):\n",
    "        data = dict(channel_name = response['items'][i]['snippet']['title'],\n",
    "                    subscribers = response['items'][i]['statistics']['subscriberCount'],\n",
    "                    views = response['items'][i]['statistics']['viewCount'],\n",
    "                    videos = response['items'][i]['statistics']['videoCount'],\n",
    "                    playlist_id = response['items'][i]['contentDetails']['relatedPlaylists']['uploads'])\n",
    "        all_data.append(data)\n",
    "    \n",
    "    return all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0f2fbdae",
   "metadata": {},
   "outputs": [],
   "source": [
    "channel_stats = get_channel_stats(youtube, channel_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9d16c505",
   "metadata": {},
   "outputs": [],
   "source": [
    "channel_data = pd.DataFrame(channel_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "718e4514",
   "metadata": {},
   "outputs": [
    {
     "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_name</th>\n",
       "      <th>subscribers</th>\n",
       "      <th>views</th>\n",
       "      <th>videos</th>\n",
       "      <th>playlist_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PewDiePie</td>\n",
       "      <td>111000000</td>\n",
       "      <td>28331774499</td>\n",
       "      <td>4480</td>\n",
       "      <td>UU-lHJZR3Gqxm24_Vd_AJ5Yw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CaseyNeistat</td>\n",
       "      <td>12400000</td>\n",
       "      <td>3036126087</td>\n",
       "      <td>1068</td>\n",
       "      <td>UUtinbF-Q-fVthA0qrFQTgXQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Marques Brownlee</td>\n",
       "      <td>15600000</td>\n",
       "      <td>2990024108</td>\n",
       "      <td>1438</td>\n",
       "      <td>UUBJycsmduvYEL83R_U4JriQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gordon Ramsay</td>\n",
       "      <td>19000000</td>\n",
       "      <td>3562637727</td>\n",
       "      <td>1231</td>\n",
       "      <td>UUIEv3lZ_tNXHzL3ox-_uUGQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MrBeast</td>\n",
       "      <td>95700000</td>\n",
       "      <td>15737927763</td>\n",
       "      <td>721</td>\n",
       "      <td>UUX6OQ3DkcsbYNE6H8uQQuVA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       channel_name subscribers        views videos               playlist_id\n",
       "0         PewDiePie   111000000  28331774499   4480  UU-lHJZR3Gqxm24_Vd_AJ5Yw\n",
       "1      CaseyNeistat    12400000   3036126087   1068  UUtinbF-Q-fVthA0qrFQTgXQ\n",
       "2  Marques Brownlee    15600000   2990024108   1438  UUBJycsmduvYEL83R_U4JriQ\n",
       "3     Gordon Ramsay    19000000   3562637727   1231  UUIEv3lZ_tNXHzL3ox-_uUGQ\n",
       "4           MrBeast    95700000  15737927763    721  UUX6OQ3DkcsbYNE6H8uQQuVA"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cb35561f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "channel_name    object\n",
       "subscribers      int64\n",
       "views            int64\n",
       "videos           int64\n",
       "playlist_id     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel_data['subscribers'] = pd.to_numeric(channel_data['subscribers'])\n",
    "channel_data['views'] = pd.to_numeric(channel_data['views'])\n",
    "channel_data['videos'] = pd.to_numeric(channel_data['videos'])\n",
    "channel_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9b58098e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(10,8)})\n",
    "ax = sns.barplot(x='channel_name', y='subscribers', data=channel_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "84f268c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x='channel_name', y='views', data=channel_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3bb7b2cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x='channel_name', y='videos', data=channel_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40df2020",
   "metadata": {},
   "source": [
    "## Get Video Ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "546f96b3",
   "metadata": {},
   "outputs": [
    {
     "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_name</th>\n",
       "      <th>subscribers</th>\n",
       "      <th>views</th>\n",
       "      <th>videos</th>\n",
       "      <th>playlist_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PewDiePie</td>\n",
       "      <td>111000000</td>\n",
       "      <td>28331774499</td>\n",
       "      <td>4480</td>\n",
       "      <td>UU-lHJZR3Gqxm24_Vd_AJ5Yw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CaseyNeistat</td>\n",
       "      <td>12400000</td>\n",
       "      <td>3036126087</td>\n",
       "      <td>1068</td>\n",
       "      <td>UUtinbF-Q-fVthA0qrFQTgXQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Marques Brownlee</td>\n",
       "      <td>15600000</td>\n",
       "      <td>2990024108</td>\n",
       "      <td>1438</td>\n",
       "      <td>UUBJycsmduvYEL83R_U4JriQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gordon Ramsay</td>\n",
       "      <td>19000000</td>\n",
       "      <td>3562637727</td>\n",
       "      <td>1231</td>\n",
       "      <td>UUIEv3lZ_tNXHzL3ox-_uUGQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MrBeast</td>\n",
       "      <td>95700000</td>\n",
       "      <td>15737927763</td>\n",
       "      <td>721</td>\n",
       "      <td>UUX6OQ3DkcsbYNE6H8uQQuVA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       channel_name  subscribers        views  videos  \\\n",
       "0         PewDiePie    111000000  28331774499    4480   \n",
       "1      CaseyNeistat     12400000   3036126087    1068   \n",
       "2  Marques Brownlee     15600000   2990024108    1438   \n",
       "3     Gordon Ramsay     19000000   3562637727    1231   \n",
       "4           MrBeast     95700000  15737927763     721   \n",
       "\n",
       "                playlist_id  \n",
       "0  UU-lHJZR3Gqxm24_Vd_AJ5Yw  \n",
       "1  UUtinbF-Q-fVthA0qrFQTgXQ  \n",
       "2  UUBJycsmduvYEL83R_U4JriQ  \n",
       "3  UUIEv3lZ_tNXHzL3ox-_uUGQ  \n",
       "4  UUX6OQ3DkcsbYNE6H8uQQuVA  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c07a133d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'UU-lHJZR3Gqxm24_Vd_AJ5Yw'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playlist_id = channel_data.loc[channel_data['channel_name']=='PewDiePie', 'playlist_id'].iloc[0]\n",
    "playlist_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e0cd31d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_video_ids(youtube, playlist_id):\n",
    "    request = youtube.playlistItems().list(\n",
    "                part='contentDetails',\n",
    "                playlistId = playlist_id,\n",
    "                maxResults = 50)\n",
    "    response = request.execute()\n",
    "    \n",
    "    video_ids = []\n",
    "    \n",
    "    for i in range(len(response['items'])):\n",
    "        video_ids.append(response['items'][i]['contentDetails']['videoId'])\n",
    "        \n",
    "    next_page_token = response.get('nextPageToken')\n",
    "    more_pages = True\n",
    "    \n",
    "    while more_pages:\n",
    "        if next_page_token is None:\n",
    "            more_pages = False\n",
    "        else:\n",
    "            request = youtube.playlistItems().list(\n",
    "                        part='contentDetails',\n",
    "                        playlistId = playlist_id,\n",
    "                        maxResults = 50,\n",
    "                        pageToken = next_page_token)\n",
    "            response = request.execute()\n",
    "        \n",
    "            for i in range(len(response['items'])):\n",
    "                video_ids.append(response['items'][i]['contentDetails']['videoId'])\n",
    "            \n",
    "            next_page_token = response.get('nextPageToken')\n",
    "    \n",
    "    return video_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ab127b07",
   "metadata": {},
   "outputs": [],
   "source": [
    "video_ids = get_video_ids(youtube, playlist_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e68974d5",
   "metadata": {},
   "outputs": [
    {
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       " 'HyAuduhaJdw',\n",
       " 'su045ytF_z4',\n",
       " 'yMWel-ct3nw',\n",
       " 'bOgApS67xZ0',\n",
       " 'aAluwbON3jY',\n",
       " 'qRuQJ_dnxRs',\n",
       " 'HuIb3XTQVJw',\n",
       " 'E2J4cSQie8A',\n",
       " 'GkPhhR_1hTg',\n",
       " 'yMuEeUyMfUo',\n",
       " '2k-9D4ZMU2s',\n",
       " 'k2oSkpSzYzU',\n",
       " 'IXdpjC7NHWc',\n",
       " 'Nqh8dSzQV-g',\n",
       " '0looVXRbaDk',\n",
       " 'tQeBRMp4S7s',\n",
       " 'RzQYEsL4MtM',\n",
       " 'b_gitOw1TZU',\n",
       " 'cwA3aptsFsk',\n",
       " 'L0tz_8e1-ck',\n",
       " '4E96vYUG7bw',\n",
       " 'x5mlFY7XuOA',\n",
       " 'pNar3Dh9zDk',\n",
       " 'whceteDJKK0',\n",
       " 'By_Cn5ixYLg',\n",
       " 'G5DbYJI-TCA',\n",
       " 'QUyOXOsXGtA',\n",
       " 'Dauk89UVftM',\n",
       " 'xol-zeXb1_c',\n",
       " '_uigJsr9sFA',\n",
       " 'mJ95wsJcBb4',\n",
       " 'hjCsOmDmjEU',\n",
       " 'uFtFMvRZ6oo',\n",
       " 'lGsZLjBjPlw',\n",
       " 'R6Ri6MiETrE',\n",
       " 'tTzmUKEPerA',\n",
       " 'PX5QgITQAwk',\n",
       " 'qCLmXN1aRrw',\n",
       " 'qFHxkZdz4HA',\n",
       " 'GHrolaULX5E',\n",
       " 'wYT1Qq6mo4I',\n",
       " 'RKBplWbWt8g',\n",
       " 'zczDybwtRTw',\n",
       " 'WuMzBTDz9DI',\n",
       " 'byxEledFpEE',\n",
       " 'p8L-LjWHpjw',\n",
       " 'wCkTXKF3LwA',\n",
       " 'avuHvnsRQrs',\n",
       " 'acLyyBCXDkw',\n",
       " '9hPxn8fOcZ4',\n",
       " 'SX5kKwf5swk',\n",
       " '3CZYMfE5vLM',\n",
       " '2kpR0BdouNE',\n",
       " 'ygGb3N_Nko4',\n",
       " 'WURdSJTTVwk',\n",
       " 'BrMMYtkiEK0',\n",
       " 'OoRlp3--ckU',\n",
       " 'qH9gc-PfoVo',\n",
       " 'HVl0zT0fXes',\n",
       " '5nZxIXyjyEo',\n",
       " 'HKPbLSQDGd4',\n",
       " 'FZGakQjkyKI',\n",
       " 'oJ2IlT9-t00',\n",
       " 'bkBaHes7AVM',\n",
       " 'FFGViT9Dji8',\n",
       " 'M-8urAhcyxs',\n",
       " 'MXiopMXJADg',\n",
       " 'd38MrVnBFXg',\n",
       " 'LoRoODZcRqc',\n",
       " 'HbZjUyFNlXg',\n",
       " 'xyXGWB0NVDc',\n",
       " 'HBDNbpWSUx8',\n",
       " '3DsKvEL4HVk',\n",
       " 'w4GReha-6DI',\n",
       " 'QAPUK5gqCQ8',\n",
       " 'aMWNOaFGsXo',\n",
       " 'HeZtOmiJGOQ',\n",
       " 'eoUVBL1Ou-4',\n",
       " '0S6lczYYyuk',\n",
       " 'MMSzuqd9f68',\n",
       " 'ggsqvFsgoKk',\n",
       " 'QstOY5aDfQE',\n",
       " 'FfOMENqRVo4',\n",
       " '9MXIiQuvT0A',\n",
       " '44xhGvcdl90',\n",
       " 'WimOwPwU_vI',\n",
       " 'r6-ku5s3JS0',\n",
       " '0Pl_m4IB9Mc',\n",
       " 'H1ZhGrd2ofU',\n",
       " 'T4bhTjB3T-E',\n",
       " 'wILTPumur0E',\n",
       " '8PLgOaVXmGU',\n",
       " 'RVFFK-T39LE',\n",
       " '6Dh-RL__uN4',\n",
       " 'Mp2xMrmg8-A',\n",
       " 'q0G-qLlIans',\n",
       " 'hokkA77ib3c',\n",
       " 'Eh8kTn065Qs',\n",
       " '9-NijEwNYnA',\n",
       " 'vJTXWxX6qZo',\n",
       " 'EXfTZzT1ilw',\n",
       " 'B7q4YsqsGyE',\n",
       " '6Qy1HmJ5V1I',\n",
       " 'cdKLib5m4ZQ',\n",
       " 'Bp5zAGVP0K0',\n",
       " 'zd8Yg7wRX7c',\n",
       " 'x4aD_cJAoJE',\n",
       " 'Ud3N7BFsDG0',\n",
       " 'gvJ_vJ0NUts',\n",
       " '0ERp2aVIHTA',\n",
       " 'sesCaT0VrO4',\n",
       " 'BzvJADDVJ_k',\n",
       " 'dJY8iT341F4',\n",
       " 'VVwKZfS3BOM',\n",
       " 'CBwJF75wZnw',\n",
       " '7a-n8hELH24',\n",
       " 'iiP4IUf8L2A',\n",
       " 'KfrIbN1mG0M',\n",
       " '7qnKCXqzYXg',\n",
       " 'KtzBsKbPkHA',\n",
       " 'ohdWzsXSv8w',\n",
       " 'SDX9az1Au5o',\n",
       " 'uxd3Grp-UqE',\n",
       " '08aBvA9U8w4',\n",
       " '1n7cV5veMIo',\n",
       " 'XkquWd_aDvY',\n",
       " 'f_XW6qZR4lM',\n",
       " 'rv8UnA_1Os8',\n",
       " '9F-WPOCOLpI',\n",
       " 'jOdZUhGyhIU',\n",
       " 'K5owJ0Wg7PI',\n",
       " 'BlCXgpozrZg',\n",
       " '2UsBZ9Hmtk0',\n",
       " 'IT3UodbpE5E',\n",
       " 'wutdQ_kzNn0',\n",
       " 'iLEOBHpRBYo',\n",
       " 'AY3OX809dzM',\n",
       " 'GXuuVuKIEc4',\n",
       " '70ZTpF3StWQ',\n",
       " 'n17YESArkPE',\n",
       " 'rnWBwnnvvOg',\n",
       " 'WesTxFXDJV4',\n",
       " 'GaRTSHAM0as',\n",
       " '8n5XNYSLSag',\n",
       " 'MT8I0Or9PUs',\n",
       " '_tOHd5JTU6U',\n",
       " 'rPRf1HcFXCo',\n",
       " ...]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video_ids"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c6fd74b",
   "metadata": {},
   "source": [
    "## Get Video Details\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "46f5718f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_video_details(youtube, video_ids):\n",
    "    all_video_stats = []\n",
    "    \n",
    "    for i in range(0, len(video_ids), 50):\n",
    "        request = youtube.videos().list(\n",
    "                    part = 'snippet, statistics',\n",
    "                    id = ','.join(video_ids[i:i+50]))\n",
    "        response = request.execute()\n",
    "        \n",
    "        for video in response['items']:\n",
    "            video_stats = dict(title = video['snippet']['title'],\n",
    "                               publishing_date = video['snippet']['publishedAt'],\n",
    "                               views = video['statistics'].get('viewCount'),\n",
    "                               likes = video['statistics'].get('likeCount'),\n",
    "                               comments = video['statistics'].get('commentCount'))\n",
    "            all_video_stats.append(video_stats)\n",
    "    \n",
    "    return all_video_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "7ae52f70",
   "metadata": {},
   "outputs": [],
   "source": [
    "video_details = get_video_details(youtube, video_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "5bf69539",
   "metadata": {},
   "outputs": [],
   "source": [
    "video_data = pd.DataFrame(video_details)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "3118472a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>This show is PEAK Entertainment!</td>\n",
       "      <td>2022-05-23</td>\n",
       "      <td>2704141.0</td>\n",
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       "      <td>7833.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>First time driving in Japan...</td>\n",
       "      <td>2022-05-21</td>\n",
       "      <td>4342216.0</td>\n",
       "      <td>369837.0</td>\n",
       "      <td>22539.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cant believe markiplier did this to me</td>\n",
       "      <td>2022-05-18</td>\n",
       "      <td>3544362.0</td>\n",
       "      <td>282116.0</td>\n",
       "      <td>10543.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>whats next?</td>\n",
       "      <td>2022-05-15</td>\n",
       "      <td>5798739.0</td>\n",
       "      <td>540515.0</td>\n",
       "      <td>26310.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>It finally happened!</td>\n",
       "      <td>2022-05-09</td>\n",
       "      <td>7481188.0</td>\n",
       "      <td>795065.0</td>\n",
       "      <td>45162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>4475</th>\n",
       "      <td>Dual Minecraft Lets Play! Episode [003] - Expl...</td>\n",
       "      <td>2010-12-19</td>\n",
       "      <td>817002.0</td>\n",
       "      <td>11484.0</td>\n",
       "      <td>1665.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4476</th>\n",
       "      <td>Dual Minecraft Lets Play! Episode [002] - New ...</td>\n",
       "      <td>2010-12-19</td>\n",
       "      <td>1909826.0</td>\n",
       "      <td>32708.0</td>\n",
       "      <td>4669.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4477</th>\n",
       "      <td>Call of Duty: Black Ops: Wager Match: Gun Game</td>\n",
       "      <td>2010-12-16</td>\n",
       "      <td>4175954.0</td>\n",
       "      <td>221530.0</td>\n",
       "      <td>47637.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4478</th>\n",
       "      <td>Blacklight Tango Down: Team Deathmatch 38-4 (P...</td>\n",
       "      <td>2010-12-10</td>\n",
       "      <td>1842891.0</td>\n",
       "      <td>42265.0</td>\n",
       "      <td>5218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4479</th>\n",
       "      <td>Minecraft Multiplayer Fun</td>\n",
       "      <td>2010-10-03</td>\n",
       "      <td>21030249.0</td>\n",
       "      <td>895866.0</td>\n",
       "      <td>162522.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4480 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  title publishing_date  \\\n",
       "0                      This show is PEAK Entertainment!      2022-05-23   \n",
       "1                        First time driving in Japan...      2022-05-21   \n",
       "2                cant believe markiplier did this to me      2022-05-18   \n",
       "3                                           whats next?      2022-05-15   \n",
       "4                                  It finally happened!      2022-05-09   \n",
       "...                                                 ...             ...   \n",
       "4475  Dual Minecraft Lets Play! Episode [003] - Expl...      2010-12-19   \n",
       "4476  Dual Minecraft Lets Play! Episode [002] - New ...      2010-12-19   \n",
       "4477     Call of Duty: Black Ops: Wager Match: Gun Game      2010-12-16   \n",
       "4478  Blacklight Tango Down: Team Deathmatch 38-4 (P...      2010-12-10   \n",
       "4479                          Minecraft Multiplayer Fun      2010-10-03   \n",
       "\n",
       "           views     likes  comments  \n",
       "0      2704141.0  198842.0    7833.0  \n",
       "1      4342216.0  369837.0   22539.0  \n",
       "2      3544362.0  282116.0   10543.0  \n",
       "3      5798739.0  540515.0   26310.0  \n",
       "4      7481188.0  795065.0   45162.0  \n",
       "...          ...       ...       ...  \n",
       "4475    817002.0   11484.0    1665.0  \n",
       "4476   1909826.0   32708.0    4669.0  \n",
       "4477   4175954.0  221530.0   47637.0  \n",
       "4478   1842891.0   42265.0    5218.0  \n",
       "4479  21030249.0  895866.0  162522.0  \n",
       "\n",
       "[4480 rows x 5 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video_data['publishing_date'] = pd.to_datetime(video_data['publishing_date']).dt.date\n",
    "video_data['views'] = pd.to_numeric(video_data['views'])\n",
    "video_data['likes'] = pd.to_numeric(video_data['likes'])\n",
    "video_data['comments'] = pd.to_numeric(video_data['comments'])\n",
    "video_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "184338b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>publishing_date</th>\n",
       "      <th>views</th>\n",
       "      <th>likes</th>\n",
       "      <th>comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>bitch lasagna</td>\n",
       "      <td>2018-10-05</td>\n",
       "      <td>304686203.0</td>\n",
       "      <td>12626231.0</td>\n",
       "      <td>1243750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>Congratulations</td>\n",
       "      <td>2019-03-31</td>\n",
       "      <td>223020014.0</td>\n",
       "      <td>10330901.0</td>\n",
       "      <td>989563.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1866</th>\n",
       "      <td>LEVEL 7 | I’M NOT CRAZY  (OUTLAST IRL GAMEPLAY)</td>\n",
       "      <td>2016-02-10</td>\n",
       "      <td>131404466.0</td>\n",
       "      <td>2897084.0</td>\n",
       "      <td>153164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3333</th>\n",
       "      <td>A Funny Montage</td>\n",
       "      <td>2013-06-04</td>\n",
       "      <td>92725755.0</td>\n",
       "      <td>1888117.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1496</th>\n",
       "      <td>THE RUBY PLAYBUTTON / YouTube 50 Mil Sub Rewar...</td>\n",
       "      <td>2016-12-18</td>\n",
       "      <td>89888147.0</td>\n",
       "      <td>6032835.0</td>\n",
       "      <td>623400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>YouTube Rewind 2018, but it's actually good</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>85107312.0</td>\n",
       "      <td>9588459.0</td>\n",
       "      <td>660817.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2945</th>\n",
       "      <td>FUNNY MONTAGE.. #2</td>\n",
       "      <td>2014-04-05</td>\n",
       "      <td>73514423.0</td>\n",
       "      <td>1302101.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3197</th>\n",
       "      <td>Jabba the Hutt (PewDiePie Song) by Schmoyoho</td>\n",
       "      <td>2013-09-14</td>\n",
       "      <td>71525356.0</td>\n",
       "      <td>1730843.0</td>\n",
       "      <td>207219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>Unboxing 100 MIL YouTube AWARD!!</td>\n",
       "      <td>2019-09-10</td>\n",
       "      <td>70671083.0</td>\n",
       "      <td>5068073.0</td>\n",
       "      <td>336534.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>Minecraft Part 1</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>52078194.0</td>\n",
       "      <td>2579398.0</td>\n",
       "      <td>153024.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  title publishing_date  \\\n",
       "946                                       bitch lasagna      2018-10-05   \n",
       "790                                     Congratulations      2019-03-31   \n",
       "1866    LEVEL 7 | I’M NOT CRAZY  (OUTLAST IRL GAMEPLAY)      2016-02-10   \n",
       "3333                                    A Funny Montage      2013-06-04   \n",
       "1496  THE RUBY PLAYBUTTON / YouTube 50 Mil Sub Rewar...      2016-12-18   \n",
       "877         YouTube Rewind 2018, but it's actually good      2018-12-27   \n",
       "2945                                 FUNNY MONTAGE.. #2      2014-04-05   \n",
       "3197       Jabba the Hutt (PewDiePie Song) by Schmoyoho      2013-09-14   \n",
       "630                    Unboxing 100 MIL YouTube AWARD!!      2019-09-10   \n",
       "709                                    Minecraft Part 1      2019-06-21   \n",
       "\n",
       "            views       likes   comments  \n",
       "946   304686203.0  12626231.0  1243750.0  \n",
       "790   223020014.0  10330901.0   989563.0  \n",
       "1866  131404466.0   2897084.0   153164.0  \n",
       "3333   92725755.0   1888117.0        NaN  \n",
       "1496   89888147.0   6032835.0   623400.0  \n",
       "877    85107312.0   9588459.0   660817.0  \n",
       "2945   73514423.0   1302101.0        NaN  \n",
       "3197   71525356.0   1730843.0   207219.0  \n",
       "630    70671083.0   5068073.0   336534.0  \n",
       "709    52078194.0   2579398.0   153024.0  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10_videos = video_data.sort_values(by='views', ascending=False).head(10)\n",
    "top10_videos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "16c448a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = sns.barplot(x='views', y='title', data=top10_videos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "069f206a",
   "metadata": {},
   "outputs": [
    {
     "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>title</th>\n",
       "      <th>publishing_date</th>\n",
       "      <th>views</th>\n",
       "      <th>likes</th>\n",
       "      <th>comments</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>This show is PEAK Entertainment!</td>\n",
       "      <td>2022-05-23</td>\n",
       "      <td>2704141.0</td>\n",
       "      <td>198842.0</td>\n",
       "      <td>7833.0</td>\n",
       "      <td>May</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>First time driving in Japan...</td>\n",
       "      <td>2022-05-21</td>\n",
       "      <td>4342216.0</td>\n",
       "      <td>369837.0</td>\n",
       "      <td>22539.0</td>\n",
       "      <td>May</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cant believe markiplier did this to me</td>\n",
       "      <td>2022-05-18</td>\n",
       "      <td>3544362.0</td>\n",
       "      <td>282116.0</td>\n",
       "      <td>10543.0</td>\n",
       "      <td>May</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>whats next?</td>\n",
       "      <td>2022-05-15</td>\n",
       "      <td>5798739.0</td>\n",
       "      <td>540515.0</td>\n",
       "      <td>26310.0</td>\n",
       "      <td>May</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>It finally happened!</td>\n",
       "      <td>2022-05-09</td>\n",
       "      <td>7481188.0</td>\n",
       "      <td>795065.0</td>\n",
       "      <td>45162.0</td>\n",
       "      <td>May</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4475</th>\n",
       "      <td>Dual Minecraft Lets Play! Episode [003] - Expl...</td>\n",
       "      <td>2010-12-19</td>\n",
       "      <td>817002.0</td>\n",
       "      <td>11484.0</td>\n",
       "      <td>1665.0</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4476</th>\n",
       "      <td>Dual Minecraft Lets Play! Episode [002] - New ...</td>\n",
       "      <td>2010-12-19</td>\n",
       "      <td>1909826.0</td>\n",
       "      <td>32708.0</td>\n",
       "      <td>4669.0</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4477</th>\n",
       "      <td>Call of Duty: Black Ops: Wager Match: Gun Game</td>\n",
       "      <td>2010-12-16</td>\n",
       "      <td>4175954.0</td>\n",
       "      <td>221530.0</td>\n",
       "      <td>47637.0</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4478</th>\n",
       "      <td>Blacklight Tango Down: Team Deathmatch 38-4 (P...</td>\n",
       "      <td>2010-12-10</td>\n",
       "      <td>1842891.0</td>\n",
       "      <td>42265.0</td>\n",
       "      <td>5218.0</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4479</th>\n",
       "      <td>Minecraft Multiplayer Fun</td>\n",
       "      <td>2010-10-03</td>\n",
       "      <td>21030249.0</td>\n",
       "      <td>895866.0</td>\n",
       "      <td>162522.0</td>\n",
       "      <td>Oct</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4480 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  title publishing_date  \\\n",
       "0                      This show is PEAK Entertainment!      2022-05-23   \n",
       "1                        First time driving in Japan...      2022-05-21   \n",
       "2                cant believe markiplier did this to me      2022-05-18   \n",
       "3                                           whats next?      2022-05-15   \n",
       "4                                  It finally happened!      2022-05-09   \n",
       "...                                                 ...             ...   \n",
       "4475  Dual Minecraft Lets Play! Episode [003] - Expl...      2010-12-19   \n",
       "4476  Dual Minecraft Lets Play! Episode [002] - New ...      2010-12-19   \n",
       "4477     Call of Duty: Black Ops: Wager Match: Gun Game      2010-12-16   \n",
       "4478  Blacklight Tango Down: Team Deathmatch 38-4 (P...      2010-12-10   \n",
       "4479                          Minecraft Multiplayer Fun      2010-10-03   \n",
       "\n",
       "           views     likes  comments month  \n",
       "0      2704141.0  198842.0    7833.0   May  \n",
       "1      4342216.0  369837.0   22539.0   May  \n",
       "2      3544362.0  282116.0   10543.0   May  \n",
       "3      5798739.0  540515.0   26310.0   May  \n",
       "4      7481188.0  795065.0   45162.0   May  \n",
       "...          ...       ...       ...   ...  \n",
       "4475    817002.0   11484.0    1665.0   Dec  \n",
       "4476   1909826.0   32708.0    4669.0   Dec  \n",
       "4477   4175954.0  221530.0   47637.0   Dec  \n",
       "4478   1842891.0   42265.0    5218.0   Dec  \n",
       "4479  21030249.0  895866.0  162522.0   Oct  \n",
       "\n",
       "[4480 rows x 6 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video_data['month'] = pd.to_datetime(video_data['publishing_date']).dt.strftime('%b')\n",
    "video_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "cd0ec0ab",
   "metadata": {},
   "outputs": [
    {
     "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>month</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apr</td>\n",
       "      <td>343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aug</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dec</td>\n",
       "      <td>383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Feb</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jan</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jul</td>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jun</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mar</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>May</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nov</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Oct</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Sep</td>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month  size\n",
       "0    Apr   343\n",
       "1    Aug   370\n",
       "2    Dec   383\n",
       "3    Feb   297\n",
       "4    Jan   355\n",
       "5    Jul   384\n",
       "6    Jun   354\n",
       "7    Mar   404\n",
       "8    May   373\n",
       "9    Nov   392\n",
       "10   Oct   410\n",
       "11   Sep   415"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "videos_per_month = video_data.groupby('month', as_index=False).size()\n",
    "videos_per_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "5d94cbd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "sort_order = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "082234fc",
   "metadata": {},
   "outputs": [
    {
     "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>month</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jan</th>\n",
       "      <td>Jan</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feb</th>\n",
       "      <td>Feb</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mar</th>\n",
       "      <td>Mar</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr</th>\n",
       "      <td>Apr</td>\n",
       "      <td>343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>May</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jun</th>\n",
       "      <td>Jun</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jul</th>\n",
       "      <td>Jul</td>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aug</th>\n",
       "      <td>Aug</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sep</th>\n",
       "      <td>Sep</td>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oct</th>\n",
       "      <td>Oct</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nov</th>\n",
       "      <td>Nov</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dec</th>\n",
       "      <td>Dec</td>\n",
       "      <td>383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      month  size\n",
       "month            \n",
       "Jan     Jan   355\n",
       "Feb     Feb   297\n",
       "Mar     Mar   404\n",
       "Apr     Apr   343\n",
       "May     May   373\n",
       "Jun     Jun   354\n",
       "Jul     Jul   384\n",
       "Aug     Aug   370\n",
       "Sep     Sep   415\n",
       "Oct     Oct   410\n",
       "Nov     Nov   392\n",
       "Dec     Dec   383"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "videos_per_month.index = pd.CategoricalIndex(videos_per_month['month'], categories=sort_order, ordered=True)\n",
    "videos_per_month = videos_per_month.sort_index()\n",
    "videos_per_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "a64d4a3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax2 = sns.barplot(x='month', y='size', data=videos_per_month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "ee901c4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "video_data.to_csv('video_details(PewDiePie).csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}