Code_for_project.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dad72839-fa54-4f0f-b3b6-088a1c6b3191",
   "metadata": {},
   "outputs": [],
   "source": [
    "from googleapiclient.discovery import build\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "api_key = '' #add google api key here\n",
    "channel_ids = [\n",
    "    'UCWCHFXZIRSWT7Y28yj1T0AA',\n",
    "    'UCbQofpGfudo6nVDG8DyMFtg',\n",
    "    'UCrk2rBFB41auTTAC_3eZf9Q',\n",
    "    'UCGwuxdEeCf0TIA2RbPOj-8g',\n",
    "    'UCnBT5HobLD5_iyHsZNL85Ng',\n",
    "    'UCXmZkD_OFXayyq8Pr3c13ow',\n",
    "    'UCfTfuTTALrsxUWjisrdOWtA',\n",
    "    'UCTPQZm46dXYJ6VcQcd7VahA'\n",
    "]\n",
    "youtube = build('youtube', 'v3', developerKey=api_key)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "19958a01-84b2-4ee2-a7be-40e21769eb00",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_channel_stats(youtube, channel_ids):\n",
    "    all_data = []\n",
    "    request = youtube.channels().list(\n",
    "        part='snippet,contentDetails,statistics',\n",
    "        id=','.join(channel_ids)\n",
    "    )\n",
    "    response = request.execute()\n",
    "    for i in range(len(response['items'])):\n",
    "        data = dict(\n",
    "            Channel_name=response['items'][i]['snippet']['title'],\n",
    "            Subscribers=response['items'][i]['statistics']['subscriberCount'],\n",
    "            Views=response['items'][i]['statistics']['viewCount'],\n",
    "            Total_videos=response['items'][i]['statistics']['videoCount'],\n",
    "            playlist_id=response['items'][i]['contentDetails']['relatedPlaylists']['uploads']\n",
    "        )\n",
    "        all_data.append(data)\n",
    "    return all_data\n",
    "\n",
    "channel_statistics = get_channel_stats(youtube, channel_ids)\n",
    "channel_data = pd.DataFrame(channel_statistics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4ba8322c-1040-4ef2-ac23-568ae4481345",
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    {
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       "<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>Total_videos</th>\n",
       "      <th>playlist_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>University of Toronto</td>\n",
       "      <td>57600</td>\n",
       "      <td>9625783</td>\n",
       "      <td>1198</td>\n",
       "      <td>UUXmZkD_OFXayyq8Pr3c13ow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stanford Graduate School of Business</td>\n",
       "      <td>2290000</td>\n",
       "      <td>156929616</td>\n",
       "      <td>3427</td>\n",
       "      <td>UUGwuxdEeCf0TIA2RbPOj-8g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UC Berkeley Inspires</td>\n",
       "      <td>5230</td>\n",
       "      <td>1137556</td>\n",
       "      <td>332</td>\n",
       "      <td>UUnBT5HobLD5_iyHsZNL85Ng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UW Sustainability</td>\n",
       "      <td>255</td>\n",
       "      <td>25471</td>\n",
       "      <td>84</td>\n",
       "      <td>UUrk2rBFB41auTTAC_3eZf9Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>University of Adelaide</td>\n",
       "      <td>17800</td>\n",
       "      <td>11355137</td>\n",
       "      <td>1117</td>\n",
       "      <td>UUTPQZm46dXYJ6VcQcd7VahA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cornell University</td>\n",
       "      <td>84700</td>\n",
       "      <td>23076794</td>\n",
       "      <td>949</td>\n",
       "      <td>UUfTfuTTALrsxUWjisrdOWtA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>The University of Melbourne</td>\n",
       "      <td>119000</td>\n",
       "      <td>26189906</td>\n",
       "      <td>2560</td>\n",
       "      <td>UUbQofpGfudo6nVDG8DyMFtg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Social Responsibility at The University of Man...</td>\n",
       "      <td>273</td>\n",
       "      <td>95447</td>\n",
       "      <td>341</td>\n",
       "      <td>UUWCHFXZIRSWT7Y28yj1T0AA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Channel_name Subscribers      Views  \\\n",
       "0                              University of Toronto       57600    9625783   \n",
       "1               Stanford Graduate School of Business     2290000  156929616   \n",
       "2                               UC Berkeley Inspires        5230    1137556   \n",
       "3                                  UW Sustainability         255      25471   \n",
       "4                             University of Adelaide       17800   11355137   \n",
       "5                                 Cornell University       84700   23076794   \n",
       "6                        The University of Melbourne      119000   26189906   \n",
       "7  Social Responsibility at The University of Man...         273      95447   \n",
       "\n",
       "  Total_videos               playlist_id  \n",
       "0         1198  UUXmZkD_OFXayyq8Pr3c13ow  \n",
       "1         3427  UUGwuxdEeCf0TIA2RbPOj-8g  \n",
       "2          332  UUnBT5HobLD5_iyHsZNL85Ng  \n",
       "3           84  UUrk2rBFB41auTTAC_3eZf9Q  \n",
       "4         1117  UUTPQZm46dXYJ6VcQcd7VahA  \n",
       "5          949  UUfTfuTTALrsxUWjisrdOWtA  \n",
       "6         2560  UUbQofpGfudo6nVDG8DyMFtg  \n",
       "7          341  UUWCHFXZIRSWT7Y28yj1T0AA  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel_data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "654a27ae-a39f-4e06-91d7-1a273fee98da",
   "metadata": {},
   "outputs": [],
   "source": [
    "keywords = [\n",
    "    'Environment', 'Eco-friendly', 'Green', 'Climate Change', \n",
    "    'Renewable Energy', 'Carbon Footprint', 'Recycling', 'Biodiversity', \n",
    "    'Conservation', 'Solar Energy', 'Wind Energy', 'Sustainable Development', \n",
    "    'Clean Energy', 'Energy Efficiency', 'Water Conservation', 'Zero Waste', \n",
    "    'Organic', 'Ecosystem', 'Pollution', 'Sustainable Agriculture', \n",
    "    'Carbon Neutral', 'Global Warming', 'Greenhouse Gas', 'Sustainable Practices', \n",
    "    'Electric Vehicles', 'Composting', 'Environmental Protection', \n",
    "    'Environmental Impact', 'Deforestation', 'Ecology', 'Sustainable Living', \n",
    "    'Renewable Resources', 'Natural Resources', 'Air Quality', 'Sustainable Design', \n",
    "    'Environmental Awareness', 'Climate Action', 'Eco-Conscious', 'Sustainable Cities', \n",
    "    'Waste Reduction', 'Green Technology', 'Sustainable Materials', 'Green Energy', \n",
    "    'Circular Economy', 'Low Carbon', 'Green Building', 'Wildlife Conservation', \n",
    "    'Marine Conservation', 'Carbon Emissions', 'Sustainable Transport', \n",
    "    'Sustainable Tourism', 'Green Infrastructure', 'Green Products', \n",
    "    'Environmental Justice', 'Clean Water', 'Habitat Restoration', 'Carbon Offset', \n",
    "    'Eco-Tourism', 'Sustainable Fashion', 'Energy Conservation', 'Green Manufacturing', \n",
    "    'Organic Farming', 'Sustainable Supply Chain', 'Climate Resilience', 'Green Policies', \n",
    "    'Sustainable Development Goals', 'Eco-Innovation', 'Greenhouse Effect', \n",
    "    'Environmental Sustainability', 'Eco-Design', 'Renewable Technologies', \n",
    "    'Sustainable Energy', 'Environmental Education', 'Sustainable Architecture', \n",
    "    'Green Economy', 'Eco-Labels', 'Environmental Stewardship', 'Climate Mitigation', \n",
    "    'Eco-Friendly Products', 'Green Living', 'Environmental Health', 'Sustainable Fisheries', \n",
    "    'Sustainable Business', 'Environmental Ethics', 'Climate Adaptation', 'Eco-Friendly Packaging', \n",
    "    'Green Chemistry', 'Low Impact', 'Biodegradable', 'Eco-System Services', \n",
    "    'Environmental Policy', 'Green Investment', 'Nature Conservation', 'Eco-Management', \n",
    "    'Eco-Systems', 'Carbon Sequestration', 'Sustainable Innovation', 'Environmental Science', \n",
    "    'Green Jobs', 'Environmental Footprint', 'Resource Efficiency', 'Eco-Friendly Cleaning', \n",
    "    'Eco-Conservation', 'Green Procurement', 'Sustainable Mobility'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cdb05ccc-62ee-40e2-ae51-c44a4cb7d124",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Published_date</th>\n",
       "      <th>Views</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Can we revive our extinct ecosystems with musi...</td>\n",
       "      <td>2023-02-16 02:04:04+00:00</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Dedication or deception? How greenwashing impa...</td>\n",
       "      <td>2023-02-16 01:56:48+00:00</td>\n",
       "      <td>87</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Are agricultural subsidies harming our environ...</td>\n",
       "      <td>2022-10-20 22:58:31+00:00</td>\n",
       "      <td>285</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>Industrial Greening - Research Tuesdays Februa...</td>\n",
       "      <td>2021-02-09 14:38:26+00:00</td>\n",
       "      <td>789</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>A Twin Study - The role of genetics and enviro...</td>\n",
       "      <td>2020-04-03 06:58:00+00:00</td>\n",
       "      <td>3189</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
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      "text/plain": [
       "                                                 Title  \\\n",
       "116  Can we revive our extinct ecosystems with musi...   \n",
       "119  Dedication or deception? How greenwashing impa...   \n",
       "126  Are agricultural subsidies harming our environ...   \n",
       "236  Industrial Greening - Research Tuesdays Februa...   \n",
       "281  A Twin Study - The role of genetics and enviro...   \n",
       "\n",
       "               Published_date Views Likes Comments  \n",
       "116 2023-02-16 02:04:04+00:00   128     0        0  \n",
       "119 2023-02-16 01:56:48+00:00    87     1        0  \n",
       "126 2022-10-20 22:58:31+00:00   285     8        0  \n",
       "236 2021-02-09 14:38:26+00:00   789    16        0  \n",
       "281 2020-04-03 06:58:00+00:00  3189    27        4  "
      ]
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     "execution_count": 6,
     "metadata": {},
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    }
   ],
   "source": [
    "playlist_id = channel_data.loc[channel_data['Channel_name']=='University of Adelaide', 'playlist_id'].iloc[0]\n",
    "def get_video_ids(youtube, playlist_id):\n",
    "\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",
    "    return video_ids\n",
    "video_ids = get_video_ids(youtube, playlist_id)\n",
    "#video_ids\n",
    "\n",
    "def get_videos_details(youtube, video_ids):\n",
    "    all_video_stats = []\n",
    "    \n",
    "    for i in range(2, 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",
    "                           Published_date = video['snippet']['publishedAt'],\n",
    "                           Views=video['statistics'].get('viewCount', 0),\n",
    "                           Likes=video['statistics'].get('likeCount', 0),\n",
    "                           Comments=video['statistics'].get('commentCount', 0)\n",
    "                           )\n",
    "\n",
    "            all_video_stats.append(video_stats)                    \n",
    "\n",
    "    return all_video_stats\n",
    "video_details = get_videos_details(youtube, video_ids)\n",
    "df = pd.DataFrame(video_details)\n",
    "\n",
    "#Apply filters\n",
    "\n",
    "df['Published_date'] = pd.to_datetime(df['Published_date'])\n",
    "# Filter videos published after 2020\n",
    "df = df[(df['Published_date'] >= '2020-01-01') & (df['Published_date'] <= '2024-06-30')]\n",
    "\n",
    "\n",
    "pattern = '|'.join(keywords)\n",
    "filtered_Adelaide = df[df['Title'].str.contains(pattern, case=False, na=False)]\n",
    "filtered_Adelaide.to_csv('University of Adelaide.csv', index=False)\n",
    "filtered_Adelaide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9cbc0b56-d70c-45b8-8e48-2e5a9ec0f3ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Likes: 52\n",
      "Total Comments: 4\n",
      "Total Views: 4478\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/3925256077.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_Adelaide['Views'] = filtered_Adelaide['Views'].astype(int)\n",
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/3925256077.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_Adelaide['Likes'] = filtered_Adelaide['Likes'].astype(int)\n",
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/3925256077.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_Adelaide['Comments'] = filtered_Adelaide['Comments'].astype(int)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_Adelaide['Views'] = filtered_Adelaide['Views'].astype(int)\n",
    "filtered_Adelaide['Likes'] = filtered_Adelaide['Likes'].astype(int)\n",
    "filtered_Adelaide['Comments'] = filtered_Adelaide['Comments'].astype(int)\n",
    "total_likes_Adelaide = filtered_Adelaide['Likes'].sum()\n",
    "total_comments_Adelaide = filtered_Adelaide['Comments'].sum()\n",
    "total_views_Adelaide= filtered_Adelaide['Views'].sum()\n",
    "\n",
    "print(\"Total Likes:\", total_likes_Adelaide)\n",
    "print(\"Total Comments:\", total_comments_Adelaide)\n",
    "print(\"Total Views:\", total_views_Adelaide)\n",
    "Adelaide_total = len(filtered_Adelaide)\n",
    "Adelaide_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6d26e5e0-efa4-4a6b-9692-8aed5940528d",
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   "outputs": [
    {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Green agriculture gets a boost from New York’s...</td>\n",
       "      <td>2024-01-16 14:00:06+00:00</td>\n",
       "      <td>2411</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Cornell expert discusses poor air quality and ...</td>\n",
       "      <td>2023-06-07 21:19:10+00:00</td>\n",
       "      <td>1467</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>PolyForm celebrates mission, spirit of Human E...</td>\n",
       "      <td>2021-05-19 17:35:03+00:00</td>\n",
       "      <td>2190</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Climate change reduced farming productivity by...</td>\n",
       "      <td>2021-04-01 15:57:34+00:00</td>\n",
       "      <td>5677</td>\n",
       "      <td>58</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>Cornell Leadership Sessions: Back to green, ca...</td>\n",
       "      <td>2020-09-17 17:36:44+00:00</td>\n",
       "      <td>2570</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>Minorities consider poverty an environmental i...</td>\n",
       "      <td>2020-03-12 20:05:33+00:00</td>\n",
       "      <td>699</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>Cornell at AAAS: Predicting biological invasio...</td>\n",
       "      <td>2020-02-12 15:25:07+00:00</td>\n",
       "      <td>388</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Title  \\\n",
       "29   Green agriculture gets a boost from New York’s...   \n",
       "52   Cornell expert discusses poor air quality and ...   \n",
       "182  PolyForm celebrates mission, spirit of Human E...   \n",
       "191  Climate change reduced farming productivity by...   \n",
       "225  Cornell Leadership Sessions: Back to green, ca...   \n",
       "274  Minorities consider poverty an environmental i...   \n",
       "282  Cornell at AAAS: Predicting biological invasio...   \n",
       "\n",
       "               Published_date Views Likes Comments  \n",
       "29  2024-01-16 14:00:06+00:00  2411    29        0  \n",
       "52  2023-06-07 21:19:10+00:00  1467    56        3  \n",
       "182 2021-05-19 17:35:03+00:00  2190    33        1  \n",
       "191 2021-04-01 15:57:34+00:00  5677    58       36  \n",
       "225 2020-09-17 17:36:44+00:00  2570    51        0  \n",
       "274 2020-03-12 20:05:33+00:00   699    24        4  \n",
       "282 2020-02-12 15:25:07+00:00   388    10        0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playlist_id = channel_data.loc[channel_data['Channel_name']=='Cornell University', 'playlist_id'].iloc[0]\n",
    "def get_video_ids(youtube, playlist_id):\n",
    "\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",
    "    return video_ids\n",
    "video_ids = get_video_ids(youtube, playlist_id)\n",
    "#video_ids\n",
    "\n",
    "def get_videos_details(youtube, video_ids):\n",
    "    all_video_stats = []\n",
    "    \n",
    "    for i in range(2, 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",
    "                           Published_date = video['snippet']['publishedAt'],\n",
    "                           Views=video['statistics'].get('viewCount', 0),\n",
    "                           Likes=video['statistics'].get('likeCount', 0),\n",
    "                           Comments=video['statistics'].get('commentCount', 0)\n",
    "                           )\n",
    "\n",
    "            all_video_stats.append(video_stats)                    \n",
    "\n",
    "    return all_video_stats\n",
    "video_details = get_videos_details(youtube, video_ids)\n",
    "df = pd.DataFrame(video_details)\n",
    "\n",
    "#Apply filters\n",
    "\n",
    "df['Published_date'] = pd.to_datetime(df['Published_date'])\n",
    "# Filter videos published after 2020\n",
    "df = df[(df['Published_date'] >= '2020-01-01') & (df['Published_date'] <= '2024-06-30')]\n",
    "\n",
    "\n",
    "pattern = '|'.join(keywords)\n",
    "filtered_Cornell = df[df['Title'].str.contains(pattern, case=False, na=False)]\n",
    "filtered_Cornell.to_csv('University of Cornell.csv', index=False)\n",
    "filtered_Cornell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "58d0cc81-3f03-4fea-95fb-33139d7b9e69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Likes: 261\n",
      "Total Comments: 44\n",
      "Total Views: 15402\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/3546563410.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_Cornell['Views'] = filtered_Cornell['Views'].astype(int)\n",
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/3546563410.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_Cornell['Likes'] = filtered_Cornell['Likes'].astype(int)\n",
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/3546563410.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_Cornell['Comments'] = filtered_Cornell['Comments'].astype(int)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_Cornell['Views'] = filtered_Cornell['Views'].astype(int)\n",
    "filtered_Cornell['Likes'] = filtered_Cornell['Likes'].astype(int)\n",
    "filtered_Cornell['Comments'] = filtered_Cornell['Comments'].astype(int)\n",
    "total_likes_Cornell = filtered_Cornell['Likes'].sum()\n",
    "total_comments_Cornell = filtered_Cornell['Comments'].sum()\n",
    "total_views_Cornell = filtered_Cornell['Views'].sum()\n",
    "\n",
    "print(\"Total Likes:\", total_likes_Cornell)\n",
    "print(\"Total Comments:\", total_comments_Cornell)\n",
    "print(\"Total Views:\", total_views_Cornell)\n",
    "Cornell_total = len(filtered_Cornell)\n",
    "Cornell_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6deb50e3-9bca-491c-940f-39fc27c8ae39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>Published_date</th>\n",
       "      <th>Views</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>What is the impact of climate change on the mi...</td>\n",
       "      <td>2024-04-16 13:56:59+00:00</td>\n",
       "      <td>275</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Sustainable Future – Challenge Accepted! | Gre...</td>\n",
       "      <td>2023-05-18 14:49:12+00:00</td>\n",
       "      <td>22326</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Sustainable Future – Challenge Accepted! | Cli...</td>\n",
       "      <td>2023-05-04 16:06:51+00:00</td>\n",
       "      <td>64296</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Campus Question: What are you doing to reduce ...</td>\n",
       "      <td>2023-04-29 23:22:16+00:00</td>\n",
       "      <td>733</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>Tuesday Tours - Green Spaces at U of T St. Geo...</td>\n",
       "      <td>2021-10-26 18:33:56+00:00</td>\n",
       "      <td>341</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>Tuesday Tours - Green Spaces at UTSC</td>\n",
       "      <td>2021-10-19 17:13:49+00:00</td>\n",
       "      <td>399</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>Tuesday Tour: Check out UTM's green spaces!</td>\n",
       "      <td>2021-10-12 20:44:05+00:00</td>\n",
       "      <td>416</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>Ken Greenberg, honorary degree recipient, spri...</td>\n",
       "      <td>2021-06-09 15:07:42+00:00</td>\n",
       "      <td>646</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Title  \\\n",
       "59   What is the impact of climate change on the mi...   \n",
       "183  Sustainable Future – Challenge Accepted! | Gre...   \n",
       "187  Sustainable Future – Challenge Accepted! | Cli...   \n",
       "189  Campus Question: What are you doing to reduce ...   \n",
       "353  Tuesday Tours - Green Spaces at U of T St. Geo...   \n",
       "355               Tuesday Tours - Green Spaces at UTSC   \n",
       "356        Tuesday Tour: Check out UTM's green spaces!   \n",
       "401  Ken Greenberg, honorary degree recipient, spri...   \n",
       "\n",
       "               Published_date  Views Likes Comments  \n",
       "59  2024-04-16 13:56:59+00:00    275     8        0  \n",
       "183 2023-05-18 14:49:12+00:00  22326    56        2  \n",
       "187 2023-05-04 16:06:51+00:00  64296    16        0  \n",
       "189 2023-04-29 23:22:16+00:00    733    19        0  \n",
       "353 2021-10-26 18:33:56+00:00    341     4        0  \n",
       "355 2021-10-19 17:13:49+00:00    399     8        0  \n",
       "356 2021-10-12 20:44:05+00:00    416     8        0  \n",
       "401 2021-06-09 15:07:42+00:00    646    11        1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playlist_id = channel_data.loc[channel_data['Channel_name']=='University of Toronto', 'playlist_id'].iloc[0]\n",
    "def get_video_ids(youtube, playlist_id):\n",
    "\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",
    "    return video_ids\n",
    "video_ids = get_video_ids(youtube, playlist_id)\n",
    "#video_ids\n",
    "\n",
    "def get_videos_details(youtube, video_ids):\n",
    "    all_video_stats = []\n",
    "    \n",
    "    for i in range(2, 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",
    "                           Published_date = video['snippet']['publishedAt'],\n",
    "                           Views=video['statistics'].get('viewCount', 0),\n",
    "                           Likes=video['statistics'].get('likeCount', 0),\n",
    "                           Comments=video['statistics'].get('commentCount', 0)\n",
    "                           )\n",
    "\n",
    "            all_video_stats.append(video_stats)                    \n",
    "\n",
    "    return all_video_stats\n",
    "video_details = get_videos_details(youtube, video_ids)\n",
    "df = pd.DataFrame(video_details)\n",
    "\n",
    "#Apply filters\n",
    "\n",
    "df['Published_date'] = pd.to_datetime(df['Published_date'])\n",
    "# Filter videos published after 2020\n",
    "df = df[(df['Published_date'] >= '2020-01-01') & (df['Published_date'] <= '2024-06-30')]\n",
    "\n",
    "\n",
    "pattern = '|'.join(keywords)\n",
    "filtered_Toronto = df[df['Title'].str.contains(pattern, case=False, na=False)]\n",
    "filtered_Toronto.to_csv('University of Toronto.csv', index=False)\n",
    "filtered_Toronto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "507b6140-01b6-4d4e-826f-87486ceec02a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Likes: 130\n",
      "Total Comments: 3\n",
      "Total Views: 89432\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/955934177.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_Toronto['Views'] = filtered_Toronto['Views'].astype(int)\n",
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/955934177.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_Toronto['Likes'] = filtered_Toronto['Likes'].astype(int)\n",
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/955934177.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_Toronto['Comments'] = filtered_Toronto['Comments'].astype(int)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_Toronto['Views'] = filtered_Toronto['Views'].astype(int)\n",
    "filtered_Toronto['Likes'] = filtered_Toronto['Likes'].astype(int)\n",
    "filtered_Toronto['Comments'] = filtered_Toronto['Comments'].astype(int)\n",
    "total_likes_Toronto = filtered_Toronto['Likes'].sum()\n",
    "total_comments_Toronto = filtered_Toronto['Comments'].sum()\n",
    "total_views_Toronto = filtered_Toronto['Views'].sum()\n",
    "\n",
    "print(\"Total Likes:\", total_likes_Toronto)\n",
    "print(\"Total Comments:\", total_comments_Toronto)\n",
    "print(\"Total Views:\", total_views_Toronto)\n",
    "Toronto_total = len(filtered_Toronto)\n",
    "Toronto_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d271b0bc-a4ae-44ef-8740-3eb4467bdc8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
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       "      <th></th>\n",
       "      <th>Title</th>\n",
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       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Climate Kids: How did climate change start?</td>\n",
       "      <td>2024-05-31 03:33:16+00:00</td>\n",
       "      <td>1248</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>MCF Seminar Series: The 2022 Climate Change Ac...</td>\n",
       "      <td>2024-05-30 05:18:26+00:00</td>\n",
       "      <td>276</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>MCFA Seminar Series: Climate Change and Intern...</td>\n",
       "      <td>2023-12-06 22:37:45+00:00</td>\n",
       "      <td>249</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>Discover the Environmental Engineering Systems...</td>\n",
       "      <td>2023-11-14 03:11:14+00:00</td>\n",
       "      <td>490</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>MCFA Seminar Series: First Nations' voices and...</td>\n",
       "      <td>2023-09-21 04:35:08+00:00</td>\n",
       "      <td>207</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>Are nature-based carbon offsets based on good ...</td>\n",
       "      <td>2023-07-31 03:53:25+00:00</td>\n",
       "      <td>824</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>University of Melbourne Health and Biomedical ...</td>\n",
       "      <td>2023-06-16 06:50:22+00:00</td>\n",
       "      <td>413</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>MCFA Seminar Series: The visual life of climat...</td>\n",
       "      <td>2023-03-14 01:24:44+00:00</td>\n",
       "      <td>277</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>MEA &amp; The University of Melbourne – Supporting...</td>\n",
       "      <td>2023-03-13 22:52:32+00:00</td>\n",
       "      <td>40307</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>Mothering in Crisis: Family, Disaster and Clim...</td>\n",
       "      <td>2022-12-07 03:23:12+00:00</td>\n",
       "      <td>352</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>Smart Sustainable Cities Webinar</td>\n",
       "      <td>2022-11-22 01:55:51+00:00</td>\n",
       "      <td>546</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>Renewable and Sustainable Energy Webinar</td>\n",
       "      <td>2022-10-26 23:42:34+00:00</td>\n",
       "      <td>859</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>Traditional Knowledge in Fiji for Anticipating...</td>\n",
       "      <td>2022-09-29 06:41:43+00:00</td>\n",
       "      <td>227</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>GSWC2022 - Ecosystem of Support</td>\n",
       "      <td>2022-04-08 04:23:04+00:00</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>Let's be Change Makers! Join Green Impact in 2022</td>\n",
       "      <td>2022-03-25 00:05:25+00:00</td>\n",
       "      <td>894</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>Making the most of 2022 to accelerate climate ...</td>\n",
       "      <td>2022-03-24 05:47:36+00:00</td>\n",
       "      <td>495</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>International Law of Climate Change: Profound ...</td>\n",
       "      <td>2021-10-27 23:43:56+00:00</td>\n",
       "      <td>920</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>Bite-sized Lecture: Is organic food better for...</td>\n",
       "      <td>2021-09-17 01:36:52+00:00</td>\n",
       "      <td>999</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>Moving South-East Asia towards clean energy</td>\n",
       "      <td>2021-06-21 03:18:06+00:00</td>\n",
       "      <td>386</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>Adapting to climate change in the atolls: Insi...</td>\n",
       "      <td>2021-05-31 00:33:06+00:00</td>\n",
       "      <td>274</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>Mining threats to biodiversity and the consequ...</td>\n",
       "      <td>2021-05-18 05:16:06+00:00</td>\n",
       "      <td>343</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>Water Security Series - What do Victoria’s new...</td>\n",
       "      <td>2021-04-22 01:58:54+00:00</td>\n",
       "      <td>583</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>Melbourne Climate Futures - planning your care...</td>\n",
       "      <td>2021-04-14 03:45:40+00:00</td>\n",
       "      <td>408</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>UniMelb Green Impact Awards Ceremony 2020</td>\n",
       "      <td>2020-11-26 06:33:52+00:00</td>\n",
       "      <td>187</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>Water Security Series - Environmental Outcomes...</td>\n",
       "      <td>2020-09-11 02:12:19+00:00</td>\n",
       "      <td>146</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Title  \\\n",
       "64         Climate Kids: How did climate change start?   \n",
       "65   MCF Seminar Series: The 2022 Climate Change Ac...   \n",
       "106  MCFA Seminar Series: Climate Change and Intern...   \n",
       "121  Discover the Environmental Engineering Systems...   \n",
       "155  MCFA Seminar Series: First Nations' voices and...   \n",
       "213  Are nature-based carbon offsets based on good ...   \n",
       "225  University of Melbourne Health and Biomedical ...   \n",
       "258  MCFA Seminar Series: The visual life of climat...   \n",
       "261  MEA & The University of Melbourne – Supporting...   \n",
       "280  Mothering in Crisis: Family, Disaster and Clim...   \n",
       "284                   Smart Sustainable Cities Webinar   \n",
       "300           Renewable and Sustainable Energy Webinar   \n",
       "330  Traditional Knowledge in Fiji for Anticipating...   \n",
       "456                    GSWC2022 - Ecosystem of Support   \n",
       "469  Let's be Change Makers! Join Green Impact in 2022   \n",
       "472  Making the most of 2022 to accelerate climate ...   \n",
       "553  International Law of Climate Change: Profound ...   \n",
       "612  Bite-sized Lecture: Is organic food better for...   \n",
       "671        Moving South-East Asia towards clean energy   \n",
       "700  Adapting to climate change in the atolls: Insi...   \n",
       "708  Mining threats to biodiversity and the consequ...   \n",
       "736  Water Security Series - What do Victoria’s new...   \n",
       "756  Melbourne Climate Futures - planning your care...   \n",
       "852          UniMelb Green Impact Awards Ceremony 2020   \n",
       "914  Water Security Series - Environmental Outcomes...   \n",
       "\n",
       "               Published_date  Views Likes Comments  \n",
       "64  2024-05-31 03:33:16+00:00   1248    30        0  \n",
       "65  2024-05-30 05:18:26+00:00    276     4        0  \n",
       "106 2023-12-06 22:37:45+00:00    249     8        0  \n",
       "121 2023-11-14 03:11:14+00:00    490     4        0  \n",
       "155 2023-09-21 04:35:08+00:00    207     6        0  \n",
       "213 2023-07-31 03:53:25+00:00    824     9        1  \n",
       "225 2023-06-16 06:50:22+00:00    413     8        1  \n",
       "258 2023-03-14 01:24:44+00:00    277     2        0  \n",
       "261 2023-03-13 22:52:32+00:00  40307     1        0  \n",
       "280 2022-12-07 03:23:12+00:00    352     7        1  \n",
       "284 2022-11-22 01:55:51+00:00    546    13        0  \n",
       "300 2022-10-26 23:42:34+00:00    859    22        0  \n",
       "330 2022-09-29 06:41:43+00:00    227     7        0  \n",
       "456 2022-04-08 04:23:04+00:00     58     1        0  \n",
       "469 2022-03-25 00:05:25+00:00    894    13        0  \n",
       "472 2022-03-24 05:47:36+00:00    495    11        0  \n",
       "553 2021-10-27 23:43:56+00:00    920    17        0  \n",
       "612 2021-09-17 01:36:52+00:00    999    17        3  \n",
       "671 2021-06-21 03:18:06+00:00    386    17        0  \n",
       "700 2021-05-31 00:33:06+00:00    274     7        0  \n",
       "708 2021-05-18 05:16:06+00:00    343     5        1  \n",
       "736 2021-04-22 01:58:54+00:00    583    11        2  \n",
       "756 2021-04-14 03:45:40+00:00    408     4        0  \n",
       "852 2020-11-26 06:33:52+00:00    187     6        0  \n",
       "914 2020-09-11 02:12:19+00:00    146     2        0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playlist_id = channel_data.loc[channel_data['Channel_name']=='The University of Melbourne', 'playlist_id'].iloc[0]\n",
    "def get_video_ids(youtube, playlist_id):\n",
    "\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",
    "    return video_ids\n",
    "video_ids = get_video_ids(youtube, playlist_id)\n",
    "#video_ids\n",
    "\n",
    "def get_videos_details(youtube, video_ids):\n",
    "    all_video_stats = []\n",
    "    \n",
    "    for i in range(2, 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",
    "                           Published_date = video['snippet']['publishedAt'],\n",
    "                           Views=video['statistics'].get('viewCount', 0),\n",
    "                           Likes=video['statistics'].get('likeCount', 0),\n",
    "                           Comments=video['statistics'].get('commentCount', 0)\n",
    "                           )\n",
    "\n",
    "            all_video_stats.append(video_stats)                    \n",
    "\n",
    "    return all_video_stats\n",
    "video_details = get_videos_details(youtube, video_ids)\n",
    "df = pd.DataFrame(video_details)\n",
    "\n",
    "#Apply filters\n",
    "\n",
    "df['Published_date'] = pd.to_datetime(df['Published_date'])\n",
    "# Filter videos published after 2020\n",
    "df = df[(df['Published_date'] >= '2020-01-01') & (df['Published_date'] <= '2024-06-30')]\n",
    "\n",
    "\n",
    "pattern = '|'.join(keywords)\n",
    "filtered_Melbourn = df[df['Title'].str.contains(pattern, case=False, na=False)]\n",
    "filtered_Melbourn.to_csv('University of Melbourn.csv', index=False)\n",
    "filtered_Melbourn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fe05c594-5f94-424a-9265-d48b61238aa3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Likes: 232\n",
      "Total Comments: 9\n",
      "Total Views: 51968\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/1840309755.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_Melbourn['Views'] = filtered_Melbourn['Views'].astype(int)\n",
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/1840309755.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_Melbourn['Likes'] = filtered_Melbourn['Likes'].astype(int)\n",
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/1840309755.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_Melbourn['Comments'] = filtered_Melbourn['Comments'].astype(int)\n"
     ]
    }
   ],
   "source": [
    "filtered_Melbourn['Views'] = filtered_Melbourn['Views'].astype(int)\n",
    "filtered_Melbourn['Likes'] = filtered_Melbourn['Likes'].astype(int)\n",
    "filtered_Melbourn['Comments'] = filtered_Melbourn['Comments'].astype(int)\n",
    "total_likes_Melbourn = filtered_Melbourn['Likes'].sum()\n",
    "total_comments_Melbourn = filtered_Melbourn['Comments'].sum()\n",
    "total_views_Melbourn = filtered_Melbourn['Views'].sum()\n",
    "\n",
    "print(\"Total Likes:\", total_likes_Melbourn)\n",
    "print(\"Total Comments:\", total_comments_Melbourn)\n",
    "print(\"Total Views:\", total_views_Melbourn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "51676122-169a-42ff-a5fe-a456936147e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Melbourn_total = len(filtered_Melbourn)\n",
    "Melbourn_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "76fdedc5-b5e5-4e47-a3aa-3079aa50e0ef",
   "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>Published_date</th>\n",
       "      <th>Views</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Water and Carbon Capture for Climate Resilience</td>\n",
       "      <td>2024-03-01 22:19:57+00:00</td>\n",
       "      <td>513</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>How Climate Change Affects You</td>\n",
       "      <td>2023-11-09 01:17:53+00:00</td>\n",
       "      <td>181</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Coping with Global Warming: Past, Present, and...</td>\n",
       "      <td>2022-10-18 20:10:56+00:00</td>\n",
       "      <td>274</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Light the Way Salon: Energy, Climate and the E...</td>\n",
       "      <td>2021-02-28 02:34:27+00:00</td>\n",
       "      <td>163</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>UC Berkeley Light the Way Global Celebration: ...</td>\n",
       "      <td>2020-11-20 17:48:14+00:00</td>\n",
       "      <td>140</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Robinson gift supports biotech ecosystem on UC...</td>\n",
       "      <td>2020-10-28 13:20:46+00:00</td>\n",
       "      <td>1069</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>What did COVID-19 do to the Bay Area’s air qua...</td>\n",
       "      <td>2020-08-16 15:00:55+00:00</td>\n",
       "      <td>3795</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Title  \\\n",
       "24     Water and Carbon Capture for Climate Resilience   \n",
       "33                      How Climate Change Affects You   \n",
       "74   Coping with Global Warming: Past, Present, and...   \n",
       "171  Light the Way Salon: Energy, Climate and the E...   \n",
       "183  UC Berkeley Light the Way Global Celebration: ...   \n",
       "189  Robinson gift supports biotech ecosystem on UC...   \n",
       "219  What did COVID-19 do to the Bay Area’s air qua...   \n",
       "\n",
       "               Published_date Views Likes Comments  \n",
       "24  2024-03-01 22:19:57+00:00   513    15        0  \n",
       "33  2023-11-09 01:17:53+00:00   181     5        1  \n",
       "74  2022-10-18 20:10:56+00:00   274     4        0  \n",
       "171 2021-02-28 02:34:27+00:00   163     3        0  \n",
       "183 2020-11-20 17:48:14+00:00   140     4        0  \n",
       "189 2020-10-28 13:20:46+00:00  1069     9        0  \n",
       "219 2020-08-16 15:00:55+00:00  3795    45        1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playlist_id = channel_data.loc[channel_data['Channel_name']=='UC Berkeley Inspires', 'playlist_id'].iloc[0]\n",
    "def get_video_ids(youtube, playlist_id):\n",
    "\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",
    "    return video_ids\n",
    "video_ids = get_video_ids(youtube, playlist_id)\n",
    "#video_ids\n",
    "\n",
    "def get_videos_details(youtube, video_ids):\n",
    "    all_video_stats = []\n",
    "    \n",
    "    for i in range(2, 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",
    "                           Published_date = video['snippet']['publishedAt'],\n",
    "                           Views=video['statistics'].get('viewCount', 0),\n",
    "                           Likes=video['statistics'].get('likeCount', 0),\n",
    "                           Comments=video['statistics'].get('commentCount', 0)\n",
    "                           )\n",
    "\n",
    "            all_video_stats.append(video_stats)                    \n",
    "\n",
    "    return all_video_stats\n",
    "video_details = get_videos_details(youtube, video_ids)\n",
    "df = pd.DataFrame(video_details)\n",
    "\n",
    "#Apply filters\n",
    "\n",
    "df['Published_date'] = pd.to_datetime(df['Published_date'])\n",
    "# Filter videos published after 2020\n",
    "df = df[(df['Published_date'] >= '2020-01-01') & (df['Published_date'] <= '2024-06-30')]\n",
    "\n",
    "\n",
    "\n",
    "pattern = '|'.join(keywords)\n",
    "filtered_Berkley = df[df['Title'].str.contains(pattern, case=False, na=False)]\n",
    "filtered_Berkley.to_csv('University of Berkley.csv', index=False)\n",
    "filtered_Berkley"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "47dd4c74-75b7-462c-b89c-96b418df95be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Likes: 85\n",
      "Total Comments: 2\n",
      "Total Views: 6135\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/2446324992.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_Berkley['Views'] = filtered_Berkley['Views'].astype(int)\n",
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/2446324992.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_Berkley['Likes'] = filtered_Berkley['Likes'].astype(int)\n",
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/2446324992.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_Berkley['Comments'] = filtered_Berkley['Comments'].astype(int)\n"
     ]
    }
   ],
   "source": [
    "filtered_Berkley['Views'] = filtered_Berkley['Views'].astype(int)\n",
    "filtered_Berkley['Likes'] = filtered_Berkley['Likes'].astype(int)\n",
    "filtered_Berkley['Comments'] = filtered_Berkley['Comments'].astype(int)\n",
    "total_likes_Berkley = filtered_Berkley['Likes'].sum()\n",
    "total_comments_Berkley = filtered_Berkley['Comments'].sum()\n",
    "total_views_Berkley = filtered_Berkley['Views'].sum()\n",
    "\n",
    "print(\"Total Likes:\", total_likes_Berkley)\n",
    "print(\"Total Comments:\", total_comments_Berkley)\n",
    "print(\"Total Views:\", total_views_Berkley)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2a7e45e8-5d7f-42f2-9c55-4ffc872b31ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Berkley_total = len(filtered_Berkley)\n",
    "Berkley_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7cc30ee8-09f9-4152-8c99-878eebc8d0ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Likes: 495\n",
      "Total Comments: 44\n",
      "Total Views: 116230\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/1498455177.py:72: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_Stanford['Views'] = filtered_Stanford['Views'].astype(int)\n",
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/1498455177.py:73: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_Stanford['Likes'] = filtered_Stanford['Likes'].astype(int)\n",
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/1498455177.py:74: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_Stanford['Comments'] = filtered_Stanford['Comments'].astype(int)\n"
     ]
    }
   ],
   "source": [
    "playlist_id = channel_data.loc[channel_data['Channel_name']=='Stanford Graduate School of Business', 'playlist_id'].iloc[0]\n",
    "def get_video_ids(youtube, playlist_id):\n",
    "\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",
    "    return video_ids\n",
    "video_ids = get_video_ids(youtube, playlist_id)\n",
    "#video_ids\n",
    "\n",
    "def get_videos_details(youtube, video_ids):\n",
    "    all_video_stats = []\n",
    "    \n",
    "    for i in range(2, 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",
    "                           Published_date = video['snippet']['publishedAt'],\n",
    "                           Views=video['statistics'].get('viewCount', 0),\n",
    "                           Likes=video['statistics'].get('likeCount', 0),\n",
    "                           Comments=video['statistics'].get('commentCount', 0)\n",
    "                           )\n",
    "\n",
    "            all_video_stats.append(video_stats)                    \n",
    "\n",
    "    return all_video_stats\n",
    "video_details = get_videos_details(youtube, video_ids)\n",
    "df = pd.DataFrame(video_details)\n",
    "\n",
    "#Apply filters\n",
    "\n",
    "df['Published_date'] = pd.to_datetime(df['Published_date'])\n",
    "# Filter videos published after 2020\n",
    "df = df[(df['Published_date'] >= '2020-01-01') & (df['Published_date'] <= '2024-06-30')]\n",
    "\n",
    "\n",
    "\n",
    "pattern = '|'.join(keywords)\n",
    "filtered_Stanford = df[df['Title'].str.contains(pattern, case=False, na=False)]\n",
    "filtered_Stanford.to_csv('University of Stanford.csv', index=False)\n",
    "filtered_Stanford\n",
    "filtered_Stanford['Views'] = filtered_Stanford['Views'].astype(int)\n",
    "filtered_Stanford['Likes'] = filtered_Stanford['Likes'].astype(int)\n",
    "filtered_Stanford['Comments'] = filtered_Stanford['Comments'].astype(int)\n",
    "total_likes_Stanford = filtered_Stanford['Likes'].sum()\n",
    "total_comments_Stanford = filtered_Stanford['Comments'].sum()\n",
    "total_views_Stanford = filtered_Stanford['Views'].sum()\n",
    "\n",
    "print(\"Total Likes:\", total_likes_Stanford)\n",
    "print(\"Total Comments:\", total_comments_Stanford)\n",
    "print(\"Total Views:\", total_views_Stanford)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8c96f358-ac8e-4e1e-b244-4d236963fdc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stanford_total = len(filtered_Stanford)\n",
    "Stanford_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a157f944-a9a2-41e2-beb3-33634e45b0d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Published_date</th>\n",
       "      <th>Views</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>The University of Manchester supporting local ...</td>\n",
       "      <td>2024-05-20 14:55:10+00:00</td>\n",
       "      <td>128</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Outstanding contribution to social and environ...</td>\n",
       "      <td>2024-05-09 19:11:07+00:00</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
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       "      <td>42</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Outstanding teaching innovation in social resp...</td>\n",
       "      <td>2024-05-09 17:21:45+00:00</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>34</th>\n",
       "      <td>Students: find out how to use your course asse...</td>\n",
       "      <td>2024-02-01 14:12:30+00:00</td>\n",
       "      <td>165</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>How could our University Living Lab enhance yo...</td>\n",
       "      <td>2024-02-01 14:06:11+00:00</td>\n",
       "      <td>372</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>47</th>\n",
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       "      <td>2023-11-13 23:54:52+00:00</td>\n",
       "      <td>40</td>\n",
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       "      <th>60</th>\n",
       "      <td>Greening Labs – Manchester Evening News interview</td>\n",
       "      <td>2023-10-26 13:10:02+00:00</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>74</th>\n",
       "      <td>Outstanding public contribution: Ardwick Clima...</td>\n",
       "      <td>2023-05-11 18:51:21+00:00</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Outstanding contribution to social innovation ...</td>\n",
       "      <td>2023-05-11 18:25:00+00:00</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Outstanding contribution to environmental sust...</td>\n",
       "      <td>2023-05-11 17:42:41+00:00</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Outstanding contribution to environmental sust...</td>\n",
       "      <td>2023-05-11 17:39:38+00:00</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Supporting environmental sustainability</td>\n",
       "      <td>2022-10-12 09:17:36+00:00</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Say hello to our Environmental Sustainability ...</td>\n",
       "      <td>2022-06-15 04:30:06+00:00</td>\n",
       "      <td>53</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Outstanding contribution to environmental sust...</td>\n",
       "      <td>2022-05-10 17:46:06+00:00</td>\n",
       "      <td>101</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Outstanding contribution to environmental sust...</td>\n",
       "      <td>2022-05-10 17:40:40+00:00</td>\n",
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       "      <td>6</td>\n",
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       "      <th>126</th>\n",
       "      <td>Outstanding contribution to social innovation ...</td>\n",
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       "      <td>119</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <th>134</th>\n",
       "      <td>Our Greater Manchester Graduate - Matthew Peti...</td>\n",
       "      <td>2021-10-21 09:09:21+00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>135</th>\n",
       "      <td>How The University of Manchester is supporting...</td>\n",
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       "      <td>1760</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
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       "      <th>161</th>\n",
       "      <td>Outstanding contribution to environmental sust...</td>\n",
       "      <td>2021-05-12 17:38:14+00:00</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>Outstanding contribution to environmental sust...</td>\n",
       "      <td>2021-05-12 17:35:04+00:00</td>\n",
       "      <td>53</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <th>166</th>\n",
       "      <td>Social innovation and environmental impact thr...</td>\n",
       "      <td>2021-05-12 17:21:06+00:00</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Environmental Sustainability - Social responsi...</td>\n",
       "      <td>2021-01-27 15:54:24+00:00</td>\n",
       "      <td>778</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Outstanding Contribution to Environmental Sust...</td>\n",
       "      <td>2020-05-07 18:19:54+00:00</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Outstanding Contribution to Environmental Sust...</td>\n",
       "      <td>2020-05-07 18:19:44+00:00</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Outstanding Contribution to Environmental Sust...</td>\n",
       "      <td>2020-05-07 18:14:34+00:00</td>\n",
       "      <td>237</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Outstanding Contribution, Social Innovation &amp; ...</td>\n",
       "      <td>2020-05-07 17:58:06+00:00</td>\n",
       "      <td>133</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Title  \\\n",
       "7    The University of Manchester supporting local ...   \n",
       "13   Outstanding contribution to social and environ...   \n",
       "18   Outstanding contribution to environmental sust...   \n",
       "24   Outstanding teaching innovation in social resp...   \n",
       "34   Students: find out how to use your course asse...   \n",
       "35   How could our University Living Lab enhance yo...   \n",
       "47   Greening labs - Making the SMI Hub genuinely s...   \n",
       "60   Greening Labs – Manchester Evening News interview   \n",
       "74   Outstanding public contribution: Ardwick Clima...   \n",
       "79   Outstanding contribution to social innovation ...   \n",
       "82   Outstanding contribution to environmental sust...   \n",
       "83   Outstanding contribution to environmental sust...   \n",
       "100            Supporting environmental sustainability   \n",
       "112  Say hello to our Environmental Sustainability ...   \n",
       "123  Outstanding contribution to environmental sust...   \n",
       "124  Outstanding contribution to environmental sust...   \n",
       "126  Outstanding contribution to social innovation ...   \n",
       "134  Our Greater Manchester Graduate - Matthew Peti...   \n",
       "135  How The University of Manchester is supporting...   \n",
       "161  Outstanding contribution to environmental sust...   \n",
       "162  Outstanding contribution to environmental sust...   \n",
       "166  Social innovation and environmental impact thr...   \n",
       "173  Environmental Sustainability - Social responsi...   \n",
       "183  Outstanding Contribution to Environmental Sust...   \n",
       "184  Outstanding Contribution to Environmental Sust...   \n",
       "185  Outstanding Contribution to Environmental Sust...   \n",
       "188  Outstanding Contribution, Social Innovation & ...   \n",
       "\n",
       "               Published_date Views Likes Comments  \n",
       "7   2024-05-20 14:55:10+00:00   128     2        0  \n",
       "13  2024-05-09 19:11:07+00:00    69     0        0  \n",
       "18  2024-05-09 17:53:47+00:00    42     0        0  \n",
       "24  2024-05-09 17:21:45+00:00    88     0        0  \n",
       "34  2024-02-01 14:12:30+00:00   165     2        0  \n",
       "35  2024-02-01 14:06:11+00:00   372     0        0  \n",
       "47  2023-11-13 23:54:52+00:00    40     0        0  \n",
       "60  2023-10-26 13:10:02+00:00    30     0        0  \n",
       "74  2023-05-11 18:51:21+00:00   102     0        0  \n",
       "79  2023-05-11 18:25:00+00:00    89     0        0  \n",
       "82  2023-05-11 17:42:41+00:00    47     0        0  \n",
       "83  2023-05-11 17:39:38+00:00    58     1        0  \n",
       "100 2022-10-12 09:17:36+00:00   120     0        0  \n",
       "112 2022-06-15 04:30:06+00:00    53     3        0  \n",
       "123 2022-05-10 17:46:06+00:00   101     2        0  \n",
       "124 2022-05-10 17:40:40+00:00    88     6        0  \n",
       "126 2022-05-10 17:28:38+00:00   119     2        0  \n",
       "134 2021-10-21 09:09:21+00:00    71     0        0  \n",
       "135 2021-10-11 09:01:25+00:00  1760    14        0  \n",
       "161 2021-05-12 17:38:14+00:00    47     1        0  \n",
       "162 2021-05-12 17:35:04+00:00    53     3        0  \n",
       "166 2021-05-12 17:21:06+00:00    81     0        0  \n",
       "173 2021-01-27 15:54:24+00:00   778    12        0  \n",
       "183 2020-05-07 18:19:54+00:00    83     0        0  \n",
       "184 2020-05-07 18:19:44+00:00   129     0        0  \n",
       "185 2020-05-07 18:14:34+00:00   237     2        0  \n",
       "188 2020-05-07 17:58:06+00:00   133     1        0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playlist_id = channel_data.loc[channel_data['Channel_name']=='Social Responsibility at The University of Manchester', 'playlist_id'].iloc[0]\n",
    "def get_video_ids(youtube, playlist_id):\n",
    "\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",
    "    return video_ids\n",
    "video_ids = get_video_ids(youtube, playlist_id)\n",
    "#video_ids\n",
    "\n",
    "def get_videos_details(youtube, video_ids):\n",
    "    all_video_stats = []\n",
    "    \n",
    "    for i in range(2, 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",
    "                           Published_date = video['snippet']['publishedAt'],\n",
    "                           Views=video['statistics'].get('viewCount', 0),\n",
    "                           Likes=video['statistics'].get('likeCount', 0),\n",
    "                           Comments=video['statistics'].get('commentCount', 0)\n",
    "                           )\n",
    "\n",
    "            all_video_stats.append(video_stats)                    \n",
    "\n",
    "    return all_video_stats\n",
    "video_details = get_videos_details(youtube, video_ids)\n",
    "df = pd.DataFrame(video_details)\n",
    "\n",
    "#Apply filters\n",
    "\n",
    "df['Published_date'] = pd.to_datetime(df['Published_date'])\n",
    "# Filter videos published after 2020\n",
    "df = df[(df['Published_date'] >= '2020-01-01') & (df['Published_date'] <= '2024-06-30')]\n",
    "\n",
    "\n",
    "\n",
    "pattern = '|'.join(keywords)\n",
    "filtered_Manchester = df[df['Title'].str.contains(pattern, case=False, na=False)]\n",
    "filtered_Manchester.to_csv('University of Manchester.csv', index=False)\n",
    "filtered_Manchester"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8daec719-5feb-4bcc-ae70-54fbe5c723df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Likes: 51\n",
      "Total Comments: 0\n",
      "Total Views: 5083\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/3513215214.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_Manchester['Views'] = filtered_Manchester['Views'].astype(int)\n",
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/3513215214.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_Manchester['Likes'] = filtered_Manchester['Likes'].astype(int)\n",
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/3513215214.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_Manchester['Comments'] = filtered_Manchester['Comments'].astype(int)\n"
     ]
    }
   ],
   "source": [
    "filtered_Manchester['Views'] = filtered_Manchester['Views'].astype(int)\n",
    "filtered_Manchester['Likes'] = filtered_Manchester['Likes'].astype(int)\n",
    "filtered_Manchester['Comments'] = filtered_Manchester['Comments'].astype(int)\n",
    "total_likes_Manchester = filtered_Manchester['Likes'].sum()\n",
    "total_comments_Manchester = filtered_Manchester['Comments'].sum()\n",
    "total_views_Manchester = filtered_Manchester['Views'].sum()\n",
    "\n",
    "print(\"Total Likes:\", total_likes_Manchester)\n",
    "print(\"Total Comments:\", total_comments_Manchester)\n",
    "print(\"Total Views:\", total_views_Manchester)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c0359e4d-f58a-4c59-b5b5-82c08b441c9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Manchester_total = len(filtered_Manchester)\n",
    "Manchester_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c985daf9-f85a-4163-b809-7263e6dec7e1",
   "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>Published_date</th>\n",
       "      <th>Views</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Creating a Sustainable Campus: UW's new Green ...</td>\n",
       "      <td>2024-05-22 17:07:23+00:00</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Creating a Sustainable Campus: Student move-ou...</td>\n",
       "      <td>2024-02-15 23:26:18+00:00</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>UW Greenhouse Gas emissions webinar</td>\n",
       "      <td>2023-12-15 00:14:47+00:00</td>\n",
       "      <td>114</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>UW Green Office: Going Paperless</td>\n",
       "      <td>2022-04-30 00:32:54+00:00</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>UW Recycling: Green Office Trash Talk</td>\n",
       "      <td>2021-11-17 23:44:24+00:00</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Husky Green Award celebration</td>\n",
       "      <td>2021-10-21 10:45:51+00:00</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Sustainability Stories: Waste Reduction</td>\n",
       "      <td>2021-05-05 18:55:04+00:00</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>UW Recycling campus Green Space tour</td>\n",
       "      <td>2020-11-30 23:10:58+00:00</td>\n",
       "      <td>106</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Post-screening discussion of \"The Story of Pla...</td>\n",
       "      <td>2020-07-13 18:42:38+00:00</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Q&amp;A with Susan Thoman of the Compost Manufactu...</td>\n",
       "      <td>2020-05-14 04:41:19+00:00</td>\n",
       "      <td>104</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Virtual Trash Talk with UW Recycling for Earth...</td>\n",
       "      <td>2020-05-07 19:15:35+00:00</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>UW Recycling Zero Waste talk with with Heather...</td>\n",
       "      <td>2020-05-04 22:26:27+00:00</td>\n",
       "      <td>104</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Title  \\\n",
       "2   Creating a Sustainable Campus: UW's new Green ...   \n",
       "7   Creating a Sustainable Campus: Student move-ou...   \n",
       "9                 UW Greenhouse Gas emissions webinar   \n",
       "15                   UW Green Office: Going Paperless   \n",
       "17              UW Recycling: Green Office Trash Talk   \n",
       "19                      Husky Green Award celebration   \n",
       "22            Sustainability Stories: Waste Reduction   \n",
       "34               UW Recycling campus Green Space tour   \n",
       "41  Post-screening discussion of \"The Story of Pla...   \n",
       "45  Q&A with Susan Thoman of the Compost Manufactu...   \n",
       "46  Virtual Trash Talk with UW Recycling for Earth...   \n",
       "48  UW Recycling Zero Waste talk with with Heather...   \n",
       "\n",
       "              Published_date Views Likes Comments  \n",
       "2  2024-05-22 17:07:23+00:00    63     1        1  \n",
       "7  2024-02-15 23:26:18+00:00    39     1        0  \n",
       "9  2023-12-15 00:14:47+00:00   114     2        0  \n",
       "15 2022-04-30 00:32:54+00:00    24     2        0  \n",
       "17 2021-11-17 23:44:24+00:00    51     0        0  \n",
       "19 2021-10-21 10:45:51+00:00    92     2        0  \n",
       "22 2021-05-05 18:55:04+00:00    57     0        0  \n",
       "34 2020-11-30 23:10:58+00:00   106     2        0  \n",
       "41 2020-07-13 18:42:38+00:00    71     0        0  \n",
       "45 2020-05-14 04:41:19+00:00   104     1        0  \n",
       "46 2020-05-07 19:15:35+00:00    58     1        0  \n",
       "48 2020-05-04 22:26:27+00:00   104     2        0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playlist_id = channel_data.loc[channel_data['Channel_name']=='UW Sustainability', 'playlist_id'].iloc[0]\n",
    "def get_video_ids(youtube, playlist_id):\n",
    "\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",
    "    return video_ids\n",
    "video_ids = get_video_ids(youtube, playlist_id)\n",
    "#video_ids\n",
    "\n",
    "def get_videos_details(youtube, video_ids):\n",
    "    all_video_stats = []\n",
    "    \n",
    "    for i in range(2, 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",
    "                           Published_date = video['snippet']['publishedAt'],\n",
    "                           Views=video['statistics'].get('viewCount', 0),\n",
    "                           Likes=video['statistics'].get('likeCount', 0),\n",
    "                           Comments=video['statistics'].get('commentCount', 0)\n",
    "                           )\n",
    "\n",
    "            all_video_stats.append(video_stats)                    \n",
    "\n",
    "    return all_video_stats\n",
    "video_details = get_videos_details(youtube, video_ids)\n",
    "df = pd.DataFrame(video_details)\n",
    "\n",
    "#Apply filters\n",
    "\n",
    "df['Published_date'] = pd.to_datetime(df['Published_date'])\n",
    "# Filter videos published after 2020\n",
    "df = df[(df['Published_date'] >= '2020-01-01') & (df['Published_date'] <= '2024-06-30')]\n",
    "\n",
    "\n",
    "pattern = '|'.join(keywords)\n",
    "filtered_washington = df[df['Title'].str.contains(pattern, case=False, na=False)]\n",
    "filtered_washington.to_csv('University of Washington.csv', index=False)\n",
    "filtered_washington"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "be4e430c-e488-4c22-b14c-8d3a4d1106f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Likes: 14\n",
      "Total Comments: 1\n",
      "Total Views: 883\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/880325023.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_washington['Views'] = filtered_washington['Views'].astype(int)\n",
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/880325023.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_washington['Likes'] = filtered_washington['Likes'].astype(int)\n",
      "/var/folders/y2/ptx7cj557677j5gj_rh5gs000000gn/T/ipykernel_3673/880325023.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_washington['Comments'] = filtered_washington['Comments'].astype(int)\n"
     ]
    }
   ],
   "source": [
    "filtered_washington['Views'] = filtered_washington['Views'].astype(int)\n",
    "filtered_washington['Likes'] = filtered_washington['Likes'].astype(int)\n",
    "filtered_washington['Comments'] = filtered_washington['Comments'].astype(int)\n",
    "total_likes_washington = filtered_washington['Likes'].sum()\n",
    "total_comments_washington = filtered_washington['Comments'].sum()\n",
    "total_views_washington = filtered_washington['Views'].sum()\n",
    "print(\"Total Likes:\", total_likes_washington)\n",
    "print(\"Total Comments:\", total_comments_washington)\n",
    "print(\"Total Views:\", total_views_washington)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6b9e75f1-8fe2-4dc4-8704-3d9822c563d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "washington_total = len(filtered_washington)\n",
    "washington_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2f4568f-9b26-44ca-85f9-3232c42b3fad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Creating a pie chart\n",
    "data = {\n",
    "    'University': ['Melbourn', 'Berkley', 'Stanford', 'Manchester', 'Washington', 'Toronto','Cornell', 'Adelaide'],\n",
    "    'Videos': [len(filtered_Melbourn), len(filtered_Berkley), len(filtered_Stanford), len(filtered_Manchester), len(filtered_washington), len(filtered_Toronto), len(filtered_Cornell), len(filtered_Adelaide)]\n",
    "}\n",
    "\n",
    "df_pie = pd.DataFrame(data)\n",
    "\n",
    "# Plotting the pie chart\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(df_pie['Videos'], labels=df_pie['University'], autopct='%1.1f%%', startangle=140)\n",
    "plt.title('Proportion of Environmental Sustainability Videos by University')\n",
    "plt.savefig('proportion_of_videos_by_university.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8287ba4c-8b5c-43ef-bf41-934fa98f3166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Trend of videos in Graph\n",
    "plt.figure(figsize=(14, 7))\n",
    "\n",
    "# Plotting the data for each university with clear labels and distinct colors\n",
    "plt.plot(filtered_Melbourn['Published_date'], filtered_Melbourn.index, linestyle='-', marker='o', label='University of Melbourne', color='blue')\n",
    "plt.plot(filtered_Berkley['Published_date'], filtered_Berkley.index, linestyle='-', marker='o', label='University of Berkeley', color='green')\n",
    "plt.plot(filtered_Stanford['Published_date'], filtered_Stanford.index, linestyle='-', marker='o', label='University of Stanford', color='red')\n",
    "plt.plot(filtered_Manchester['Published_date'], filtered_Manchester.index, linestyle='-', marker='o', label='University of Manchester', color='purple')\n",
    "plt.plot(filtered_washington['Published_date'], filtered_washington.index, linestyle='-', marker='o', label='University of Washington', color='orange')\n",
    "plt.plot(filtered_Toronto['Published_date'], filtered_Toronto.index, linestyle='-', marker='o', label='University of Toronto', color='brown')\n",
    "plt.plot(filtered_Cornell['Published_date'], filtered_Cornell.index, linestyle='-', marker='o', label='University of Cornell', color='pink')\n",
    "plt.plot(filtered_Adelaide['Published_date'], filtered_Adelaide.index, linestyle='-', marker='o', label='University of Adelaide', color='grey')\n",
    "\n",
    "# Setting the title and labels\n",
    "plt.title('Trend of Posting videos by University Over Time', fontsize=16)\n",
    "plt.xlabel('Date', fontsize=14)\n",
    "plt.ylabel('Video Index', fontsize=14)\n",
    "\n",
    "# Enabling grid for better readability\n",
    "plt.grid(True)\n",
    "\n",
    "# Formatting the x-axis for date representation\n",
    "plt.xticks(rotation=45)\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
    "plt.gca().xaxis.set_major_locator(mdates.AutoDateLocator())\n",
    "\n",
    "# Adding legend to indicate which line represents which university\n",
    "plt.legend()\n",
    "\n",
    "# Inverting y-axis to show the most recent video at the top\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "# Moving y-axis ticks to the right for better alignment with labels\n",
    "plt.gca().yaxis.tick_right()\n",
    "\n",
    "# Saving the figure and displaying the plot\n",
    "plt.savefig('environmental_sustainability_videos_over_time.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "afa5baba-6e58-40a6-ab35-a210202e0e6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "data = {\n",
    "    'University': [' Melbourne', ' Berkeley', ' Stanford', ' Manchester', ' Washington', ' Toronto', 'Cornell', 'Adelaide'],\n",
    "    'Likes': [total_likes_Melbourn, total_likes_Berkley, total_likes_Stanford, total_likes_Manchester, total_likes_washington, total_likes_Toronto, total_likes_Cornell, total_likes_Adelaide],\n",
    "    'Comments': [total_comments_Melbourn, total_comments_Berkley, total_comments_Stanford, total_comments_Manchester, total_comments_washington, total_comments_Toronto, total_comments_Cornell, total_comments_Adelaide],\n",
    "    'Views': [total_views_Melbourn, total_views_Berkley, total_views_Stanford, total_views_Manchester, total_views_washington, total_views_Toronto, total_views_Cornell, total_views_Adelaide],\n",
    "'Videos': [len(filtered_Melbourn), len(filtered_Berkley), len(filtered_Stanford), len(filtered_Manchester), len(filtered_washington),len(filtered_Toronto), len(filtered_Cornell), len(filtered_Adelaide)]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Plotting the data\n",
    "fig, ax1 = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "# Set the width of the bars\n",
    "bar_width = 0.2\n",
    "\n",
    "# Set the positions of the bars\n",
    "r1 = range(len(df['University']))\n",
    "r2 = [x + bar_width for x in r1]\n",
    "r3 = [x + bar_width for x in r2]\n",
    "\n",
    "# Plotting Likes, Comments, and Videos on the primary y-axis\n",
    "ax1.set_xlabel('Universities', fontweight='bold')\n",
    "ax1.set_ylabel('Likes, Comments, and Videos', fontweight='bold')\n",
    "ax1.bar(r1, df['Likes'], color='b', width=bar_width, edgecolor='grey', label='Likes')\n",
    "ax1.bar(r2, df['Comments'], color='g', width=bar_width, edgecolor='grey', label='Comments')\n",
    "ax1.bar(r3, df['Videos'], color='y', width=bar_width, edgecolor='grey', label='Videos')\n",
    "\n",
    "# Creating a secondary y-axis for Views\n",
    "ax2 = ax1.twinx()\n",
    "ax2.set_ylabel('Views', fontweight='bold')\n",
    "ax2.plot(r1, df['Views'], color='r', marker='o', linestyle='-', linewidth=2, label='Views')\n",
    "\n",
    "# Adding legends\n",
    "ax1.legend(loc='upper left')\n",
    "ax2.legend(loc='upper right')\n",
    "\n",
    "# Adding title\n",
    "plt.title('Videos, Likes, Comments, and Views for Universities (01.01.2020 - 30.06.2024)')\n",
    "\n",
    "# Rotating x-axis labels for better readability\n",
    "plt.xticks([r + bar_width for r in range(len(df['University']))], df['University'], rotation=45)\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.savefig('university_engagements.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4c027aa-fd84-4c42-979a-2007d11d8b7b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (Youtube-env)",
   "language": "python",
   "name": "youtube-env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}