youtube-data-analysis.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":2087509,"sourceType":"datasetVersion","datasetId":1251585}],"dockerImageVersionId":30732,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-06-30T05:38:00.043129Z","iopub.execute_input":"2024-06-30T05:38:00.043573Z","iopub.status.idle":"2024-06-30T05:38:00.053729Z","shell.execute_reply.started":"2024-06-30T05:38:00.043535Z","shell.execute_reply":"2024-06-30T05:38:00.052204Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stdout","text":"/kaggle/input/top5000youtubechannels/top-5000-youtube-channels.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"df = pd.read_csv('/kaggle/input/top5000youtubechannels/top-5000-youtube-channels.csv')","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:00.056452Z","iopub.execute_input":"2024-06-30T05:38:00.056941Z","iopub.status.idle":"2024-06-30T05:38:00.079456Z","shell.execute_reply.started":"2024-06-30T05:38:00.056898Z","shell.execute_reply":"2024-06-30T05:38:00.078300Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"df.head(5)","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:00.080710Z","iopub.execute_input":"2024-06-30T05:38:00.081032Z","iopub.status.idle":"2024-06-30T05:38:00.095035Z","shell.execute_reply.started":"2024-06-30T05:38:00.081004Z","shell.execute_reply":"2024-06-30T05:38:00.093761Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"  Rank Grade                Channel name Video Uploads Subscribers  \\\n0  1st  A++                       Zee TV         82757    18752951   \n1  2nd  A++                     T-Series         12661    61196302   \n2  3rd  A++   Cocomelon - Nursery Rhymes           373    19238251   \n3  4th  A++                    SET India         27323    31180559   \n4  5th  A++                          WWE         36756    32852346   \n\n   Video views  \n0  20869786591  \n1  47548839843  \n2   9793305082  \n3  22675948293  \n4  26273668433  ","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>Rank</th>\n      <th>Grade</th>\n      <th>Channel name</th>\n      <th>Video Uploads</th>\n      <th>Subscribers</th>\n      <th>Video views</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1st</td>\n      <td>A++</td>\n      <td>Zee TV</td>\n      <td>82757</td>\n      <td>18752951</td>\n      <td>20869786591</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2nd</td>\n      <td>A++</td>\n      <td>T-Series</td>\n      <td>12661</td>\n      <td>61196302</td>\n      <td>47548839843</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3rd</td>\n      <td>A++</td>\n      <td>Cocomelon - Nursery Rhymes</td>\n      <td>373</td>\n      <td>19238251</td>\n      <td>9793305082</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4th</td>\n      <td>A++</td>\n      <td>SET India</td>\n      <td>27323</td>\n      <td>31180559</td>\n      <td>22675948293</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5th</td>\n      <td>A++</td>\n      <td>WWE</td>\n      <td>36756</td>\n      <td>32852346</td>\n      <td>26273668433</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.describe().T","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:00.097245Z","iopub.execute_input":"2024-06-30T05:38:00.097647Z","iopub.status.idle":"2024-06-30T05:38:00.120721Z","shell.execute_reply.started":"2024-06-30T05:38:00.097615Z","shell.execute_reply":"2024-06-30T05:38:00.119687Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"              count          mean           std   min           25%  \\\nVideo views  5000.0  1.071449e+09  2.003844e+09  75.0  1.862329e+08   \n\n                     50%           75%           max  \nVideo views  482054780.0  1.124368e+09  4.754884e+10  ","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>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Video views</th>\n      <td>5000.0</td>\n      <td>1.071449e+09</td>\n      <td>2.003844e+09</td>\n      <td>75.0</td>\n      <td>1.862329e+08</td>\n      <td>482054780.0</td>\n      <td>1.124368e+09</td>\n      <td>4.754884e+10</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:00.121863Z","iopub.execute_input":"2024-06-30T05:38:00.122171Z","iopub.status.idle":"2024-06-30T05:38:00.135360Z","shell.execute_reply.started":"2024-06-30T05:38:00.122147Z","shell.execute_reply":"2024-06-30T05:38:00.134206Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 5000 entries, 0 to 4999\nData columns (total 6 columns):\n #   Column         Non-Null Count  Dtype \n---  ------         --------------  ----- \n 0   Rank           5000 non-null   object\n 1   Grade          5000 non-null   object\n 2   Channel name   5000 non-null   object\n 3   Video Uploads  5000 non-null   object\n 4   Subscribers    5000 non-null   object\n 5   Video views    5000 non-null   int64 \ndtypes: int64(1), object(5)\nmemory usage: 234.5+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"df.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:00.136842Z","iopub.execute_input":"2024-06-30T05:38:00.137194Z","iopub.status.idle":"2024-06-30T05:38:00.151664Z","shell.execute_reply.started":"2024-06-30T05:38:00.137165Z","shell.execute_reply":"2024-06-30T05:38:00.150296Z"},"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"Rank             0\nGrade            0\nChannel name     0\nVideo Uploads    0\nSubscribers      0\nVideo views      0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"len(df.Rank.unique())","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:00.153338Z","iopub.execute_input":"2024-06-30T05:38:00.153963Z","iopub.status.idle":"2024-06-30T05:38:00.166974Z","shell.execute_reply.started":"2024-06-30T05:38:00.153921Z","shell.execute_reply":"2024-06-30T05:38:00.165825Z"},"trusted":true},"execution_count":35,"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"5000"},"metadata":{}}]},{"cell_type":"code","source":"print(len(df.Grade.unique()))\ndf.Grade.unique()","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:00.168757Z","iopub.execute_input":"2024-06-30T05:38:00.169196Z","iopub.status.idle":"2024-06-30T05:38:00.184447Z","shell.execute_reply.started":"2024-06-30T05:38:00.169156Z","shell.execute_reply":"2024-06-30T05:38:00.183180Z"},"trusted":true},"execution_count":36,"outputs":[{"name":"stdout","text":"6\n","output_type":"stream"},{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"array(['A++ ', 'A+ ', 'A ', '\\xa0 ', 'A- ', 'B+ '], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"df.head(20)","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:00.187729Z","iopub.execute_input":"2024-06-30T05:38:00.188543Z","iopub.status.idle":"2024-06-30T05:38:00.204842Z","shell.execute_reply.started":"2024-06-30T05:38:00.188506Z","shell.execute_reply":"2024-06-30T05:38:00.203503Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"    Rank Grade                Channel name Video Uploads Subscribers  \\\n0    1st  A++                       Zee TV         82757    18752951   \n1    2nd  A++                     T-Series         12661    61196302   \n2    3rd  A++   Cocomelon - Nursery Rhymes           373    19238251   \n3    4th  A++                    SET India         27323    31180559   \n4    5th  A++                          WWE         36756    32852346   \n5    6th  A++                   Movieclips         30243    17149705   \n6    7th  A++                   netd müzik          8500    11373567   \n7    8th  A++        ABS-CBN Entertainment        100147    12149206   \n8    9th  A++              Ryan ToysReview          1140    16082927   \n9   10th  A++                  Zee Marathi         74607     2841811   \n10  11th   A+              5-Minute Crafts          2085    33492951   \n11  12th   A+              Canal KondZilla           822    39409726   \n12  13th   A+             Like Nastya Vlog           150     7662886   \n13  14th   A+                        Ozuna            50    18824912   \n14  15th   A+                   Wave Music         16119    15899764   \n15  16th   A+                  Ch3Thailand         49239    11569723   \n16  17th   A+              WORLDSTARHIPHOP          4778    15830098   \n17  18th   A+              Vlad and Nikita            53         --    \n18  19th   A+                      Badabun          3060    23603062   \n19  20th   A+            WorkpointOfficial         24287    17687229   \n\n    Video views  \n0   20869786591  \n1   47548839843  \n2    9793305082  \n3   22675948293  \n4   26273668433  \n5   16618094724  \n6   23898730764  \n7   17202609850  \n8   24518098041  \n9    2591830307  \n10   8587520379  \n11  19291034467  \n12   2540099931  \n13   8727783225  \n14  10989179147  \n15   9388600275  \n16  11102158475  \n17   1428274554  \n18   5860444053  \n19  14022189654  ","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>Rank</th>\n      <th>Grade</th>\n      <th>Channel name</th>\n      <th>Video Uploads</th>\n      <th>Subscribers</th>\n      <th>Video views</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1st</td>\n      <td>A++</td>\n      <td>Zee TV</td>\n      <td>82757</td>\n      <td>18752951</td>\n      <td>20869786591</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2nd</td>\n      <td>A++</td>\n      <td>T-Series</td>\n      <td>12661</td>\n      <td>61196302</td>\n      <td>47548839843</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3rd</td>\n      <td>A++</td>\n      <td>Cocomelon - Nursery Rhymes</td>\n      <td>373</td>\n      <td>19238251</td>\n      <td>9793305082</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4th</td>\n      <td>A++</td>\n      <td>SET India</td>\n      <td>27323</td>\n      <td>31180559</td>\n      <td>22675948293</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5th</td>\n      <td>A++</td>\n      <td>WWE</td>\n      <td>36756</td>\n      <td>32852346</td>\n      <td>26273668433</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6th</td>\n      <td>A++</td>\n      <td>Movieclips</td>\n      <td>30243</td>\n      <td>17149705</td>\n      <td>16618094724</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7th</td>\n      <td>A++</td>\n      <td>netd müzik</td>\n      <td>8500</td>\n      <td>11373567</td>\n      <td>23898730764</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8th</td>\n      <td>A++</td>\n      <td>ABS-CBN Entertainment</td>\n      <td>100147</td>\n      <td>12149206</td>\n      <td>17202609850</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9th</td>\n      <td>A++</td>\n      <td>Ryan ToysReview</td>\n      <td>1140</td>\n      <td>16082927</td>\n      <td>24518098041</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>10th</td>\n      <td>A++</td>\n      <td>Zee Marathi</td>\n      <td>74607</td>\n      <td>2841811</td>\n      <td>2591830307</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>11th</td>\n      <td>A+</td>\n      <td>5-Minute Crafts</td>\n      <td>2085</td>\n      <td>33492951</td>\n      <td>8587520379</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>12th</td>\n      <td>A+</td>\n      <td>Canal KondZilla</td>\n      <td>822</td>\n      <td>39409726</td>\n      <td>19291034467</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>13th</td>\n      <td>A+</td>\n      <td>Like Nastya Vlog</td>\n      <td>150</td>\n      <td>7662886</td>\n      <td>2540099931</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>14th</td>\n      <td>A+</td>\n      <td>Ozuna</td>\n      <td>50</td>\n      <td>18824912</td>\n      <td>8727783225</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>15th</td>\n      <td>A+</td>\n      <td>Wave Music</td>\n      <td>16119</td>\n      <td>15899764</td>\n      <td>10989179147</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>16th</td>\n      <td>A+</td>\n      <td>Ch3Thailand</td>\n      <td>49239</td>\n      <td>11569723</td>\n      <td>9388600275</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>17th</td>\n      <td>A+</td>\n      <td>WORLDSTARHIPHOP</td>\n      <td>4778</td>\n      <td>15830098</td>\n      <td>11102158475</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>18th</td>\n      <td>A+</td>\n      <td>Vlad and Nikita</td>\n      <td>53</td>\n      <td>--</td>\n      <td>1428274554</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>19th</td>\n      <td>A+</td>\n      <td>Badabun</td>\n      <td>3060</td>\n      <td>23603062</td>\n      <td>5860444053</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>20th</td>\n      <td>A+</td>\n      <td>WorkpointOfficial</td>\n      <td>24287</td>\n      <td>17687229</td>\n      <td>14022189654</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df = df.replace('--', np.nan, regex=True)\ndf.head(20)","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:00.206335Z","iopub.execute_input":"2024-06-30T05:38:00.206764Z","iopub.status.idle":"2024-06-30T05:38:00.239472Z","shell.execute_reply.started":"2024-06-30T05:38:00.206732Z","shell.execute_reply":"2024-06-30T05:38:00.238143Z"},"trusted":true},"execution_count":38,"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"    Rank Grade                Channel name Video Uploads Subscribers  \\\n0    1st  A++                       Zee TV         82757    18752951   \n1    2nd  A++                     T-Series         12661    61196302   \n2    3rd  A++   Cocomelon - Nursery Rhymes           373    19238251   \n3    4th  A++                    SET India         27323    31180559   \n4    5th  A++                          WWE         36756    32852346   \n5    6th  A++                   Movieclips         30243    17149705   \n6    7th  A++                   netd müzik          8500    11373567   \n7    8th  A++        ABS-CBN Entertainment        100147    12149206   \n8    9th  A++              Ryan ToysReview          1140    16082927   \n9   10th  A++                  Zee Marathi         74607     2841811   \n10  11th   A+              5-Minute Crafts          2085    33492951   \n11  12th   A+              Canal KondZilla           822    39409726   \n12  13th   A+             Like Nastya Vlog           150     7662886   \n13  14th   A+                        Ozuna            50    18824912   \n14  15th   A+                   Wave Music         16119    15899764   \n15  16th   A+                  Ch3Thailand         49239    11569723   \n16  17th   A+              WORLDSTARHIPHOP          4778    15830098   \n17  18th   A+              Vlad and Nikita            53         NaN   \n18  19th   A+                      Badabun          3060    23603062   \n19  20th   A+            WorkpointOfficial         24287    17687229   \n\n    Video views  \n0   20869786591  \n1   47548839843  \n2    9793305082  \n3   22675948293  \n4   26273668433  \n5   16618094724  \n6   23898730764  \n7   17202609850  \n8   24518098041  \n9    2591830307  \n10   8587520379  \n11  19291034467  \n12   2540099931  \n13   8727783225  \n14  10989179147  \n15   9388600275  \n16  11102158475  \n17   1428274554  \n18   5860444053  \n19  14022189654  ","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>Rank</th>\n      <th>Grade</th>\n      <th>Channel name</th>\n      <th>Video Uploads</th>\n      <th>Subscribers</th>\n      <th>Video views</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1st</td>\n      <td>A++</td>\n      <td>Zee TV</td>\n      <td>82757</td>\n      <td>18752951</td>\n      <td>20869786591</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2nd</td>\n      <td>A++</td>\n      <td>T-Series</td>\n      <td>12661</td>\n      <td>61196302</td>\n      <td>47548839843</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3rd</td>\n      <td>A++</td>\n      <td>Cocomelon - Nursery Rhymes</td>\n      <td>373</td>\n      <td>19238251</td>\n      <td>9793305082</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4th</td>\n      <td>A++</td>\n      <td>SET India</td>\n      <td>27323</td>\n      <td>31180559</td>\n      <td>22675948293</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5th</td>\n      <td>A++</td>\n      <td>WWE</td>\n      <td>36756</td>\n      <td>32852346</td>\n      <td>26273668433</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6th</td>\n      <td>A++</td>\n      <td>Movieclips</td>\n      <td>30243</td>\n      <td>17149705</td>\n      <td>16618094724</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7th</td>\n      <td>A++</td>\n      <td>netd müzik</td>\n      <td>8500</td>\n      <td>11373567</td>\n      <td>23898730764</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8th</td>\n      <td>A++</td>\n      <td>ABS-CBN Entertainment</td>\n      <td>100147</td>\n      <td>12149206</td>\n      <td>17202609850</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9th</td>\n      <td>A++</td>\n      <td>Ryan ToysReview</td>\n      <td>1140</td>\n      <td>16082927</td>\n      <td>24518098041</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>10th</td>\n      <td>A++</td>\n      <td>Zee Marathi</td>\n      <td>74607</td>\n      <td>2841811</td>\n      <td>2591830307</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>11th</td>\n      <td>A+</td>\n      <td>5-Minute Crafts</td>\n      <td>2085</td>\n      <td>33492951</td>\n      <td>8587520379</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>12th</td>\n      <td>A+</td>\n      <td>Canal KondZilla</td>\n      <td>822</td>\n      <td>39409726</td>\n      <td>19291034467</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>13th</td>\n      <td>A+</td>\n      <td>Like Nastya Vlog</td>\n      <td>150</td>\n      <td>7662886</td>\n      <td>2540099931</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>14th</td>\n      <td>A+</td>\n      <td>Ozuna</td>\n      <td>50</td>\n      <td>18824912</td>\n      <td>8727783225</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>15th</td>\n      <td>A+</td>\n      <td>Wave Music</td>\n      <td>16119</td>\n      <td>15899764</td>\n      <td>10989179147</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>16th</td>\n      <td>A+</td>\n      <td>Ch3Thailand</td>\n      <td>49239</td>\n      <td>11569723</td>\n      <td>9388600275</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>17th</td>\n      <td>A+</td>\n      <td>WORLDSTARHIPHOP</td>\n      <td>4778</td>\n      <td>15830098</td>\n      <td>11102158475</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>18th</td>\n      <td>A+</td>\n      <td>Vlad and Nikita</td>\n      <td>53</td>\n      <td>NaN</td>\n      <td>1428274554</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>19th</td>\n      <td>A+</td>\n      <td>Badabun</td>\n      <td>3060</td>\n      <td>23603062</td>\n      <td>5860444053</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>20th</td>\n      <td>A+</td>\n      <td>WorkpointOfficial</td>\n      <td>24287</td>\n      <td>17687229</td>\n      <td>14022189654</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:00.241571Z","iopub.execute_input":"2024-06-30T05:38:00.241917Z","iopub.status.idle":"2024-06-30T05:38:00.252298Z","shell.execute_reply.started":"2024-06-30T05:38:00.241888Z","shell.execute_reply":"2024-06-30T05:38:00.251179Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"Rank               0\nGrade              0\nChannel name       0\nVideo Uploads      6\nSubscribers      387\nVideo views        0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"perc_missing = df.isnull().sum()*100/len(df)\nperc_missing","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:00.253919Z","iopub.execute_input":"2024-06-30T05:38:00.254539Z","iopub.status.idle":"2024-06-30T05:38:00.269089Z","shell.execute_reply.started":"2024-06-30T05:38:00.254496Z","shell.execute_reply":"2024-06-30T05:38:00.267982Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"Rank             0.00\nGrade            0.00\nChannel name     0.00\nVideo Uploads    0.12\nSubscribers      7.74\nVideo views      0.00\ndtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"import seaborn as sns\nsns.heatmap(df.isnull())","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:00.270615Z","iopub.execute_input":"2024-06-30T05:38:00.270976Z","iopub.status.idle":"2024-06-30T05:38:00.806986Z","shell.execute_reply.started":"2024-06-30T05:38:00.270947Z","shell.execute_reply":"2024-06-30T05:38:00.805650Z"},"trusted":true},"execution_count":41,"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"<Axes: >"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"df = df.dropna(axis=0)","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:00.808746Z","iopub.execute_input":"2024-06-30T05:38:00.809172Z","iopub.status.idle":"2024-06-30T05:38:00.819604Z","shell.execute_reply.started":"2024-06-30T05:38:00.809133Z","shell.execute_reply":"2024-06-30T05:38:00.818082Z"},"trusted":true},"execution_count":42,"outputs":[]},{"cell_type":"code","source":"sns.heatmap(df.isnull())","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:00.823613Z","iopub.execute_input":"2024-06-30T05:38:00.824057Z","iopub.status.idle":"2024-06-30T05:38:01.406193Z","shell.execute_reply.started":"2024-06-30T05:38:00.824027Z","shell.execute_reply":"2024-06-30T05:38:01.405017Z"},"trusted":true},"execution_count":43,"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":"<Axes: >"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"df.Rank.str[0:-2]","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:01.407648Z","iopub.execute_input":"2024-06-30T05:38:01.408000Z","iopub.status.idle":"2024-06-30T05:38:01.418382Z","shell.execute_reply.started":"2024-06-30T05:38:01.407971Z","shell.execute_reply":"2024-06-30T05:38:01.417379Z"},"trusted":true},"execution_count":44,"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"0           1\n1           2\n2           3\n3           4\n4           5\n        ...  \n4995    4,996\n4996    4,997\n4997    4,998\n4998    4,999\n4999    5,000\nName: Rank, Length: 4610, dtype: object"},"metadata":{}}]},{"cell_type":"code","source":"df.Rank = df.Rank.replace('st','', regex=True)\ndf.Rank = df.Rank.replace('nd','', regex=True)\ndf.Rank = df.Rank.replace('rd','', regex=True)\ndf.Rank = df.Rank.replace('th','', regex=True)\ndf.Rank = df.Rank.replace(',','', regex=True)","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:01.419606Z","iopub.execute_input":"2024-06-30T05:38:01.419929Z","iopub.status.idle":"2024-06-30T05:38:01.448865Z","shell.execute_reply.started":"2024-06-30T05:38:01.419895Z","shell.execute_reply":"2024-06-30T05:38:01.447651Z"},"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"code","source":"df.Rank","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:01.450229Z","iopub.execute_input":"2024-06-30T05:38:01.450629Z","iopub.status.idle":"2024-06-30T05:38:01.463291Z","shell.execute_reply.started":"2024-06-30T05:38:01.450589Z","shell.execute_reply":"2024-06-30T05:38:01.462112Z"},"trusted":true},"execution_count":46,"outputs":[{"execution_count":46,"output_type":"execute_result","data":{"text/plain":"0          1\n1          2\n2          3\n3          4\n4          5\n        ... \n4995    4996\n4996    4997\n4997    4998\n4998    4999\n4999    5000\nName: Rank, Length: 4610, dtype: object"},"metadata":{}}]},{"cell_type":"code","source":"df.Rank = df.Rank.astype('float')","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:01.464620Z","iopub.execute_input":"2024-06-30T05:38:01.464937Z","iopub.status.idle":"2024-06-30T05:38:01.474284Z","shell.execute_reply.started":"2024-06-30T05:38:01.464911Z","shell.execute_reply":"2024-06-30T05:38:01.472988Z"},"trusted":true},"execution_count":47,"outputs":[]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:01.475980Z","iopub.execute_input":"2024-06-30T05:38:01.476531Z","iopub.status.idle":"2024-06-30T05:38:01.491381Z","shell.execute_reply.started":"2024-06-30T05:38:01.476473Z","shell.execute_reply":"2024-06-30T05:38:01.490226Z"},"trusted":true},"execution_count":48,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nIndex: 4610 entries, 0 to 4999\nData columns (total 6 columns):\n #   Column         Non-Null Count  Dtype  \n---  ------         --------------  -----  \n 0   Rank           4610 non-null   float64\n 1   Grade          4610 non-null   object \n 2   Channel name   4610 non-null   object \n 3   Video Uploads  4610 non-null   object \n 4   Subscribers    4610 non-null   object \n 5   Video views    4610 non-null   int64  \ndtypes: float64(1), int64(1), object(4)\nmemory usage: 252.1+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"df['Video Uploads'] = df['Video Uploads'].astype('float')","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:01.492885Z","iopub.execute_input":"2024-06-30T05:38:01.493791Z","iopub.status.idle":"2024-06-30T05:38:01.503519Z","shell.execute_reply.started":"2024-06-30T05:38:01.493755Z","shell.execute_reply":"2024-06-30T05:38:01.502294Z"},"trusted":true},"execution_count":49,"outputs":[]},{"cell_type":"code","source":"df['Subscribers'] = df['Subscribers'].astype('float')","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:01.504862Z","iopub.execute_input":"2024-06-30T05:38:01.505207Z","iopub.status.idle":"2024-06-30T05:38:01.521275Z","shell.execute_reply.started":"2024-06-30T05:38:01.505180Z","shell.execute_reply":"2024-06-30T05:38:01.520127Z"},"trusted":true},"execution_count":50,"outputs":[]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:01.523042Z","iopub.execute_input":"2024-06-30T05:38:01.523538Z","iopub.status.idle":"2024-06-30T05:38:01.540841Z","shell.execute_reply.started":"2024-06-30T05:38:01.523493Z","shell.execute_reply":"2024-06-30T05:38:01.539435Z"},"trusted":true},"execution_count":51,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nIndex: 4610 entries, 0 to 4999\nData columns (total 6 columns):\n #   Column         Non-Null Count  Dtype  \n---  ------         --------------  -----  \n 0   Rank           4610 non-null   float64\n 1   Grade          4610 non-null   object \n 2   Channel name   4610 non-null   object \n 3   Video Uploads  4610 non-null   float64\n 4   Subscribers    4610 non-null   float64\n 5   Video views    4610 non-null   int64  \ndtypes: float64(3), int64(1), object(2)\nmemory usage: 252.1+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"df.Grade","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:01.542473Z","iopub.execute_input":"2024-06-30T05:38:01.543097Z","iopub.status.idle":"2024-06-30T05:38:01.553315Z","shell.execute_reply.started":"2024-06-30T05:38:01.543061Z","shell.execute_reply":"2024-06-30T05:38:01.552116Z"},"trusted":true},"execution_count":52,"outputs":[{"execution_count":52,"output_type":"execute_result","data":{"text/plain":"0       A++ \n1       A++ \n2       A++ \n3       A++ \n4       A++ \n        ... \n4995     B+ \n4996     B+ \n4997     B+ \n4998     B+ \n4999     B+ \nName: Grade, Length: 4610, dtype: object"},"metadata":{}}]},{"cell_type":"code","source":"df.Grade.unique()","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:38:01.554967Z","iopub.execute_input":"2024-06-30T05:38:01.555322Z","iopub.status.idle":"2024-06-30T05:38:01.567400Z","shell.execute_reply.started":"2024-06-30T05:38:01.555293Z","shell.execute_reply":"2024-06-30T05:38:01.566183Z"},"trusted":true},"execution_count":53,"outputs":[{"execution_count":53,"output_type":"execute_result","data":{"text/plain":"array(['A++ ', 'A+ ', 'A ', 'A- ', 'B+ '], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"# Map the grades and handle unmapped values by filling with a default value (e.g., 0)\ndf['Grade'] = df['Grade'].map({'A++ ': 1, 'A+ ': 2, 'A ': 3, 'A- ': 4, 'B+ ': 5})","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:41:08.750965Z","iopub.execute_input":"2024-06-30T05:41:08.751380Z","iopub.status.idle":"2024-06-30T05:41:08.758998Z","shell.execute_reply.started":"2024-06-30T05:41:08.751348Z","shell.execute_reply":"2024-06-30T05:41:08.757759Z"},"trusted":true},"execution_count":63,"outputs":[]},{"cell_type":"code","source":"df.Grade.dtype","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:41:17.181645Z","iopub.execute_input":"2024-06-30T05:41:17.182864Z","iopub.status.idle":"2024-06-30T05:41:17.189565Z","shell.execute_reply.started":"2024-06-30T05:41:17.182821Z","shell.execute_reply":"2024-06-30T05:41:17.188540Z"},"trusted":true},"execution_count":64,"outputs":[{"execution_count":64,"output_type":"execute_result","data":{"text/plain":"dtype('int64')"},"metadata":{}}]},{"cell_type":"code","source":"# Find Average Views For Each Channel\ndf['avg_views'] = df['Video views']/df['Video Uploads']\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:48:18.341393Z","iopub.execute_input":"2024-06-30T05:48:18.342181Z","iopub.status.idle":"2024-06-30T05:48:18.358895Z","shell.execute_reply.started":"2024-06-30T05:48:18.342141Z","shell.execute_reply":"2024-06-30T05:48:18.357745Z"},"trusted":true},"execution_count":71,"outputs":[{"execution_count":71,"output_type":"execute_result","data":{"text/plain":"   Rank  Grade                Channel name  Video Uploads  Subscribers  \\\n0   1.0      1                      Zee TV        82757.0   18752951.0   \n1   2.0      1                    T-Series        12661.0   61196302.0   \n2   3.0      1  Cocomelon - Nursery Rhymes          373.0   19238251.0   \n3   4.0      1                   SET India        27323.0   31180559.0   \n4   5.0      1                         WWE        36756.0   32852346.0   \n\n   Video views  new     avg_views  \n0  20869786591    1  2.521815e+05  \n1  47548839843    1  3.755536e+06  \n2   9793305082    1  2.625551e+07  \n3  22675948293    1  8.299216e+05  \n4  26273668433    1  7.148130e+05  ","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>Rank</th>\n      <th>Grade</th>\n      <th>Channel name</th>\n      <th>Video Uploads</th>\n      <th>Subscribers</th>\n      <th>Video views</th>\n      <th>new</th>\n      <th>avg_views</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>1</td>\n      <td>Zee TV</td>\n      <td>82757.0</td>\n      <td>18752951.0</td>\n      <td>20869786591</td>\n      <td>1</td>\n      <td>2.521815e+05</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.0</td>\n      <td>1</td>\n      <td>T-Series</td>\n      <td>12661.0</td>\n      <td>61196302.0</td>\n      <td>47548839843</td>\n      <td>1</td>\n      <td>3.755536e+06</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.0</td>\n      <td>1</td>\n      <td>Cocomelon - Nursery Rhymes</td>\n      <td>373.0</td>\n      <td>19238251.0</td>\n      <td>9793305082</td>\n      <td>1</td>\n      <td>2.625551e+07</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>1</td>\n      <td>SET India</td>\n      <td>27323.0</td>\n      <td>31180559.0</td>\n      <td>22675948293</td>\n      <td>1</td>\n      <td>8.299216e+05</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>1</td>\n      <td>WWE</td>\n      <td>36756.0</td>\n      <td>32852346.0</td>\n      <td>26273668433</td>\n      <td>1</td>\n      <td>7.148130e+05</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:48:41.751581Z","iopub.execute_input":"2024-06-30T05:48:41.751974Z","iopub.status.idle":"2024-06-30T05:48:41.765695Z","shell.execute_reply.started":"2024-06-30T05:48:41.751941Z","shell.execute_reply":"2024-06-30T05:48:41.764180Z"},"trusted":true},"execution_count":72,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nIndex: 4610 entries, 0 to 4999\nData columns (total 8 columns):\n #   Column         Non-Null Count  Dtype  \n---  ------         --------------  -----  \n 0   Rank           4610 non-null   float64\n 1   Grade          4610 non-null   int64  \n 2   Channel name   4610 non-null   object \n 3   Video Uploads  4610 non-null   float64\n 4   Subscribers    4610 non-null   float64\n 5   Video views    4610 non-null   int64  \n 6   new            4610 non-null   int64  \n 7   avg_views      4610 non-null   float64\ndtypes: float64(4), int64(3), object(1)\nmemory usage: 324.1+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"# Find Out Top Five Channels With Maximum Number of Video Upload\ndf.sort_values(by='Video Uploads', ascending = False).head()","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:51:06.791168Z","iopub.execute_input":"2024-06-30T05:51:06.791646Z","iopub.status.idle":"2024-06-30T05:51:06.811247Z","shell.execute_reply.started":"2024-06-30T05:51:06.791608Z","shell.execute_reply":"2024-06-30T05:51:06.810150Z"},"trusted":true},"execution_count":75,"outputs":[{"execution_count":75,"output_type":"execute_result","data":{"text/plain":"        Rank  Grade Channel name  Video Uploads  Subscribers  Video views  \\\n3453  3454.0      5   AP Archive       422326.0     746325.0    548619569   \n1149  1150.0      4     YTN NEWS       355996.0     820108.0   1640347646   \n2223  2224.0      5    SBS Drama       335521.0    1418619.0   1565758044   \n323    324.0      3     GMA News       269065.0    2599175.0   2786949164   \n2956  2957.0      5          MLB       267649.0    1434206.0   1329206392   \n\n      new     avg_views  \n3453    5   1299.042846  \n1149    4   4607.769879  \n2223    5   4666.646928  \n323     3  10357.902975  \n2956    5   4966.229622  ","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>Rank</th>\n      <th>Grade</th>\n      <th>Channel name</th>\n      <th>Video Uploads</th>\n      <th>Subscribers</th>\n      <th>Video views</th>\n      <th>new</th>\n      <th>avg_views</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3453</th>\n      <td>3454.0</td>\n      <td>5</td>\n      <td>AP Archive</td>\n      <td>422326.0</td>\n      <td>746325.0</td>\n      <td>548619569</td>\n      <td>5</td>\n      <td>1299.042846</td>\n    </tr>\n    <tr>\n      <th>1149</th>\n      <td>1150.0</td>\n      <td>4</td>\n      <td>YTN NEWS</td>\n      <td>355996.0</td>\n      <td>820108.0</td>\n      <td>1640347646</td>\n      <td>4</td>\n      <td>4607.769879</td>\n    </tr>\n    <tr>\n      <th>2223</th>\n      <td>2224.0</td>\n      <td>5</td>\n      <td>SBS Drama</td>\n      <td>335521.0</td>\n      <td>1418619.0</td>\n      <td>1565758044</td>\n      <td>5</td>\n      <td>4666.646928</td>\n    </tr>\n    <tr>\n      <th>323</th>\n      <td>324.0</td>\n      <td>3</td>\n      <td>GMA News</td>\n      <td>269065.0</td>\n      <td>2599175.0</td>\n      <td>2786949164</td>\n      <td>3</td>\n      <td>10357.902975</td>\n    </tr>\n    <tr>\n      <th>2956</th>\n      <td>2957.0</td>\n      <td>5</td>\n      <td>MLB</td>\n      <td>267649.0</td>\n      <td>1434206.0</td>\n      <td>1329206392</td>\n      <td>5</td>\n      <td>4966.229622</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2024-06-30T05:53:49.231004Z","iopub.execute_input":"2024-06-30T05:53:49.231427Z","iopub.status.idle":"2024-06-30T05:53:49.246007Z","shell.execute_reply.started":"2024-06-30T05:53:49.231394Z","shell.execute_reply":"2024-06-30T05:53:49.244766Z"},"trusted":true},"execution_count":80,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nIndex: 4610 entries, 0 to 4999\nData columns (total 8 columns):\n #   Column         Non-Null Count  Dtype  \n---  ------         --------------  -----  \n 0   Rank           4610 non-null   float64\n 1   Grade          4610 non-null   int64  \n 2   Channel name   4610 non-null   object \n 3   Video Uploads  4610 non-null   float64\n 4   Subscribers    4610 non-null   float64\n 5   Video views    4610 non-null   int64  \n 6   new            4610 non-null   int64  \n 7   avg_views      4610 non-null   float64\ndtypes: float64(4), int64(3), object(1)\nmemory usage: 324.1+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"df1 = df.drop(columns = ['Channel name'])","metadata":{"execution":{"iopub.status.busy":"2024-06-30T06:03:36.397407Z","iopub.execute_input":"2024-06-30T06:03:36.397929Z","iopub.status.idle":"2024-06-30T06:03:36.407357Z","shell.execute_reply.started":"2024-06-30T06:03:36.397891Z","shell.execute_reply":"2024-06-30T06:03:36.405419Z"},"trusted":true},"execution_count":82,"outputs":[]},{"cell_type":"code","source":"df1.corr()","metadata":{"execution":{"iopub.status.busy":"2024-06-30T06:03:44.359952Z","iopub.execute_input":"2024-06-30T06:03:44.360399Z","iopub.status.idle":"2024-06-30T06:03:44.381194Z","shell.execute_reply.started":"2024-06-30T06:03:44.360367Z","shell.execute_reply":"2024-06-30T06:03:44.379869Z"},"trusted":true},"execution_count":83,"outputs":[{"execution_count":83,"output_type":"execute_result","data":{"text/plain":"                   Rank     Grade  Video Uploads  Subscribers  Video views  \\\nRank           1.000000  0.865083      -0.073180    -0.383329    -0.402873   \nGrade          0.865083  1.000000      -0.088773    -0.429213    -0.477423   \nVideo Uploads -0.073180 -0.088773       1.000000     0.011362     0.087830   \nSubscribers   -0.383329 -0.429213       0.011362     1.000000     0.791241   \nVideo views   -0.402873 -0.477423       0.087830     0.791241     1.000000   \nnew            0.865083  1.000000      -0.088773    -0.429213    -0.477423   \navg_views     -0.153670 -0.155231      -0.064408     0.289386     0.294422   \n\n                    new  avg_views  \nRank           0.865083  -0.153670  \nGrade          1.000000  -0.155231  \nVideo Uploads -0.088773  -0.064408  \nSubscribers   -0.429213   0.289386  \nVideo views   -0.477423   0.294422  \nnew            1.000000  -0.155231  \navg_views     -0.155231   1.000000  ","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>Rank</th>\n      <th>Grade</th>\n      <th>Video Uploads</th>\n      <th>Subscribers</th>\n      <th>Video views</th>\n      <th>new</th>\n      <th>avg_views</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Rank</th>\n      <td>1.000000</td>\n      <td>0.865083</td>\n      <td>-0.073180</td>\n      <td>-0.383329</td>\n      <td>-0.402873</td>\n      <td>0.865083</td>\n      <td>-0.153670</td>\n    </tr>\n    <tr>\n      <th>Grade</th>\n      <td>0.865083</td>\n      <td>1.000000</td>\n      <td>-0.088773</td>\n      <td>-0.429213</td>\n      <td>-0.477423</td>\n      <td>1.000000</td>\n      <td>-0.155231</td>\n    </tr>\n    <tr>\n      <th>Video Uploads</th>\n      <td>-0.073180</td>\n      <td>-0.088773</td>\n      <td>1.000000</td>\n      <td>0.011362</td>\n      <td>0.087830</td>\n      <td>-0.088773</td>\n      <td>-0.064408</td>\n    </tr>\n    <tr>\n      <th>Subscribers</th>\n      <td>-0.383329</td>\n      <td>-0.429213</td>\n      <td>0.011362</td>\n      <td>1.000000</td>\n      <td>0.791241</td>\n      <td>-0.429213</td>\n      <td>0.289386</td>\n    </tr>\n    <tr>\n      <th>Video views</th>\n      <td>-0.402873</td>\n      <td>-0.477423</td>\n      <td>0.087830</td>\n      <td>0.791241</td>\n      <td>1.000000</td>\n      <td>-0.477423</td>\n      <td>0.294422</td>\n    </tr>\n    <tr>\n      <th>new</th>\n      <td>0.865083</td>\n      <td>1.000000</td>\n      <td>-0.088773</td>\n      <td>-0.429213</td>\n      <td>-0.477423</td>\n      <td>1.000000</td>\n      <td>-0.155231</td>\n    </tr>\n    <tr>\n      <th>avg_views</th>\n      <td>-0.153670</td>\n      <td>-0.155231</td>\n      <td>-0.064408</td>\n      <td>0.289386</td>\n      <td>0.294422</td>\n      <td>-0.155231</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Which Grade Has A Maximum Number of Video Uploads\ndf.sort_values(by ='Video Uploads', ascending = False)['Grade'].head(1)","metadata":{"execution":{"iopub.status.busy":"2024-06-30T06:19:11.248317Z","iopub.execute_input":"2024-06-30T06:19:11.248755Z","iopub.status.idle":"2024-06-30T06:19:11.259419Z","shell.execute_reply.started":"2024-06-30T06:19:11.248723Z","shell.execute_reply":"2024-06-30T06:19:11.258281Z"},"trusted":true},"execution_count":89,"outputs":[{"execution_count":89,"output_type":"execute_result","data":{"text/plain":"3453    5\nName: Grade, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"# Which Grade Has The Highest Average Views\ndf.sort_values(by ='avg_views', ascending = False)['Grade'].head(1)","metadata":{"execution":{"iopub.status.busy":"2024-06-30T06:19:13.860779Z","iopub.execute_input":"2024-06-30T06:19:13.861360Z","iopub.status.idle":"2024-06-30T06:19:13.872989Z","shell.execute_reply.started":"2024-06-30T06:19:13.861305Z","shell.execute_reply":"2024-06-30T06:19:13.871323Z"},"trusted":true},"execution_count":90,"outputs":[{"execution_count":90,"output_type":"execute_result","data":{"text/plain":"628    3\nName: Grade, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"# Which Grade Has The Highest Number of Subscribers? \ndf.sort_values(by ='Subscribers', ascending = False)['Grade'].head(1)","metadata":{"execution":{"iopub.status.busy":"2024-06-30T07:15:17.531112Z","iopub.execute_input":"2024-06-30T07:15:17.531646Z","iopub.status.idle":"2024-06-30T07:15:17.550416Z","shell.execute_reply.started":"2024-06-30T07:15:17.531607Z","shell.execute_reply":"2024-06-30T07:15:17.549298Z"},"trusted":true},"execution_count":106,"outputs":[{"execution_count":106,"output_type":"execute_result","data":{"text/plain":"        Rank  Grade Channel name  Video Uploads  Subscribers  Video views  \\\n1470  1471.0      4    PewDiePie         3601.0   65995310.0  18684630003   \n\n      new     avg_views  \n1470    4  5.188734e+06  ","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>Rank</th>\n      <th>Grade</th>\n      <th>Channel name</th>\n      <th>Video Uploads</th>\n      <th>Subscribers</th>\n      <th>Video views</th>\n      <th>new</th>\n      <th>avg_views</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1470</th>\n      <td>1471.0</td>\n      <td>4</td>\n      <td>PewDiePie</td>\n      <td>3601.0</td>\n      <td>65995310.0</td>\n      <td>18684630003</td>\n      <td>4</td>\n      <td>5.188734e+06</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"sns.barplot(x='Grade',y='Subscribers',data=df1)","metadata":{"execution":{"iopub.status.busy":"2024-06-30T07:14:39.965552Z","iopub.execute_input":"2024-06-30T07:14:39.965936Z","iopub.status.idle":"2024-06-30T07:14:40.340299Z","shell.execute_reply.started":"2024-06-30T07:14:39.965903Z","shell.execute_reply":"2024-06-30T07:14:40.339010Z"},"trusted":true},"execution_count":105,"outputs":[{"execution_count":105,"output_type":"execute_result","data":{"text/plain":"<Axes: xlabel='Grade', ylabel='Subscribers'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"# Which Grade Has The Highest Video Views\ndf.sort_values(by ='Video views', ascending = False)['Grade'].head(1)","metadata":{"execution":{"iopub.status.busy":"2024-06-30T06:35:52.342082Z","iopub.execute_input":"2024-06-30T06:35:52.342490Z","iopub.status.idle":"2024-06-30T06:35:52.353584Z","shell.execute_reply.started":"2024-06-30T06:35:52.342447Z","shell.execute_reply":"2024-06-30T06:35:52.352550Z"},"trusted":true},"execution_count":92,"outputs":[{"execution_count":92,"output_type":"execute_result","data":{"text/plain":"1    1\nName: Grade, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"sns.barplot(x='Grade',y='Video views',data=df1)","metadata":{"execution":{"iopub.status.busy":"2024-06-30T07:14:02.686298Z","iopub.execute_input":"2024-06-30T07:14:02.686703Z","iopub.status.idle":"2024-06-30T07:14:03.056048Z","shell.execute_reply.started":"2024-06-30T07:14:02.686668Z","shell.execute_reply":"2024-06-30T07:14:03.054981Z"},"trusted":true},"execution_count":103,"outputs":[{"execution_count":103,"output_type":"execute_result","data":{"text/plain":"<Axes: xlabel='Grade', ylabel='Video views'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"df.columns","metadata":{"execution":{"iopub.status.busy":"2024-06-30T07:10:03.435259Z","iopub.execute_input":"2024-06-30T07:10:03.435702Z","iopub.status.idle":"2024-06-30T07:10:03.443015Z","shell.execute_reply.started":"2024-06-30T07:10:03.435669Z","shell.execute_reply":"2024-06-30T07:10:03.441917Z"},"trusted":true},"execution_count":93,"outputs":[{"execution_count":93,"output_type":"execute_result","data":{"text/plain":"Index(['Rank', 'Grade', 'Channel name', 'Video Uploads', 'Subscribers',\n       'Video views', 'new', 'avg_views'],\n      dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"sns.countplot(data=df, x=df['Grade'])","metadata":{"execution":{"iopub.status.busy":"2024-06-30T07:12:11.326444Z","iopub.execute_input":"2024-06-30T07:12:11.327851Z","iopub.status.idle":"2024-06-30T07:12:11.570020Z","shell.execute_reply.started":"2024-06-30T07:12:11.327804Z","shell.execute_reply":"2024-06-30T07:12:11.568602Z"},"trusted":true},"execution_count":100,"outputs":[{"execution_count":100,"output_type":"execute_result","data":{"text/plain":"<Axes: xlabel='Grade', ylabel='count'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAkQAAAGwCAYAAABIC3rIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAnzUlEQVR4nO3de3BUZZ7/8U8nIU3ApGOA3CSyQRxuctHghl40yyUmRIYRZZ2VQUFBKLXjChHIxnW4eIsyA3jD4MwsRnehEF3BkYxAAAkrhltmMgQUCpCt4JIODJg0BEhC0r8/5sdZWxiBmKQ7PO9XVVfR5zw5/T22Vbzr9Olg83q9XgEAABgsyN8DAAAA+BtBBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjhfh7gLagsbFRR48eVXh4uGw2m7/HAQAAV8Dr9erUqVOKj49XUNAPXwMiiK7A0aNHlZCQ4O8xAABAExw5ckRdu3b9wTUE0RUIDw+X9Nf/oBEREX6eBgAAXAmPx6OEhATr7/EfQhBdgQsfk0VERBBEAAC0MVdyuws3VQMAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMF6IvwcAAMBUbz79ib9HaLMyF4xu1uNxhQgAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8vwZRbm6ubr/9doWHhys6OlpjxozR/v37fdYMHTpUNpvN5/HYY4/5rCkvL9eoUaPUoUMHRUdHa+bMmTp//rzPms2bN+u2226T3W5Xjx49lJ+f39KnBwAA2gi/BlFRUZFcLpe2bdumwsJC1dfXKy0tTTU1NT7rpkyZooqKCusxf/58a19DQ4NGjRqluro6ffHFF3r33XeVn5+v2bNnW2sOHz6sUaNGadiwYSotLdW0adP06KOPat26da12rgAAIHCF+PPF165d6/M8Pz9f0dHRKikpUUpKirW9Q4cOio2NveQx1q9fry+//FIbNmxQTEyMBg4cqOeff17Z2dmaO3euQkNDtWTJEiUmJmrBggWSpN69e+vzzz/XokWLlJ6eftExa2trVVtbaz33eDzNcboAACBABdQ9RNXV1ZKkqKgon+3Lli1T586ddcsttygnJ0dnzpyx9hUXF6tfv36KiYmxtqWnp8vj8Wjv3r3WmtTUVJ9jpqenq7i4+JJz5ObmyuFwWI+EhIRmOT8AABCY/HqF6LsaGxs1bdo0DRkyRLfccou1/Re/+IW6deum+Ph47d69W9nZ2dq/f78++ugjSZLb7faJIUnWc7fb/YNrPB6Pzp49q7CwMJ99OTk5ysrKsp57PB6iCACAa1jABJHL5dKePXv0+eef+2yfOnWq9ed+/fopLi5OI0aM0KFDh3TTTTe1yCx2u112u71Fjg0AAAJPQHxklpmZqTVr1uizzz5T165df3BtcnKyJOngwYOSpNjYWFVWVvqsufD8wn1Hf2tNRETERVeHAACAefwaRF6vV5mZmVq1apU2bdqkxMTEy/5MaWmpJCkuLk6S5HQ6VVZWpmPHjllrCgsLFRERoT59+lhrNm7c6HOcwsJCOZ3OZjoTAADQlvk1iFwul/7zP/9Ty5cvV3h4uNxut9xut86ePStJOnTokJ5//nmVlJTof/7nf/T73/9eEyZMUEpKivr37y9JSktLU58+ffTQQw/pz3/+s9atW6dnn31WLpfL+tjrscce09dff61Zs2Zp3759euutt7Ry5UpNnz7db+cOAAACh1+DKC8vT9XV1Ro6dKji4uKsx/vvvy9JCg0N1YYNG5SWlqZevXrp6aef1tixY/XJJ59YxwgODtaaNWsUHBwsp9OpBx98UBMmTNBzzz1nrUlMTFRBQYEKCws1YMAALViwQL/73e8u+ZV7AABgHr/eVO31en9wf0JCgoqKii57nG7duukPf/jDD64ZOnSo/vSnP13VfAAAwAwBcVM1AACAPxFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADj+TWIcnNzdfvttys8PFzR0dEaM2aM9u/f77Pm3Llzcrlc6tSpk6677jqNHTtWlZWVPmvKy8s1atQodejQQdHR0Zo5c6bOnz/vs2bz5s267bbbZLfb1aNHD+Xn57f06QEAgDbCr0FUVFQkl8ulbdu2qbCwUPX19UpLS1NNTY21Zvr06frkk0/0wQcfqKioSEePHtV9991n7W9oaNCoUaNUV1enL774Qu+++67y8/M1e/Zsa83hw4c1atQoDRs2TKWlpZo2bZoeffRRrVu3rlXPFwAABCab1+v1+nuIC44fP67o6GgVFRUpJSVF1dXV6tKli5YvX65/+qd/kiTt27dPvXv3VnFxsQYPHqxPP/1UP/3pT3X06FHFxMRIkpYsWaLs7GwdP35coaGhys7OVkFBgfbs2WO91gMPPKCqqiqtXbv2snN5PB45HA5VV1crIiKiZU4eAGCcN5/+xN8jtFmZC0Zfds3V/P0dUPcQVVdXS5KioqIkSSUlJaqvr1dqaqq1plevXrrxxhtVXFwsSSouLla/fv2sGJKk9PR0eTwe7d2711rz3WNcWHPhGN9XW1srj8fj8wAAANeugAmixsZGTZs2TUOGDNEtt9wiSXK73QoNDVVkZKTP2piYGLndbmvNd2Powv4L+35ojcfj0dmzZy+aJTc3Vw6Hw3okJCQ0yzkCAIDAFDBB5HK5tGfPHq1YscLfoygnJ0fV1dXW48iRI/4eCQAAtKAQfw8gSZmZmVqzZo22bNmirl27WttjY2NVV1enqqoqn6tElZWVio2Ntdbs2LHD53gXvoX23TXf/2ZaZWWlIiIiFBYWdtE8drtddru9Wc4NAAAEPr9eIfJ6vcrMzNSqVau0adMmJSYm+uxPSkpSu3bttHHjRmvb/v37VV5eLqfTKUlyOp0qKyvTsWPHrDWFhYWKiIhQnz59rDXfPcaFNReOAQAAzObXK0Qul0vLly/Xxx9/rPDwcOueH4fDobCwMDkcDk2ePFlZWVmKiopSRESEnnzySTmdTg0ePFiSlJaWpj59+uihhx7S/Pnz5Xa79eyzz8rlcllXeR577DG9+eabmjVrliZNmqRNmzZp5cqVKigo8Nu5AwCAwOHXK0R5eXmqrq7W0KFDFRcXZz3ef/99a82iRYv005/+VGPHjlVKSopiY2P10UcfWfuDg4O1Zs0aBQcHy+l06sEHH9SECRP03HPPWWsSExNVUFCgwsJCDRgwQAsWLNDvfvc7paent+r5AgCAwBRQv4coUPF7iAAALYHfQ9R01/TvIQIAAPAHgggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABivSUE0fPhwVVVVXbTd4/Fo+PDhP3YmAACAVtWkINq8ebPq6uou2n7u3Dn993//948eCgAAoDWFXM3i3bt3W3/+8ssv5Xa7recNDQ1au3atbrjhhuabDgAAoBVcVRANHDhQNptNNpvtkh+NhYWF6Y033mi24QAAAFrDVX1kdvjwYR06dEher1c7duzQ4cOHrcf//u//yuPxaNKkSVd8vC1btmj06NGKj4+XzWbT6tWrffY//PDDVoBdeIwcOdJnzcmTJzV+/HhFREQoMjJSkydP1unTp33W7N69W3feeafat2+vhIQEzZ8//2pOGwAAXOOu6gpRt27dJEmNjY3N8uI1NTUaMGCAJk2apPvuu++Sa0aOHKl33nnHem632332jx8/XhUVFSosLFR9fb0eeeQRTZ06VcuXL5f01xu909LSlJqaqiVLlqisrEyTJk1SZGSkpk6d2iznAQAA2rarCqLvOnDggD777DMdO3bsokCaPXv2FR0jIyNDGRkZP7jGbrcrNjb2kvu++uorrV27Vjt37tSgQYMkSW+88Ybuvvtu/frXv1Z8fLyWLVumuro6LV26VKGhoerbt69KS0u1cOFCgggAAEhqYhD99re/1eOPP67OnTsrNjZWNpvN2mez2a44iK7E5s2bFR0dreuvv17Dhw/XCy+8oE6dOkmSiouLFRkZacWQJKWmpiooKEjbt2/Xvffeq+LiYqWkpCg0NNRak56erldeeUXffvutrr/++otes7a2VrW1tdZzj8fTbOcDAAACT5OC6IUXXtCLL76o7Ozs5p7Hx8iRI3XfffcpMTFRhw4d0jPPPKOMjAwVFxcrODhYbrdb0dHRPj8TEhKiqKgo6xtwbrdbiYmJPmtiYmKsfZcKotzcXM2bN6+FzgoAAASaJgXRt99+q/vvv7+5Z7nIAw88YP25X79+6t+/v2666SZt3rxZI0aMaLHXzcnJUVZWlvXc4/EoISGhxV4PAAD4V5N+MeP999+v9evXN/csl9W9e3d17txZBw8elCTFxsbq2LFjPmvOnz+vkydPWvcdxcbGqrKy0mfNhed/694ku92uiIgInwcAALh2NekKUY8ePfTLX/5S27ZtU79+/dSuXTuf/f/yL//SLMN93zfffKMTJ04oLi5OkuR0OlVVVaWSkhIlJSVJkjZt2qTGxkYlJydba/7t3/5N9fX11pyFhYXq2bPnJT8uAwAA5rF5vV7v1f7Q9+/J8Tmgzaavv/76io5z+vRp62rPrbfeqoULF2rYsGGKiopSVFSU5s2bp7Fjxyo2NlaHDh3SrFmzdOrUKZWVlVlfv8/IyFBlZaWWLFlife1+0KBB1tfuq6ur1bNnT6WlpSk7O1t79uzRpEmTtGjRoiv+lpnH45HD4VB1dTVXiwAAzebNpz/x9whtVuaC0ZddczV/fzfpCtHhw4eb8mMX2bVrl4YNG2Y9v3DfzsSJE5WXl6fdu3fr3XffVVVVleLj45WWlqbnn3/e53cRLVu2TJmZmRoxYoSCgoI0duxYvf7669Z+h8Oh9evXy+VyKSkpSZ07d9bs2bP5yj0AALA06QqRabhCBABoCVwharqAuEJ0uX+eY+nSpU05LAAAgF80+Wv331VfX689e/aoqqrqkv/oKwAAQCBrUhCtWrXqom2NjY16/PHHddNNN/3ooQAAAFpTk34P0SUPFBSkrKwsLVq0qLkOCQAA0CqaLYgk6dChQzp//nxzHhIAAKDFNekjs+/+sxaS5PV6VVFRoYKCAk2cOLFZBgMAAGgtTQqiP/3pTz7Pg4KC1KVLFy1YsOCy30ADAAAINE0Kos8++6y55wAAAPCbJgXRBcePH9f+/fslST179lSXLl2aZSgAAIDW1KSbqmtqajRp0iTFxcUpJSVFKSkpio+P1+TJk3XmzJnmnhEAAKBFNSmIsrKyVFRUpE8++URVVVWqqqrSxx9/rKKiIj399NPNPSMAAECLatJHZv/1X/+lDz/8UEOHDrW23X333QoLC9PPf/5z5eXlNdd8AAAALa5JV4jOnDmjmJiYi7ZHR0fzkRkAAGhzmhRETqdTc+bM0blz56xtZ8+e1bx58+R0OpttOAAAgNbQpI/MXn31VY0cOVJdu3bVgAEDJEl//vOfZbfbtX79+mYdEAAAoKU1KYj69eunAwcOaNmyZdq3b58kady4cRo/frzCwsKadUAAAICW1qQgys3NVUxMjKZMmeKzfenSpTp+/Liys7ObZTgAAIDW0KR7iN5++2316tXrou19+/bVkiVLfvRQAAAAralJQeR2uxUXF3fR9i5duqiiouJHDwUAANCamhRECQkJ2rp160Xbt27dqvj4+B89FAAAQGtq0j1EU6ZM0bRp01RfX6/hw4dLkjZu3KhZs2bxm6oBAECb06Qgmjlzpk6cOKEnnnhCdXV1kqT27dsrOztbOTk5zTogAABAS2tSENlsNr3yyiv65S9/qa+++kphYWG6+eabZbfbm3s+AACAFtekILrguuuu0+23395cswAAAPhFk26qBgAAuJYQRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeH4Noi1btmj06NGKj4+XzWbT6tWrffZ7vV7Nnj1bcXFxCgsLU2pqqg4cOOCz5uTJkxo/frwiIiIUGRmpyZMn6/Tp0z5rdu/erTvvvFPt27dXQkKC5s+f39KnBgAA2hC/BlFNTY0GDBigxYsXX3L//Pnz9frrr2vJkiXavn27OnbsqPT0dJ07d85aM378eO3du1eFhYVas2aNtmzZoqlTp1r7PR6P0tLS1K1bN5WUlOhXv/qV5s6dq9/85jctfn4AAKBtCPHni2dkZCgjI+OS+7xer1599VU9++yzuueeeyRJ7733nmJiYrR69Wo98MAD+uqrr7R27Vrt3LlTgwYNkiS98cYbuvvuu/XrX/9a8fHxWrZsmerq6rR06VKFhoaqb9++Ki0t1cKFC33C6btqa2tVW1trPfd4PM185gAAIJAE7D1Ehw8fltvtVmpqqrXN4XAoOTlZxcXFkqTi4mJFRkZaMSRJqampCgoK0vbt2601KSkpCg0Ntdakp6dr//79+vbbby/52rm5uXI4HNYjISGhJU4RAAAEiIANIrfbLUmKiYnx2R4TE2Ptc7vdio6O9tkfEhKiqKgonzWXOsZ3X+P7cnJyVF1dbT2OHDny408IAAAELL9+ZBao7Ha77Ha7v8cAAACtJGCvEMXGxkqSKisrfbZXVlZa+2JjY3Xs2DGf/efPn9fJkyd91lzqGN99DQAAYLaADaLExETFxsZq48aN1jaPx6Pt27fL6XRKkpxOp6qqqlRSUmKt2bRpkxobG5WcnGyt2bJli+rr6601hYWF6tmzp66//vpWOhsAABDI/BpEp0+fVmlpqUpLSyX99Ubq0tJSlZeXy2azadq0aXrhhRf0+9//XmVlZZowYYLi4+M1ZswYSVLv3r01cuRITZkyRTt27NDWrVuVmZmpBx54QPHx8ZKkX/ziFwoNDdXkyZO1d+9evf/++3rttdeUlZXlp7MGAACBxq/3EO3atUvDhg2znl+IlIkTJyo/P1+zZs1STU2Npk6dqqqqKt1xxx1au3at2rdvb/3MsmXLlJmZqREjRigoKEhjx47V66+/bu13OBxav369XC6XkpKS1LlzZ82ePftvfuUeAACYx+b1er3+HiLQeTweORwOVVdXKyIiwt/jAACuEW8+/Ym/R2izMheMvuyaq/n7m2+ZAYBhilL+0d8jtFn/uKXI3yOghQTsTdUAAACthSACAADGI4gAAIDxCCIAAGA8gggAABiPb5kBaBVD3hji7xHarK1PbvX3CMA1jytEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMF5AB9HcuXNls9l8Hr169bL2nzt3Ti6XS506ddJ1112nsWPHqrKy0ucY5eXlGjVqlDp06KDo6GjNnDlT58+fb+1TAQAAASzE3wNcTt++fbVhwwbreUjI/408ffp0FRQU6IMPPpDD4VBmZqbuu+8+bd26VZLU0NCgUaNGKTY2Vl988YUqKio0YcIEtWvXTi+99FKrnwsAAAhMAR9EISEhio2NvWh7dXW1/v3f/13Lly/X8OHDJUnvvPOOevfurW3btmnw4MFav369vvzyS23YsEExMTEaOHCgnn/+eWVnZ2vu3LkKDQ1t7dMBAAABKKA/MpOkAwcOKD4+Xt27d9f48eNVXl4uSSopKVF9fb1SU1Ottb169dKNN96o4uJiSVJxcbH69eunmJgYa016ero8Ho/27t37N1+ztrZWHo/H5wEAAK5dAR1EycnJys/P19q1a5WXl6fDhw/rzjvv1KlTp+R2uxUaGqrIyEifn4mJiZHb7ZYkud1unxi6sP/Cvr8lNzdXDofDeiQkJDTviQEAgIAS0B+ZZWRkWH/u37+/kpOT1a1bN61cuVJhYWEt9ro5OTnKysqynns8HqIIAIBrWEBfIfq+yMhI/eQnP9HBgwcVGxururo6VVVV+ayprKy07jmKjY296FtnF55f6r6kC+x2uyIiInweAADg2tWmguj06dM6dOiQ4uLilJSUpHbt2mnjxo3W/v3796u8vFxOp1OS5HQ6VVZWpmPHjllrCgsLFRERoT59+rT6/AAAIDAF9EdmM2bM0OjRo9WtWzcdPXpUc+bMUXBwsMaNGyeHw6HJkycrKytLUVFRioiI0JNPPimn06nBgwdLktLS0tSnTx899NBDmj9/vtxut5599lm5XC7Z7XY/nx0AAAgUAR1E33zzjcaNG6cTJ06oS5cuuuOOO7Rt2zZ16dJFkrRo0SIFBQVp7Nixqq2tVXp6ut566y3r54ODg7VmzRo9/vjjcjqd6tixoyZOnKjnnnvOX6cEAAACUEAH0YoVK35wf/v27bV48WItXrz4b67p1q2b/vCHPzT3aAAA4BrSpu4hAgAAaAkEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeCH+HgBoSeXP9fP3CG3WjbPL/D0CALQao64QLV68WH/3d3+n9u3bKzk5WTt27PD3SAAAIAAYE0Tvv/++srKyNGfOHP3xj3/UgAEDlJ6ermPHjvl7NAAA4GfGfGS2cOFCTZkyRY888ogkacmSJSooKNDSpUv1r//6r832Okkz32u2Y5mo5FcT/D0CAMBARgRRXV2dSkpKlJOTY20LCgpSamqqiouLL1pfW1ur2tpa63l1dbUkyePxXPa1GmrPNsPE5rqS/8ZX49S5hmY9nkma+704f/Z8sx7PJM39XtSc571oquZ+L87WnmnW45nkSt6LC2u8Xu9l1xoRRH/5y1/U0NCgmJgYn+0xMTHat2/fRetzc3M1b968i7YnJCS02Iz4K8cbj/l7BFyQ6/D3BPj/HNm8FwHDwXsRKGYtvvK1p06dkuMy750RQXS1cnJylJWVZT1vbGzUyZMn1alTJ9lsNj9O9uN4PB4lJCToyJEjioiI8Pc4RuO9CBy8F4GF9yNwXAvvhdfr1alTpxQfH3/ZtUYEUefOnRUcHKzKykqf7ZWVlYqNjb1ovd1ul91u99kWGRnZkiO2qoiIiDb7P/e1hvcicPBeBBbej8DR1t+Ly10ZusCIb5mFhoYqKSlJGzdutLY1NjZq48aNcjqdfpwMAAAEAiOuEElSVlaWJk6cqEGDBunv//7v9eqrr6qmpsb61hkAADCXMUH0z//8zzp+/Lhmz54tt9utgQMHau3atRfdaH0ts9vtmjNnzkUfB6L18V4EDt6LwML7EThMey9s3iv5LhoAAMA1zIh7iAAAAH4IQQQAAIxHEAEAAOMRRAAAwHgEkQG2bNmi0aNHKz4+XjabTatXr/b3SMbKzc3V7bffrvDwcEVHR2vMmDHav3+/v8cyUl5envr372/90jmn06lPP/3U32NB0ssvvyybzaZp06b5exTjzJ07VzabzefRq1cvf4/VKggiA9TU1GjAgAFavPgq/uEXtIiioiK5XC5t27ZNhYWFqq+vV1pammpqavw9mnG6du2ql19+WSUlJdq1a5eGDx+ue+65R3v37vX3aEbbuXOn3n77bfXv39/foxirb9++qqiosB6ff/65v0dqFcb8HiKTZWRkKCMjw99jQNLatWt9nufn5ys6OlolJSVKSUnx01RmGj16tM/zF198UXl5edq2bZv69u3rp6nMdvr0aY0fP16//e1v9cILL/h7HGOFhIRc8p+1utZxhQjwo+rqaklSVFSUnycxW0NDg1asWKGamhr+OR8/crlcGjVqlFJTU/09itEOHDig+Ph4de/eXePHj1d5ebm/R2oVXCEC/KSxsVHTpk3TkCFDdMstt/h7HCOVlZXJ6XTq3Llzuu6667Rq1Sr16dPH32MZacWKFfrjH/+onTt3+nsUoyUnJys/P189e/ZURUWF5s2bpzvvvFN79uxReHi4v8drUQQR4Ccul0t79uwx5vP5QNSzZ0+VlpaqurpaH374oSZOnKiioiKiqJUdOXJETz31lAoLC9W+fXt/j2O0795e0b9/fyUnJ6tbt25auXKlJk+e7MfJWh5BBPhBZmam1qxZoy1btqhr167+HsdYoaGh6tGjhyQpKSlJO3fu1Guvvaa3337bz5OZpaSkRMeOHdNtt91mbWtoaNCWLVv05ptvqra2VsHBwX6c0FyRkZH6yU9+ooMHD/p7lBZHEAGtyOv16sknn9SqVau0efNmJSYm+nskfEdjY6Nqa2v9PYZxRowYobKyMp9tjzzyiHr16qXs7GxiyI9Onz6tQ4cO6aGHHvL3KC2OIDLA6dOnfer+8OHDKi0tVVRUlG688UY/TmYel8ul5cuX6+OPP1Z4eLjcbrckyeFwKCwszM/TmSUnJ0cZGRm68cYbderUKS1fvlybN2/WunXr/D2accLDwy+6j65jx47q1KkT99e1shkzZmj06NHq1q2bjh49qjlz5ig4OFjjxo3z92gtjiAywK5duzRs2DDreVZWliRp4sSJys/P99NUZsrLy5MkDR061Gf7O++8o4cffrj1BzLYsWPHNGHCBFVUVMjhcKh///5at26d7rrrLn+PBvjNN998o3HjxunEiRPq0qWL7rjjDm3btk1dunTx92gtzub1er3+HgIAAMCf+D1EAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRABwhR5++GGNGTPG32MAaAEEEYA2y+1266mnnlKPHj3Uvn17xcTEaMiQIcrLy9OZM2f8PR6ANoR/ywxAm/T1119ryJAhioyM1EsvvaR+/frJbrerrKxMv/nNb3TDDTfoZz/72UU/V19fr3bt2vlhYgCBjCtEANqkJ554QiEhIdq1a5d+/vOfq3fv3urevbvuueceFRQUaPTo0ZIkm82mvLw8/exnP1PHjh314osvqqGhQZMnT1ZiYqLCwsLUs2dPvfbaaz7Hb2hoUFZWliIjI9WpUyfNmjVL3/+nHxsbG5Wbm2sdZ8CAAfrwww9b7b8BgOZDEAFoc06cOKH169fL5XKpY8eOl1xjs9msP8+dO1f33nuvysrKNGnSJDU2Nqpr16764IMP9OWXX2r27Nl65plntHLlSutnFixYoPz8fC1dulSff/65Tp48qVWrVvm8Rm5urt577z0tWbJEe/fu1fTp0/Xggw+qqKioZU4cQIvhX7sH0OZs375dgwcP1kcffaR7773X2t65c2edO3dOkuRyufTKK6/IZrNp2rRpWrRo0Q8eMzMzU26327rCEx8fr+nTp2vmzJmSpPPnzysxMVFJSUlavXq1amtrFRUVpQ0bNsjpdFrHefTRR3XmzBktX768uU8bQAviHiIA14wdO3aosbFR48ePV21trbV90KBBF61dvHixli5dqvLycp09e1Z1dXUaOHCgJKm6uloVFRVKTk621oeEhGjQoEHWx2YHDx7UmTNndNddd/kct66uTrfeemsLnB2AlkQQAWhzevToIZvNpv379/ts7969uyQpLCzMZ/v3P1ZbsWKFZsyYoQULFsjpdCo8PFy/+tWvtH379iue4fTp05KkgoIC3XDDDT777Hb7FR8HQGDgHiIAbU6nTp1011136c0331RNTc1V//zWrVv1D//wD3riiSd06623qkePHjp06JC13+FwKC4uzieQzp8/r5KSEut5nz59ZLfbVV5erh49evg8EhISftwJAmh1XCEC0Ca99dZbGjJkiAYNGqS5c+eqf//+CgoK0s6dO7Vv3z4lJSX9zZ+9+eab9d5772ndunVKTEzUf/zHf2jnzp1KTEy01jz11FN6+eWXdfPNN6tXr15auHChqqqqrP3h4eGaMWOGpk+frsbGRt1xxx2qrq7W1q1bFRERoYkTJ7bk6QNoZtxUDaDNqqio0EsvvaSCggJ98803stvt6tOnj+6//3498cQT6tChg2w2m1atWuXzG6Zra2v12GOPadWqVbLZbBo3bpwcDoc+/fRTlZaWSvrrFaEZM2bonXfeUVBQkCZNmqS//OUvqq6u1urVqyVJXq9Xr7/+uvLy8vT1118rMjJSt912m5555hmlpKS0/n8QAE1GEAEAAONxDxEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADj/T8mLMdHIOyOUQAAAABJRU5ErkJggg=="},"metadata":{}}]},{"cell_type":"code","source":"sns.barplot(x='Grade',y='Video Uploads',data=df1)","metadata":{"execution":{"iopub.status.busy":"2024-06-30T07:13:26.531007Z","iopub.execute_input":"2024-06-30T07:13:26.531402Z","iopub.status.idle":"2024-06-30T07:13:27.051120Z","shell.execute_reply.started":"2024-06-30T07:13:26.531371Z","shell.execute_reply":"2024-06-30T07:13:27.050150Z"},"trusted":true},"execution_count":102,"outputs":[{"execution_count":102,"output_type":"execute_result","data":{"text/plain":"<Axes: xlabel='Grade', ylabel='Video Uploads'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}