final-draft
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index.html
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@ -115,6 +115,7 @@
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- Access API
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- Access API
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- Channel upload playlist
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- Channel upload playlist
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- Video statistics
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- Video statistics
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- `pandas` dataframe
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</section>
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</section>
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<section data-markdown>
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<section data-markdown>
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### 4. Get YouTube video statistics
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### 4. Get YouTube video statistics
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@ -132,40 +133,44 @@
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```
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```
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</section>
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</section>
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<section>
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<section data-markdown>
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<pre><code data-line-numbers="3|5-16|17-18|18-29|30-32"># tubestates/youtube_api.py
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```python [|3|5-16|17-18|20-29|30-32]
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# tubestates/youtube_api.py
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upload_playlist_ID = channel_data['upload_playlist_ID']
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upload_playlist_ID = channel_data['upload_playlist_ID']
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video_response = []
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video_response = []
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next_page_token = None
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next_page_token = None
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while True:
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while True:
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# obtaining video ID + titles
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# obtaining video ID + titles
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playlist_request = self.youtube.playlistItems().list(
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playlist_request = self.youtube.playlistItems().list(
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part='snippet,contentDetails',
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part='snippet,contentDetails',
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maxResults=50, # API Limit is 50
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maxResults=50, # API Limit is 50
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pageToken=next_page_token,
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pageToken=next_page_token,
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playlistId=upload_playlist_ID,
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playlistId=upload_playlist_ID,
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)
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)
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playlist_response = playlist_request.execute()
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playlist_response = playlist_request.execute()
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# isolating video ID
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# isolating video ID
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vid_subset = [ vid_ID['contentDetails']['videoId']
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vid_subset = [ vid_ID['contentDetails']['videoId']
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for vid_ID in playlist_response['items'] ]
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for vid_ID in playlist_response['items'] ]
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# retrieving video statistics
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# retrieving video statistics
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vid_info_subset_request = self.youtube.videos().list(
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vid_info_subset_request = self.youtube.videos().list(
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part='snippet,contentDetails,statistics',
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part='snippet,contentDetails,statistics',
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id=vid_subset
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id=vid_subset
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)
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)
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vid_info_subset_response = vid_info_subset_request.execute()
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vid_info_subset_response = vid_info_subset_request.execute()
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video_response.append(vid_info_subset_response)
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video_response.append(vid_info_subset_response)
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# obtaining page token
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# obtaining page token
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next_page_token = playlist_response.get('nextPageToken') # get method used because token may not exist
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next_page_token = playlist_response.get('nextPageToken') # get method used because token may not exist
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if next_page_token is None:
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if next_page_token is None:
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break
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break
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df = pd.json_normalize(video_response, 'items')
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df = pd.json_normalize(video_response, 'items')
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return df
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return df
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</code></pre>
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</section>
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<section data-markdown>
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### Video statistics
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</section>
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</section>
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<section data-markdown>
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<section data-markdown>
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## How does TubeStats work?
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## How does TubeStats work?
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@ -202,7 +207,7 @@ return df
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</section>
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</section>
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<section data-markdown>
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<section data-markdown>
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## 6. Testing
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## 6. Testing
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```python [|16-20]
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```python [|15-20]
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# tests/tests_youtube_api.py
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# tests/tests_youtube_api.py
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from tubestats.youtube_api import create_api, YouTubeAPI
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from tubestats.youtube_api import create_api, YouTubeAPI
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from tests.test_settings import set_channel_ID_test_case
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from tests.test_settings import set_channel_ID_test_case
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</section>
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</section>
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<section data-markdown>
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<section data-markdown>
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## Somethings I would like to discuss
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## Somethings I would like to discuss
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- DataFrame and memory
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- Error handling
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- Async?
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</section>
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</section>
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<section data-markdown>
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<section data-markdown>
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df = self.df
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### DataFrame immutability and memory?
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df = df[['snippet.publishedAt',
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```python []
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'snippet.title',
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df = self.df
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...
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df = df[['snippet.publishedAt',
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'statistics.favoriteCount',
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'snippet.title',
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'statistics.commentCount']]
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...
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'statistics.favoriteCount',
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df = df.fillna(0)
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'statistics.commentCount']]
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# changing dtypes
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df = df.fillna(0)
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df = df.astype({'statistics.viewCount': 'int',
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...
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'statistics.commentCount': 'int',})
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# applying natural log to view count as data is tail heavy
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df['statistics.viewCount_NLOG'] = df['statistics.viewCount'].apply(lambda x : np.log(x))
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df = df.sort_values(by='snippet.publishedAt_REFORMATED', ascending=True)
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df = df.astype({'statistics.viewCount': 'int',
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return DataFrame)
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...
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'statistics.commentCount': 'int',})
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df['statistics.viewCount_NLOG'] = df['statistics.viewCount'].apply(lambda x : np.log(x))
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df = df.sort_values(by='snippet.publishedAt_REFORMATED', ascending=True)
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</section>
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</section>
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<section data-markdown>
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## What did I learn
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- Project based learning
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- 'minimal viable product'
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</section>
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<section data-markdown>
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## Conclusion
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- Analysing consistency
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- YouTube Data API --> Heroku
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- Share your work!
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</section>
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<section data-markdown>
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## Acknowledgements
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- Menno
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</div>
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</div>
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</div>
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</div>
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