Use YouTube API to get view trends of popular MOOC course

view_count_youtube_api.py

import os
import pandas as pd

import google_auth_oauthlib.flow
import googleapiclient.discovery
import googleapiclient.errors
import matplotlib 

scopes = ["https://www.googleapis.com/auth/youtube.readonly"]

# Disable OAuthlib's HTTPS verification when running locally.
# *DO NOT* leave this option enabled in production.
os.environ["OAUTHLIB_INSECURE_TRANSPORT"] = "1"

api_service_name = "youtube"
api_version = "v3"
client_secrets_file = "YOUR_CLIENT_SECRET_FILE.json"

# Get credentials and create an API client
flow = google_auth_oauthlib.flow.InstalledAppFlow.from_client_secrets_file(
    client_secrets_file, scopes)
credentials = flow.run_console()
youtube = googleapiclient.discovery.build(
    api_service_name, api_version, credentials=credentials)


course_dict = {
    "Stanford CS 230": "PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb",
    "Stanford CS 224N": "PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z",
    "Stanford CS 231N": "PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk",
    "Stanford CS 229": "PLa-Bt050gYuhEeLRG8YBmFxwLvTJ5FqPS",
    "fast.ai Course": "PLCdvEQLhYkYmKTKWTrH7bHtQ1CsKZaQBl",
    "MIT Linear Algebra": "PL49CF3715CB9EF31D",
    "Caltech Learning from Data": "PLD63A284B7615313A",
    "How to Start a Startup": "PL11qn6zM2Y3bMZdChxEqHKaCaKUjwItGL",
    # really any playlist from Youtube
}

def get_view_ts(course_name, api_client):
    """
    For a given channel_id, retrive the list of videos and 
    store the video title and video id in a dataframe.

    return: pandas.DataFrame()
    """
    request = youtube.playlistItems().list(
    part="snippet",
    maxResults=50,
    playlistId=course_dict.get(course_name)
    )
    response = request.execute()

    df_video_ids = pd.DataFrame(
        columns = ['course_name', 'title', 'video_id']
    )

    for r in response.get('items'):
        data = r.get('snippet')
        title = data.get('title')
        video_id = data.get('resourceId').get('videoId')
        df_video_ids = df_video_ids.append(
            {'course_name': course_name, 'title' : title, 'video_id': video_id}, 
            ignore_index=True
        )

    df_views = pd.DataFrame(columns = ['video_id', 'views'])

    for video_id in df_video_ids.video_id:
        request = youtube.videos().list(
                part="statistics",
                id=video_id
            )
        response = request.execute()
        view_count = (response.get('items')[0]
                .get('statistics')
                .get('viewCount')
        )
        df_views = df_views.append(
            {'video_id' : video_id, 'views': float(view_count)}, 
            ignore_index=True
        )
    df = df_video_ids.merge(df_views, on='video_id')
    df['view_ratio'] = df.views / df.views[0]
    
    return df


df_result = pd.DataFrame(columns = ['course_name', 'title', 'views'])

for course_name in course_dict.keys():
    df_per_course = get_view_ts(course_name, youtube)
    df_result = df_result.append(
        df_per_course["course_name", "title", "views"]
    )

df_pivot = (
    df_result
        .loc[:, ['course_name', 'views']]
        .pivot(columns='course_name', values='views')
)

ax = (
    df_pivot.plot(
        title = 'View Trends Overtime',
        kind='line',
        figsize=[15,10]
    )
)

ax.set_xlabel("Video # in the YoutTube Playlist", size=14)
ax.set_ylabel("% Views relative to First Video", size=14)
vals = ax.get_yticks()
_ = ax.set_yticklabels(['{:,.1%}'.format(x) for x in vals])