iot youtube project

! pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib

import os import pandas as pd import seaborn as sns import matplotlib.pyplot as plt from googleapiclient.discovery import build

API_KEY=os.environ["API_KEY"] channel_id="UCMo9XBmvZ7EobpH4fYp9TNA" youtube = build('youtube', 'v3', developerKey=API_KEY)

print(API_KEY)

# Function to get the channels stats # It will also contain the upload playlist ID we can use to grab videos. def get_channel_stats(youtube, channel_id): request = youtube.channels().list( part="snippet,contentDetails,statistics", id=channel_id ) response = request.execute() return response['items']

# This will get us a list of videos from a playlist. # Note a page of results has a max value of 50 so we will # need to loop over our results with a pageToken def get_video_list(youtube, upload_id): video_list = [] request = youtube.playlistItems().list( part="snippet,contentDetails", playlistId=upload_id, maxResults=50 ) next_page = True while next_page: response = request.execute() data = response['items'] for video in data: video_id = video['contentDetails']['videoId'] if video_id not in video_list: video_list.append(video_id) # Do we have more pages? if 'nextPageToken' in response.keys(): next_page = True request = youtube.playlistItems().list( part="snippet,contentDetails", playlistId=upload_id, pageToken=response['nextPageToken'], maxResults=50 ) else: next_page = False return video_list

# Once we have our video list we can pass it to this function to get details. # Again we have a max of 50 at a time so we will use a for loop to break up our list. def get_video_details(youtube, video_list): stats_list=[] # Can only get 50 videos at a time. for i in range(0, len(video_list), 50): request= youtube.videos().list( part="snippet,contentDetails,statistics", id=video_list[i:i+50] ) data = request.execute() for video in data['items']: title=video['snippet']['title'] published=video['snippet']['publishedAt'] description=video['snippet']['description'] tag_count= len(video['snippet']['tags']) view_count=video['statistics'].get('viewCount',0) like_count=video['statistics'].get('likeCount',0) dislike_count=video['statistics'].get('dislikeCount',0) comment_count=video['statistics'].get('commentCount',0) stats_dict=dict(title=title, description=description, published=published, tag_count=tag_count, view_count=view_count, like_count=like_count, dislike_count=dislike_count, comment_count=comment_count) stats_list.append(stats_dict) return stats_list

channel_stats = get_channel_stats(youtube, channel_id)

upload_id = channel_stats[0]['contentDetails']['relatedPlaylists']['uploads'] upload_id

video_list = get_video_list(youtube, upload_id)

video_data = get_video_details(youtube, video_list)

df=pd.DataFrame(video_data) df['title_length'] = df['title'].str.len() df["view_count"] = pd.to_numeric(df["view_count"]) df["like_count"] = pd.to_numeric(df["like_count"]) df["dislike_count"] = pd.to_numeric(df["dislike_count"]) df["comment_count"] = pd.to_numeric(df["comment_count"]) # reaction used later add up likes + dislikes + comments df["reactions"] = df["like_count"] + df["dislike_count"] + df["comment_count"] + df["comment_count"] df.to_csv("GMM-Data.csv") df.head()

# Histogram of tag count. chart = sns.distplot( a=df["tag_count"], hist=True, kde=False, rug=False ) chart.set(xlabel='Number of Tags Used', ylabel='Occurrences')

chart = sns.distplot( a=df["title_length"], hist=True, kde=False, rug=False ) chart.set(xlabel='Title Length', ylabel='Occurrences')

df_highest_views = df.nlargest(10, 'view_count') df_highest_views['title'] = df_highest_views['title'].str[:40] df_highest_views['view_count_millions'] = df_highest_views['view_count'] / 1000000 df_highest_views

sns.set(rc={'figure.figsize':(20,5)}) plot = sns.barplot(x="view_count_millions", y="title", data=df_highest_views, palette="bright") plot.set(xlabel='Millions of Views', ylabel='') plot.set_title('Most Viewed Videos') plt.xlim(20, 32)

# Most Interations ( Like + Dislikes + Comments) df_highest_reactions = df.nlargest(10, 'reactions') df_highest_reactions['title'] = df_highest_reactions['title'].str[:40] df_highest_reactions['reactions'] = df_highest_reactions['reactions'] / 1000 df_highest_reactions

sns.set(rc={'figure.figsize':(20,5)}) plot = sns.barplot(y="title", x="reactions", data=df_highest_reactions, palette="bright") plot.set(xlabel='Thousands of Reactions', ylabel='') plt.xticks(rotation=45) plot.set_title('Most Video Reactions') plt.xlim(300, 700)