Top Streams for a game Twitch

top_streams_twitch.py

# I am getting only the top 25 streamers for the first game
headers = {
    'Authorization' : 'Bearer '+str(access_token),
}
topstreamsforgame_response = requests.get('https://api.twitch.tv/helix/streams?game_id='+str(topgames_df['id'][0])+'&first=25', headers=headers)

# Load the JSON
topstreamsforgame_response_json = json.loads(topstreamsforgame_response.text)

# Extracting data from the JSON
topstreamsforgame_data = topstreamsforgame_response_json['data']

# Converting into a DataFrame
topstreamsforgame_df = pd.DataFrame.from_dict(json_normalize(topstreamsforgame_data), orient='columns')

# FOR loop to get top 25 streamers for rest of the games in our list
# To keep the dashboard lightweight and relevant, I am using only the Top 20 Games and Top 25 Streamers per game

for i in range(1,19) :
    
    headers = {
        'Authorization' : 'Bearer '+str(access_token),
    }
    topstreamsforgame_response = requests.get('https://api.twitch.tv/helix/streams?game_id='+str(topgames_df['id'][i])+'&first=25', headers=headers)
    
    topstreamsforgame_response_json = json.loads(topstreamsforgame_response.text)
    topstreamsforgame_data = topstreamsforgame_response_json['data']
    topstreamsforgame_df_temp = pd.DataFrame.from_dict(json_normalize(topstreamsforgame_data), orient='columns')
    
    frames = [topstreamsforgame_df, topstreamsforgame_df_temp]
    topstreamsforgame_df = pd.concat(frames, ignore_index=True)

# Look at the data we retrieved
topstreamsforgame_df.info()