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Extract, Transform, and Load YouTube Analytics Data in Python

The CData Python Connector for YouTube Analytics enables you to create ETL applications and pipelines for YouTube Analytics data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for YouTube Analytics and the petl framework, you can build YouTube Analytics-connected applications and pipelines for extracting, transforming, and loading YouTube Analytics data. This article shows how to connect to YouTube Analytics with the CData Python Connector and use petl and pandas to extract, transform, and load YouTube Analytics data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live YouTube Analytics data in Python. When you issue complex SQL queries from YouTube Analytics, the driver pushes supported SQL operations, like filters and aggregations, directly to YouTube Analytics and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to YouTube Analytics Data

Connecting to YouTube Analytics data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

YouTube Analytics uses the OAuth authentication standard. You can use the embedded CData OAuth credentials or you can register an application with Google to obtain your own.

In addition to the OAuth values, to access YouTube Analytics data set ChannelId to the Id of a YouTube channel. You can obtain the channel Id in the advanced account settings for your channel. If not specified, the channel of the currently authenticated user will be used.

If you want to generate content owner reports, specify the ContentOwnerId property. This is the Id of the copyright holder for content in YouTube's rights management system. The content owner is the person or organization that claims videos and sets their monetization policy.

After installing the CData YouTube Analytics Connector, follow the procedure below to install the other required modules and start accessing YouTube Analytics through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for YouTube Analytics Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.youtubeanalytics as mod

You can now connect with a connection string. Use the connect function for the CData YouTube Analytics Connector to create a connection for working with YouTube Analytics data.

cnxn = mod.connect("ContentOwnerId=MyContentOwnerId;ChannelId=MyChannelId;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query YouTube Analytics

Use SQL to create a statement for querying YouTube Analytics. In this article, we read data from the Groups entity.

sql = "SELECT Snippet_Title, ContentDetails_ItemCount FROM Groups WHERE Mine = 'True'"

Extract, Transform, and Load the YouTube Analytics Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the YouTube Analytics data. In this example, we extract YouTube Analytics data, sort the data by the ContentDetails_ItemCount column, and load the data into a CSV file.

Loading YouTube Analytics Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'ContentDetails_ItemCount')

etl.tocsv(table2,'groups_data.csv')

In the following example, we add new rows to the Groups table.

Adding New Rows to YouTube Analytics

table1 = [ ['Snippet_Title','ContentDetails_ItemCount'], ['NewSnippet_Title1','NewContentDetails_ItemCount1'], ['NewSnippet_Title2','NewContentDetails_ItemCount2'], ['NewSnippet_Title3','NewContentDetails_ItemCount3'] ]

etl.appenddb(table1, cnxn, 'Groups')

With the CData Python Connector for YouTube Analytics, you can work with YouTube Analytics data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the YouTube Analytics Python Connector to start building Python apps and scripts with connectivity to YouTube Analytics data. Reach out to our Support Team if you have any questions.



Full Source Code


import petl as etl
import pandas as pd
import cdata.youtubeanalytics as mod

cnxn = mod.connect("ContentOwnerId=MyContentOwnerId;ChannelId=MyChannelId;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Snippet_Title, ContentDetails_ItemCount FROM Groups WHERE Mine = 'True'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'ContentDetails_ItemCount')

etl.tocsv(table2,'groups_data.csv')

table3 = [ ['Snippet_Title','ContentDetails_ItemCount'], ['NewSnippet_Title1','NewContentDetails_ItemCount1'], ['NewSnippet_Title2','NewContentDetails_ItemCount2'], ['NewSnippet_Title3','NewContentDetails_ItemCount3'] ]

etl.appenddb(table3, cnxn, 'Groups')