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Extract, Transform, and Load YouTube Data in Python

The CData Python Connector for YouTube enables you to create ETL applications and pipelines for YouTube 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 and the petl framework, you can build YouTube-connected applications and pipelines for extracting, transforming, and loading YouTube data. This article shows how to connect to YouTube with the CData Python Connector and use petl and pandas to extract, transform, and load YouTube data.

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

Connecting to YouTube Data

Connecting to YouTube 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 uses the OAuth authentication standard. To access Google APIs on behalf on individual users, you can use the embedded CData credentials or you can register your own OAuth app.

OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, you will need to register an application to obtain the OAuth JWT values.

After installing the CData YouTube Connector, follow the procedure below to install the other required modules and start accessing YouTube 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 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.youtube as mod

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

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

Create a SQL Statement to Query YouTube

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

sql = "SELECT Title, ViewCount FROM Videos WHERE MyRating = 'like'"

Extract, Transform, and Load the YouTube Data

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

Loading YouTube Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData Python Connector for YouTube, you can work with YouTube 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 Python Connector to start building Python apps and scripts with connectivity to YouTube 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.youtube as mod

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

sql = "SELECT Title, ViewCount FROM Videos WHERE MyRating = 'like'"

table1 = etl.fromdb(cnxn,sql)

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

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