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Python Connector Libraries for YouTube Analytics Data Connectivity. Integrate YouTube Analytics with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

How to Visualize YouTube Analytics Data in Python with pandas

Use pandas and other modules to analyze and visualize live YouTube Analytics data in Python.

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, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build YouTube Analytics-connected Python applications and scripts for visualizing YouTube Analytics data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to YouTube Analytics data, execute queries, and visualize the results.

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.

Follow the procedure below to install the required modules and start accessing YouTube Analytics through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize YouTube Analytics Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with YouTube Analytics data.

engine = create_engine("youtubeanalytics:///?ContentOwnerId=MyContentOwnerId&ChannelId=MyChannelId&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to YouTube Analytics

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT Snippet_Title, ContentDetails_ItemCount FROM Groups WHERE Mine = 'True'", engine)

Visualize YouTube Analytics Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the YouTube Analytics data. The show method displays the chart in a new window.

df.plot(kind="bar", x="Snippet_Title", y="ContentDetails_ItemCount")

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for YouTube Analytics 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 pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("youtubeanalytics:///?ContentOwnerId=MyContentOwnerId&ChannelId=MyChannelId&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Snippet_Title, ContentDetails_ItemCount FROM Groups WHERE Mine = 'True'", engine)

df.plot(kind="bar", x="Snippet_Title", y="ContentDetails_ItemCount")