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Use pandas to Visualize YouTube Data in Python

The CData Python Connector for YouTube enables you use pandas and other modules to analyze and visualize live YouTube 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, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build YouTube-connected Python applications and scripts for visualizing YouTube data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to YouTube 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 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.

Follow the procedure below to install the required modules and start accessing YouTube 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 Data in Python

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

engine = create_engine("youtube:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to YouTube

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

df = pandas.read_sql("SELECT Title, ViewCount FROM Videos WHERE MyRating = 'like'", engine)

Visualize YouTube Data

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

df.plot(kind="bar", x="Title", y="ViewCount")

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 pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("youtube:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Title, ViewCount FROM Videos WHERE MyRating = 'like'", engine)

df.plot(kind="bar", x="Title", y="ViewCount")