Use pandas to Visualize Facebook Ads Data in Python

Ready to get started?

Download for a free trial:

Download Now

Learn more:

Facebook Ads Python Connector

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

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

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

Connecting to Facebook Ads Data

Connecting to Facebook Ads 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.

Most tables require user authentication as well as application authentication. Facebook uses the OAuth authentication standard. To authenticate to Facebook, you can use the embedded OAuthClientId, OAuthClientSecret, and CallbackURL or you can obtain your own by registering an app with Facebook.

See the Getting Started chapter of the help documentation for a guide to using OAuth.

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

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

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

Execute SQL to Facebook Ads

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

df = pandas.read_sql("SELECT AccountId, Name FROM AdAccounts WHERE Name = 'Acct Name'", engine)

Visualize Facebook Ads Data

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

df.plot(kind="bar", x="AccountId", y="Name")

Free Trial & More Information

Download a free, 30-day trial of the Facebook Ads Python Connector to start building Python apps and scripts with connectivity to Facebook Ads 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("facebookads:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT AccountId, Name FROM AdAccounts WHERE Name = 'Acct Name'", engine)

df.plot(kind="bar", x="AccountId", y="Name")