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

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Use SQLAlchemy ORMs to Access Facebook Ads Data in Python


The CData Python Connector for Facebook Ads enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Facebook Ads data.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Facebook Ads and the SQLAlchemy toolkit, you can build Facebook Ads-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Facebook Ads data to query, update, delete, and insert Facebook Ads data.

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 CData Connector 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 SQLAlchemy and start accessing Facebook Ads through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:

pip install sqlalchemy pip install sqlalchemy.orm

Be sure to import the appropriate modules:

from sqlalchemy import create_engine, String, Column from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker

Model 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.

NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.

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

Declare a Mapping Class for Facebook Ads Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the AdAccounts table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base() class AdAccounts(base): __tablename__ = "AdAccounts" AccountId = Column(String,primary_key=True) Name = Column(String) ...

Query Facebook Ads Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("facebookads:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(AdAccounts).filter_by(Name="Acct Name"): print("AccountId: ", instance.AccountId) print("Name: ", instance.Name) print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

AdAccounts_table = AdAccounts.metadata.tables["AdAccounts"] for instance in session.execute(AdAccounts_table.select().where(AdAccounts_table.c.Name == "Acct Name")): print("AccountId: ", instance.AccountId) print("Name: ", instance.Name) print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Insert Facebook Ads Data

To insert Facebook Ads data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to Facebook Ads.

new_rec = AdAccounts(AccountId="placeholder", Name="Acct Name") session.add(new_rec) session.commit()

Update Facebook Ads Data

To update Facebook Ads data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to Facebook Ads.

updated_rec = session.query(AdAccounts).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Name = "Acct Name" session.commit()

Delete Facebook Ads Data

To delete Facebook Ads data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided records (rows).

deleted_rec = session.query(AdAccounts).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() session.delete(deleted_rec) session.commit()

Free Trial & More Information

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