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

Use SQLAlchemy ORMs to Access LinkedIn Ads Data in Python

The CData Python Connector for LinkedIn Ads enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of LinkedIn 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 LinkedIn Ads and the SQLAlchemy toolkit, you can build LinkedIn Ads-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to LinkedIn Ads data to query LinkedIn Ads data.

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

Connecting to LinkedIn Ads Data

Connecting to LinkedIn 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.

LinkedIn Ads uses the OAuth authentication standard. OAuth requires the authenticating user to interact with LinkedIn using the browser. See the OAuth section in the Help documentation for a guide.

Follow the procedure below to install SQLAlchemy and start accessing LinkedIn 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 LinkedIn Ads Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with LinkedIn 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("linkedinads:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:portNumber&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for LinkedIn 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 Analytics 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 Analytics(base): __tablename__ = "Analytics" VisibilityCode = Column(String,primary_key=True) Comment = Column(String) ...

Query LinkedIn 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("linkedinads:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:portNumber&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Analytics).filter_by(EntityId="238"): print("VisibilityCode: ", instance.VisibilityCode) print("Comment: ", instance.Comment) 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

Analytics_table = Analytics.metadata.tables["Analytics"] for instance in session.execute( == "238")): print("VisibilityCode: ", instance.VisibilityCode) print("Comment: ", instance.Comment) print("---------")

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

Free Trial & More Information

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