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

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How to use SQLAlchemy ORM to access MailChimp Data in Python


Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of MailChimp data.

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

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

Connecting to MailChimp Data

Connecting to MailChimp 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.

You can set the APIKey to the key you generate in your account settings, or, instead of providing your APIKey, you can use the OAuth standard to authenticate the application. OAuth can be used to enable other users to access their own data. To authenticate using OAuth, you will need to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL by registering an app with MailChimp.

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

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with MailChimp 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("mailchimp:///?APIKey=myAPIKey")

Declare a Mapping Class for MailChimp 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 Lists 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 Lists(base): __tablename__ = "Lists" Name = Column(String,primary_key=True) Stats_AvgSubRate = Column(String) ...

Query MailChimp 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("mailchimp:///?APIKey=myAPIKey") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Lists).filter_by(Contact_Country="US"): print("Name: ", instance.Name) print("Stats_AvgSubRate: ", instance.Stats_AvgSubRate) 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

Lists_table = Lists.metadata.tables["Lists"] for instance in session.execute(Lists_table.select().where(Lists_table.c.Contact_Country == "US")): print("Name: ", instance.Name) print("Stats_AvgSubRate: ", instance.Stats_AvgSubRate) print("---------")

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

Insert MailChimp Data

To insert MailChimp 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 MailChimp.

new_rec = Lists(Name="placeholder", Contact_Country="US") session.add(new_rec) session.commit()

Update MailChimp Data

To update MailChimp 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 MailChimp.

updated_rec = session.query(Lists).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Contact_Country = "US" session.commit()

Delete MailChimp Data

To delete MailChimp 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(Lists).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 MailChimp to start building Python apps and scripts with connectivity to MailChimp data. Reach out to our Support Team if you have any questions.