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

Use SQLAlchemy ORMs to Access Act CRM Data in Python

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

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

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

Connecting to Act CRM Data

Connecting to Act CRM 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.

The User and Password properties, under the Authentication section, must be set to valid Act! user credentials. In addition to the authentication values, see the following:

  • Connecting to Act! Premium

    In addition to the authentication values, the URL to Act! is also required; for example

    Additionally, you must specify the ActDatabase you will connect to. This is found by going to the About Act! Premium menu of your account, at the top right of the page, in the ? menu. Use the Database Name in the window that appears.

  • Connecting to Act! Premium Cloud

    To connect to your Act! Premium Cloud account, you also need to specify the ActCloudName property. This property is found in the URL address of the Cloud account; for example

Note that retrieving ActCRM metadata can be expensive. It is advised that you set the CacheMetadata property to store the metadata locally.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Act CRM 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("actcrm:///?URL=")

Declare a Mapping Class for Act CRM 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 Activities 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 Activities(base): __tablename__ = "Activities" ActivityDisplayName = Column(String,primary_key=True) Subject = Column(String) ...

Query Act CRM 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("actcrm:///?URL=") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Activities).filter_by(Subject="Sample subject"): print("ActivityDisplayName: ", instance.ActivityDisplayName) print("Subject: ", instance.Subject) 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

Activities_table = Activities.metadata.tables["Activities"] for instance in session.execute( == "Sample subject")): print("ActivityDisplayName: ", instance.ActivityDisplayName) print("Subject: ", instance.Subject) print("---------")

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

Insert Act CRM Data

To insert Act CRM 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 Act CRM.

new_rec = Activities(ActivityDisplayName="placeholder", Subject="Sample subject") session.add(new_rec) session.commit()

Update Act CRM Data

To update Act CRM 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 Act CRM.

updated_rec = session.query(Activities).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Subject = "Sample subject" session.commit()

Delete Act CRM Data

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