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

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How to use SQLAlchemy ORM to access Basecamp Data in Python


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

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

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

Connecting to Basecamp Data

Connecting to Basecamp 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.

Basecamp uses basic or OAuth 2.0 authentication. To use basic authentication you will need the user and password that you use for logging in to Basecamp. To authenticate to Basecamp via OAuth 2.0, you will need to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties by registering an app with Basecamp.

See the Getting Started section in the help documentation for a connection guide.

Additionally, you will need to specify the AccountId connection property. This can be copied from the URL after you log in.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Basecamp 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("basecamp:///?User=test@northwind.db&Password=test123")

Declare a Mapping Class for Basecamp 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 Projects 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 Projects(base): __tablename__ = "Projects" Name = Column(String,primary_key=True) DocumentsCount = Column(String) ...

Query Basecamp 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("basecamp:///?User=test@northwind.db&Password=test123") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Projects).filter_by(Drafts="True"): print("Name: ", instance.Name) print("DocumentsCount: ", instance.DocumentsCount) 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

Projects_table = Projects.metadata.tables["Projects"] for instance in session.execute(Projects_table.select().where(Projects_table.c.Drafts == "True")): print("Name: ", instance.Name) print("DocumentsCount: ", instance.DocumentsCount) print("---------")

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

Insert Basecamp Data

To insert Basecamp 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 Basecamp.

new_rec = Projects(Name="placeholder", Drafts="True") session.add(new_rec) session.commit()

Update Basecamp Data

To update Basecamp 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 Basecamp.

updated_rec = session.query(Projects).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Drafts = "True" session.commit()

Delete Basecamp Data

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