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

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How to use SQLAlchemy ORM to access Asana Data in Python


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

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

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

Connecting to Asana Data

Connecting to Asana 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 optionally set the following to refine the data returned from Asana.

  • WorkspaceId: Set this to the globally unique identifier (gid) associated with your Asana Workspace to only return projects from the specified workspace. To get your workspace id, navigate to https://app.asana.com/api/1.0/workspaces while logged into Asana. This displays a JSON object containing your workspace name and Id.
  • ProjectId: Set this to the globally unique identifier (gid) associated with your Asana Project to only return data mapped under the specified project. Project IDs can be found in the URL of your project's Overview page. This will be the numbers directly after /0/.

Connect Using OAuth Authentication

You must use OAuth to authenticate with Asana. OAuth requires the authenticating user to interact with Asana using the browser. 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 Asana 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 Asana Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Asana 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("asana:///?OAuthClientId=YourClientId&OAuthClientSecret=YourClientSecret&CallbackURL='http://localhost:33333'&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Asana 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" Id = Column(String,primary_key=True) WorkspaceId = Column(String) ...

Query Asana 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("asana:///?OAuthClientId=YourClientId&OAuthClientSecret=YourClientSecret&CallbackURL='http://localhost:33333'&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(projects).filter_by(Archived="true"): print("Id: ", instance.Id) print("WorkspaceId: ", instance.WorkspaceId) 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.Archived == "true")): print("Id: ", instance.Id) print("WorkspaceId: ", instance.WorkspaceId) 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 Asana to start building Python apps and scripts with connectivity to Asana data. Reach out to our Support Team if you have any questions.