Ready to get started?

Download a free trial of the Microsoft Planner Connector to get started:

 Download Now

Learn more:

Microsoft Planner Icon Microsoft Planner Python Connector

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

How to use SQLAlchemy ORM to access Microsoft Planner Data in Python

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

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

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

Connecting to Microsoft Planner Data

Connecting to Microsoft Planner 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 connect without setting any connection properties for your user credentials. Below are the minimum connection properties required to connect.

  • InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
  • Tenant (optional): Set this if you wish to authenticate to a different tenant than your default. This is required to work with an organization not on your default Tenant.

When you connect the Driver opens the MS Planner OAuth endpoint in your default browser. Log in and grant permissions to the Driver. The Driver then completes the OAuth process.

  1. Extracts the access token from the callback URL and authenticates requests.
  2. Obtains a new access token when the old one expires.
  3. Saves OAuth values in OAuthSettingsLocation to be persisted across connections.

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

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

Declare a Mapping Class for Microsoft Planner 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 Tasks 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 Tasks(base): __tablename__ = "Tasks" TaskId = Column(String,primary_key=True) startDateTime = Column(String) ...

Query Microsoft Planner 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("microsoftplanner:///?OAuthClientId=MyApplicationId&OAuthClientSecret=MySecretKey&CallbackURL=http://localhost:33333&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Tasks).filter_by(TaskId="BCrvyMoiLEafem-3RxIESmUAHbLK"): print("TaskId: ", instance.TaskId) print("startDateTime: ", instance.startDateTime) 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

Tasks_table = Tasks.metadata.tables["Tasks"] for instance in session.execute( == "BCrvyMoiLEafem-3RxIESmUAHbLK")): print("TaskId: ", instance.TaskId) print("startDateTime: ", instance.startDateTime) print("---------")

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

Insert Microsoft Planner Data

To insert Microsoft Planner 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 Microsoft Planner.

new_rec = Tasks(TaskId="placeholder", TaskId="BCrvyMoiLEafem-3RxIESmUAHbLK") session.add(new_rec) session.commit()

Update Microsoft Planner Data

To update Microsoft Planner 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 Microsoft Planner.

updated_rec = session.query(Tasks).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.TaskId = "BCrvyMoiLEafem-3RxIESmUAHbLK" session.commit()

Delete Microsoft Planner Data

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