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

Use SQLAlchemy ORMs to Access Microsoft Teams Data in Python

The CData Python Connector for Microsoft Teams enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Microsoft Teams 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 Teams and the SQLAlchemy toolkit, you can build Microsoft Teams-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Microsoft Teams data to query, update, delete, and insert Microsoft Teams data.

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

Connecting to Microsoft Teams Data

Connecting to Microsoft Teams 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 to MS Teams using the embedded OAuth connectivity. When you connect, the MS Teams OAuth endpoint opens in your browser. Log in and grant permissions to complete the OAuth process. See the OAuth section in the online Help documentation for more information on other OAuth authentication flows.

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

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

Declare a Mapping Class for Microsoft Teams 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 Teams 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 Teams(base): __tablename__ = "Teams" subject = Column(String,primary_key=True) location_displayName = Column(String) ...

Query Microsoft Teams 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("msteams:///?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(Teams).filter_by(Id="Jq74mCczmFXk1tC10GB"): print("subject: ", instance.subject) print("location_displayName: ", instance.location_displayName) 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

Teams_table = Teams.metadata.tables["Teams"] for instance in session.execute( == "Jq74mCczmFXk1tC10GB")): print("subject: ", instance.subject) print("location_displayName: ", instance.location_displayName) print("---------")

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

Insert Microsoft Teams Data

To insert Microsoft Teams 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 Teams.

new_rec = Teams(subject="placeholder", Id="Jq74mCczmFXk1tC10GB") session.add(new_rec) session.commit()

Update Microsoft Teams Data

To update Microsoft Teams 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 Teams.

updated_rec = session.query(Teams).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Id = "Jq74mCczmFXk1tC10GB" session.commit()

Delete Microsoft Teams Data

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