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Use pandas to Visualize Microsoft Teams Data in Python

The CData Python Connector for Microsoft Teams enables you use pandas and other modules to analyze and visualize live Microsoft Teams data in Python.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Microsoft Teams, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Microsoft Teams-connected Python applications and scripts for visualizing Microsoft Teams data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Microsoft Teams data, execute queries, and visualize the results.

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 driver 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 the required modules and start accessing Microsoft Teams through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize 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.

engine = create_engine("msteams:///?OAuthClientId=MyApplicationId&OAuthClientSecret=MySecretKey&CallbackURL=http://localhost:33333&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Microsoft Teams

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT subject, location_displayName FROM Teams WHERE Id = 'Jq74mCczmFXk1tC10GB'", engine)

Visualize Microsoft Teams Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Microsoft Teams data. The show method displays the chart in a new window.

df.plot(kind="bar", x="subject", y="location_displayName")
plt.show()

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.



Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("msteams:///?OAuthClientId=MyApplicationId&OAuthClientSecret=MySecretKey&CallbackURL=http://localhost:33333&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT subject, location_displayName FROM Teams WHERE Id = 'Jq74mCczmFXk1tC10GB'", engine)

df.plot(kind="bar", x="subject", y="location_displayName")
plt.show()