How to Visualize Microsoft Planner Data in Python with pandas

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 Planner, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Microsoft Planner-connected Python applications and scripts for visualizing Microsoft Planner data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Microsoft Planner 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 Planner data in Python. When you issue complex SQL queries from Microsoft Planner, the driver 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 the required modules and start accessing Microsoft Planner 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 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.

engine = create_engine("microsoftplanner:///?OAuthClientId=MyApplicationId&OAuthClientSecret=MySecretKey&CallbackURL=http://localhost:33333&InitiateOAuth=GETANDREFRESH")

Execute SQL to Microsoft Planner

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

df = pandas.read_sql("SELECT TaskId, startDateTime FROM Tasks WHERE TaskId = 'BCrvyMoiLEafem-3RxIESmUAHbLK'", engine)

Visualize Microsoft Planner Data

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

df.plot(kind="bar", x="TaskId", y="startDateTime")
plt.show()

👁 Microsoft Planner data in a Python plot (Salesforce is shown).

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.

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Full Source Code

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

engine = create_engine("microsoftplanner:///?OAuthClientId=MyApplicationId&OAuthClientSecret=MySecretKey&CallbackURL=http://localhost:33333&InitiateOAuth=GETANDREFRESH")
df = pandas.read_sql("SELECT TaskId, startDateTime FROM Tasks WHERE TaskId = 'BCrvyMoiLEafem-3RxIESmUAHbLK'", engine)

df.plot(kind="bar", x="TaskId", y="startDateTime")
plt.show()