Extract, Transform, and Load Microsoft Planner Data in Python

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Microsoft Planner Python Connector

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

The CData Python Connector for Microsoft Planner enables you to create ETL applications and pipelines for Microsoft Planner data in Python with petl.

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 and the petl framework, you can build Microsoft Planner-connected applications and pipelines for extracting, transforming, and loading Microsoft Planner data. This article shows how to connect to Microsoft Planner with the CData Python Connector and use petl and pandas to extract, transform, and load 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 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.

After installing the CData Microsoft Planner Connector, follow the procedure below to install the other required modules and start accessing Microsoft Planner through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for Microsoft Planner Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.microsoftplanner as mod

You can now connect with a connection string. Use the connect function for the CData Microsoft Planner Connector to create a connection for working with Microsoft Planner data.

cnxn = mod.connect("OAuthClientId=MyApplicationId;OAuthClientSecret=MySecretKey;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Microsoft Planner

Use SQL to create a statement for querying Microsoft Planner. In this article, we read data from the Tasks entity.

sql = "SELECT TaskId, startDateTime FROM Tasks WHERE TaskId = 'BCrvyMoiLEafem-3RxIESmUAHbLK'"

Extract, Transform, and Load the Microsoft Planner Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Microsoft Planner data. In this example, we extract Microsoft Planner data, sort the data by the startDateTime column, and load the data into a CSV file.

Loading Microsoft Planner Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'startDateTime')


In the following example, we add new rows to the Tasks table.

Adding New Rows to Microsoft Planner

table1 = [ ['TaskId','startDateTime'], ['NewTaskId1','NewstartDateTime1'], ['NewTaskId2','NewstartDateTime2'], ['NewTaskId3','NewstartDateTime3'] ]

etl.appenddb(table1, cnxn, 'Tasks')

With the CData Python Connector for Microsoft Planner, you can work with Microsoft Planner data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the Microsoft Planner Python Connector to start building Python apps and scripts with connectivity to Microsoft Planner data. Reach out to our Support Team if you have any questions.

Full Source Code

import petl as etl
import pandas as pd
import cdata.microsoftplanner as mod

cnxn = mod.connect("OAuthClientId=MyApplicationId;OAuthClientSecret=MySecretKey;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT TaskId, startDateTime FROM Tasks WHERE TaskId = 'BCrvyMoiLEafem-3RxIESmUAHbLK'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'startDateTime')


table3 = [ ['TaskId','startDateTime'], ['NewTaskId1','NewstartDateTime1'], ['NewTaskId2','NewstartDateTime2'], ['NewTaskId3','NewstartDateTime3'] ]

etl.appenddb(table3, cnxn, 'Tasks')