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Extract, Transform, and Load OneNote Data in Python

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

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

Connecting to OneNote Data

Connecting to OneNote 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.

OneNote uses the OAuth authentication standard. To authenticate using OAuth, you will need to create an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties. See the Help documentation for more information.

After installing the CData OneNote Connector, follow the procedure below to install the other required modules and start accessing OneNote 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 OneNote 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.onenote as mod

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

Use SQL to create a statement for querying OneNote. In this article, we read data from the Notebooks entity.

sql = "SELECT Id, notebook_displayName FROM Notebooks WHERE Id = 'Jq74mCczmFXk1tC10GB'"

Extract, Transform, and Load the OneNote Data

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

Loading OneNote Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

etl.tocsv(table2,'notebooks_data.csv')

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

Adding New Rows to OneNote

table1 = [ ['Id','notebook_displayName'], ['NewId1','Newnotebook_displayName1'], ['NewId2','Newnotebook_displayName2'], ['NewId3','Newnotebook_displayName3'] ]

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

With the CData Python Connector for OneNote, you can work with OneNote 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 OneNote Python Connector to start building Python apps and scripts with connectivity to OneNote 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.onenote as mod

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

sql = "SELECT Id, notebook_displayName FROM Notebooks WHERE Id = 'Jq74mCczmFXk1tC10GB'"

table1 = etl.fromdb(cnxn,sql)

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

etl.tocsv(table2,'notebooks_data.csv')

table3 = [ ['Id','notebook_displayName'], ['NewId1','Newnotebook_displayName1'], ['NewId2','Newnotebook_displayName2'], ['NewId3','Newnotebook_displayName3'] ]

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