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

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

Connecting to SharePoint Data

Connecting to SharePoint 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.

Set the URL property to the base SharePoint site or to a sub-site. This allows you to query any lists and other SharePoint entities defined for the site or sub-site.

The User and Password properties, under the Authentication section, must be set to valid SharePoint user credentials when using SharePoint On-Premise.

If you are connecting to SharePoint Online, set the SharePointEdition to SHAREPOINTONLINE along with the User and Password connection string properties. For more details on connecting to SharePoint Online, see the "Getting Started" chapter of the help documentation

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

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

cnxn = mod.connect("User=myuseraccount;Password=mypassword;Auth Scheme=NTLM;URL=http://sharepointserver/mysite;SharePointEdition=SharePointOnPremise;")

Create a SQL Statement to Query SharePoint

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

sql = "SELECT Name, Revenue FROM MyCustomList WHERE Location = 'Chapel Hill'"

Extract, Transform, and Load the SharePoint Data

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

Loading SharePoint Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to SharePoint

table1 = [ ['Name','Revenue'], ['NewName1','NewRevenue1'], ['NewName2','NewRevenue2'], ['NewName3','NewRevenue3'] ]

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

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

cnxn = mod.connect("User=myuseraccount;Password=mypassword;Auth Scheme=NTLM;URL=http://sharepointserver/mysite;SharePointEdition=SharePointOnPremise;")

sql = "SELECT Name, Revenue FROM MyCustomList WHERE Location = 'Chapel Hill'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Name','Revenue'], ['NewName1','NewRevenue1'], ['NewName2','NewRevenue2'], ['NewName3','NewRevenue3'] ]

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