Ready to get started?

Download a free trial of the SharePoint Connector to get started:

 Download Now

Learn more:

SharePoint Icon SharePoint Python Connector

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

How to Visualize SharePoint Data in Python with pandas

Use pandas and other modules to analyze and visualize live 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, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build SharePoint-connected Python applications and scripts for visualizing SharePoint data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to SharePoint data, execute queries, and visualize the results.

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

Follow the procedure below to install the required modules and start accessing SharePoint 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 SharePoint Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with SharePoint data.

engine = create_engine("sharepoint:///?User=myuseraccount&Password=mypassword&Auth Scheme=NTLM&URL=http://sharepointserver/mysite&SharePointEdition=SharePointOnPremise")

Execute SQL to SharePoint

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

df = pandas.read_sql("SELECT Name, Revenue FROM MyCustomList WHERE Location = 'Chapel Hill'", engine)

Visualize SharePoint Data

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

df.plot(kind="bar", x="Name", y="Revenue")

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for SharePoint 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 pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("sharepoint:///?User=myuseraccount&Password=mypassword&Auth Scheme=NTLM&URL=http://sharepointserver/mysite&SharePointEdition=SharePointOnPremise")
df = pandas.read_sql("SELECT Name, Revenue FROM MyCustomList WHERE Location = 'Chapel Hill'", engine)

df.plot(kind="bar", x="Name", y="Revenue")