How to Visualize SharePoint Data in Python with pandas via CData Connect AI

Jerod Johnson
Jerod Johnson
Director, Technology Evangelism
Use the CData Connect AI Python SDK with pandas and Matplotlib 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 Connect AI Python SDK, the pandas and Matplotlib modules, you can build SharePoint-connected Python applications and scripts for visualizing SharePoint data. This article shows how to connect to Connect AI and use the pandas and Matplotlib built-in functions to query SharePoint data and visualize the results.

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so pandas can read query results directly from the SDK connection object. There is no driver to install per source and no SQLAlchemy engine to configure: connect with a Personal Access Token and pass the connection straight to pandas.read_sql.

About SharePoint Data Integration

Accessing and integrating live data from SharePoint has never been easier with CData. Customers rely on CData connectivity to:

  • Access data from a wide range of SharePoint versions, including Windows SharePoint Services 3.0, Microsoft Office SharePoint Server 2007 and above, and SharePoint Online.
  • Access all of SharePoint thanks to support for Hidden and Lookup columns.
  • Recursively scan folders to create a relational model of all SharePoint data.
  • Use SQL stored procedures to upload and download documents and attachments.

Most customers rely on CData solutions to integrate SharePoint data into their database or data warehouse, while others integrate their SharePoint data with preferred data tools, like Power BI, Tableau, or Excel.

For more information on how customers are solving problems with CData's SharePoint solutions, refer to our blog: Drivers in Focus: Collaboration Tools.


Getting Started


Connect to SharePoint in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

  1. Log into Connect AI, click Sources, and then click Add Connection
  2. Adding a Connection
  3. Select "SharePoint" from the Add Connection panel
  4. Selecting a data source
  5. Enter the necessary authentication properties to connect to SharePoint.

    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

    Configuring a connection (Salesforce is shown)
  6. Click Save & Test
  7. Navigate to the Permissions tab and update the user-based permissions. Updating permissions

Generate a Personal Access Token (PAT)

The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.

  1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
  2. On the Settings page, go to the Access Tokens section and click Create PAT.
  3. Give the PAT a name and click Create. Creating a new PAT
  4. The PAT is only visible at creation, so copy it and store it securely.

Install Required Modules

Install the SDK (with the pandas extra) and Matplotlib using the pip utility:

pip install "cdata-connect-ai[full]"
pip install matplotlib

Visualize SharePoint Data in Python

Import the modules, then connect to Connect AI with your account email and PAT. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, SharePoint1).

import pandas
import matplotlib.pyplot as plt
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

Query SharePoint with pandas

Use the read_sql function from pandas to execute a SQL statement and store the result set in a DataFrame. Pass the SDK connection directly, no engine required.

df = pandas.read_sql(
    "SELECT Name, Revenue "
    "FROM [SharePoint1].[SharePoint].[MyCustomList] "
    "WHERE Location = 'Chapel Hill'",
    conn,
)

Note: pandas may print a UserWarning stating that it only officially supports SQLAlchemy connectables. This is expected when passing a DB-API connection directly—the query runs correctly and the warning is safe to ignore.

Visualize SharePoint Data

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

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

conn.close()
The Matplotlib chart of the query results (Salesforce is shown)

More Information and Free Trial

Now you can read live SharePoint data into pandas through the CData Connect AI Python SDK. For more information on connecting to SharePoint (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start working with live SharePoint data in Python.



Full Source Code

import pandas
import matplotlib.pyplot as plt
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

df = pandas.read_sql(
    "SELECT Name, Revenue "
    "FROM [SharePoint1].[SharePoint].[MyCustomList] "
    "WHERE Location = 'Chapel Hill'",
    conn,
)

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

conn.close()

Ready to get started?

Learn more about CData Connect AI or sign up for free trial access:

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