How to Visualize Outlook Data in Python with pandas

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData API Driver for Python, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Outlook-connected Python applications and scripts for visualizing Outlook data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Outlook data, execute queries, and visualize the results.

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

Connecting to Outlook Data

Connecting to Outlook 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.

Using OAuth Authentication

Microsoft Graph API uses OAuth 2.0 for authentication. You must register an application in the Microsoft Azure Portal to obtain OAuth credentials (Client ID and Client Secret).

Obtaining OAuth Credentials

1. Log in to the Azure Portal.
2. Navigate to Azure Active Directory > App registrations.
3. Click New registration to create a new application.
4. Enter an application name and select the appropriate account types.
5. Set the Redirect URI to your application's callback URL (e.g., http://localhost:33333 for desktop apps).
6. Click Register to create the application.
7. On the application overview page, copy the Application (client) ID - this is your OAuthClientId.
8. Navigate to Certificates & secrets and create a new client secret.
9. Copy the client secret value - this is your OAuthClientSecret.
10. Navigate to API permissions and add the required Microsoft Graph API permissions:
    • Mail.Read - For accessing email messages
    • Contacts.Read - For accessing contacts
    • Calendars.Read - For accessing calendar events
    • Tasks.Read - For accessing To Do tasks
    • offline_access - For obtaining refresh tokens
11. Click Grant admin consent to grant these permissions.

Connecting with OAuth

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to OAuth.
• InitiateOAuth: Set this to GETANDREFRESH. The CData API Profile for Outlook will automatically walk through the OAuth process in order to obtain the access token.
• OAuthClientId: Set this to the Application (client) ID from Azure Portal.
• OAuthClientSecret: Set this to the client secret value from Azure Portal.
• TenantId: Set this to your Azure AD tenant identifier (GUID or domain name like 'contoso.onmicrosoft.com').
• CallbackURL: Set this to the Redirect URI you specified in your app registration (e.g., http://localhost:33333 for desktop apps).

Example connection string

Profile=C:\profiles\Outlook.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;TenantId=your_tenant_id;CallbackUrl=http://localhost:33333;

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

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

engine = create_engine("api:///?Profile=C:\profiles\Outlook.apip&AuthScheme=OAuth&InitiateOAuth=GETANDREFRESH&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&TenantId=your_tenant_id&CallbackUrl=http://localhost:33333")

Execute SQL to Outlook

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

df = pandas.read_sql("SELECT , FROM CalendarGroupCalendars WHERE CalendarGroupId = 'group_id'", engine)

Visualize Outlook Data

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

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

👁 Outlook data in a Python plot (Salesforce is shown).

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Outlook data. Reach out to our Support Team if you have any questions.

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Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("api:///?Profile=C:\profiles\Outlook.apip&AuthScheme=OAuth&InitiateOAuth=GETANDREFRESH&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&TenantId=your_tenant_id&CallbackUrl=http://localhost:33333")
df = pandas.read_sql("SELECT , FROM CalendarGroupCalendars WHERE CalendarGroupId = 'group_id'", engine)

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