How to Visualize Microsoft Exchange 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 Python Connector for Exchange, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Microsoft Exchange-connected Python applications and scripts for visualizing Microsoft Exchange data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Microsoft Exchange data, execute queries, and visualize the results.

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

Connecting to Microsoft Exchange Data

Connecting to Microsoft Exchange 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.

Specify the User and Password to connect to Exchange. Additionally, specify the address of the Exchange server you are connecting to and the Platform associated with the server.

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

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

engine = create_engine("exchange:///?User='[email protected]'&Password='myPassword'&Server='https://outlook.office365.com/EWS/Exchange.asmx'&Platform='Exchange_Online'")

Execute SQL to Microsoft Exchange

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

df = pandas.read_sql("SELECT GivenName, Size FROM Contacts WHERE BusinnessAddress_City = 'Raleigh'", engine)

Visualize Microsoft Exchange Data

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

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Exchange to start building Python apps and scripts with connectivity to Microsoft Exchange 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("exchange:///?User='[email protected]'&Password='myPassword'&Server='https://outlook.office365.com/EWS/Exchange.asmx'&Platform='Exchange_Online'")
df = pandas.read_sql("SELECT GivenName, Size FROM Contacts WHERE BusinnessAddress_City = 'Raleigh'", engine)

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