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

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

Connecting to Power BI XMLA Data

Connecting to Power BI XMLA 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.

By default, use Azure AD to connect to Microsoft Power BI XMLA. Azure AD is Microsoft’s multi-tenant, cloud-based directory and identity management service. It is user-based authentication that requires that you set AuthScheme to AzureAD.

For more information on other authentication schemes, refer to the Help documentation.

Follow the procedure below to install the required modules and start accessing Power BI XMLA 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 Power BI XMLA Data in Python

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

engine = create_engine("powerbixmla:///?AuthScheme=AzureADInitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Power BI XMLA

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

df = pandas.read_sql("SELECT Country, Education FROM Customer WHERE Country = 'Australia'", engine)

Visualize Power BI XMLA Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Power BI XMLA to start building Python apps and scripts with connectivity to Power BI XMLA 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("powerbixmla:///?AuthScheme=AzureADInitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Country, Education FROM Customer WHERE Country = 'Australia'", engine)

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