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

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

Connecting to Azure Data Catalog Data

Connecting to Azure Data Catalog 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.

You can optionally set the following to read the different catalog data returned from Azure Data Catalog.

Connect Using OAuth Authentication

You must use OAuth to authenticate with Azure Data Catalog. OAuth requires the authenticating user to interact with Azure Data Catalog using the browser. For more information, refer to the OAuth section in the help documentation.

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

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

engine = create_engine("azuredatacatalog:///?InitiateOAuth=GETANDREFRESH")

Execute SQL to Azure Data Catalog

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

df = pandas.read_sql("SELECT DslAddressDatabase, Type FROM Tables WHERE Name = 'FactProductInventory'", engine)

Visualize Azure Data Catalog Data

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

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

👁 Azure Data Catalog data in a Python plot (Salesforce is shown).

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Azure Data Catalog to start building Python apps and scripts with connectivity to Azure Data Catalog 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("azuredatacatalog:///?InitiateOAuth=GETANDREFRESH")
df = pandas.read_sql("SELECT DslAddressDatabase, Type FROM Tables WHERE Name = 'FactProductInventory'", engine)

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