Use pandas to Visualize Azure Table Data in Python

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Azure Python Connector

Python Connector Libraries for Azure Storage Data Connectivity. Integrate Azure Storage with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

The CData Python Connector for Azure Table enables you use pandas and other modules to analyze and visualize live Azure Table data in Python.

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 Table, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Azure Table-connected Python applications and scripts for visualizing Azure Table data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Azure Table 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 Table data in Python. When you issue complex SQL queries from Azure Table, the driver pushes supported SQL operations, like filters and aggregations, directly to Azure Table and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Azure Table Data

Connecting to Azure Table 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 your AccessKey and your Account to connect. Set the Account property to the Storage Account Name and set AccessKey to one of the Access Keys. Either the Primary or Secondary Access Keys can be used. To obtain these values, navigate to the Storage Accounts blade in the Azure portal. You can obtain the access key by selecting your account and clicking Access Keys in the Settings section.

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

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

engine = create_engine("azuretables:///?AccessKey=myAccessKey&Account=myAccountName")

Execute SQL to Azure Table

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

df = pandas.read_sql("SELECT Name, Price FROM NorthwindProducts WHERE ShipCity = 'New York'", engine)

Visualize Azure Table Data

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

df.plot(kind="bar", x="Name", y="Price")

Free Trial & More Information

Download a free, 30-day trial of the Azure Table Python Connector to start building Python apps and scripts with connectivity to Azure Table 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("azuretables:///?AccessKey=myAccessKey&Account=myAccountName")
df = pandas.read_sql("SELECT Name, Price FROM NorthwindProducts WHERE ShipCity = 'New York'", engine)

df.plot(kind="bar", x="Name", y="Price")