Use pandas to Visualize Cosmos DB Data in Python

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Cosmos DB Python Connector

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

The CData Python Connector for Cosmos DB enables you use pandas and other modules to analyze and visualize live Cosmos DB 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 Cosmos DB, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Cosmos DB-connected Python applications and scripts for visualizing Cosmos DB data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Cosmos DB data, execute queries, and visualize the results.

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

Connecting to Cosmos DB Data

Connecting to Cosmos DB 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.

To obtain the connection string needed to connect to a Cosmos DB account using the SQL API, log in to the Azure Portal, select Azure Cosmos DB, and select your account. In the Settings section, click Connection String and set the following values:

  • AccountEndpoint: The Cosmos DB account URL from the Keys blade of the Cosmos DB account
  • AccountKey: In the Azure portal, navigate to the Cosmos DB service and select your Azure Cosmos DB account. From the resource menu, go to the Keys page. Find the PRIMARY KEY value and set AccountKey to this value.

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

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

engine = create_engine("cosmosdb:///?AccountEndpoint=myAccountEndpoint&AccountKey=myAccountKey")

Execute SQL to Cosmos DB

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

df = pandas.read_sql("SELECT City, CompanyName FROM Customers WHERE Name = 'Morris Park Bake Shop'", engine)

Visualize Cosmos DB Data

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

df.plot(kind="bar", x="City", y="CompanyName")

Free Trial & More Information

Download a free, 30-day trial of the Cosmos DB Python Connector to start building Python apps and scripts with connectivity to Cosmos DB 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("cosmosdb:///?AccountEndpoint=myAccountEndpoint&AccountKey=myAccountKey")
df = pandas.read_sql("SELECT City, CompanyName FROM Customers WHERE Name = 'Morris Park Bake Shop'", engine)

df.plot(kind="bar", x="City", y="CompanyName")