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Get the Report →How to Visualize Redis Data in Python with pandas
Use pandas and other modules to analyze and visualize live Redis 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 Redis, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Redis-connected Python applications and scripts for visualizing Redis data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Redis data, execute queries, and visualize the results.
With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Redis data in Python. When you issue complex SQL queries from Redis, the driver pushes supported SQL operations, like filters and aggregations, directly to Redis and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
Connecting to Redis Data
Connecting to Redis 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.
Set the following connection properties to connect to a Redis instance:
- Server: Set this to the name or address of the server your Redis instance is running on. You can specify the port in Port.
- Password: Set this to the password used to authenticate with a password-protected Redis instance , using the Redis AUTH command.
Set UseSSL to negotiate SSL/TLS encryption when you connect.
Follow the procedure below to install the required modules and start accessing Redis 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 Redis Data in Python
You can now connect with a connection string. Use the create_engine function to create an Engine for working with Redis data.
engine = create_engine("redis:///?Server=127.0.0.1&Port=6379&Password=myPassword")
Execute SQL to Redis
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 Country = 'US'", engine)
Visualize Redis Data
With the query results stored in a DataFrame, use the plot function to build a chart to display the Redis data. The show method displays the chart in a new window.
df.plot(kind="bar", x="City", y="CompanyName") plt.show()

Free Trial & More Information
Download a free, 30-day trial of the CData Python Connector for Redis to start building Python apps and scripts with connectivity to Redis 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("redis:///?Server=127.0.0.1&Port=6379&Password=myPassword") df = pandas.read_sql("SELECT City, CompanyName FROM Customers WHERE Country = 'US'", engine) df.plot(kind="bar", x="City", y="CompanyName") plt.show()