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Get the Report →How to Visualize Avro Data in Python with pandas
Use pandas and other modules to analyze and visualize live Avro 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 Avro, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Avro-connected Python applications and scripts for visualizing Avro data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Avro data, execute queries, and visualize the results.
With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Avro data in Python. When you issue complex SQL queries from Avro, the driver pushes supported SQL operations, like filters and aggregations, directly to Avro and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
Connecting to Avro Data
Connecting to Avro 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.
Connect to your local Avro file(s) by setting the URI connection property to the location of the Avro file.Follow the procedure below to install the required modules and start accessing Avro 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 Avro Data in Python
You can now connect with a connection string. Use the create_engine function to create an Engine for working with Avro data.
engine = create_engine("avro:///?URI=C:/folder/table.avroInitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
Execute SQL to Avro
Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.
df = pandas.read_sql("SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = 'value_2'", engine)
Visualize Avro Data
With the query results stored in a DataFrame, use the plot function to build a chart to display the Avro data. The show method displays the chart in a new window.
df.plot(kind="bar", x="Id", y="Column1") plt.show()

Free Trial & More Information
Download a free, 30-day trial of the CData Python Connector for Avro to start building Python apps and scripts with connectivity to Avro 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("avro:///?URI=C:/folder/table.avroInitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") df = pandas.read_sql("SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = 'value_2'", engine) df.plot(kind="bar", x="Id", y="Column1") plt.show()