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

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

Connecting to MySQL Data

Connecting to MySQL 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.

The Server and Port properties must be set to a MySQL server. If IntegratedSecurity is set to false, then User and Password must be set to valid user credentials. Optionally, Database can be set to connect to a specific database. If not set, tables from all databases will be returned.

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

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

engine = create_engine("mysql:///?User=myUser&Password=myPassword&Database=NorthWind&Server=myServer&Port=3306")

Execute SQL to MySQL

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

df = pandas.read_sql("SELECT ShipName, Freight FROM Orders WHERE ShipCountry = 'USA'", engine)

Visualize MySQL Data

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

df.plot(kind="bar", x="ShipName", y="Freight")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for MySQL to start building Python apps and scripts with connectivity to MySQL 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("mysql:///?User=myUser&Password=myPassword&Database=NorthWind&Server=myServer&Port=3306")
df = pandas.read_sql("SELECT ShipName, Freight FROM Orders WHERE ShipCountry = 'USA'", engine)

df.plot(kind="bar", x="ShipName", y="Freight")
plt.show()