Use pandas to Visualize DB2 Data in Python

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IBM DB2 Python Connector

Python Connector Libraries for IBM DB2 Data Connectivity. Integrate IBM DB2 with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

The CData Python Connector for DB2 enables you use pandas and other modules to analyze and visualize live DB2 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 DB2, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build DB2-connected Python applications and scripts for visualizing DB2 data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to DB2 data, execute queries, and visualize the results.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live DB2 data in Python. When you issue complex SQL queries from DB2, the driver pushes supported SQL operations, like filters and aggregations, directly to DB2 and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to DB2 Data

Connecting to DB2 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.

Follow the procedure below to install the required modules and start accessing DB2 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 DB2 Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with DB2 data.

engine = create_engine("db2:///?Server=")

Execute SQL to DB2

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

df = pandas.read_sql("SELECT OrderName, Freight FROM Orders WHERE ShipCity = 'New York'", engine)

Visualize DB2 Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the DB2 data. The show method displays the chart in a new window.

df.plot(kind="bar", x="OrderName", y="Freight")

Free Trial & More Information

Download a free, 30-day trial of the DB2 Python Connector to start building Python apps and scripts with connectivity to DB2 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("db2:///?Server=")
df = pandas.read_sql("SELECT OrderName, Freight FROM Orders WHERE ShipCity = 'New York'", engine)

df.plot(kind="bar", x="OrderName", y="Freight")