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

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

Connecting to xBase Data

Connecting to xBase 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 DataSource property must be set to the name of the folder that contains the .dbf files. Specify the IncludeFiles property to work with xBase table files having extensions that differ from .dbf. Specify multiple extensions in a comma-separated list.

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

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

engine = create_engine("xbase:///?DataSource=MyDBFFilesFolder")

Execute SQL to xBase

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

df = pandas.read_sql("SELECT Company, Total FROM Invoices WHERE Class = 'ASSET'", engine)

Visualize xBase Data

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

df.plot(kind="bar", x="Company", y="Total")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for xBase to start building Python apps and scripts with connectivity to xBase 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("xbase:///?DataSource=MyDBFFilesFolder")
df = pandas.read_sql("SELECT Company, Total FROM Invoices WHERE Class = 'ASSET'", engine)

df.plot(kind="bar", x="Company", y="Total")
plt.show()