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Extract, Transform, and Load xBase Data in Python

The CData Python Connector for xBase enables you to create ETL applications and pipelines for xBase data in Python with petl.

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 and the petl framework, you can build xBase-connected applications and pipelines for extracting, transforming, and loading xBase data. This article shows how to connect to xBase with the CData Python Connector and use petl and pandas to extract, transform, and load xBase data.

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.

After installing the CData xBase Connector, follow the procedure below to install the other required modules and start accessing xBase through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for xBase Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.xbase as mod

You can now connect with a connection string. Use the connect function for the CData xBase Connector to create a connection for working with xBase data.

cnxn = mod.connect("DataSource=MyDBFFilesFolder;")

Create a SQL Statement to Query xBase

Use SQL to create a statement for querying xBase. In this article, we read data from the Invoices entity.

sql = "SELECT Company, Total FROM Invoices WHERE Class = 'ASSET'"

Extract, Transform, and Load the xBase Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the xBase data. In this example, we extract xBase data, sort the data by the Total column, and load the data into a CSV file.

Loading xBase Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Total')

etl.tocsv(table2,'invoices_data.csv')

With the CData Python Connector for xBase, you can work with xBase data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the xBase Python Connector 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 petl as etl
import pandas as pd
import cdata.xbase as mod

cnxn = mod.connect("DataSource=MyDBFFilesFolder;")

sql = "SELECT Company, Total FROM Invoices WHERE Class = 'ASSET'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Total')

etl.tocsv(table2,'invoices_data.csv')