How to Build an ETL App for WordPress Data in Python with CData

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Wordpress and the petl framework, you can build WordPress-connected applications and pipelines for extracting, transforming, and loading WordPress data. This article shows how to connect to WordPress with the CData Python Connector and use petl and pandas to extract, transform, and load WordPress data.

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

Connecting to WordPress Data

Connecting to WordPress 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.

To connect to WordPress, set the URL property and other authentication properties. WordPress supports Basic (User and Password) and OAuth2.0 authentication, though Basic is recommended for a testing environment only. To connect with OAuth register an app with WordPress.

See the Getting Started guide in the CData driver documentation for more information.

After installing the CData WordPress Connector, follow the procedure below to install the other required modules and start accessing WordPress 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 WordPress 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.wordpress as mod

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

cnxn = mod.connect("Url=http://www.yourwordpresshost.com;InitiateOAuth=GETANDREFRESH;")

Create a SQL Statement to Query WordPress

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

sql = "SELECT Id, Name FROM Categories WHERE Id = '1'"

Extract, Transform, and Load the WordPress Data

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

Loading WordPress Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

In the following example, we add new rows to the Categories table.

Adding New Rows to WordPress

table1 = [ ['Id','Name'], ['NewId1','NewName1'], ['NewId2','NewName2'], ['NewId3','NewName3'] ]

etl.appenddb(table1, cnxn, 'Categories')

With the CData Python Connector for Wordpress, you can work with WordPress 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 CData Python Connector for Wordpress to start building Python apps and scripts with connectivity to WordPress data. Reach out to our Support Team if you have any questions.

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Full Source Code

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

cnxn = mod.connect("Url=http://www.yourwordpresshost.com;InitiateOAuth=GETANDREFRESH;")

sql = "SELECT Id, Name FROM Categories WHERE Id = '1'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Id','Name'], ['NewId1','NewName1'], ['NewId2','NewName2'], ['NewId3','NewName3'] ]

etl.appenddb(table3, cnxn, 'Categories')