Alteryx to Python (pandas) and Back: A Simple Guide for Getting Started
by Nathan Purvis
Background
Alteryx and Python (specifically pandas) are both powerful tools for data preparation, each with their own strengths. Whilst Alteryx offers an intuitive, drag-and-drop interface that eases the process of building complex workflows, Python can provide greater flexibility and scalability for automation and integration.
Many data professionals can find themselves working with both of these tools, whether expanding their skill set, integrating Python into existing Alteryx workflows, or switching between them based on project needs. However, understanding how common Alteryx functions translate into pandas code and vice versa can take time.
The Resource
To make this process easier, I’ve created a simple reference guide in the form of a GitHub repository. This was inspired by an earlier resource created by my Information Lab colleague, Dan Farmer, that helps users to transition from Alteryx to SQL. It includes:
Side-by-side comparisons of Alteryx tools and their pandas equivalents
Screenshots & code snippets to help bridge the gap between visual workflows and scripting
An easy-to-follow structure for those new to pandas or looking to quickly skip to certain topics
Who This is For
This guide is designed for:
Alteryx users exploring Python, specifically pandas
Data professionals working across both tools
Anyone looking for a quick and simple reference
Check It Out
You can find the full guide here: https://github.com/DataNath/alteryx_to_python
As usual, please feel free to provide any feedback, criticisms or suggestions you may have for future content! You can find links to help documentation for both tools below.