Summary

Pandas is a powerful tool for cleaning, transforming, manipulating, or enriching data, among many other potential uses. As a result it has become a standard tool for data engineers for a wide range of applications. Matt Harrison is a Python expert with a long history of working with data who now spends his time on consulting and training. He recently wrote a book on effective patterns for Pandas code, and in this episode he shares advice on how to write efficient data processing routines that will scale with your data volumes, while being understandable and maintainable.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I’m interviewing Matt Harrison about useful tips for using Pandas for data engineering projects

Interview

Introduction
How did you get involved in the area of data management?
What are the main tasks that you have seen Pandas used for in a data engineering context?
What are some of the common mistakes that can lead to poor performance when scaling to large data sets?
What are some of the utility features that you have found most helpful for data processing?
One of the interesting add-ons to Pandas is its integration with Arrow. What are some of the considerations for how and when to use the Arrow capabilities vs. out-of-the-box Pandas?
Pandas is a tool that spans data processing and data science. What are some of the ways that data engineers should think about writing their code to make it accessible to data scientists for supporting collaboration across data workflows?
Pandas is often used for transformation logic. What are some of the ways that engineers should approach the design of their code to make it understandable and maintainable?
- How can data engineers support testing their transformations?
There are a number of projects that aim to scale Pandas logic across cores and clusters. What are some of the considerations for when to use one of these tools, and how to select the proper framework? (e.g. Dask, Modin, Ray, etc.)
What are some anti-patterns that engineers should guard against when using Pandas for data processing?
What are the most interesting, innovative, or unexpected ways that you have seen Pandas used for data processing?
When is Pandas the wrong choice for data processing?
What are some of the projects related to Pandas that you are keeping an eye on?

Contact Info

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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Links

Metasnake
Snowflake Schema
OLAP
Panel Data
NumPy
Dask
- Podcast Episode
Parquet
Arrow
Feather
Zen of Python
Joel Grus’ I Don’t Like Notebooks presentation
Pandas Method Chaining
Effective Pandas Book (affiliate link with 20% discount code applied)
- Podcast.__init__ Episode
pytest
- Podcast.__init__ Episode
Great Expectations
- Podcast Episode
Hypothesis
- Podcast.__init__ Episode
Papermill
- Podcast Episode
Jupytext
Koalas
Modin
- Podcast.__init__ Episode
Spark
Ray
- Podcast.__init__ Episode
Spark Pandas API
Vaex
Rapids
Terality
H2O
H2O DataTable
Fugue
Ibis
Multi-process Pandas
PandaPy
Polars
Google Colab

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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