Pandas got 3x faster

To be clear, this doesn't speed-up pandas, per se. It uses a different library (Modin) as a drop-in replacement:

https://github.com/modin-project/modin

Modin uses Ray, a distributed computation library. There was a similar article on HN a year ago that hyped "making pandas faster" by replacing it with Ray:

https://news.ycombinator.com/item?id=16510610

Why link to a blog post instead of the Modin [1] project directly, which is the reason for the speed improvement?

Also the title "Pandas got 3X faster" seems to contradict the conclusion in the article, which reports the result was < 2x faster.

[1] https://github.com/modin-project/modin

The Modin project should work on merging the implementation to the original Pandas project, so that parallel / non-parallel algorithms could be mixed. Drop in replacements don't work, as they are not 100% compatible with the original project.

I would prefer to pass an optional parallel=true parameter to some functions in the API, or have a configuration setting that can fall back to non-parallel implementation.

If you are interested in learning more about this, there is also a recent PyData talk: https://rise.cs.berkeley.edu/blog/modin-talk-at-pydata-nyc-2...

Imagine my disappointment when I found out this isn't about real pandas.

Can someone shed light on what the trade-offs are by using this?

As someone who has never heard of pandas, the logo is hilarious.