diff --git a/README.md b/README.md index 4386eeb05..3b5574860 100644 --- a/README.md +++ b/README.md @@ -49,24 +49,19 @@ There are three steps to writing dataframe-agnostic code using Narwhals: - if you started with Modin, you'll get Modin back (and compute will be distributed) - if you started with cuDF, you'll get cuDF back (and compute will happen on GPU) -## Package size +## What about Ibis? Like Ibis, Narwhals aims to enable dataframe-agnostic code. However, Narwhals comes with **zero** dependencies, is about as lightweight as it gets, and is aimed at library developers rather than at end users. It also does -not aim to support as many backends, preferring to instead focus on dataframes. +not aim to support as many backends, preferring to instead focus on dataframes. So, which should you use? -The projects are not in competition, and the comparison is intended only to help you choose the right tool -for the right task. +- If you need to run complicated analyses and aren't too bothered about package size: Ibis! +- If you're a library maintainer and want the thinnest-possible layer to get cross-dataframe library support: Narwhals! Here is the package size increase which would result from installing each tool in a non-pandas environment: -

- Comparison between Narwhals (0.3 MB) and Ibis (~310 MB) -

+![image](https://github.com/MarcoGorelli/narwhals/assets/33491632/a8dfba78-feb1-48c1-960a-5b9b03585fa5) ## Example