From 5bc0921a5b99d33f402203d34744872d2e0499a9 Mon Sep 17 00:00:00 2001 From: Marco Edward Gorelli Date: Sat, 20 Apr 2024 18:23:54 +0100 Subject: [PATCH] docs: show table of import times and package sizes in README --- README.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index ee3867ec8c95..df7a61e2d808 100644 --- a/README.md +++ b/README.md @@ -157,11 +157,9 @@ Polars is very fast. In fact, it is one of the best performing solutions availab ### Lightweight -Polars is also very lightweight. It comes with zero required dependencies, and this shows in the import times: +Polars is also very lightweight, as shown by its relatively small package size and low import times. -- polars: 70ms -- numpy: 104ms -- pandas: 520ms +![Comparison of Polars, pandas, NumPy, and PyArrow package sizes and import times - Polars is lowest](https://github.com/pola-rs/polars/assets/33491632/390f9258-734a-4878-ba68-3a1f2d7f639b) ### Handles larger-than-RAM data