From 52713b53b1c4430101eacaa9ef4968128810c287 Mon Sep 17 00:00:00 2001 From: Marco Gorelli <33491632+MarcoGorelli@users.noreply.github.com> Date: Sat, 1 Jun 2024 15:14:17 +0100 Subject: [PATCH] update related projects page --- docs/related.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/docs/related.md b/docs/related.md index c4a5e6cb9..76661a24d 100644 --- a/docs/related.md +++ b/docs/related.md @@ -9,21 +9,23 @@ Standardised way of interchanging data between libraries, see Project which aims to "provide a standard interface that encapsulates implementation details of dataframe libraries. This will allow users and third-party libraries to write code that interacts and operates with a standard dataframe, and not with specific implementations.", see [here](https://data-apis.org/dataframe-api/draft/). +The Narwhals author was originally involved, but left due to irreconcilable differences in vision. + Some notable difference are: - Narwhals just uses a subset of the Polars API, whereas the dataframe standard introduces a new API -- Narwhals supports expressions and separates lazy and eager execution +- Narwhals supports expressions, and separates lazy and eager execution - Narwhals is a standalone, independent project, whereas the dataframe standard aims to be upstreamed and implemented by major dataframe libraries. -The projects are not in competition and have different goals. - ## Ibis [Presents itself as a dataframe standard](https://voltrondata.com/resources/open-source-standards), and dispatches to 20+ backends. Some differences with Narwhals are: -- Narwhals is ~1000 times lighter and is aimed at library maintainers as opposed to end users +- Narwhals is aimed at library maintainers, Ibis more to end users +- Narwhals has zero required dependencies, whereas Ibis requires pandas and PyArrow. For users starting + from non-pandas environments, the difference in the relative size increase is ~1000x - Narwhals only supports 4 backends, Ibis more than 20 - Narwhals is focused on fundamental dataframe operations, Ibis on SQL backends