From e31644fd0c1980f0d290200d885e315a87c856a3 Mon Sep 17 00:00:00 2001 From: Jan Frenzel <jan.frenzel@tu-dresden.de> Date: Tue, 28 Sep 2021 08:24:33 +0200 Subject: [PATCH] Apply 1 suggestion(s) to 1 file(s) --- .../docs/software/data_analytics_with_python.md | 16 +++++----------- 1 file changed, 5 insertions(+), 11 deletions(-) diff --git a/doc.zih.tu-dresden.de/docs/software/data_analytics_with_python.md b/doc.zih.tu-dresden.de/docs/software/data_analytics_with_python.md index 0a00f99f3..f17215e7c 100644 --- a/doc.zih.tu-dresden.de/docs/software/data_analytics_with_python.md +++ b/doc.zih.tu-dresden.de/docs/software/data_analytics_with_python.md @@ -85,17 +85,11 @@ For more examples of using pandarallel check out ### Dask -**Dask** is an open-source library for parallel computing. -Dask is a flexible library for parallel -computing in Python. - -Dask natively scales Python. -It provides advanced parallelism, enabling performance at -scale for some of the popular tools. -For instance: Dask arrays scale NumPy workflows, Dask -dataframes scale Pandas workflows, -Dask-ML scales machine learning programming interfaces like Scikit-Learn and -XGBoost. +[Dask](https://dask.org/) is a flexible and open-source library for parallel computing in Python. +It replaces some Python data structures with parallel versions in order to provide advanced +parallelism for analytics, enabling performance at scale for some of the popular tools. For +instance: Dask arrays replace NumPy arrays, Dask dataframes replace Pandas dataframes. +Furthermore, Dask-ML scales machine learning APIs like Scikit-Learn and XGBoost. Dask is composed of two parts: -- GitLab