docs: Small improvements Ecosystem page (#14176)

pola-rs · Feb 1, 2024 · 937c23b · 937c23b
1 parent ff32460
commit 937c23b
Showing 1 changed file with 25 additions and 19 deletions.
diff --git a/docs/user-guide/ecosystem.md b/docs/user-guide/ecosystem.md
@@ -1,4 +1,4 @@
-# Integrations and ecosystem
+# Ecosystem
 
 ## Introduction
 
@@ -12,56 +12,62 @@ On this page you can find a non-exhaustive list of libraries and tools that supp
 - [Machine learning](#machine-learning)
 - [Other](#other)
 
-#### [Apache Arrow](https://arrow.apache.org/)
+---
 
-Apache Arrow enables zero-copy reads of data within the same process, meaning that data can be directly accessed in its in-memory format without the need for copying or serialisation. This enhances performance when integrating with different tools using Apache Arrow. Polars is compatible with a wide range of libraries that also make use of Apache Arrow, like Pandas and DuckDB.
+### Apache Arrow
+
+[Apache Arrow](https://arrow.apache.org/) enables zero-copy reads of data within the same process, meaning that data can be directly accessed in its in-memory format without the need for copying or serialisation. This enhances performance when integrating with different tools using Apache Arrow. Polars is compatible with a wide range of libraries that also make use of Apache Arrow, like Pandas and DuckDB.
 
 ### Data visualisation
 
-#### [hvPlot](https://hvplot.holoviz.org/)
+#### hvPlot
 
-hvPlot is available as the default plotting backend for Polars making it simple to create interactive and static visualisations. You can use hvPlot by using the feature flag `plot` during installing.
+[hvPlot](https://hvplot.holoviz.org/) is available as the default plotting backend for Polars making it simple to create interactive and static visualisations. You can use hvPlot by using the feature flag `plot` during installing.
 
 ```python
 pip install 'polars[plot]'
 ```
 
-#### [Matplotlib](https://matplotlib.org/)
+#### Matplotlib
 
-Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible.
+[Matplotlib](https://matplotlib.org/) is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible.
 
-#### [Plotly Dash](https://github.com/plotly/dash)
+#### Plotly
 
-Dash is the original low-code framework for rapidly building data apps in Python. Learn more about how to build fast Dash apps at [Plotly.com](https://plotly.com/blog/polars-to-build-fast-dash-apps-for-large-datasets/).
+[Plotly](https://plotly.com/python/) is an interactive, open-source, and browser-based graphing library for Python. Built on top of plotly.js, it ships with over 30 chart types, including scientific charts, 3D graphs, statistical charts, SVG maps, financial charts, and more.
 
 #### [Seaborn](https://seaborn.pydata.org/)
 
 Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
 
 ### IO
 
-#### [Delta Lake](https://github.com/delta-io/delta-rs)
+#### Delta Lake
 
-The Delta Lake project aims to unlock the power of the Deltalake for as many users and projects as possible by providing native low-level APIs aimed at developers and integrators, as well as a high-level operations API that lets you query, inspect, and operate your Delta Lake with ease.
+The [Delta Lake](https://github.com/delta-io/delta-rs) project aims to unlock the power of the Deltalake for as many users and projects as possible by providing native low-level APIs aimed at developers and integrators, as well as a high-level operations API that lets you query, inspect, and operate your Delta Lake with ease.
 
 Read how to use Delta Lake with Polars [at Delta Lake](https://delta-io.github.io/delta-rs/integrations/delta-lake-polars/#reading-a-delta-lake-table-with-polars).
 
 ### Machine Learning
 
-#### [Scikit Learn](https://scikit-learn.org/stable/)
+#### Scikit Learn
 
-Since Scikit Learn 1.4, all transformers support Polars output. See the change log for [more details](https://scikit-learn.org/dev/whats_new/v1.4.html#changes-impacting-all-modules).
+Since [Scikit Learn](https://scikit-learn.org/stable/) 1.4, all transformers support Polars output. See the change log for [more details](https://scikit-learn.org/dev/whats_new/v1.4.html#changes-impacting-all-modules).
 
 ### Other
 
-#### [Great Tables](https://posit-dev.github.io/great-tables/articles/intro.html)
+#### DuckDB
+
+[DuckDB](https://duckdb.org) is a high-performance analytical database system. It is designed to be fast, reliable, portable, and easy to use. DuckDB provides a rich SQL dialect, with support far beyond basic SQL. DuckDB supports arbitrary and nested correlated subqueries, window functions, collations, complex types (arrays, structs), and more. Read about integration with Polars [on the DuckDB website](https://duckdb.org/docs/guides/python/polars).
+
+#### Great Tables
 
-With Great Tables anyone can make wonderful-looking tables in Python. Here is a [blog post](https://posit-dev.github.io/great-tables/blog/polars-styling/) on how to use Great Tables with Polars.
+With [Great Tables](https://posit-dev.github.io/great-tables/articles/intro.html) anyone can make wonderful-looking tables in Python. Here is a [blog post](https://posit-dev.github.io/great-tables/blog/polars-styling/) on how to use Great Tables with Polars.
 
-#### [LanceDB](https://lancedb.com/)
+#### LanceDB
 
-LanceDB is a developer-friendly, serverless vector database for AI applications. They have added a direct integration with Polars. LanceDB can ingest Polars dataframes, return results as polars dataframes, and export the entire table as a polars lazyframe. You can find a quick tutorial in their blog [LanceDB + Polars](https://blog.lancedb.com/lancedb-polars-2d5eb32a8aa3)
+[LanceDB](https://lancedb.com/) is a developer-friendly, serverless vector database for AI applications. They have added a direct integration with Polars. LanceDB can ingest Polars dataframes, return results as polars dataframes, and export the entire table as a polars lazyframe. You can find a quick tutorial in their blog [LanceDB + Polars](https://blog.lancedb.com/lancedb-polars-2d5eb32a8aa3)
 
-#### [Mage](https://www.mage.ai)
+#### Mage
 
-Open-source data pipeline tool for transforming and integrating data. Learn about integration between Polars and Mage at [docs.mage.ai](https://docs.mage.ai/integrations/polars).
+[Mage](https://www.mage.ai) is an open-source data pipeline tool for transforming and integrating data. Learn about integration between Polars and Mage at [docs.mage.ai](https://docs.mage.ai/integrations/polars).