Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

docs: Small improvements Ecosystem page #14176

Merged
merged 3 commits into from
Feb 1, 2024
Merged

docs: Small improvements Ecosystem page #14176

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
7a04e67
add duckdb, update plotly and adjust page name
r-brink Feb 1, 2024
01f9b10
remove links from titles and to text
r-brink Feb 1, 2024
b9382af
formatting
r-brink Feb 1, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
44 changes: 25 additions & 19 deletions docs/user-guide/ecosystem.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
# Integrations and ecosystem
# Ecosystem

## Introduction

Expand All @@ -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.

stinodego marked this conversation as resolved.
Show resolved Hide resolved
### 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).
Loading