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32 changes: 22 additions & 10 deletions content/en/community/ecosystem.md
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Expand Up @@ -8,7 +8,7 @@ developers to build powerful and more focused data tools. The creation
of libraries that complement pandas' functionality also allows pandas
development to remain focused around its original requirements.

This is an community-maintained list of projects that build on pandas in order
This is a community-maintained list of projects that build on pandas in order
to provide tools in the PyData space. The pandas core development team does not necessarily endorse any particular project on this list or have any knowledge of the maintenance status of any particular library.

For a more complete list of projects that depend on pandas, see the [libraries.io usage page for
Expand Down Expand Up @@ -496,17 +496,29 @@ You can find more information about the Hugging Face Dataset Hub in the [documen

## Out-of-core

### [Bodo](https://bodo.ai/)
### [Bodo](https://github.com/bodo-ai/Bodo)

Bodo is a high-performance Python computing engine that automatically parallelizes and
optimizes your code through compilation using HPC (high-performance computing) techniques.
Designed to operate with native pandas dataframes, Bodo compiles your pandas code to execute
across multiple cores on a single machine or distributed clusters of multiple compute nodes efficiently.
Bodo also makes distributed pandas dataframes queryable with SQL.

The community edition of Bodo is free to use on up to 8 cores. Beyond that, Bodo offers a paid
enterprise edition. Free licenses of Bodo (for more than 8 cores) are available
[upon request](https://www.bodo.ai/contact) for academic and non-profit use.
Bodo is a high-performance compute engine for Python data processing.
Using an auto-parallelizing just-in-time (JIT) compiler, Bodo simplifies scaling Pandas
workloads from laptops to clusters without major code changes.
Under the hood, Bodo relies on MPI-based high-performance computing (HPC) technology—making it
both easier to use and often much faster than alternatives.
Bodo also provides a SQL engine that can query distributed pandas dataframes efficiently.

```python
import pandas as pd
import bodo

@bodo.jit
def process_data():
df = pd.read_parquet("my_data.pq")
df2 = pd.DataFrame({"A": df.apply(lambda r: 0 if r.A == 0 else (r.B // r.A), axis=1)})
df2.to_parquet("out.pq")

process_data()
```


### [Cylon](https://cylondata.org/)

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2 changes: 1 addition & 1 deletion content/en/index.html
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Expand Up @@ -121,7 +121,7 @@ <h4>Previous versions</h4>
<ul id="show-more-releases" class="collapse">
{% for release in releases[5:] %}
<li class="small">
{{ release.name }} ({{ release.published.strftime("%Y-%m-%d") }})<br/>
{{ release.name }} ({{ release.published.strftime("%b %d, %Y") }})<br/>
<a href="https://pandas.pydata.org/pandas-docs/stable/whatsnew/{{ release.tag }}.html">changelog</a> |
<a href="https://pandas.pydata.org/pandas-docs/version/{{ release.name }}/">docs</a> |
<a href="{{ release.url }}">code</a>
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