Update source content

content/en/about/team.md

@@ -41,8 +41,6 @@ If you want to support pandas development, you can find information in the [dona
 
 ## Governance
 
-Wes McKinney is the Benevolent Dictator for Life (BDFL).
-
 The project governance is available in the [project governance page]({{ base_url }}about/governance.html).
 
 ## Workgroups

content/en/community/ecosystem.md

@@ -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
@@ -158,7 +158,7 @@ df = pd.read_csv("data.csv")
 df  # discover interesting insights!
 ```
 
-By printing out a dataframe, Lux automatically [recommends a set of visualizations](https://raw.githubusercontent.com/lux-org/lux-resources/master/readme_img/demohighlight.gif) that highlights interesting trends and patterns in the dataframe. Users can leverage any existing pandas commands without modifying their code, while being able to visualize their pandas data structures (e.g., DataFrame, Series, Index) at the same time. Lux also offers a [powerful, intuitive language](https://lux-api.readthedocs.io/en/latest/source/guide/vis.html>) that allow users to create  Altair, matplotlib, or Vega-Lite visualizations without having to think at the level of code.
+By printing out a dataframe, Lux automatically [recommends a set of visualizations](https://raw.githubusercontent.com/lux-org/lux-resources/master/readme_img/demohighlight.gif) that highlights interesting trends and patterns in the dataframe. Users can leverage any existing pandas commands without modifying their code, while being able to visualize their pandas data structures (e.g., DataFrame, Series, Index) at the same time. Lux also offers a [powerful, intuitive language](https://lux-api.readthedocs.io/en/latest/source/guide/vis.html) that allow users to create  Altair, matplotlib, or Vega-Lite visualizations without having to think at the level of code.
 
 ### [D-Tale](https://github.com/man-group/dtale)
 
@@ -205,7 +205,7 @@ standard output formats (HTML, HTML presentation slides, LaTeX, PDF,
 ReStructuredText, Markdown, Python) through 'Download As' in the web
 interface and `jupyter convert` in a shell.
 
-Pandas DataFrames implement `_repr_html_`and `_repr_latex` methods which
+Pandas DataFrames implement `_repr_html_` and `_repr_latex` methods which
 are utilized by Jupyter Notebook for displaying (abbreviated) HTML or
 LaTeX tables. LaTeX output is properly escaped. (Note: HTML tables may
 or may not be compatible with non-HTML Jupyter output formats.)
@@ -342,7 +342,7 @@ It supports the following data types:
 
 - pandas data types
 - data types defined in the [NTV format](https://loco-philippe.github.io/ES/JSON%20semantic%20format%20(JSON-NTV).htm)
-- data types defined in [Table Schema specification](http://dataprotocols.org/json-table-schema/#field-types-and-formats)
+- data types defined in [Table Schema specification](https://datapackage.org/standard/table-schema/)
 
 The interface is always reversible (conversion round trip) with two formats (JSON-NTV and JSON-TableSchema).
 
@@ -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/)
 
@@ -676,7 +688,7 @@ units aware.
 
 ### [Text Extensions](https://ibm.biz/text-extensions-for-pandas)
 
-Text Extensions for Pandas provides extension types to cover common data structures for representing natural language data, plus library integrations that convert the outputs of popular natural language processing libraries into Pandas DataFrames.
+Text Extensions for Pandas provides extension types to cover common data structures for representing natural language data, plus library integrations that convert the outputs of popular natural language processing libraries into pandas DataFrames.
 
 ## Accessors
 

content/en/config.yml

@@ -132,16 +132,14 @@ workgroups:
     contact: [email protected]
     responsibilities: "Keep the pandas infrastructure up and working. In particular the servers for the website, benchmarks, CI and others needed."
     members:
-    - Marc Garcia
-    - Matthew Roeschke
+    - William Ayd
     - Thomas Li
   communications:
     name: Communications
     contact: [email protected]
     responsibilities: "Share relevant information with the broader community, mainly via our social networks, as well as being the main point of contact between NumFOCUS and the core team."
     members:
     - Marco Gorelli
-    - Marc Garcia
 sponsors:
   active:
   - name: "NumFOCUS"

content/en/index.html

@@ -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>