Merge branch 'main' into books

Author: InessaPawson

content/en/about.md

@@ -15,23 +15,24 @@ The NumPy Steering Council is the project's governing body. Its role is to ensur
 - Sebastian Berg
 - Ralf Gommers
 - Charles Harris
-- Stephan Hoyer
 - Inessa Pawson
 - Matti Picus
 - Stéfan van der Walt
 - Melissa Weber Mendonça
-- Eric Wieser
+- Marten van Kerkwijk
+- Nathan Goldbaum
 
 Emeritus:
 
 - Alex Griffing (2015-2017)
 - Allan Haldane (2015-2021)
-- Marten van Kerkwijk (2017-2019)
 - Travis Oliphant (project founder, 2005-2012)
 - Nathaniel Smith (2012-2021)
 - Julian Taylor (2013-2021)
 - Jaime Fernández del Río (2014-2021)
 - Pauli Virtanen (2008-2021)
+- Eric Wieser (2017-2025)
+- Stephan Hoyer (2017-2025)
 
 To contact the NumPy Steering Council, please email [email protected].
 
@@ -71,7 +72,7 @@ NumPy receives direct funding from the following sources:
 Institutional Partners are organizations that support the project by employing people that contribute to NumPy as part of their job. Current Institutional Partners include:
 
 - UC Berkeley (Stéfan van der Walt)
-- Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça)
+- Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça, Mateusz Sokol, Rohit Goswami)
 - NVIDIA (Sebastian Berg)
 
 {{< partners >}}

content/en/case-studies/cricket-analytics.md

@@ -141,7 +141,7 @@ for various kinds of cricket related sporting analytics such as:
   and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/)
   are used for tactical analysis.
 
-* **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provide useful insights into relationship between various datasets.
+* **Data Visualization:** Data graphing and visualization provide useful insights into relationship between various datasets.
 
 ## Summary
 

content/en/community.md

@@ -58,12 +58,12 @@ NumPy also organizes in-person sprints for its team and interested contributors
 
 The NumPy project doesn't organize its own conferences. The conferences that have traditionally been most popular with NumPy maintainers, contributors and users are the SciPy and PyData conference series:
 
-- [SciPy US](https://conference.scipy.org)
-- [EuroSciPy](https://www.euroscipy.org)
-- [SciPy Latin America](https://pythoncientifico.ar/)
-- [SciPy India](https://scipy.in)
-- [SciPy Japan](https://www.scipyjapan.scipy.org/)
-- [PyData conferences](https://pydata.org/event-schedule/) (15-20 events a year spread over many countries)
+- SciPy US
+- EuroSciPy
+- SciPy Latin America
+- SciPy India
+- SciPyData Japan
+- PyData conferences (15-20 events a year spread over many countries)
 
 Many of these conferences include tutorial days that cover NumPy and/or sprints where you can learn how to contribute to NumPy or related open source projects.
 

content/en/config.yaml

@@ -11,7 +11,7 @@ params:
     # Hero subtitle (optional)
     subtitle: The fundamental package for scientific computing with Python
     # Button text
-    buttontext: "Latest release: NumPy 2.2. View all releases"
+    buttontext: "Latest release: NumPy 2.3. View all releases"
     # Where the main hero button links to
     buttonlink: "/news/#releases"
     # Hero image (from static/images/___)
@@ -89,7 +89,7 @@ params:
             link: https://numpy.org/doc/stable
           - text: Learn
             link: /learn
-          - text: Citing Numpy
+          - text: Citing NumPy
             link: /citing-numpy
           - text: Roadmap
             link: https://numpy.org/neps/roadmap.html

content/en/contribute.md

@@ -86,12 +86,15 @@ opportunities abound.
 
 ### Translating website content
 
-We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy
-accessible to users in their native language. Volunteer translators are at the heart
-of this effort.  See
-[here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n)
-for background; comment on [this GitHub
-issue](https://github.com/numpy/numpy.org/issues/55) to sign up.
+We are working on translating [numpy.org](https://numpy.org) into multiple languages to make 
+its content more accessible to NumPy users all over the globe. (See
+[NEP 28](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n)
+for background.) Volunteer translators are at the heart of this effort. If you'd like to help, join the 
+*translation* channel on the 
+[Scientific Python Discord server](https://discord.com/channels/786703927705862175/1131695137370669158).
+
+To get familiar with our translation process, read the guide 
+[How to translate content using Crowdin](https://scientific-python-translations.github.io/translate/).
 
 ### Community coordination and outreach
 

content/en/learn.md

@@ -66,7 +66,7 @@ Try these advanced resources for a better understanding of NumPy concepts like a
 * [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *by Jaime Fernández* (2016)
 * [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *by Ralf Gommers* (2019)
 * [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *by Matti Picus* (2019)
-* [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris* (2019)
+* [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stéfan van der Walt, Charles Harris* (2019)
 * [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) *by Travis Oliphant* (2019)
 
 ***

content/en/news.md

@@ -1,10 +1,29 @@
 ---
 title: "News"
 sidebar: false
-newsHeader: "NumPy 2.2.0 released!"
-date: 2024-12-08
+newsHeader: "NumPy 2.3.0 released!"
+date: 2025-06-07
 ---
 
+### NumPy 2.3.0 released
+
+_7 Jun, 2025_ -- The NumPy 2.3.0 release improves free threaded Python support
+and annotations together with the usual set of bug fixes. It is unusual in the
+number of expired deprecations, code modernizations, and style cleanups. The
+latter may not be visible to users, but is important for code maintenance over
+the long term. Note that we have also upgraded from manylinux2014 to
+manylinux_2_28. Highlights are:
+
+- Interactive examples in the NumPy documentation.
+- Building NumPy with OpenMP Parallelization.
+- Preliminary support for Windows on ARM.
+- Improved support for free threaded Python.
+- Improved annotations.
+
+This release supports Python versions 3.11-3.13, Python 3.14 will be
+supported when it is released.
+
+
 ### NumPy 2.2.0 released
 
 _8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back
@@ -442,6 +461,10 @@ Here is a list of NumPy releases, with links to release notes. Bugfix
 releases (only the `z` changes in the `x.y.z` version number) have no new
 features; minor releases (the `y` increases) do.
 
+- NumPy 2.3.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.3.0)) -- _7 Jun 2025_.
+- NumPy 2.2.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.6)) -- _17 May 2025_.
+- NumPy 2.2.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.5)) -- _19 Apr 2025_.
+- NumPy 2.2.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.4)) -- _16 Mar 2025_.
 - NumPy 2.2.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.3)) -- _13 Feb 2025_.
 - NumPy 2.2.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.2)) -- _18 Jan 2025_.
 - NumPy 2.2.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.1)) -- _21 Dec 2024_.

content/en/tabcontents.yaml

@@ -2,7 +2,7 @@ params:
   machinelearning:
     paras:
     - para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://www.scipy.org). As machine learning grows, so does the list of libraries built on NumPy. [TensorFlow’s](https://www.tensorflow.org) deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. [PyTorch](https://pytorch.org), another deep learning library, is popular among researchers in computer vision and natural language processing.
-      para2: Statistical techniques called [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [CatBoost](https://catboost.ai) — one of the fastest inference engines. [Yellowbrick](https://www.scikit-yb.org/en/latest/) and [Eli5](https://eli5.readthedocs.io/en/latest/) offer machine learning visualizations.
+      para2: Statistical techniques called [ensemble methods](https://scikit-learn.org/stable/modules/ensemble.html) such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [CatBoost](https://catboost.ai) — one of the fastest inference engines. [Yellowbrick](https://www.scikit-yb.org/en/latest/) and [Eli5](https://eli5.readthedocs.io/en/latest/) offer machine learning visualizations.
 
   arraylibraries:
     intro:
@@ -173,7 +173,7 @@ params:
         - url: https://pystan.readthedocs.io/en/latest/
           label: PyStan
         - url: https://docs.pymc.io/
-          label: PyMC3
+          label: PyMC
         - url: https://arviz-devs.github.io/arviz/
           label: ArviZ
         - url: https://emcee.readthedocs.io/
@@ -250,7 +250,7 @@ params:
     examples:
     - text: "<b>Extract, Transform, Load: </b>[Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)"
     - text: "<b>Exploratory analysis: </b>[Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)"
-    - text: "<b>Model and evaluate: </b>[scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)"
+    - text: "<b>Model and evaluate: </b>[scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC](https://docs.pymc.io), [spaCy](https://spacy.io)"
     - text: "<b>Report in a dashboard: </b>[Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://voila.readthedocs.io/)"
 
     content:

content/es/tabcontents.yaml

@@ -2,7 +2,7 @@ params:
   machinelearning:
     paras:
       - para1: NumPy constituye la base de potentes librerías de aprendizaje automático como [scikit-learn](https://scikit-learn.org) y [SciPy](https://www.scipy.org). A medida que crece el aprendizaje automático, también lo hace la lista de librerías basadas en NumPy. Las capacidades de aprendizaje profundo de [TensorFlow](https://www.tensorflow.org) tienen amplias aplicaciones— entre ellas el reconocimiento de voz e imágenes, las aplicaciones basadas en texto, el análisis de series de tiempo y la detección de vídeo. [PyTorch](https://pytorch.org), otra librería de aprendizaje profundo, es popular entre los investigadores de visión artificial y procesamiento del lenguaje natural.
-        para2: Las técnicas estadísticas denominadas métodos [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205), como binning, bagging, stacking y boosting, se encuentran entre los algoritmos de ML implementados por herramientas como [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/) y [CatBoost](https://catboost.ai) — uno de los motores de inferencia más rápidos. [Yellowbrick](https://www.scikit-yb.org/en/latest/) y [Eli5](https://eli5.readthedocs.io/en/latest/) ofrecen visualizaciones de aprendizaje automático.
+        para2: Las técnicas estadísticas denominadas [métodos ensemble](https://scikit-learn.org/stable/modules/ensemble.html), como binning, bagging, stacking y boosting, se encuentran entre los algoritmos de ML implementados por herramientas como [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/) y [CatBoost](https://catboost.ai) — uno de los motores de inferencia más rápidos. [Yellowbrick](https://www.scikit-yb.org/en/latest/) y [Eli5](https://eli5.readthedocs.io/en/latest/) ofrecen visualizaciones de aprendizaje automático.
   arraylibraries:
     intro:
       - text: La API de NumPy es el punto de partida cuando se escriben librerías para explotar hardware innovador, crear tipos de arreglos especializadas o añadir capacidades más allá de lo que NumPy proporciona.
@@ -168,7 +168,7 @@ params:
           - url: https://pystan.readthedocs.io/en/latest/
             label: PyStan
           - url: https://docs.pymc.io/
-            label: PyMC3
+            label: PyMC
           - url: https://arviz-devs.github.io/arviz/
             label: ArviZ
           - url: https://emcee.readthedocs.io/
@@ -240,7 +240,7 @@ params:
     examples:
       - text: "<b>Extraer, Transformar, Cargar: </b>[Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)"
       - text: "<b>Análisis Exploratorio: </b>[Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)"
-      - text: "<b>Modelado y evaluación: </b>[scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)"
+      - text: "<b>Modelado y evaluación: </b>[scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC](https://docs.pymc.io), [spaCy](https://spacy.io)"
       - text: "<b>Informes en un panel de control: </b>[Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)"
     content:
       - text: Para grandes volúmenes de datos, [Dask](https://dask.org) y [Ray](https://ray.io/) están diseñados para escalarse. Las implementaciones estables se basan en el versionado de datos ([DVC](https://dvc.org)), rastreo de experimentos ([MLFlow](https://mlflow.org)), y automatización del flujo de trabajo ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) y [Prefect](https://www.prefect.io)).

content/ja/tabcontents.yaml

@@ -3,7 +3,7 @@ params:
     paras:
       - 
         para1: NumPyは、[scikit-learn](https://scikit-learn.org)や[SciPy](https://www.scipy.org)のような強力な機械学習ライブラリの基礎を形成しています。機械学習の技術分野が成長するにつれ、NumPyをベースにしたライブラリの数も増えています。[TensorFlow](https://www.tensorflow.org)の深層学習機能は、音声認識や画像認識、テキストベースのアプリケーション、時系列分析、動画検出など、幅広い応用用途があります。[PyTorch](https://pytorch.org)も、コンピュータビジョンや自然言語処理の研究者に人気のある深層学習ライブラリです。[MXNet](https://github.com/apache/incubator-mxnet)もAIパッケージの一つで、深層学習の設計図やテンプレート機能を提供しています。
-        para2: '[ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205)法と呼ばれる統計的手法であるビンニング、バギング、スタッキングや、[XGBoost](https://github.com/dmlc/xgboost)、[LightGBM](https://lightgbm.readthedocs.io/en/latest/)、[CatBoost](https://catboost.ai)などのツールで実装されているブースティングなどは、機械学習アルゴリズムの一つであり、最速の推論エンジンの一つです。[Yellowbrick](https://www.scikit-yb.org/en/latest/)や[Eli5](https://eli5.readthedocs.io/en/latest/)は機械学習の可視化機能を提供しています。'
+        para2: '[ensemble](https://scikit-learn.org/stable/modules/ensemble.html)法と呼ばれる統計的手法であるビンニング、バギング、スタッキングや、[XGBoost](https://github.com/dmlc/xgboost)、[LightGBM](https://lightgbm.readthedocs.io/en/latest/)、[CatBoost](https://catboost.ai)などのツールで実装されているブースティングなどは、機械学習アルゴリズムの一つであり、最速の推論エンジンの一つです。[Yellowbrick](https://www.scikit-yb.org/en/latest/)や[Eli5](https://eli5.readthedocs.io/en/latest/)は機械学習の可視化機能を提供しています。'
   arraylibraries:
     intro:
       - 
@@ -224,7 +224,7 @@ params:
             label: PyStan
           - 
             url: https://docs.pymc.io/
-            label: PyMC3
+            label: PyMC
           - 
             url: https://arviz-devs.github.io/arviz/
             label: ArviZ
@@ -326,7 +326,7 @@ params:
       - 
         text: "<b>探索的解析: </b>[Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)"
       - 
-        text: "<b>モデリングと評価: </b>[scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)"
+        text: "<b>モデリングと評価: </b>[scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC](https://docs.pymc.io), [spaCy](https://spacy.io)"
       - 
         text: "<b>ダッシュボードでのレポート: </b>[Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)"
     content: