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Deploying to main from @ numpy/numpy.org@d2f4068 🚀
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‎es/index.html

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‎es/tabcontents.yaml

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- url: https://pystan.readthedocs.io/en/latest/
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label: PyStan
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- url: https://docs.pymc.io/
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label: PyMC3
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label: PyMC
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- url: https://arviz-devs.github.io/arviz/
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label: ArviZ
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- url: https://emcee.readthedocs.io/
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examples:
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- text: "<b>Extraer, Transformar, Cargar: </b>[Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)"
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- text: "<b>Análisis Exploratorio: </b>[Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)"
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- 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)"
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- 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)"
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- 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)"
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content:
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- 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)).

‎index.html

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‎ja/index.html

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‎ja/tabcontents.yaml

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label: PyStan
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url: https://docs.pymc.io/
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label: PyMC
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url: https://arviz-devs.github.io/arviz/
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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)"
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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)"
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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)"
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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)"
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‎pt/index.html

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‎pt/tabcontents.yaml

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- url: https://pystan.readthedocs.io/en/latest/
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label: PyStan
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- url: https://docs.pymc.io/
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label: PyMC
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- url: https://arviz-devs.github.io/arviz/
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label: ArviZ
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- url: https://emcee.readthedocs.io/
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examples:
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- text: "<b>Extract, Transform, Load: </b>[Pandas](https://pandas.pydata.org),[ Intake](https://intake.readthedocs.io),[PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)"
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- text: "<b>Exploratory analysis: </b>[Jupyter](https://jupyter.org),[Seaborn](https://seaborn.pydata.org),[ Matplotlib](https://matplotlib.org),[ Altair](https://altair-viz.github.io)"
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- 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)"
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- 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)"
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- text: "<b>Report in a dashboard: </b>[Dash](https://plotly.com/dash),[ Panel](https://panel.holoviz.org),[ Voila](https://github.com/voila-dashboards/voila)"
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content:
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- text: For high data volumes, [Dask](https://dask.org) and[Ray](https://ray.io/) are designed to scale. Stabledeployments rely on data versioning ([DVC](https://dvc.org)),experiment tracking ([MLFlow](https://mlflow.org)), andworkflow automation ([Airflow](https://airflow.apache.org) and[Prefect](https://www.prefect.io)).

‎tabcontents.yaml

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- url: https://pystan.readthedocs.io/en/latest/
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label: PyStan
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- url: https://docs.pymc.io/
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label: PyMC3
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label: PyMC
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- url: https://arviz-devs.github.io/arviz/
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- url: https://emcee.readthedocs.io/
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examples:
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- text: "<b>Extract, Transform, Load: </b>[Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)"
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- text: "<b>Exploratory analysis: </b>[Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)"
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- 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)"
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- 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)"
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- text: "<b>Report in a dashboard: </b>[Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://voila.readthedocs.io/)"
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