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  machinelearning:
    - para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://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 &mdash; 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 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) &mdash; 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:
    - text: NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides.
    headers:
    - text: Array Library
    - text: Capabilities & Application areas
    libraries:
    - title: Dask
      text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale.
      img: /images/content_images/arlib/dask.png
      alttext: Dask
      url: https://dask.org/
    - title: CuPy
      text: NumPy-compatible array library for GPU-accelerated computing with Python.
      img: /images/content_images/arlib/cupy.png
      alttext: CuPy
      url: https://cupy.dev
    - title: JAX
      text: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU."
      img: /images/content_images/arlib/jax_logo_250px.png
      alttext: JAX
      url: https://docs.jax.dev
    - title: Xarray
      text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization.
      img: /images/content_images/arlib/xarray.png
      alttext: xarray
      url: https://xarray.pydata.org/en/stable/index.html
    - title: Sparse
      text: NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra.
      img: /images/content_images/arlib/sparse.png
      alttext: sparse
      url: https://sparse.pydata.org/en/latest/
    - title: PyTorch
      text: Deep learning framework that accelerates the path from research prototyping to production deployment.
      img: /images/content_images/arlib/pytorch-logo-dark.svg
      alttext: PyTorch
      url: https://pytorch.org/
    - title: TensorFlow
      text: An end-to-end platform for machine learning to easily build and deploy ML powered applications.
      img: /images/content_images/arlib/tensorflow-logo.svg
      alttext: TensorFlow
      url: https://www.tensorflow.org
    - title: Arrow
      text: A cross-language development platform for columnar in-memory data and analytics.
      img: /images/content_images/arlib/arrow.png
      alttext: arrow
      url: https://arrow.apache.org/
    - title: xtensor
      text: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis.
      img: /images/content_images/arlib/xtensor.png
      alttext: xtensor
      url: https://github.com/xtensor-stack/xtensor-python
    - title: Awkward Array
      text: Manipulate JSON-like data with NumPy-like idioms.
      img: /images/content_images/arlib/awkward.svg
      alttext: awkward
      url: https://awkward-array.org/
    - title: uarray
      text: Python backend system that decouples API from implementation; unumpy provides a NumPy API.
      img: /images/content_images/arlib/uarray.png
      alttext: uarray
      url: https://uarray.org/en/latest/
    - title: tensorly
      text: Tensor learning, algebra and backends to seamlessly use NumPy, PyTorch, TensorFlow or CuPy.
      img: /images/content_images/arlib/tensorly.png
      alttext: tensorly
      url: http://tensorly.org/stable/home.html
    - title: Blosc2
      text: Accelerated computation for in-memory, on-disk, or remote compressed arrays.
      img: /images/content_images/arlib/blosc-logo.svg
      alttext: blosc2
      url: https://www.blosc.org/python-blosc2/
  scientificdomains:
    - text: Nearly every scientist working in Python draws on the power of NumPy.
    - text: "NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant."
    libraries:
    - title: Quantum Computing
      alttext: A computer chip.
      img: /images/content_images/sc_dom_img/quantum_computing.svg
      links:
        - url: https://qutip.org
          label: QuTiP
        - url: https://pyquil-docs.rigetti.com/en/stable
          label: PyQuil
        - url: https://qiskit.org
          label: Qiskit
        - url: https://pennylane.ai
          label: PennyLane
    - title: Statistical Computing
      alttext: A line graph with the line moving up.
      img: /images/content_images/sc_dom_img/statistical_computing.svg
      links:
        - url: https://pandas.pydata.org/
          label: Pandas
        - url: https://www.statsmodels.org/
          label: statsmodels
        - url: https://xarray.pydata.org/en/stable/
          label: Xarray
        - url: https://seaborn.pydata.org/
          label: Seaborn
    - title: Signal Processing
      alttext: A bar chart with positive and negative values.
      img: /images/content_images/sc_dom_img/signal_processing.svg
      links:
        - url: https://scipy.org
          label: SciPy
        - url: https://pywavelets.readthedocs.io/
          label: PyWavelets
        - url: https://python-control.org/
          label: python-control
        - url: https://hyperspy.org/
          label: HyperSpy
    - title: Image Processing
      alttext: A photograph of the mountains.
      img: /images/content_images/sc_dom_img/image_processing.svg
      links:
        - url: https://scikit-image.org/
          label: Scikit-image
        - url: https://opencv.org/
          label: OpenCV
        - url: https://mahotas.rtfd.io/
          label: Mahotas
    - title: Graphs and Networks
      alttext: A simple graph.
      img: /images/content_images/sc_dom_img/sd6.svg
      links:
        - url: https://networkx.org/
          label: NetworkX
        - url: https://graph-tool.skewed.de/
          label: graph-tool
        - url: https://igraph.org/python/
          label: igraph
        - url: https://pygsp.rtfd.io/
          label: PyGSP
    - title: Astronomy
      alttext: A telescope.
      img: /images/content_images/sc_dom_img/astronomy_processes.svg
      links:
        - url: https://www.astropy.org/
          label: AstroPy
        - url: https://sunpy.org/
          label: SunPy
        - url: https://spacepy.github.io/
          label: SpacePy
    - title: Cognitive Psychology
      alttext: A human head with gears.
      img: /images/content_images/sc_dom_img/cognitive_psychology.svg
      links:
        - url: https://www.psychopy.org/
          label: PsychoPy
    - title: Bioinformatics
      alttext: A strand of DNA.
      img: /images/content_images/sc_dom_img/bioinformatics.svg
      links:
        - url: https://biopython.org/
          label: BioPython
        - url: http://scikit-bio.org/
          label: Scikit-Bio
        - url: https://github.com/openvax/pyensembl
          label: PyEnsembl
        - url: http://etetoolkit.org/
          label: ETE
    - title: Bayesian Inference
      alttext: A graph with a bell-shaped curve.
      img: /images/content_images/sc_dom_img/bayesian_inference.svg
      links:
        - url: https://pystan.readthedocs.io/en/latest/
          label: PyStan
        - url: https://docs.pymc.io/
          label: PyMC
        - url: https://arviz-devs.github.io/arviz/
          label: ArviZ
        - url: https://emcee.readthedocs.io/
          label: emcee
    - title: Mathematical Analysis
      alttext: Four mathematical symbols.
      img: /images/content_images/sc_dom_img/mathematical_analysis.svg
      links:
        - url: https://scipy.org
          label: SciPy
        - url: https://www.sympy.org/
          label: SymPy
        - url: https://www.cvxpy.org/
          label: cvxpy
        - url: https://fenicsproject.org/
          label: FEniCS
    - title: Chemistry
      alttext: A test tube.
      img: /images/content_images/sc_dom_img/chemistry.svg
      links:
        - url: https://cantera.org/
          label: Cantera
        - url: https://www.mdanalysis.org/
          label: MDAnalysis
        - url: https://github.com/rdkit/rdkit
          label: RDKit
        - url: https://www.pybamm.org/
          label: PyBaMM
    - title: Geoscience
      alttext: The Earth.
      img: /images/content_images/sc_dom_img/geoscience.svg
      links:
        - url: https://pangeo.io/
          label: Pangeo
        - url: https://simpeg.xyz/
          label: Simpeg
        - url: https://github.com/obspy/obspy/wiki
          label: ObsPy
        - url: https://www.fatiando.org/
          label: Fatiando a Terra
    - title: Geographic Processing
      alttext: A map.
      img: /images/content_images/sc_dom_img/GIS.svg
      links:
        - url: https://shapely.readthedocs.io/
          label: Shapely
        - url: https://geopandas.org/
          label: GeoPandas
        - url: https://python-visualization.github.io/folium
          label: Folium
    - title: Architecture & Engineering
      alttext: A microprocessor development board.
      img: /images/content_images/sc_dom_img/robotics.svg
      links:
        - url: https://compas.dev/
          label: COMPAS
        - url: https://cityenergyanalyst.com/
          label: City Energy Analyst
        - url: https://nortikin.github.io/sverchok/
          label: Sverchok
  datascience:
    intro: "NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:"
    image1:
    - img: /images/content_images/ds-landscape.png
      alttext: Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'.
    image2:
    - img: /images/content_images/data-science.png
      alttext: Diagram of three overlapping circles. The circles are labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'.
    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), [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:
    - text: For high data volumes, [Dask](https://dask.org) and [Ray](https://ray.io/) are designed to scale. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) and [Prefect](https://www.prefect.io)).
  visualization:
    images:
    - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries
      img: /images/content_images/v_matplotlib.png
      alttext: A streamplot made in matplotlib
    - url: https://github.com/yhat/ggpy
      img: /images/content_images/v_ggpy.png
      alttext: A scatter-plot graph made in ggpy
    - url: https://www.journaldev.com/19692/python-plotly-tutorial
      img: /images/content_images/v_plotly.png
      alttext:  A box-plot made in plotly
    - url: https://altair-viz.github.io/gallery/streamgraph.html
      img: /images/content_images/v_altair.png
      alttext:  A streamgraph made in altair
    - url: https://seaborn.pydata.org
      img: /images/content_images/v_seaborn.png
      alttext:  A pairplot of two types of graph, a plot-graph and a frequency graph made in seaborn"
    - url: https://pyvista.org
      img: /images/content_images/v_pyvista.png
      alttext:  A 3D volume rendering made in PyVista.
    - url: https://napari.org
      img: /images/content_images/v_napari.png
      alttext:  A multi-dimensionan image made in napari.
    - url: https://vispy.org/gallery/index.html
      img: /images/content_images/v_vispy.png
      alttext:  A Voronoi diagram made in vispy.
    content:
    - text: NumPy is an essential component in the burgeoning
            [Python visualization landscape](https://pyviz.org/overviews/index.html),
            which includes [Matplotlib](https://matplotlib.org),
            [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly),
            [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/),
            [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://napari.org/),
            and [PyVista](https://pyvista.org), to name a few.
    - text: NumPy's accelerated processing of large arrays allows researchers to visualize
            datasets far larger than native Python could handle.
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