NumPy

The fundamental package for scientific computing with Python

Powerful N-dimensional arrays

Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.

Numerical computing tools

NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.

Interoperable

NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.

Performant

The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.

Easy to use

NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level.

Open source

Try NumPy

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• Scientific Domains
• Array libraries
• Data Science
• Machine Learning
• Visualization

• Nearly every scientist working in Python draws on the power of NumPy.

  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.

  Quantum Computing	Statistical Computing	Signal Processing	Image Processing	3-D Visualization	Symbolic Computing	Astronomy Processes	Cognitive Psychology
  QuTiP	Pandas	SciPy	Scikit-image	Mayavi	SymPy	AstroPy	PsychoPy
  PyQuil	statsmodels	PyWavelets	OpenCV	Napari		SunPy
  Qiskit	Seaborn					SpacePy

  Bioinformatics	Bayesian Inference	Mathematical Analysis	Simulation Modeling	Multi-variate Analysis	Geographic Processing	Interactive Computing
  BioPython	PyStan	SciPy	PyDSTool	PyChem	Shapely	Jupyter
  Scikit-Bio	PyMC3	SymPy			GeoPandas	IPython
  PyEnsembl		cvxpy			Folium	Binder
  		FEniCS

• 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.

  	Array Library	Capabilities & Application areas
  	Dask	Distributed arrays and advanced parallelism for analytics, enabling performance at scale.
  	CuPy	NumPy-compatible array library for GPU-accelerated computing with Python.
  	JAX	Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU.
  	Xarray	Labeled, indexed multi-dimensional arrays for advanced analytics and visualization
  	Sparse	NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra.
  	PyTorch	Deep learning framework that accelerates the path from research prototyping to production deployment.
  	TensorFlow	An end-to-end platform for machine learning to easily build and deploy ML powered applications.
  	MXNet	Deep learning framework suited for flexible research prototyping and production.
  	Arrow	A cross-language development platform for columnar in-memory data and analytics.
  	xtensor	Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis.
  	XND	Develop libraries for array computing, recreating NumPy's foundational concepts.
  	uarray	Python backend system that decouples API from implementation; unumpy provides a NumPy API.
  	TensorLy	Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy.

• NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:

  • Extract, Transform, Load: Pandas, Intake, PyJanitor
  • Exploratory analysis: Jupyter, Seaborn, Matplotlib, Altair
  • Model and evaluate: scikit-learn, statsmodels, PyMC3, spaCy
  • Report in a dashboard: Dash, Panel, Voila

  For high data volumes, Dask and Ray are designed to scale. Stable deployments rely on data versioning (DVC), experiment tracking (MLFlow), and workflow automation (Airflow and Prefect).

• NumPy forms the basis of powerful machine learning libraries like scikit-learn and SciPy. As machine learning grows, so does the list of libraries built on NumPy. TensorFlow’s deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. PyTorch, another deep learning library, is popular among researchers in computer vision and natural language processing. MXNet is another AI package, providing blueprints and templates for deep learning.

  Statistical techniques called ensemble methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as XGBoost, LightGBM, and CatBoost — one of the fastest inference engines. Yellowbrick and Eli5 offer machine learning visualizations.