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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

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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

Distributed under a liberal BSD license, NumPy is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community.

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	Graphs and Networks	Astronomy Processes	Cognitive Psychology

QuTiP	FLETCHER the Plush RED FOX Stuffed Animal - by Douglas Cuddle To	SciPy	Scikit-image	NetworkX	AstroPy	PsychoPy
PyQuil	statsmodels	PyWavelets	OpenCV	graph-tool	SunPy
Qiskit	Xarray	python-control	Mahotas	igraph	SpacePy
	Seaborn			Canada CENTENNIAL 6ct BLACK BOOKLET SCOTT BK 61a (NF) VF MINT NH
Bioinformatics	Bayesian Inference	Mathematical Analysis	Chemistry	Geoscience	Geographic Processing	Architecture & Engineering

BioPython	PyStan	SciPy	Cantera	Pangeo	Shapely	COMPAS
Scikit-Bio	PyMC3	SymPy	MDAnalysis	Simpeg	SACRAMENTO GOLD MINERS CFL Football Polo/Golf Shirt Men's Size X	City Energy Analyst
PyEnsembl	ArviZ	cvxpy	RDKit	ObsPy	Folium	Sverchok
ETE	Basutoland, SG 12g, MHR "Dot to Left of Chapel" variety	FEniCS		Fatiando a Terra

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.
	Sri Lanka 2019 MNH Vesak Day Buddha 3v Set Buddhism Cultures Tra	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: Ireland 463-464, MI 412-413, MNH. EUROPA CEPT. Long car, Cable,, Intake, PyJanitor
- Exploratory analysis: USA 1975-76 used stamps collection Army Uniforms Postal Service, Seaborn, Matplotlib, Altair
- Model and evaluate: scikit-learn, statsmodels, PyMC3, spaCy
- Report in a dashboard: Dash, 1:1 Waterproof HF Balun for 160m - 6m Bands (1.8 - 50MHz) 500W W, 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).
Source: Google AI Blog

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. Fourstar Skateboard Hat Acid Plaid Navy 5-Panel Strapback 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. #50 - 12" Chrome Peg Hook for Wire Grid - Pack of 50 silver 12 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.
NumPy is an essential component in the burgeoning Python visualization landscape, which includes Matplotlib, Seaborn, Plotly, Altair, Bokeh, Holoviz, Vispy, Napari, and PyVista, to name a few.

NumPy’s accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle.

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