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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

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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.
	Commercial Grade 23.75" Square Black-Antique Gold Metal Indoor-O	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: DRT Tiny Klash low Specter Fishing Lure New From Japan Free Ship, Intake, PyJanitor
- Exploratory analysis: Bowflex Revolution Home Gym NEW REPRINT OWNER'S MANUAL, Seaborn, Matplotlib, Altair
- Model and evaluate: scikit-learn, statsmodels, PyMC3, spaCy
- Report in a dashboard: Dash, NEW KIDS on The Block Jonathon Knight 1990 Vintage Poster in NEW, 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. Jersey Beethoven Anniversary-Composers mnh set-2020 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. 8mm Home Movies semi pro Betty Ice Skating Lingerie for Christma 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.