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NumPy

The fundamental package for scientific computing with Python

NumPy 2.5.0 released

2026-06-21

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.

Open source

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

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.

Try NumPy

Use the interactive shell to try NumPy in the browser

"""
To try the examples in the browser:
1. Type code in the input cell and press
   Shift + Enter to execute
2. Or copy paste the code, and click on
   the "Run" button in the toolbar
"""

# The standard way to import NumPy:
import numpy as np

# Create a 2-D array, set every second element in
# some rows and find max per row:

x = np.arange(15, dtype=np.int64).reshape(3, 5)
x[1:, ::2] = -99
x
# array([[  0,   1,   2,   3,   4],
#        [-99,   6, -99,   8, -99],
#        [-99,  11, -99,  13, -99]])

x.max(axis=1)
# array([ 4,  8, 13])

# Generate normally distributed random numbers:
rng = np.random.default_rng()
samples = rng.normal(size=2500)
samples

ECOSYSTEM

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.

- PsychoPy

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.
	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.
	Awkward Array	Manipulate JSON-like data with NumPy-like idioms.
	uarray	Python backend system that decouples API from implementation; unumpy provides a NumPy API.
	tensorly	Tensor learning, algebra and backends to seamlessly use NumPy, PyTorch, TensorFlow or CuPy.
	Blosc2	Accelerated computation for in-memory, on-disk, or remote compressed arrays.

Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'.

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, PyMC, 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, Dagster and Prefect).

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'.

An animated gif showing a three-dimensional graph of embeddings made in Tensorflow.

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. 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.

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.

A pairplot of two types of graph, a plot-graph and a frequency graph made in seaborn"

A multi-dimensionan image made in napari.

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.

CASE STUDIES

First Image of a Black Hole

How NumPy, together with libraries like SciPy and Matplotlib that depend on NumPy, enabled the Event Horizon Telescope to produce the first ever image of a black hole

Detection of Gravitational Waves

Two orbs orbiting each other. They are displacing gravity around them.

In 1916, Albert Einstein predicted gravitational waves; 100 years later their existence was confirmed by LIGO scientists using NumPy.

Sports Analytics

Cricket Analytics is changing the game by improving player and team performance through statistical modelling and predictive analytics. NumPy enables many of these analyses.

Pose Estimation using deep learning

DeepLabCut uses NumPy for accelerating scientific studies that involve observing animal behavior for better understanding of motor control, across species and timescales.

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