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Install Documentation Learn Community About Us News Contribute
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NumPy
The fundamental package for scientific computing with Python
Latest release: NumPy 2.0. View all releases
NumPy 2.1 released!
2024-08-18
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

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
     
   * QuTiP
   * PyQuil
   * Qiskit
   * PennyLane
 * Statistical Computing
     
   * Pandas
   * statsmodels
   * Xarray
   * Seaborn
 * Signal Processing
     
   * SciPy
   * PyWavelets
   * python-control
   * HyperSpy
 * Image Processing
     
   * Scikit-image
   * OpenCV
   * Mahotas
 * Graphs and Networks
     
   * NetworkX
   * graph-tool
   * igraph
   * PyGSP
 * Astronomy
     
   * AstroPy
   * SunPy
   * SpacePy
 * Cognitive Psychology
     
   * PsychoPy
 * Bioinformatics
     
   * BioPython
   * Scikit-Bio
   * PyEnsembl
   * ETE
 * Bayesian Inference
     
   * PyStan
   * PyMC3
   * ArviZ
   * emcee
 * Mathematical Analysis
     
   * SciPy
   * SymPy
   * cvxpy
   * FEniCS
 * Chemistry
     
   * Cantera
   * MDAnalysis
   * RDKit
   * PyBaMM
 * Geoscience
     
   * Pangeo
   * Simpeg
   * ObsPy
   * Fatiando a Terra
 * Geographic Processing
     
   * Shapely
   * GeoPandas
   * Folium
 * Architecture & Engineering
     
   * COMPAS
   * City Energy Analyst
   * Sverchok

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 LibraryCapabilities & Application areasDaskDistributed arrays and advanced
parallelism for analytics, enabling performance at scale.CuPyNumPy-compatible
array library for GPU-accelerated computing with Python.JAXComposable
transformations of NumPy programs: differentiate, vectorize, just-in-time
compilation to GPU/TPU.XarrayLabeled, indexed multi-dimensional arrays for
advanced analytics and visualization.SparseNumPy-compatible sparse array library
that integrates with Dask and SciPy's sparse linear algebra.PyTorchDeep learning
framework that accelerates the path from research prototyping to production
deployment.TensorFlowAn end-to-end platform for machine learning to easily build
and deploy ML powered applications.ArrowA cross-language development platform
for columnar in-memory data and analytics.xtensorMulti-dimensional arrays with
broadcasting and lazy computing for numerical analysis.Awkward ArrayManipulate
JSON-like data with NumPy-like idioms.uarrayPython backend system that decouples
API from implementation; unumpy provides a NumPy API.tensorlyTensor learning,
algebra and backends to seamlessly use NumPy, 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, Dagster 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. 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.

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
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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