NumPyの高速で多機能なベクトル化計算、インデックス処理、ブロードキャスティングのコンセプトは、今日の配列計算のデファクト・スタンダードです。

NumPyは、様々な数学関数、乱数生成器、線形代数ルーチン、フーリエ変換などを提供しています。

相互運用性

NumPyは、幅広いハードウェアとコンピューティング・プラットフォームをサポートしており、分散処理、GPU、疎行列ライブラリにも対応しています。

高パフォーマンス

NumPyの中核は最適化されたC言語のコードです。Pythonの柔軟性を、コンパイルされたコードの高速さとともに享受できます。

使いやすさ

NumPyの高水準なシンタックスは、どんなバックグラウンドや経験値のプログラマーでも利用でき、生産性を高めることができます。

オープンソース

寛容なBSDライセンスで公開されています。NumPyは活発で、互いを尊重し、多様性を認め合うコミュニティによって、 GitHub上でオープンに開発されています.

Try NumPy

Enable the interactive shell

import numpy as np

エコシステム

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	Pandas	SciPy	Scikit-image	NetworkX	AstroPy	PsychoPy
PyQuil	statsmodels	PyWavelets	OpenCV	graph-tool	SunPy
Qiskit	Xarray	python-control	Mahotas	igraph	SpacePy
	Seaborn			PyGSP
Bioinformatics	Bayesian Inference	Mathematical Analysis	Chemistry	Geoscience	Geographic Processing	Architecture & Engineering

BioPython	PyStan	SciPy	Cantera	Pangeo	Shapely	COMPAS
Scikit-Bio	PyMC3	SymPy	MDAnalysis	Simpeg	GeoPandas	City Energy Analyst
PyEnsembl	ArviZ	cvxpy	RDKit	ObsPy	Folium	Sverchok
ETE	emcee	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.
	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 andRay are designed to scale. Stabledeployments rely on data versioning (DVC),experiment tracking (MLFlow), andworkflow automation (Airflow andPrefect).
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. 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.
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.

エコシステム

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