NumPy

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 ComputingQuTiPPyQuilQiskitPennyLaneStatistical ComputingPandasstatsmodelsXarraySeabornSignal ProcessingSciPyPyWaveletspython-controlHyperSpyImage ProcessingScikit-imageOpenCVMahotasGraphs and NetworksNetworkXgraph-tooligraphPyGSPAstronomyAstroPySunPySpacePyCognitive PsychologyPsychoPyBioinformaticsBioPythonScikit-BioPyEnsemblETEBayesian InferencePyStanPyMCArviZemceeMathematical AnalysisSciPySymPycvxpyFEniCSChemistryCanteraMDAnalysisRDKitPyBaMMGeosciencePangeoSimpegObsPyFatiando a TerraGeographic ProcessingShapelyGeoPandasFoliumArchitecture & EngineeringCOMPASCity Energy AnalystSverchok

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.Blosc2Accelerated computation for in-memory, on-disk, or remote compressed arrays.

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, PyJanitorExploratory analysis: Jupyter, Seaborn, Matplotlib, AltairModel and evaluate: scikit-learn, statsmodels, PyMC, spaCyReport in a dashboard: Dash, Panel, VoilaFor 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 BlogNumPy 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.

NumPy 的核心功能是提供高性能的多维数组对象（ndarray）以及配套的数学函数、随机数生成器、线性代数算法和傅里叶变换等工具。它通过向量化操作替代显式循环，支持广播机制，使数组计算简洁高效，是现代科学计算和 AI 数据处理的基础库。

NumPy 适用于几乎所有需要数值计算的场景，包括：数据科学中的数组预处理与统计计算、机器学习中的特征矩阵操作与模型训练前的数据转换、科学计算中的线性代数求解与信号处理（如傅里叶变换），以及深度学习框架（如 PyTorch、TensorFlow）的底层数据接口和后端运算支持。

NumPy 的技术特点主要包括：高性能 C 语言实现，支持向量化运算和广播；内存布局优化，支持视图与副本机制，减少数据拷贝；丰富且统一的数学函数库（如 np.sum、np.dot、np.fft）；与 C/C++/Fortran 代码无缝集成，具备良好的兼容性和可扩展性；采用宽松的 BSD 许可证，开源免费，拥有活跃且多元化的社区维护。