Official source and binary releases¶

For each official release of NumPy and SciPy, we provide source code (tarball),as well as binary wheels for several major platforms (Windows, OSX, Linux).

The only prerequisite for NumPy is Python itself. If you don’t have Python yet and want the simplest way to get started, we recommend you use the Anaconda Distribution - it includes Python, NumPy, and other commonly used packages for scientific computing and data science. NumPy can be installed with conda, with pip, or with a package manager on macOS and Linux. Download Numpy (Numerical Python) - This is a Python-based library whose main purpose is to implement a fast and sophisticated multi-dimensional array that will help in scientific computing.

Source code repository access¶

The most recent development versions of NumPy and SciPy are available throughthe official repositories hosted on GitHub.

To check out the latest NumPy sources:

To check out the latest SciPy sources:

Build instructions¶

Build instructions for SciPy can be found in its documentation.The latest version can be found at:https://docs.scipy.org/doc/scipy-dev/reference/building/index.html

Third-party/vendor package managers¶

Below is a partial list of third-party and operating system vendor packagemanagers containing NumPy and SciPy packages.

These packages are not maintained by the NumPy and SciPydevelopers; this list is provided only as a convenience. Thesepackages may not always provide the most up-to-date version of thesoftware, and may be unmaintained.

IMPORTANT: If you experience problems with these packages (especiallythose related to installation/build errors), please report the problem tothe package maintainer first, rather than to the NumPy/SciPy mailing lists.

Download Numpy For Python 3.7 Mac

Numpy Library Download

• 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	3-D Visualization	Symbolic Computing	Astronomy Processes	Cognitive Psychology
  QuTiP	Pandas	SciPy	Scikit-image	Mayavi	SymPy	AstroPy	PsychoPy
  PyQuil	statsmodels	PyWavelets	OpenCV	Napari	SunPy
  Qiskit	Seaborn	SpacePy

  Bioinformatics	Bayesian Inference	Mathematical Analysis	Simulation Modeling	Multi-variate Analysis	Geographic Processing	Interactive Computing
  BioPython	PyStan	SciPy	PyDSTool	PyChem	Shapely	Jupyter
  Scikit-Bio	PyMC3	SymPy	GeoPandas	IPython
  PyEnsembl	cvxpy	Folium	Binder
  FEniCS

• 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 and Ray are designed to scale. Stable deployments rely on data versioning (DVC), experiment tracking (MLFlow), and workflow automation (Airflow and Prefect).

• 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, and Napari, to name a few.

  NumPy's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle.