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[NumPy](https://numpy.org/) [Install](https://numpy.org/install) [Documentation](https://numpy.org/doc/stable) [Learn](https://numpy.org/learn) [Community](https://numpy.org/community) [About Us](https://numpy.org/about) [News](https://numpy.org/news) [Contribute](https://numpy.org/contribute) [English]() [Português](https://numpy.org/pt/install/) [日本語 (Japanese)](https://numpy.org/ja/install/) [Español](https://numpy.org/es/install/) - Installing NumPy # Installing NumPy Tip This page assumes you are comfortable using a terminal and are familiar with package managers. The only prerequisite for installing 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](https://www.anaconda.com/download) - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science. The recommended method of installing NumPy depends on your preferred workflow. Below, we break down the installation methods into the following categories: - **Project-based** (e.g., uv, pixi) *(recommended for new users)* - **Environment-based** (e.g., pip, conda) *(the traditional workflow)* - **System package managers** *(not recommended for most users)* - **Building from source** *(for advanced users and development purposes)* Choose the method that best suits your needs. If you’re unsure, start with the **Environment-based** method using `conda` or `pip`. Below are the different methods for **installing NumPy**. Click on the tabs to explore each method: Project Based Environment Based System Package Managers Building from Source Recommended for new users who want a streamlined workflow. - **uv:** A modern Python package manager designed for speed and simplicity. ``` uv pip install numpy ``` - **pixi:** A cross-platform package manager for Python and other languages. ``` pixi add numpy ``` The two main tools that install Python packages are `pip` and `conda`. Their functionality partially overlaps (e.g. both can install `numpy`), however, they can also work together. We’ll discuss the major differences between pip and conda here - this is important to understand if you want to manage packages effectively. The first difference is that conda is cross-language and it can install Python, while pip is installed for a particular Python on your system and installs other packages to that same Python install only. This also means conda can install non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while pip can’t. The second difference is that pip installs from the Python Packaging Index (PyPI), while conda installs from its own channels (typically “defaults” or “conda-forge”). PyPI is the largest collection of packages by far, however, all popular packages are available for conda as well. The third difference is that conda is an integrated solution for managing packages, dependencies and environments, while with pip you may need another tool (there are many!) for dealing with environments or complex dependencies. - **Conda:** If you use conda, you can install NumPy from the defaults or conda-forge channels: ``` conda create -n my-env conda activate my-env conda install numpy ``` - **Pip:** ``` pip install numpy ``` Tip **Tip:** Use a virtual environment for better dependency management ``` python -m venv my-env source my-env/bin/activate # macOS/Linux my-env\Scripts\activate # Windows pip install numpy ``` Not recommended for most users, but available for convenience. **macOS (Homebrew):** ``` brew install numpy ``` **Linux (APT):** ``` sudo apt install python3-numpy ``` **Windows (Chocolatey):** ``` choco install numpy ``` For advanced users and developers who want to customize or debug **NumPy**. A word of warning: building Numpy from source can be a nontrivial exercise. We recommend using binaries instead if those are available for your platform via one of the above methods. For details on how to build from source, see [the building from source guide in the Numpy docs](https://numpy.org/devdocs/building/). ## Verifying the Installation[\#](https://numpy.org/install/#verifying-the-installation "Link to this heading") After installing NumPy, verify the installation by running the following in a Python shell or script: ``` import numpy as np print(np.__version__) ``` This should print the installed version of NumPy without errors. ## Troubleshooting[\#](https://numpy.org/install/#troubleshooting "Link to this heading") If your installation fails with the message below, see [Troubleshooting ImportError](https://numpy.org/doc/stable/user/troubleshooting-importerror.html). ``` IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! Importing the numpy c-extensions failed. This error can happen for different reasons, often due to issues with your setup. ``` On this page  [Install](https://numpy.org/install) [Documentation](https://numpy.org/doc/stable) [Learn](https://numpy.org/learn) [Citing NumPy](https://numpy.org/citing-numpy) [Roadmap](https://numpy.org/neps/roadmap.html) [About us](https://numpy.org/about) [Community](https://numpy.org/community) [User surveys](https://numpy.org/user-surveys) [Contribute](https://numpy.org/contribute) [Code of conduct](https://numpy.org/code-of-conduct) [Get help](https://numpy.org/gethelp) [Terms of use](https://numpy.org/terms) [Privacy](https://numpy.org/privacy) [Press kit](https://numpy.org/press-kit) © 2026 NumPy team. All rights reserved.

Tip This page assumes you are comfortable using a terminal and are familiar with package managers. The only prerequisite for installing 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](https://www.anaconda.com/download) - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science. The recommended method of installing NumPy depends on your preferred workflow. Below, we break down the installation methods into the following categories: - **Project-based** (e.g., uv, pixi) *(recommended for new users)* - **Environment-based** (e.g., pip, conda) *(the traditional workflow)* - **System package managers** *(not recommended for most users)* - **Building from source** *(for advanced users and development purposes)* Choose the method that best suits your needs. If you’re unsure, start with the **Environment-based** method using `conda` or `pip`. Below are the different methods for **installing NumPy**. Click on the tabs to explore each method: Recommended for new users who want a streamlined workflow. - **uv:** A modern Python package manager designed for speed and simplicity. ``` uv pip install numpy ``` - **pixi:** A cross-platform package manager for Python and other languages. ``` pixi add numpy ``` The two main tools that install Python packages are `pip` and `conda`. Their functionality partially overlaps (e.g. both can install `numpy`), however, they can also work together. We’ll discuss the major differences between pip and conda here - this is important to understand if you want to manage packages effectively. The first difference is that conda is cross-language and it can install Python, while pip is installed for a particular Python on your system and installs other packages to that same Python install only. This also means conda can install non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while pip can’t. The second difference is that pip installs from the Python Packaging Index (PyPI), while conda installs from its own channels (typically “defaults” or “conda-forge”). PyPI is the largest collection of packages by far, however, all popular packages are available for conda as well. The third difference is that conda is an integrated solution for managing packages, dependencies and environments, while with pip you may need another tool (there are many!) for dealing with environments or complex dependencies. - **Conda:** If you use conda, you can install NumPy from the defaults or conda-forge channels: ``` conda create -n my-env conda activate my-env conda install numpy ``` - **Pip:** ``` pip install numpy ``` Tip **Tip:** Use a virtual environment for better dependency management ``` python -m venv my-env source my-env/bin/activate # macOS/Linux my-env\Scripts\activate # Windows pip install numpy ``` Not recommended for most users, but available for convenience. **macOS (Homebrew):** ``` brew install numpy ``` **Linux (APT):** ``` sudo apt install python3-numpy ``` **Windows (Chocolatey):** ``` choco install numpy ``` For advanced users and developers who want to customize or debug **NumPy**. A word of warning: building Numpy from source can be a nontrivial exercise. We recommend using binaries instead if those are available for your platform via one of the above methods. For details on how to build from source, see [the building from source guide in the Numpy docs](https://numpy.org/devdocs/building/). ## Verifying the Installation[\#](https://numpy.org/install/#verifying-the-installation "Link to this heading") After installing NumPy, verify the installation by running the following in a Python shell or script: ``` import numpy as np print(np.__version__) ``` This should print the installed version of NumPy without errors. ## Troubleshooting[\#](https://numpy.org/install/#troubleshooting "Link to this heading") If your installation fails with the message below, see [Troubleshooting ImportError](https://numpy.org/doc/stable/user/troubleshooting-importerror.html). ``` IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! Importing the numpy c-extensions failed. This error can happen for different reasons, often due to issues with your setup. ```