Installing NumPy

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

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 .