Building and installing NumPy

Binary installers

In most use cases the best way to install NumPy on your system is by using an installable binary package for your operating system.


Good solutions for Windows are, Enthought Canopy, Anaconda (which both provide binary installers for Windows, OS X and Linux) and Python (x, y). Both of these packages include Python, NumPy and many additional packages.

A lightweight alternative is to download the Python installer from and the NumPy installer for your Python version from the Sourceforge `download site <`_.

The NumPy installer includes binaries for different CPU’s (without SSE instructions, with SSE2 or with SSE3) and installs the correct one automatically. If needed, this can be bypassed from the command line with

numpy-<1.y.z>-superpack-win32.exe /arch nosse

or sse2 or sse3 instead of nosse.


All major distributions provide packages for NumPy. These are usually reasonably up-to-date, but sometimes lag behind the most recent NumPy release.

Mac OS X

Universal binary installers for NumPy are available from the `download site <`_, and wheel packages from PyPi. With a recent version of `pip`<>`_ this will give you a binary install (from the wheel packages) compatible with at Python, Homebrew and MacPorts:

pip install numpy

Building from source

A general overview of building NumPy from source is given here, with detailed instructions for specific platforms given seperately.


Building NumPy requires the following software installed:

  1. Python 2.6.x, 2.7.x, 3.2.x or newer

    On Debian and derivatives (Ubuntu): python, python-dev (or python3-dev)

    On Windows: the official python installer at is enough

    Make sure that the Python package distutils is installed before continuing. For example, in Debian GNU/Linux, installing python-dev also installs distutils.

    Python must also be compiled with the zlib module enabled. This is practically always the case with pre-packaged Pythons.

  2. Compilers

    To build any extension modules for Python, you’ll need a C compiler. Various NumPy modules use FORTRAN 77 libraries, so you’ll also need a FORTRAN 77 compiler installed.

    Note that NumPy is developed mainly using GNU compilers. Compilers from other vendors such as Intel, Absoft, Sun, NAG, Compaq, Vast, Porland, Lahey, HP, IBM, Microsoft are only supported in the form of community feedback, and may not work out of the box. GCC 4.x (and later) compilers are recommended.

  3. Linear Algebra libraries

    NumPy does not require any external linear algebra libraries to be installed. However, if these are available, NumPy’s setup script can detect them and use them for building. A number of different LAPACK library setups can be used, including optimized LAPACK libraries such as ATLAS, MKL or the Accelerate/vecLib framework on OS X.

Basic Installation

To install NumPy run:

python install

To perform an in-place build that can be run from the source folder run:

python build_ext --inplace

The NumPy build system uses distutils and numpy.distutils. setuptools is only used when building via pip or with python Using virtualenv should work as expected.

Note: for build instructions to do development work on NumPy itself, see :ref:`development-environment`.

Parallel builds

From NumPy 1.10.0 on it’s also possible to do a parallel build with:

python build -j 4 install --prefix $HOME/.local

This will compile numpy on 4 CPUs and install it into the specified prefix. to perform a parallel in-place build, run:

python build_ext --inplace -j 4

The number of build jobs can also be specified via the environment variable NPY_NUM_BUILD_JOBS.

FORTRAN ABI mismatch

The two most popular open source fortran compilers are g77 and gfortran. Unfortunately, they are not ABI compatible, which means that concretely you should avoid mixing libraries built with one with another. In particular, if your blas/lapack/atlas is built with g77, you must use g77 when building numpy and scipy; on the contrary, if your atlas is built with gfortran, you must build numpy/scipy with gfortran. This applies for most other cases where different FORTRAN compilers might have been used.

Choosing the fortran compiler

To build with g77:

python build --fcompiler=gnu

To build with gfortran:

python build --fcompiler=gnu95

For more information see:

python build --help-fcompiler

How to check the ABI of blas/lapack/atlas

One relatively simple and reliable way to check for the compiler used to build a library is to use ldd on the library. If is a dependency, this means that g77 has been used. If is a a dependency, gfortran has been used. If both are dependencies, this means both have been used, which is almost always a very bad idea.

Disabling ATLAS and other accelerated libraries

Usage of ATLAS and other accelerated libraries in Numpy can be disabled via:

BLAS=None LAPACK=None ATLAS=None python build

Supplying additional compiler flags

Additional compiler flags can be supplied by setting the OPT, FOPT (for Fortran), and CC environment variables.

Building with ATLAS support


You can install the necessary package for optimized ATLAS with this command:

sudo apt-get install libatlas-base-dev