Building From Source on Windows


Compared to OSX and Linux, building NumPy and SciPy on Windows is difficult, largely due to the lack of compatible, open-source libraries like LAPACK or ATLAS that are necessary to build both libraries and have them perform relatively well. You can’t sudo apt-get install everything like you can on the other two platforms.

Fortunately, a lot of work has been done recently to rectify this situation. Projects such as OpenBLAS and Mingwpy are under active development to develop open-source toolchains that would allow Windows users to build and develop with NumPy and SciPy from source without issues of financial, platform, or licensing constraints.

This document will attempt to provide a general summary of the available options that users can currently avail themselves to if they so choose to build these libraries from source. However, in light of all the work currently being done, do not expect these instructions to be accurate in the long-run and be sure to check up on any of the open source projects mentioned for the most up-to-date information. For more information on all of these projects, the Mingwpy website is an excellent source of more in-depth information than this document will provide.

Python Libraries

For development purposes, you will need several Python libraries when building NumPy and SciPy. These can be installed by running the command (sudo) pip install {library}. The libraries needed are:

  1. Cython (compiling .pyx files)
  2. Nose (running unit tests)
  3. Tempita (SciPy only)


In order to build NumPy and SciPy, two compilers are needed: a C compiler and a Fortran compiler. The latter is technically not necessary for NumPy, but it is strongly encouraged to have one in order to build libraries like LAPACK or ATLAS that will significantly improve performance. For the remainder of this document, given the performance differences, NumPy will be treated as if it actually does require such libraries, hence necessitating a Fortran compiler.


The Mingw-w64 project provides Windows versions of the free GNU compilers gcc and gfortran. These are the compilers most NumPy and SciPy developers work with and hence are the best supported by build scripts in both libraries. Also, as indicated in the name, they form the basis of the ongoing Mingwpy project mentioned previously. Thus, from a long-term perspective, these compilers may be the optimal ones to use. Installation instructions can be found here.


A POSIX-compatible, Linux-like environment for Windows, Cygwin is a very useful tool, as it allows compilation and use of many Unix tools without modification. It can also be used to build libraries like ATLAS, which at the moment is very Unix-oriented, although that may be subject to change as we will discuss later on. Installation instructions for Cygwin can be found here. When using the installer (either 32-bit or 64-bit depending on your computer), make sure to search for and select packages with the keyword gcc in them. Note that if you use Cygwin’s gcc, anything built with it can only run in a Cygwin environment and not in your native Windows environment.

In addition, Cygwin also offers its own identical packages for Mingw-w64 that you can install by searching for mingw64 in the packages list and then selecting those that contain i686 if you’re using 32-bit or x86_64 if you’re using 64-bit. If you choose this option, there is no need to have a separate installation of Mingw-w64. This is because anything built with Mingw-w64 will be cross-platform compatible, so the build will work in your native Windows environment as well.

Finally, the installer may also miss several important DLL’s necessary for proper function as pointed out here, so double check that you have them marked during installation. Rest assured that even if you forget to install a package, you can always run the installer again to install additional ones.

Microsoft Visual C++ (MSVC)

NumPy and SciPy both support MSVC and its C/C++ compiler extension modules for the official binary distribution of Python. However, make sure that you download the correct version! For example, Python 2.7.x is compiled with Visual Studio 2008, and Python 3.5.1 is compiled with Visual Studio 2015. If you are using Python 2.7.x, you can also visit this link here to download the Microsoft Visual C++ Compiler for Python 2.7. If you are using Python 3.4.x and Windows 7, you should visit this link here and download the Microsoft Windows SDK for Windows 7. If you are using Python 3.5.x, you should obtain the compiler via their Visual Studio offering and download the Community Edition. If none of these configurations match your own, you will need to use one of the other build options described above. Please be aware that this option does does not come with a Fortran compiler, only a C/C++ compiler, and the only one currently known to be compatible with this compiler is the Intel Fortran compiler (ifort), which itself is difficult to obtain as will be explained in the discussion about the Intel Math Kernel Library (MKL).


As mentioned in the overview, certain libraries (math libraries to be specific) are necessary for a high performing NumPy and for building SciPy, and they are BLAS and LAPACK. There are many options available, in particular for BLAS, and we will discuss several of the options below.

Intel Math Kernel Library (MKL)

Intel has provided its own implementations of BLAS and LAPACK, and they are by far some of the best performing libraries for both NumPy and SciPy. Unfortunately, they are not free and also require their own Fortran compiler for these libraries to work. While it is possible to obtain the libraries for free via their Community License (you can click here to learn more and click here to register), it does not come with the Fortran compiler, ifort, which is necessary for building both the NumPy and SciPy libraries with MKL.

To obtain this compiler, it is necessary to download their Intel Parallel Studio XE product, which can be trialed for 30 days, but it is currently unknown what will happen to the library and header files on your hard drive after that period has expired. To download, visit this page here for more information. Note, if you are a student or educator, this option is very appealing because Intel’s academic license will provide you everything that you need free of charge. To register, visit this page here and choose the appropriate option corresponding to your current academic situations. Afterwards, click the link corresponding to Intel Parallel Studio XE and download. Note that this installation will require that you have the most up-to-date version of Visual Studio.

Finally, a brief note regarding C/C++ compilers: the Intel Parallel Studio XE software package will come with its own C/C++ compiler (icc), which will work perfectly fine when building the libraries. However, the C/C++ compiler from MSVC (cl) should work just fine as well.


ATLAS is an optimized version of BLAS that is considered to be “portably efficient” according to its website. If you want to use this library, the easiest is to use this library in combination with Mingw-w64. Precompiled libraries using this toolchain can be found here in the folder corresponding to your architecture (32-bit or 64-bit). While this setup has been shown to build NumPy successfully, it is not known yet whether it can build SciPy.

If you are so inclined to build ATLAS by hand, you must use Cygwin to build it because the library was explicitly designed for Unix environments. However, you can compile the library with either the native gcc tools or the mingww-64 tool package that you downloaded with Cygwin. Installations scripts can be found in the same location here. In the folder corresponding to your architecture, search for an install_atlas script, download the appropriate ZIP files here, fill in some of the variables with appropriate values corresponding to your directory structure (e.g. the code_home variable) and then run script. Be forewarned though that this will take a very long time (around eight hours) to install.

Finally, it should be noted that ATLAS, although open source, is not well optimized for Windows given its intended operating system environment. Thus, if performance is of the utmost importance, ATLAS may not be the best choice of libraries for building from source.


OpenBLAS is an optimized version of BLAS that is currently used in languages like Julia by default. Besides being actively worked upon, it performs about as well as the Intel libraries discussed previously. Furthermore, it is quite easy to install using Cygwin. Just search for openblas and lapack in the packages that you are downloading, and they will be automatically installed into your usr/lib directory, which is where NumPy and SciPy will search for libraries if no configuration file is provided. Please note that if you choose this route, you must use Cygwin’s Python for this setup to work. During installation, just search for python in the packages and download the appropriate interpreter. However, if you are so inclined to build OpenBLAS by hand or want to build the library in your native Windows environment, installation instructions can be found on the OpenBLAS wiki page here.


Up to this point, we have been discussing optimized versions of BLAS coupled with LAPACK. It goes without saying then that it must be possible to build NumPy and SciPy with an unoptimized (and therefore lower-performant) BLAS library. Pre-built libraries are readily available here, though be sure to check the environment in which the libraries were built. Otherwise, NumPy and SciPy will not build. However, if none of the environments match your own environment, the libraries themselves can be downloaded as ZIP files by searching for a “download” section on the BLAS and LAPACK webpages. Rough installation instructions can be found here for BLAS and on the LAPACK homepage for LAPACK. While these instructions are for Linux, you should be able to follow these instructions fairly well if you have either Cygwin or Mingw-w64 installed on your computer.

Linking Libraries to NumPy and SciPy

Now that you have obtained the libraries that you want to use to build NumPy and SciPy, it is now necessary to link those libraries to NumPy and SciPy so that they will be used during the building process. There are two ways to do this. First, you can store them in the “standard” locations, which correspond either to the Lib directory of your Python installation or one of your lib directories (e.g. /usr/lib) if you are using Cygwin. To determine the “standard” locations on your computer, navigate to the top-most level of your NumPy or SciPy directory and run python config, and the output will show you where Python is searching for libraries.

The other option is to create a configuration file, either called site.cfg or .numpy-site.cfg. If you are building both NumPy and SciPy, you should store it in your C:\Users\{username} directory of your native Windows environment or your $HOME or ~ directory if you are using Cygwin. If you are just building NumPy, you can store it in the same directory as the topmost file. Before filling it in, make sure that your configuration file can be detected by filling it with some invalid text (e.g. “asdf”) and then run python config again. An exception should be thrown because Python won’t be able to parse your configuration file.

Depending on which library you use, the exact specifics of the configuration file will vary. The site.cfg.example file, which should be located at the top of your NumPy installation, provides an excellent guide for how to fill in your configuration file given the libraries you are using. If you do not have such a file, you can find it online here.

Additional Resources

As discussed in the overview, this document is not meant to provide extremely detailed explanations on how to build NumPy and SciPy on Windows. This is largely because there is no one clearly superior way to do so at this point in time, and because the process for building these libraries on Windows is under active development, it is probable that any information will go out of date relatively soon. If you wish to receive more assistance, please reach out to the NumPy and SciPy mailing lists, which can be found here. There are many developers out there working on this issue right now, and they would certainly be happy to help you out! Google is also a good resource, as there are many people out there who use NumPy and SciPy on Windows, so it would not be surprising if your question or problem has already been addressed.