Setting up and using your development environment

Building in-place

For development, you can set up an in-place build so that changes made to .py files have effect without rebuild. First, run:

$ python build_ext -i

This allows you to import the in-place built NumPy from the repo base directory only. If you want the in-place build to be visible outside that base dir, you need to point your PYTHONPATH environment variable to this directory. Some IDEs (Spyder for example) have utilities to manage PYTHONPATH. On Linux and OSX, you can run the command:


and on Windows:

$ set PYTHONPATH=/path/to/numpy

Now editing a Python source file in NumPy allows you to immediately test and use your changes (in .py files), by simply restarting the interpreter.

Note that another way to do an inplace build visible outside the repo base dir is with python develop. Instead of adjusting PYTHONPATH, this installs a .egg-link file into your site-packages as well as adjusts the easy-install.pth there, so its a more permanent (and magical) operation.

Other build options

It’s possible to do a parallel build with numpy.distutils with the -j option; see Parallel builds for more details.

In order to install the development version of NumPy in site-packages, use python install --user.

A similar approach to in-place builds and use of PYTHONPATH but outside the source tree is to use:

$ python install --prefix /some/owned/folder
$ export PYTHONPATH=/some/owned/folder/lib/python3.4/site-packages

Using virtualenvs

A frequently asked question is “How do I set up a development version of NumPy in parallel to a released version that I use to do my job/research?”.

One simple way to achieve this is to install the released version in site-packages, by using a binary installer or pip for example, and set up the development version in a virtualenv. First install virtualenv (optionally use virtualenvwrapper), then create your virtualenv (named numpy-dev here) with:

$ virtualenv numpy-dev

Now, whenever you want to switch to the virtual environment, you can use the command source numpy-dev/bin/activate, and deactivate to exit from the virtual environment and back to your previous shell.

Running tests

Besides using, there are various ways to run the tests. Inside the interpreter, tests can be run like this:

>>> np.test()
>>> np.test('full')   # Also run tests marked as slow
>>> np.test('full', verbose=2)   # Additionally print test name/file

Or a similar way from the command line:

$ python -c "import numpy as np; np.test()"

Tests can also be run with nosetests numpy, however then the NumPy-specific nose plugin is not found which causes tests marked as KnownFailure to be reported as errors.

Running individual test files can be useful; it’s much faster than running the whole test suite or that of a whole module (example: np.random.test()). This can be done with:

$ python path_to_testfile/

That also takes extra arguments, like --pdb which drops you into the Python debugger when a test fails or an exception is raised.

Running tests with tox is also supported. For example, to build NumPy and run the test suite with Python 3.4, use:

$ tox -e py34

For more extensive info on running and writing tests, see .

Note: do not run the tests from the root directory of your numpy git repo, that will result in strange test errors.

Rebuilding & cleaning the workspace

Rebuilding NumPy after making changes to compiled code can be done with the same build command as you used previously - only the changed files will be re-built. Doing a full build, which sometimes is necessary, requires cleaning the workspace first. The standard way of doing this is (note: deletes any uncommitted files!):

$ git clean -xdf

When you want to discard all changes and go back to the last commit in the repo, use one of:

$ git checkout .
$ git reset --hard


Another frequently asked question is “How do I debug C code inside NumPy?”. The easiest way to do this is to first write a Python script that invokes the C code whose execution you want to debug. For instance

from numpy import linspace
x = np.arange(5)

Now, you can run:

$ gdb --args python -g --python

And then in the debugger:

(gdb) break array_empty_like
(gdb) run

The execution will now stop at the corresponding C function and you can step through it as usual. With the Python extensions for gdb installed (often the default on Linux), a number of useful Python-specific commands are available. For example to see where in the Python code you are, use py-list. For more details, see DebuggingWithGdb.

Instead of plain gdb you can of course use your favourite alternative debugger; run it on the python binary with arguments -g --python

Building NumPy with a Python built with debug support (on Linux distributions typically packaged as python-dbg) is highly recommended.

Understanding the code & getting started

The best strategy to better understand the code base is to pick something you want to change and start reading the code to figure out how it works. When in doubt, you can ask questions on the mailing list. It is perfectly okay if your pull requests aren’t perfect, the community is always happy to help. As a volunteer project, things do sometimes get dropped and it’s totally fine to ping us if something has sat without a response for about two to four weeks.

So go ahead and pick something that annoys or confuses you about numpy, experiment with the code, hang around for discussions or go through the reference documents to try to fix it. Things will fall in place and soon you’ll have a pretty good understanding of the project as a whole. Good Luck!