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<!DOCTYPE html>
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<title>Setting up and using your development environment — NumPy v2.6.dev0 Manual</title>
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<section id="setting-up-and-using-your-development-environment">
<span id="development-environment"></span><h1>Setting up and using your development environment<a class="headerlink" href="#setting-up-and-using-your-development-environment" title="Link to this heading">#</a></h1>
<section id="recommended-development-setup">
<span id="id1"></span><h2>Recommended development setup<a class="headerlink" href="#recommended-development-setup" title="Link to this heading">#</a></h2>
<p>Since NumPy contains parts written in C and Cython that need to be
compiled before use, make sure you have the necessary compilers and Python
development headers installed - see <a class="reference internal" href="../building/index.html#building-from-source"><span class="std std-ref">Building from source</span></a>. Building
NumPy as of version <code class="docutils literal notranslate"><span class="pre">2.0</span></code> requires C11 and C++17 compliant compilers.</p>
<p>Having compiled code also means that importing NumPy from the development
sources needs some additional steps, which are explained below. For the rest
of this chapter we assume that you have set up your git repo as described in
<a class="reference external" href="https://scikit-image.org/docs/stable/gitwash/index.html#using-git" title="(in skimage v0.26.0)"><span>Working with scikit-image source code</span></a>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>If you are having trouble building NumPy from source or setting up your
local development environment, you can try to build NumPy with GitHub
Codespaces. It allows you to create the correct development environment
right in your browser, reducing the need to install local development
environments and deal with incompatible dependencies.</p>
<p>If you have good internet connectivity and want a temporary set-up, it is
often faster to work on NumPy in a Codespaces environment. For documentation
on how to get started with Codespaces, see
<a class="reference external" href="https://docs.github.com/en/codespaces">the Codespaces docs</a>.
When creating a codespace for the <code class="docutils literal notranslate"><span class="pre">numpy/numpy</span></code> repository, the default
2-core machine type works; 4-core will build and work a bit faster (but of
course at a cost of halving your number of free usage hours). Once your
codespace has started, you can run <code class="docutils literal notranslate"><span class="pre">conda</span> <span class="pre">activate</span> <span class="pre">numpy-dev</span></code> and your
development environment is completely set up - you can then follow the
relevant parts of the NumPy documentation to build, test, develop, write
docs, and contribute to NumPy.</p>
</div>
</section>
<section id="using-virtual-environments">
<h2>Using virtual environments<a class="headerlink" href="#using-virtual-environments" title="Link to this heading">#</a></h2>
<p>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?”.</p>
<p>One simple way to achieve this is to install the released version in
site-packages, by using pip or conda for example, and set
up the development version in a virtual environment.</p>
<p>If you use conda, we recommend creating a separate virtual environment for
numpy development using the <code class="docutils literal notranslate"><span class="pre">environment.yml</span></code> file in the root of the repo
(this will create the environment and install all development dependencies at
once):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ conda env create -f environment.yml # `mamba` works too for this command
$ conda activate numpy-dev
</pre></div>
</div>
<p>If you installed Python some other way than conda, first install
<a class="reference external" href="https://virtualenv.pypa.io/">virtualenv</a> (optionally use <a class="reference external" href="https://doughellmann.com/projects/virtualenvwrapper/">virtualenvwrapper</a>), then create your
virtualenv (named <code class="docutils literal notranslate"><span class="pre">numpy-dev</span></code> here), activate it, and install all project
dependencies with:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ virtualenv numpy-dev
$ source numpy-dev/bin/activate # activate virtual environment
$ python -m pip install -r requirements/all_requirements.txt
</pre></div>
</div>
<p>Now, whenever you want to switch to the virtual environment, you can use the
command <code class="docutils literal notranslate"><span class="pre">source</span> <span class="pre">numpy-dev/bin/activate</span></code>, and <code class="docutils literal notranslate"><span class="pre">deactivate</span></code> to exit from the
virtual environment and back to your previous shell.</p>
</section>
<section id="building-from-source">
<h2>Building from source<a class="headerlink" href="#building-from-source" title="Link to this heading">#</a></h2>
<p>See <a class="reference internal" href="../building/index.html#building-from-source"><span class="std std-ref">Building from source</span></a>.</p>
</section>
<section id="testing-builds">
<span id="id2"></span><h2>Testing builds<a class="headerlink" href="#testing-builds" title="Link to this heading">#</a></h2>
<p>Before running the tests, first install the test dependencies:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ python -m pip install -r requirements/test_requirements.txt
$ python -m pip install asv # only for running benchmarks
</pre></div>
</div>
<p>To build the development version of NumPy and run tests, spawn
interactive shells with the Python import paths properly set up etc., use the
<a class="reference external" href="https://github.com/scientific-python/spin">spin</a> utility. To run tests, do
one of:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ spin test -v
$ spin test numpy/random # to run the tests in a specific module
$ spin test -v -t numpy/_core/tests/test_nditer.py::test_iter_c_order
$ spin test -p auto # to run tests in parallel threads using pytest-run-parallel
</pre></div>
</div>
<p>This builds NumPy first, so the first time it may take a few minutes.</p>
<p>You can also use <code class="docutils literal notranslate"><span class="pre">spin</span> <span class="pre">bench</span></code> for benchmarking. See <code class="docutils literal notranslate"><span class="pre">spin</span> <span class="pre">--help</span></code> for more
command line options.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>If the above commands result in <code class="docutils literal notranslate"><span class="pre">RuntimeError:</span> <span class="pre">Cannot</span> <span class="pre">parse</span> <span class="pre">version</span> <span class="pre">0+untagged.xxxxx</span></code>,
run <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">pull</span> <span class="pre">upstream</span> <span class="pre">main</span> <span class="pre">--tags</span></code>.</p>
</div>
<p>Additional arguments may be forwarded to <code class="docutils literal notranslate"><span class="pre">pytest</span></code> by passing the extra
arguments after a bare <code class="docutils literal notranslate"><span class="pre">--</span></code>. For example, to run a test method with the
<code class="docutils literal notranslate"><span class="pre">--pdb</span></code> flag forwarded to the target, run the following:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ spin test -t numpy/tests/test_scripts.py::test_f2py -- --pdb
</pre></div>
</div>
<p>You can also <a class="reference external" href="https://docs.pytest.org/en/latest/usage.html#specifying-tests-selecting-tests">match test names using python operators</a> by passing the <code class="docutils literal notranslate"><span class="pre">-k</span></code>
argument to pytest:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ spin test -v -t numpy/_core/tests/test_multiarray.py -- -k "MatMul and not vector"
</pre></div>
</div>
<p>To run “doctests” – to check that the code examples in the documentation is correct –
use the <em class="xref py py-obj">check-docs</em> spin command. It relies on the <em class="xref py py-obj">scipy-docs</em> package, which
provides several additional features on top of the standard library <code class="docutils literal notranslate"><span class="pre">doctest</span></code>
package. Install <code class="docutils literal notranslate"><span class="pre">scipy-doctest</span></code> and run one of:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ spin check-docs -v
$ spin check-docs numpy/linalg
$ spin check-docs -v -- -k 'det and not slogdet'
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Remember that all tests of NumPy should pass before committing your changes.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Some of the tests in the test suite require a large amount of
memory, and are skipped if your system does not have enough.</p>
</div>
</section>
<section id="other-build-options">
<h2>Other build options<a class="headerlink" href="#other-build-options" title="Link to this heading">#</a></h2>
<p>For more options including selecting compilers, setting custom compiler flags
and controlling parallelism, see <a class="reference external" href="https://docs.scipy.org/doc/scipy/building/compilers_and_options.html" title="(in SciPy v1.18.0)"><span>Compiler selection and customizing a build</span></a>
(from the SciPy documentation.)</p>
</section>
<section id="running-tests">
<h2>Running tests<a class="headerlink" href="#running-tests" title="Link to this heading">#</a></h2>
<p>Besides using <code class="docutils literal notranslate"><span class="pre">spin</span></code>, there are various ways to run the tests. Inside
the interpreter, tests can be run like this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">test</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">test</span><span class="p">(</span><span class="s1">'full'</span><span class="p">)</span> <span class="c1"># Also run tests marked as slow</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">test</span><span class="p">(</span><span class="s1">'full'</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="c1"># Additionally print test name/file</span>
<span class="go">An example of a successful test :</span>
<span class="go">``4686 passed, 362 skipped, 9 xfailed, 5 warnings in 213.99 seconds``</span>
</pre></div>
</div>
<p>Or a similar way from the command line:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ python -c "import numpy as np; np.test()"
</pre></div>
</div>
<p>Tests can also be run with <code class="docutils literal notranslate"><span class="pre">pytest</span> <span class="pre">numpy</span></code>, however then the NumPy-specific
plugin is not found which causes strange side effects.</p>
<p>Running individual test files can be useful; it’s much faster than running the
whole test suite or that of a whole module (example: <code class="docutils literal notranslate"><span class="pre">np.random.test()</span></code>).
This can be done with:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ python path_to_testfile/test_file.py
</pre></div>
</div>
<p>That also takes extra arguments, like <code class="docutils literal notranslate"><span class="pre">--pdb</span></code> which drops you into the Python
debugger when a test fails or an exception is raised.</p>
<p>Running tests with <a class="reference external" href="https://tox.readthedocs.io/">tox</a> is also supported. For example, to build NumPy and
run the test suite with Python 3.12, use:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ tox -e py312
</pre></div>
</div>
<p>For more extensive information, see <a class="reference internal" href="../reference/testing.html#testing-guidelines"><span class="std std-ref">Testing guidelines</span></a>.</p>
<p>Note: do not run the tests from the root directory of your numpy git repo without <code class="docutils literal notranslate"><span class="pre">spin</span></code>,
that will result in strange test errors.</p>
</section>
<section id="running-type-checks">
<h2>Running type checks<a class="headerlink" href="#running-type-checks" title="Link to this heading">#</a></h2>
<p>Changes that involve static type declarations are also executed using <code class="docutils literal notranslate"><span class="pre">spin</span></code>.
The invocation will look like the following:</p>
<blockquote>
<div><p>$ spin mypy</p>
</div></blockquote>
<p>This will look in the <code class="docutils literal notranslate"><span class="pre">typing/tests</span></code> directory for sets of operations to
test for type incompatibility.</p>
</section>
<section id="running-linting">
<h2>Running linting<a class="headerlink" href="#running-linting" title="Link to this heading">#</a></h2>
<p>Lint checks can be performed on newly added lines of Python code.</p>
<p>Install all dependent packages using pip:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ python -m pip install -r requirements/linter_requirements.txt
</pre></div>
</div>
<p>To run lint checks before committing new code, run:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ spin lint
</pre></div>
</div>
<p>If there are no errors, the output will look like:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ spin lint
Running Ruff Check...
All checks passed!
Running C API borrow-reference linter...
Scanning 548 C/C++ source files...
All checks passed! C API borrow-ref linter found no issues.
Running cython-lint...
</pre></div>
</div>
<p>In case of errors, check the error message for details:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ spin lint
Running Ruff Check...
I001 [*] Import block is un-sorted or un-formatted
--> numpy/matlib.py:12:1
|
10 | PendingDeprecationWarning, stacklevel=2)
11 |
12 | / import numpy as np
13 | |
14 | | # Matlib.py contains all functions in the numpy namespace with a few
15 | | # replacements. See doc/source/reference/routines.matlib.rst for details.
16 | | # Need * as we're copying the numpy namespace.
17 | | from numpy import * # noqa: F403
18 | | from numpy.matrixlib.defmatrix import matrix, asmatrix
| |______________________________________________________^
19 |
20 | __version__ = np.__version__
|
help: Organize imports
15 | # replacements. See doc/source/reference/routines.matlib.rst for details.
16 | # Need * as we're copying the numpy namespace.
17 | from numpy import * # noqa: F403
- from numpy.matrixlib.defmatrix import matrix, asmatrix
18 + from numpy.matrixlib.defmatrix import asmatrix, matrix
19 |
20 | __version__ = np.__version__
21 |
E501 Line too long (127 > 88)
--> numpy/matlib.py:214:89
|
212 | -------
213 | I : matrix
214 | A `n` x `M` matrix where all elements are equal to zero, except for the `k`-th diagonal, whose values are equal to one.
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
215 |
216 | See Also
|
Found 2 errors.
[*] 1 fixable with the `--fix` option.
</pre></div>
</div>
<p>To automatically fix issues that can be fixed, run:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ spin lint --fix
Running Ruff Check...
E501 Line too long (127 > 88)
--> numpy/matlib.py:214:89
|
212 | -------
213 | I : matrix
214 | A `n` x `M` matrix where all elements are equal to zero, except for the `k`-th diagonal, whose values are equal to one.
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
215 |
216 | See Also
|
Found 2 errors (1 fixed, 1 remaining).
</pre></div>
</div>
<p>It is advisable to run lint checks before pushing commits to a remote branch
since the linter runs as part of the CI pipeline.</p>
<p>For more details on Style Guidelines:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://www.python.org/dev/peps/pep-0008/">Python Style Guide</a></p></li>
<li><p><a class="reference external" href="https://numpy.org/neps/nep-0045-c_style_guide.html#nep45" title="(in NumPy Enhancement Proposals)"><span>NEP 45 — C style guide</span></a></p></li>
</ul>
</section>
<section id="rebuilding-cleaning-the-workspace">
<h2>Rebuilding & cleaning the workspace<a class="headerlink" href="#rebuilding-cleaning-the-workspace" title="Link to this heading">#</a></h2>
<p>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 (<em>note: deletes any
uncommitted files!</em>):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ git clean -xdf
</pre></div>
</div>
<p>When you want to discard all changes and go back to the last commit in the
repo, use one of:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ git checkout .
$ git reset --hard
</pre></div>
</div>
</section>
<section id="debugging">
<span id="id3"></span><h2>Debugging<a class="headerlink" href="#debugging" title="Link to this heading">#</a></h2>
<p>Another frequently asked question is “How do I debug C code inside NumPy?”.
First, ensure that you have gdb installed on your system with the Python
extensions (often the default on Linux). You can see which version of
Python is running inside gdb to verify your setup:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="n">gdb</span><span class="p">)</span> <span class="n">python</span>
<span class="o">></span><span class="kn">import</span><span class="w"> </span><span class="nn">sys</span>
<span class="o">></span><span class="nb">print</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">version_info</span><span class="p">)</span>
<span class="o">></span><span class="n">end</span>
<span class="n">sys</span><span class="o">.</span><span class="n">version_info</span><span class="p">(</span><span class="n">major</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">minor</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">micro</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">releaselevel</span><span class="o">=</span><span class="s1">'final'</span><span class="p">,</span> <span class="n">serial</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
</pre></div>
</div>
<p>Most python builds do not include debug symbols and are built with compiler
optimizations enabled. To get the best debugging experience using a debug build
of Python is encouraged, see <a class="reference internal" href="development_advanced_debugging.html#advanced-debugging"><span class="std std-ref">Advanced debugging tools</span></a>.</p>
<p>In terms of debugging, NumPy also needs to be built in a debug mode. You need to use
<code class="docutils literal notranslate"><span class="pre">debug</span></code> build type and disable optimizations to make sure <code class="docutils literal notranslate"><span class="pre">-O0</span></code> flag is used
during object building. Note that NumPy should NOT be installed in your environment
before you build with the <code class="docutils literal notranslate"><span class="pre">spin</span> <span class="pre">build</span></code> command.</p>
<p>To generate source-level debug information within the build process run:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ spin build --clean -- -Dbuildtype=debug -Ddisable-optimization=true
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>In case you are using conda environment be aware that conda sets <code class="docutils literal notranslate"><span class="pre">CFLAGS</span></code>
and <code class="docutils literal notranslate"><span class="pre">CXXFLAGS</span></code> automatically, and they will include the <code class="docutils literal notranslate"><span class="pre">-O2</span></code> flag by default.
You can safely use <code class="docutils literal notranslate"><span class="pre">unset</span> <span class="pre">CFLAGS</span> <span class="pre">&&</span> <span class="pre">unset</span> <span class="pre">CXXFLAGS</span></code> to avoid them or provide them
at the beginning of the <code class="docutils literal notranslate"><span class="pre">spin</span></code> command: <code class="docutils literal notranslate"><span class="pre">CFLAGS="-O0</span> <span class="pre">-g"</span> <span class="pre">CXXFLAGS="-O0</span> <span class="pre">-g"</span></code>.
Alternatively, to take control of these variables more permanently, you can create
<code class="docutils literal notranslate"><span class="pre">env_vars.sh</span></code> file in the <code class="docutils literal notranslate"><span class="pre"><path-to-conda-envs>/numpy-dev/etc/conda/activate.d</span></code>
directory. In this file you can export <code class="docutils literal notranslate"><span class="pre">CFLAGS</span></code> and <code class="docutils literal notranslate"><span class="pre">CXXFLAGS</span></code> variables.
For complete instructions please refer to
<a class="reference external" href="https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#saving-environment-variables">https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#saving-environment-variables</a>.</p>
</div>
<p>Next you need to write a Python script that invokes the C code whose execution
you want to debug. For instance <code class="docutils literal notranslate"><span class="pre">mytest.py</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p>Note that your test file needs to be outside the NumPy clone you have. Now, you can
run:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ spin gdb /path/to/mytest.py
</pre></div>
</div>
<p>In case you are using clang toolchain:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ spin lldb /path/to/mytest.py
</pre></div>
</div>
<p>And then in the debugger:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="n">gdb</span><span class="p">)</span> <span class="k">break</span> <span class="n">array_empty_like</span>
<span class="p">(</span><span class="n">gdb</span><span class="p">)</span> <span class="n">run</span>
</pre></div>
</div>
<p>lldb counterpart:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="n">lldb</span><span class="p">)</span> <span class="nb">breakpoint</span> <span class="nb">set</span> <span class="o">--</span><span class="n">name</span> <span class="n">array_empty_like</span>
<span class="p">(</span><span class="n">lldb</span><span class="p">)</span> <span class="n">run</span>
</pre></div>
</div>
<p>The execution will now stop at the corresponding C function and you can step
through it as usual. A number of useful Python-specific commands are available.
For example to see where in the Python code you are, use <code class="docutils literal notranslate"><span class="pre">py-list</span></code>, to see the
python traceback, use <code class="docutils literal notranslate"><span class="pre">py-bt</span></code>. For more details, see
<a class="reference external" href="https://wiki.python.org/moin/DebuggingWithGdb">DebuggingWithGdb</a>. Here are some commonly used commands:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">list</span></code>: List specified function or line.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">next</span></code>: Step program, proceeding through subroutine calls.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">step</span></code>: Continue program being debugged, after signal or breakpoint.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">print</span></code>: Print value of expression EXP.</p></li>
</ul>
<p>Rich support for Python debugging requires that the <code class="docutils literal notranslate"><span class="pre">python-gdb.py</span></code> script
distributed with Python is installed in a path where gdb can find it. If you
installed your Python build from your system package manager, you likely do
not need to manually do anything. However, if you built Python from source,
you will likely need to create a <code class="docutils literal notranslate"><span class="pre">.gdbinit</span></code> file in your home directory
pointing gdb at the location of your Python installation. For example, a
version of python installed via <a class="reference external" href="https://github.com/pyenv/pyenv">pyenv</a>
needs a <code class="docutils literal notranslate"><span class="pre">.gdbinit</span></code> file with the following contents:</p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>add-auto-load-safe-path ~/.pyenv
</pre></div>
</div>
<p>Building NumPy with a Python built with debug support (on Linux distributions
typically packaged as <code class="docutils literal notranslate"><span class="pre">python-dbg</span></code>) is highly recommended.</p>
</section>
<section id="understanding-the-code-getting-started">
<h2>Understanding the code & getting started<a class="headerlink" href="#understanding-the-code-getting-started" title="Link to this heading">#</a></h2>
<p>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.</p>
<p>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!</p>
</section>
</section>
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