.. _contributing_codebase:

{{ header }}

=============================
Contributing to the code base
=============================

.. contents:: Table of Contents:
   :local:

Code standards
--------------

Writing good code is not just about what you write. It is also about *how* you
write it. During :ref:`Continuous Integration <contributing.ci>` testing, several
tools will be run to check your code for stylistic errors.
Generating any warnings will cause the test to fail.
Thus, good style is a requirement for submitting code to pandas.

There is a tool in pandas to help contributors verify their changes before
contributing them to the project::

   ./ci/code_checks.sh

The script validates the doctests, formatting in docstrings, static typing, and
imported modules. It is possible to run the checks independently by using the
parameters ``docstring``, ``code``, ``typing``, and ``doctests``
(e.g. ``./ci/code_checks.sh doctests``).

In addition, because a lot of people use our library, it is important that we
do not make sudden changes to the code that could have the potential to break
a lot of user code as a result, that is, we need it to be as *backwards compatible*
as possible to avoid mass breakages.

In addition to ``./ci/code_checks.sh``, some extra checks are run by
``pre-commit`` - see :ref:`here <contributing.pre-commit>` for how to
run them.

Additional standards are outlined on the :ref:`pandas code style guide <code_style>`.

.. _contributing.pre-commit:

Pre-commit
----------

Additionally, :ref:`Continuous Integration <contributing.ci>` will run code formatting checks
like ``black``, ``flake8``, ``isort``, and ``cpplint`` and more using `pre-commit hooks <https://pre-commit.com/>`_
Any warnings from these checks will cause the :ref:`Continuous Integration <contributing.ci>` to fail; therefore,
it is helpful to run the check yourself before submitting code. This
can be done by installing ``pre-commit``::

    pip install pre-commit

and then running::

    pre-commit install

from the root of the pandas repository. Now all of the styling checks will be
run each time you commit changes without your needing to run each one manually.
In addition, using ``pre-commit`` will also allow you to more easily
remain up-to-date with our code checks as they change.

Note that if needed, you can skip these checks with ``git commit --no-verify``.

If you don't want to use ``pre-commit`` as part of your workflow, you can still use it
to run its checks with::

    pre-commit run --files <files you have modified>

without needing to have done ``pre-commit install`` beforehand.

If you want to run checks on all recently committed files on upstream/main you can use::

    pre-commit run --from-ref=upstream/main --to-ref=HEAD --all-files

without needing to have done ``pre-commit install`` beforehand.

.. note::

    If you have conflicting installations of ``virtualenv``, then you may get an
    error - see `here <https://github.com/pypa/virtualenv/issues/1875>`_.

    Also, due to a `bug in virtualenv <https://github.com/pypa/virtualenv/issues/1986>`_,
    you may run into issues if you're using conda. To solve this, you can downgrade
    ``virtualenv`` to version ``20.0.33``.

Optional dependencies
---------------------

Optional dependencies (e.g. matplotlib) should be imported with the private helper
``pandas.compat._optional.import_optional_dependency``. This ensures a
consistent error message when the dependency is not met.

All methods using an optional dependency should include a test asserting that an
``ImportError`` is raised when the optional dependency is not found. This test
should be skipped if the library is present.

All optional dependencies should be documented in
:ref:`install.optional_dependencies` and the minimum required version should be
set in the ``pandas.compat._optional.VERSIONS`` dict.

Backwards compatibility
-----------------------

Please try to maintain backward compatibility. pandas has lots of users with lots of
existing code, so don't break it if at all possible.  If you think breakage is required,
clearly state why as part of the pull request.  Also, be careful when changing method
signatures and add deprecation warnings where needed. Also, add the deprecated sphinx
directive to the deprecated functions or methods.

If a function with the same arguments as the one being deprecated exist, you can use
the ``pandas.util._decorators.deprecate``:

.. code-block:: python

    from pandas.util._decorators import deprecate

    deprecate('old_func', 'new_func', '1.1.0')

Otherwise, you need to do it manually:

.. code-block:: python

    import warnings


    def old_func():
        """Summary of the function.

        .. deprecated:: 1.1.0
           Use new_func instead.
        """
        warnings.warn('Use new_func instead.', FutureWarning, stacklevel=2)
        new_func()


    def new_func():
        pass

You'll also need to

1. Write a new test that asserts a warning is issued when calling with the deprecated argument
2. Update all of pandas existing tests and code to use the new argument

See :ref:`contributing.warnings` for more.

.. _contributing.type_hints:

Type hints
----------

pandas strongly encourages the use of :pep:`484` style type hints. New development should contain type hints and pull requests to annotate existing code are accepted as well!

Style guidelines
~~~~~~~~~~~~~~~~

Type imports should follow the ``from typing import ...`` convention. Some types do not need to be imported since :pep:`585` some builtin constructs, such as ``list`` and ``tuple``, can directly be used for type annotations. So rather than

.. code-block:: python

   import typing

   primes: typing.List[int] = []

You should write

.. code-block:: python

   primes: list[int] = []

``Optional`` should be  avoided in favor of the shorter ``| None``, so instead of

.. code-block:: python

   from typing import Union

   maybe_primes: list[Union[int, None]] = []

or

.. code-block:: python

   from typing import Optional

   maybe_primes: list[Optional[int]] = []

You should write

.. code-block:: python

   from __future__ import annotations  # noqa: F404

   maybe_primes: list[int | None] = []

In some cases in the code base classes may define class variables that shadow builtins. This causes an issue as described in `Mypy 1775 <https://github.com/python/mypy/issues/1775#issuecomment-310969854>`_. The defensive solution here is to create an unambiguous alias of the builtin and use that without your annotation. For example, if you come across a definition like

.. code-block:: python

   class SomeClass1:
       str = None

The appropriate way to annotate this would be as follows

.. code-block:: python

   str_type = str

   class SomeClass2:
       str: str_type = None

In some cases you may be tempted to use ``cast`` from the typing module when you know better than the analyzer. This occurs particularly when using custom inference functions. For example

.. code-block:: python

   from typing import cast

   from pandas.core.dtypes.common import is_number

   def cannot_infer_bad(obj: Union[str, int, float]):

       if is_number(obj):
           ...
       else:  # Reasonably only str objects would reach this but...
           obj = cast(str, obj)  # Mypy complains without this!
	   return obj.upper()

The limitation here is that while a human can reasonably understand that ``is_number`` would catch the ``int`` and ``float`` types mypy cannot make that same inference just yet (see `mypy #5206 <https://github.com/python/mypy/issues/5206>`_. While the above works, the use of ``cast`` is **strongly discouraged**. Where applicable a refactor of the code to appease static analysis is preferable

.. code-block:: python

   def cannot_infer_good(obj: Union[str, int, float]):

       if isinstance(obj, str):
           return obj.upper()
       else:
           ...

With custom types and inference this is not always possible so exceptions are made, but every effort should be exhausted to avoid ``cast`` before going down such paths.

pandas-specific types
~~~~~~~~~~~~~~~~~~~~~

Commonly used types specific to pandas will appear in `pandas._typing <https://github.com/pandas-dev/pandas/blob/main/pandas/_typing.py>`_ and you should use these where applicable. This module is private for now but ultimately this should be exposed to third party libraries who want to implement type checking against pandas.

For example, quite a few functions in pandas accept a ``dtype`` argument. This can be expressed as a string like ``"object"``, a ``numpy.dtype`` like ``np.int64`` or even a pandas ``ExtensionDtype`` like ``pd.CategoricalDtype``. Rather than burden the user with having to constantly annotate all of those options, this can simply be imported and reused from the pandas._typing module

.. code-block:: python

   from pandas._typing import Dtype

   def as_type(dtype: Dtype) -> ...:
       ...

This module will ultimately house types for repeatedly used concepts like "path-like", "array-like", "numeric", etc... and can also hold aliases for commonly appearing parameters like ``axis``. Development of this module is active so be sure to refer to the source for the most up to date list of available types.

Validating type hints
~~~~~~~~~~~~~~~~~~~~~

pandas uses `mypy <http://mypy-lang.org>`_ and `pyright <https://github.com/microsoft/pyright>`_ to statically analyze the code base and type hints. After making any change you can ensure your type hints are correct by running

.. code-block:: shell

   ./ci/code_checks.sh typing

A recent version of ``numpy`` (>=1.21.0) is required for type validation.

.. _contributing.ci:

Testing type hints in code using pandas
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. warning::

    * Pandas is not yet a py.typed library (:pep:`561`)!
      The primary purpose of locally declaring pandas as a py.typed library is to test and
      improve the pandas-builtin type annotations.

Until pandas becomes a py.typed library, it is possible to easily experiment with the type
annotations shipped with pandas by creating an empty file named "py.typed" in the pandas
installation folder:

.. code-block:: none

   python -c "import pandas; import pathlib; (pathlib.Path(pandas.__path__[0]) / 'py.typed').touch()"

The existence of the py.typed file signals to type checkers that pandas is already a py.typed
library. This makes type checkers aware of the type annotations shipped with pandas.

Testing with continuous integration
-----------------------------------

The pandas test suite will run automatically on `GitHub Actions <https://github.com/features/actions/>`__ and
`Azure Pipelines <https://azure.microsoft.com/en-us/services/devops/pipelines/>`__
continuous integration services, once your pull request is submitted.
However, if you wish to run the test suite on a branch prior to submitting the pull request,
then the continuous integration services need to be hooked to your GitHub repository. Instructions are here
for `GitHub Actions <https://docs.github.com/en/actions/>`__ and
`Azure Pipelines <https://docs.microsoft.com/en-us/azure/devops/pipelines/?view=azure-devops>`__.

A pull-request will be considered for merging when you have an all 'green' build. If any tests are failing,
then you will get a red 'X', where you can click through to see the individual failed tests.
This is an example of a green build.

.. image:: ../_static/ci.png

.. _contributing.tdd:


Test-driven development/code writing
------------------------------------

pandas is serious about testing and strongly encourages contributors to embrace
`test-driven development (TDD) <https://en.wikipedia.org/wiki/Test-driven_development>`_.
This development process "relies on the repetition of a very short development cycle:
first the developer writes an (initially failing) automated test case that defines a desired
improvement or new function, then produces the minimum amount of code to pass that test."
So, before actually writing any code, you should write your tests.  Often the test can be
taken from the original GitHub issue.  However, it is always worth considering additional
use cases and writing corresponding tests.

Adding tests is one of the most common requests after code is pushed to pandas.  Therefore,
it is worth getting in the habit of writing tests ahead of time so this is never an issue.

Writing tests
~~~~~~~~~~~~~

All tests should go into the ``tests`` subdirectory of the specific package.
This folder contains many current examples of tests, and we suggest looking to these for
inspiration. Please reference our :ref:`testing location guide <test_organization>` if you are unsure
where to place a new unit test.

Using ``pytest``
~~~~~~~~~~~~~~~~

Test structure
^^^^^^^^^^^^^^

pandas existing test structure is *mostly* class-based, meaning that you will typically find tests wrapped in a class.

.. code-block:: python

    class TestReallyCoolFeature:
        pass

Going forward, we are moving to a more *functional* style using the `pytest <https://docs.pytest.org/en/latest/>`__ framework, which offers a richer testing
framework that will facilitate testing and developing. Thus, instead of writing test classes, we will write test functions like this:

.. code-block:: python

    def test_really_cool_feature():
        pass

Preferred idioms
^^^^^^^^^^^^^^^^

* Functional tests named ``def test_*`` and *only* take arguments that are either fixtures or parameters.
* Use a bare ``assert`` for testing scalars and truth-testing
* Use ``tm.assert_series_equal(result, expected)`` and ``tm.assert_frame_equal(result, expected)`` for comparing :class:`Series` and :class:`DataFrame` results respectively.
* Use `@pytest.mark.parameterize <https://docs.pytest.org/en/latest/how-to/parametrize.html>`__ when testing multiple cases.
* Use `pytest.mark.xfail <https://docs.pytest.org/en/latest/reference/reference.html?#pytest.mark.xfail>`__ when a test case is expected to fail.
* Use `pytest.mark.skip <https://docs.pytest.org/en/latest/reference/reference.html?#pytest.mark.skip>`__ when a test case is never expected to pass.
* Use `pytest.param <https://docs.pytest.org/en/latest/reference/reference.html?#pytest-param>`__ when a test case needs a particular mark.
* Use `@pytest.fixture <https://docs.pytest.org/en/latest/reference/reference.html?#pytest-fixture>`__ if multiple tests can share a setup object.

.. warning::

    Do not use ``pytest.xfail`` (which is different than ``pytest.mark.xfail``) since it immediately stops the
    test and does not check if the test will fail. If this is the behavior you desire, use ``pytest.skip`` instead.

If a test is known to fail but the manner in which it fails
is not meant to be captured, use ``pytest.mark.xfail`` It is common to use this method for a test that
exhibits buggy behavior or a non-implemented feature. If
the failing test has flaky behavior, use the argument ``strict=False``. This
will make it so pytest does not fail if the test happens to pass.

Prefer the decorator ``@pytest.mark.xfail`` and the argument ``pytest.param``
over usage within a test so that the test is appropriately marked during the
collection phase of pytest. For xfailing a test that involves multiple
parameters, a fixture, or a combination of these, it is only possible to
xfail during the testing phase. To do so, use the ``request`` fixture:

.. code-block:: python

    def test_xfail(request):
        mark = pytest.mark.xfail(raises=TypeError, reason="Indicate why here")
        request.node.add_marker(mark)

xfail is not to be used for tests involving failure due to invalid user arguments.
For these tests, we need to verify the correct exception type and error message
is being raised, using ``pytest.raises`` instead.

If your test requires working with files or
network connectivity, there is more information on the :wiki:`Testing` of the wiki.


Example
^^^^^^^

Here is an example of a self-contained set of tests in a file ``pandas/tests/test_cool_feature.py``
that illustrate multiple features that we like to use. Please remember to add the Github Issue Number
as a comment to a new test.

.. code-block:: python

   import pytest
   import numpy as np
   import pandas as pd


   @pytest.mark.parametrize('dtype', ['int8', 'int16', 'int32', 'int64'])
   def test_dtypes(dtype):
       assert str(np.dtype(dtype)) == dtype


   @pytest.mark.parametrize(
       'dtype', ['float32', pytest.param('int16', marks=pytest.mark.skip),
                 pytest.param('int32', marks=pytest.mark.xfail(
                     reason='to show how it works'))])
   def test_mark(dtype):
       assert str(np.dtype(dtype)) == 'float32'


   @pytest.fixture
   def series():
       return pd.Series([1, 2, 3])


   @pytest.fixture(params=['int8', 'int16', 'int32', 'int64'])
   def dtype(request):
       return request.param


   def test_series(series, dtype):
       # GH <issue_number>
       result = series.astype(dtype)
       assert result.dtype == dtype

       expected = pd.Series([1, 2, 3], dtype=dtype)
       tm.assert_series_equal(result, expected)


A test run of this yields

.. code-block:: shell

   ((pandas) bash-3.2$ pytest  test_cool_feature.py  -v
   =========================== test session starts ===========================
   platform darwin -- Python 3.6.2, pytest-3.6.0, py-1.4.31, pluggy-0.4.0
   collected 11 items

   tester.py::test_dtypes[int8] PASSED
   tester.py::test_dtypes[int16] PASSED
   tester.py::test_dtypes[int32] PASSED
   tester.py::test_dtypes[int64] PASSED
   tester.py::test_mark[float32] PASSED
   tester.py::test_mark[int16] SKIPPED
   tester.py::test_mark[int32] xfail
   tester.py::test_series[int8] PASSED
   tester.py::test_series[int16] PASSED
   tester.py::test_series[int32] PASSED
   tester.py::test_series[int64] PASSED

Tests that we have ``parametrized`` are now accessible via the test name, for example we could run these with ``-k int8`` to sub-select *only* those tests which match ``int8``.


.. code-block:: shell

   ((pandas) bash-3.2$ pytest  test_cool_feature.py  -v -k int8
   =========================== test session starts ===========================
   platform darwin -- Python 3.6.2, pytest-3.6.0, py-1.4.31, pluggy-0.4.0
   collected 11 items

   test_cool_feature.py::test_dtypes[int8] PASSED
   test_cool_feature.py::test_series[int8] PASSED


.. _using-hypothesis:

Using ``hypothesis``
~~~~~~~~~~~~~~~~~~~~

Hypothesis is a library for property-based testing. Instead of explicitly
parametrizing a test, you can describe *all* valid inputs and let Hypothesis
try to find a failing input.  Even better, no matter how many random examples
it tries, Hypothesis always reports a single minimal counterexample to your
assertions - often an example that you would never have thought to test.

See `Getting Started with Hypothesis <https://hypothesis.works/articles/getting-started-with-hypothesis/>`_
for more of an introduction, then `refer to the Hypothesis documentation
for details <https://hypothesis.readthedocs.io/en/latest/index.html>`_.

.. code-block:: python

    import json
    from hypothesis import given, strategies as st

    any_json_value = st.deferred(lambda: st.one_of(
        st.none(), st.booleans(), st.floats(allow_nan=False), st.text(),
        st.lists(any_json_value), st.dictionaries(st.text(), any_json_value)
    ))


    @given(value=any_json_value)
    def test_json_roundtrip(value):
        result = json.loads(json.dumps(value))
        assert value == result

This test shows off several useful features of Hypothesis, as well as
demonstrating a good use-case: checking properties that should hold over
a large or complicated domain of inputs.

To keep the pandas test suite running quickly, parametrized tests are
preferred if the inputs or logic are simple, with Hypothesis tests reserved
for cases with complex logic or where there are too many combinations of
options or subtle interactions to test (or think of!) all of them.

.. _contributing.warnings:

Testing warnings
~~~~~~~~~~~~~~~~

By default, the :ref:`Continuous Integration <contributing.ci>` will fail if any unhandled warnings are emitted.

If your change involves checking that a warning is actually emitted, use
``tm.assert_produces_warning(ExpectedWarning)``.


.. code-block:: python

   import pandas._testing as tm


   df = pd.DataFrame()
   with tm.assert_produces_warning(FutureWarning):
       df.some_operation()

We prefer this to the ``pytest.warns`` context manager because ours checks that the warning's
stacklevel is set correctly. The stacklevel is what ensure the *user's* file name and line number
is printed in the warning, rather than something internal to pandas. It represents the number of
function calls from user code (e.g. ``df.some_operation()``) to the function that actually emits
the warning. Our linter will fail the build if you use ``pytest.warns`` in a test.

If you have a test that would emit a warning, but you aren't actually testing the
warning itself (say because it's going to be removed in the future, or because we're
matching a 3rd-party library's behavior), then use ``pytest.mark.filterwarnings`` to
ignore the error.

.. code-block:: python

   @pytest.mark.filterwarnings("ignore:msg:category")
   def test_thing(self):
       ...

If the test generates a warning of class ``category`` whose message starts
with ``msg``, the warning will be ignored and the test will pass.

If you need finer-grained control, you can use Python's usual
`warnings module <https://docs.python.org/3/library/warnings.html>`__
to control whether a warning is ignored / raised at different places within
a single test.

.. code-block:: python

   with warnings.catch_warnings():
       warnings.simplefilter("ignore", FutureWarning)
       # Or use warnings.filterwarnings(...)

Alternatively, consider breaking up the unit test.


Running the test suite
----------------------

The tests can then be run directly inside your Git clone (without having to
install pandas) by typing::

    pytest pandas

Often it is worth running only a subset of tests first around your changes before running the
entire suite.

The easiest way to do this is with::

    pytest pandas/path/to/test.py -k regex_matching_test_name

Or with one of the following constructs::

    pytest pandas/tests/[test-module].py
    pytest pandas/tests/[test-module].py::[TestClass]
    pytest pandas/tests/[test-module].py::[TestClass]::[test_method]

Using `pytest-xdist <https://pypi.org/project/pytest-xdist>`_, one can
speed up local testing on multicore machines. To use this feature, you will
need to install ``pytest-xdist`` via::

    pip install pytest-xdist

Two scripts are provided to assist with this.  These scripts distribute
testing across 4 threads.

On Unix variants, one can type::

    test_fast.sh

On Windows, one can type::

    test_fast.bat

This can significantly reduce the time it takes to locally run tests before
submitting a pull request.

For more, see the `pytest <https://docs.pytest.org/en/latest/>`_ documentation.

Furthermore one can run

.. code-block:: python

   pd.test()

with an imported pandas to run tests similarly.

Running the performance test suite
----------------------------------

Performance matters and it is worth considering whether your code has introduced
performance regressions. pandas is in the process of migrating to
`asv benchmarks <https://github.com/airspeed-velocity/asv>`__
to enable easy monitoring of the performance of critical pandas operations.
These benchmarks are all found in the ``pandas/asv_bench`` directory, and the
test results can be found `here <https://pandas.pydata.org/speed/pandas/>`__.

To use all features of asv, you will need either ``conda`` or
``virtualenv``. For more details please check the `asv installation
webpage <https://asv.readthedocs.io/en/latest/installing.html>`_.

To install asv::

    pip install git+https://github.com/airspeed-velocity/asv

If you need to run a benchmark, change your directory to ``asv_bench/`` and run::

    asv continuous -f 1.1 upstream/main HEAD

You can replace ``HEAD`` with the name of the branch you are working on,
and report benchmarks that changed by more than 10%.
The command uses ``conda`` by default for creating the benchmark
environments. If you want to use virtualenv instead, write::

    asv continuous -f 1.1 -E virtualenv upstream/main HEAD

The ``-E virtualenv`` option should be added to all ``asv`` commands
that run benchmarks. The default value is defined in ``asv.conf.json``.

Running the full benchmark suite can be an all-day process, depending on your
hardware and its resource utilization. However, usually it is sufficient to paste
only a subset of the results into the pull request to show that the committed changes
do not cause unexpected performance regressions.  You can run specific benchmarks
using the ``-b`` flag, which takes a regular expression. For example, this will
only run benchmarks from a ``pandas/asv_bench/benchmarks/groupby.py`` file::

    asv continuous -f 1.1 upstream/main HEAD -b ^groupby

If you want to only run a specific group of benchmarks from a file, you can do it
using ``.`` as a separator. For example::

    asv continuous -f 1.1 upstream/main HEAD -b groupby.GroupByMethods

will only run the ``GroupByMethods`` benchmark defined in ``groupby.py``.

You can also run the benchmark suite using the version of ``pandas``
already installed in your current Python environment. This can be
useful if you do not have virtualenv or conda, or are using the
``setup.py develop`` approach discussed above; for the in-place build
you need to set ``PYTHONPATH``, e.g.
``PYTHONPATH="$PWD/.." asv [remaining arguments]``.
You can run benchmarks using an existing Python
environment by::

    asv run -e -E existing

or, to use a specific Python interpreter,::

    asv run -e -E existing:python3.6

This will display stderr from the benchmarks, and use your local
``python`` that comes from your ``$PATH``.

Information on how to write a benchmark and how to use asv can be found in the
`asv documentation <https://asv.readthedocs.io/en/latest/writing_benchmarks.html>`_.

Documenting your code
---------------------

Changes should be reflected in the release notes located in ``doc/source/whatsnew/vx.y.z.rst``.
This file contains an ongoing change log for each release.  Add an entry to this file to
document your fix, enhancement or (unavoidable) breaking change.  Make sure to include the
GitHub issue number when adding your entry (using ``:issue:`1234``` where ``1234`` is the
issue/pull request number). Your entry should be written using full sentences and proper
grammar.

When mentioning parts of the API, use a Sphinx ``:func:``, ``:meth:``, or ``:class:``
directive as appropriate. Not all public API functions and methods have a
documentation page; ideally links would only be added if they resolve. You can
usually find similar examples by checking the release notes for one of the previous
versions.

If your code is a bugfix, add your entry to the relevant bugfix section. Avoid
adding to the ``Other`` section; only in rare cases should entries go there.
Being as concise as possible, the description of the bug should include how the
user may encounter it and an indication of the bug itself, e.g.
"produces incorrect results" or "incorrectly raises". It may be necessary to also
indicate the new behavior.

If your code is an enhancement, it is most likely necessary to add usage
examples to the existing documentation.  This can be done following the section
regarding :ref:`documentation <contributing_documentation>`.
Further, to let users know when this feature was added, the ``versionadded``
directive is used. The sphinx syntax for that is:

.. code-block:: rst

  .. versionadded:: 1.1.0

This will put the text *New in version 1.1.0* wherever you put the sphinx
directive. This should also be put in the docstring when adding a new function
or method (`example <https://github.com/pandas-dev/pandas/blob/v0.20.2/pandas/core/frame.py#L1495>`__)
or a new keyword argument (`example <https://github.com/pandas-dev/pandas/blob/v0.20.2/pandas/core/generic.py#L568>`__).
