Contributing to the code base¶
Table of Contents:
Writing good code is not just about what you write. It is also about how you write it. During Continuous Integration 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:
The script validates the doctests, formatting in docstrings, and
imported modules. It is possible to run the checks independently by using the
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 (including static type
checking) are run by
pre-commit - see here
for how to run them.
Additionally, Continuous Integration will run code formatting checks
flake8 (including a pandas-dev-flaker plugin),
cpplint and more using pre-commit hooks
Any warnings from these checks will cause the Continuous Integration to fail; therefore,
it is helpful to run the check yourself before submitting code. This
can be done by installing
pip install pre-commit
and then running:
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.
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
Optional dependencies and the minimum required version should be
set in the
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
from pandas.util._decorators import deprecate deprecate('old_func', 'new_func', '1.1.0')
Otherwise, you need to do it manually:
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
Write a new test that asserts a warning is issued when calling with the deprecated argument
Update all of pandas existing tests and code to use the new argument
See Testing warnings for more.
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!
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
tuple, can directly be used for type annotations. So rather than
import typing primes: typing.List[int] = 
You should write
primes: list[int] = 
Optional should be avoided in favor of the shorter
| None, so instead of
from typing import Union maybe_primes: list[Union[int, None]] = 
from typing import Optional maybe_primes: list[Optional[int]] = 
You should write
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. 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
class SomeClass1: str = None
The appropriate way to annotate this would be as follows
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
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
float types mypy cannot make that same inference just yet (see mypy #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
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.
Commonly used types specific to pandas will appear in pandas._typing 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
np.int64 or even a pandas
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
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.
pre-commit run --hook-stage manual --all-files
in your activated python environment. A recent version of
numpy (>=1.22.0) is required for type validation.
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:
python -c "import pandas; import pathlib; (pathlib.Path(pandas.__path__) / '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.
The pandas test suite will run automatically on GitHub Actions 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.
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.
pandas is serious about testing and strongly encourages contributors to embrace test-driven development (TDD). 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.
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 testing location guide if you are unsure
where to place a new unit test.
pandas existing test structure is mostly class-based, meaning that you will typically find tests wrapped in a class.
class TestReallyCoolFeature: pass
Going forward, we are moving to a more functional style using the pytest 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:
def test_really_cool_feature(): pass
Functional tests named
def test_*and only take arguments that are either fixtures or parameters.
Use a bare
assertfor testing scalars and truth-testing
Use @pytest.mark.parameterize when testing multiple cases.
Use pytest.mark.xfail when a test case is expected to fail.
Use pytest.mark.skip when a test case is never expected to pass.
Use pytest.param when a test case needs a particular mark.
Use @pytest.fixture if multiple tests can share a setup object.
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
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
will make it so pytest does not fail if the test happens to pass.
Prefer the decorator
@pytest.mark.xfail and the argument
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
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
If your test requires working with files or network connectivity, there is more information on the wiki Testing of the wiki.
Here is an example of a self-contained set of tests in a file
that illustrate multiple features that we like to use. Please remember to add the Github Issue Number
as a comment to a new test.
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
((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
((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
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.
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.
By default, the Continuous Integration will fail if any unhandled warnings are emitted.
If your change involves checking that a warning is actually emitted, use
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
ignore the error.
@pytest.mark.filterwarnings("ignore:msg:category") def test_thing(self): ...
If the test generates a warning of class
category whose message starts
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 to control whether a warning is ignored / raised at different places within a single test.
with warnings.catch_warnings(): warnings.simplefilter("ignore", FutureWarning) # Or use warnings.filterwarnings(...)
Alternatively, consider breaking up the unit test.
The tests can then be run directly inside your Git clone (without having to install pandas) by typing:
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, one can
speed up local testing on multicore machines. To use this feature, you will
need to install
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:
On Windows, one can type:
This can significantly reduce the time it takes to locally run tests before submitting a pull request.
For more, see the pytest documentation.
Furthermore one can run
with an imported pandas to run tests similarly.
Performance matters and it is worth considering whether your code has introduced
performance regressions. pandas is in the process of migrating to
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.
To use all features of asv, you will need either
virtualenv. For more details please check the asv installation
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
-E virtualenv option should be added to all
that run benchmarks. The default value is defined in
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
-b flag, which takes a regular expression. For example, this will
only run benchmarks from a
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
. as a separator. For example:
asv continuous -f 1.1 upstream/main HEAD -b groupby.GroupByMethods
will only run the
GroupByMethods benchmark defined in
You can also run the benchmark suite using the version of
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="$PWD/.." asv [remaining arguments].
You can run benchmarks using an existing Python
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
Information on how to write a benchmark and how to use asv can be found in the asv documentation.
Changes should be reflected in the release notes located in
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
1234 is the
issue/pull request number). Your entry should be written using full sentences and proper
When mentioning parts of the API, use a Sphinx
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
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
Further, to let users know when this feature was added, the
directive is used. The sphinx syntax for that is:
.. 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) or a new keyword argument (example).