Python API documentation

Docstrings

A docstring is a string literal that occurs as the first statement in a module, function, class, or method definition. Such a docstring becomes the __doc__ special attribute of that object.

>>> import numpy as np
>>> print(np.sum.__doc__)

    Sum of array elements over a given axis.

    Parameters
    ----------
    a : array_like
        Elements to sum.
    axis : None or int or tuple of ints, optional
        Axis or axes along which a sum is performed.  The default,
        axis=None, will sum all of the elements of the input array.  If
        axis is negative it counts from the last to the first axis.

        .. versionadded:: 1.7.0

        If axis is a tuple of ints, a sum is performed on all of the axes
        specified in the tuple instead of a single axis or all the axes as
        before.
    dtype : dtype, optional
        The type of the returned array and of the accumulator in which the
        elements are summed.  The dtype of `a` is used by default unless `a`
        has an integer dtype of less precision than the default platform
        integer.  In that case, if `a` is signed then the platform integer
        is used while if `a` is unsigned then an unsigned integer of the
        same precision as the platform integer is used.
    out : ndarray, optional
        Alternative output array in which to place the result. It must have
        the same shape as the expected output, but the type of the output
        values will be cast if necessary.
    keepdims : bool, optional
        If this is set to True, the axes which are reduced are left
        in the result as dimensions with size one. With this option,
        the result will broadcast correctly against the input array.

        If the default value is passed, then `keepdims` will not be
        passed through to the `sum` method of sub-classes of
        `ndarray`, however any non-default value will be.  If the
        sub-class' method does not implement `keepdims` any
        exceptions will be raised.
    initial : scalar, optional
        Starting value for the sum. See `~numpy.ufunc.reduce` for details.

        .. versionadded:: 1.15.0

    where : array_like of bool, optional
        Elements to include in the sum. See `~numpy.ufunc.reduce` for details.

        .. versionadded:: 1.17.0

    Returns
    -------
    sum_along_axis : ndarray
        An array with the same shape as `a`, with the specified
        axis removed.   If `a` is a 0-d array, or if `axis` is None, a scalar
        is returned.  If an output array is specified, a reference to
        `out` is returned.

    See Also
    --------
    ndarray.sum : Equivalent method.

    add.reduce : Equivalent functionality of `add`.

    cumsum : Cumulative sum of array elements.

    trapz : Integration of array values using the composite trapezoidal rule.

    mean, average

    Notes
    -----
    Arithmetic is modular when using integer types, and no error is
    raised on overflow.

    The sum of an empty array is the neutral element 0:

    >>> np.sum([])
    0.0

    For floating point numbers the numerical precision of sum (and
    ``np.add.reduce``) is in general limited by directly adding each number
    individually to the result causing rounding errors in every step.
    However, often numpy will use a  numerically better approach (partial
    pairwise summation) leading to improved precision in many use-cases.
    This improved precision is always provided when no ``axis`` is given.
    When ``axis`` is given, it will depend on which axis is summed.
    Technically, to provide the best speed possible, the improved precision
    is only used when the summation is along the fast axis in memory.
    Note that the exact precision may vary depending on other parameters.
    In contrast to NumPy, Python's ``math.fsum`` function uses a slower but
    more precise approach to summation.
    Especially when summing a large number of lower precision floating point
    numbers, such as ``float32``, numerical errors can become significant.
    In such cases it can be advisable to use `dtype="float64"` to use a higher
    precision for the output.

    Examples
    --------
    >>> np.sum([0.5, 1.5])
    2.0
    >>> np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)
    1
    >>> np.sum([[0, 1], [0, 5]])
    6
    >>> np.sum([[0, 1], [0, 5]], axis=0)
    array([0, 6])
    >>> np.sum([[0, 1], [0, 5]], axis=1)
    array([1, 5])
    >>> np.sum([[0, 1], [np.nan, 5]], where=[False, True], axis=1)
    array([1., 5.])

    If the accumulator is too small, overflow occurs:

    >>> np.ones(128, dtype=np.int8).sum(dtype=np.int8)
    -128

    You can also start the sum with a value other than zero:

    >>> np.sum([10], initial=5)
    15

All modules should normally have docstrings, and all functions and classes exported by a module should also have docstrings. Public methods (including the __init__ constructor) should also have docstrings. A package may be documented in the module docstring of the __init__.py file in the package directory.

String literals occurring elsewhere in Python code may also act as documentation. They are not recognized by the Python bytecode compiler and are not accessible as runtime object attributes (i.e. not assigned to __doc__), but two types of extra docstrings may be extracted by software tools:

String literals occurring immediately after a simple assignment at the top level of a module, class, or __init__ method are called “attribute docstrings”.
String literals occurring immediately after another docstring are called “additional docstrings”.

More information at https://peps.python.org/pep-0257/.

Google vs NumPy vs others

How should the docstrings be formatted? There are many variants that you can choose, and the two main popular styles are:

The main difference between the two styles is that Google uses indentation to separate sections, whereas NumPy uses underlines.

Doctest

A doctest is a way to embed test cases within the documentation of a function or module in Python. The tests are written in the form of interactive Python sessions, and they are used to make sure that the code examples in the documentation are accurate. Check for example the examples given above in the np.sum function.

Let’s take a simple Python function that calculates the area of a rectangle:

import sys
import doctest

def area(width, height):
    """Calculate the area of a rectangle.

    >>> area(5, 10)
    50
    >>> area(2, 3)
    6
    """
    return width * height

if __name__ == "__main__":
    sys.exit(doctest.testmod())

The tests are executed using:

python mymodule.py

This will run all of the doctests in the current module and report any failures. Alternatively you can run it using the doctest module from the command line:

python -m doctest mymodule.py

Exercise

Add a docstring to this function by documenting its arguments, its return value. Do not hesitate to add missing types as well in the definition of the function. You can add any useful notes as well (see the example above), and finally add a doctest to it:

def vector_sum(vec, vec2):
    output = []
    for i in range(len(vec)):
        output.append(vec[i] + vec2[i])
    return output

Use generators!

Writing documentation is a time consuming task that we easily skip if we do not get some help. Typically, we recommend to use an extension with your favourite code editor to automatically generate template docstrings (e.g. autoDocstring in VSCode and VSCodium), or use integrated chatbot based on large language models (e.g. Microsoft Copilot). In the latter case, please be sure to check the output carefully as it may seem sensible at first glance, but could turn out to be completely wrong on closer examination.

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