Python 文档字符串（docstring）

def my_func():
    """docstring-test
    line1
    line2
    line3
    """

class MyClass:
    """docstring-test
    line1
    line2
    line3
    """

print(my_func.__doc__)
# ...

print(type(MyClass.__doc__))
# <class 'str'>

def func_rest(param1, param2):
    """Summary line.

    :param param1: Description of param1
    :type param1: int
    :param param2: Description of param2
    :type param2: str
    :returns: Description of return value
    :rtype: bool
    """
    return True

def func_numpy(param1, param2):
    """Summary line.

    Extended description of function.

    Parameters
    ----------
    param1 : int
        Description of param1
    param2 : str
        Description of param2

    Returns
    -------
    bool
        Description of return value
    """
    return True

def func_google(param1, param2):
    """Summary line.

    Extended description of function.

    Args:
        param1 (int): Description of param1
        param2 (str): Description of param2

    Returns:
        bool: Description of return value

    """
    return True

class Series(base.IndexOpsMixin, NDFrame):  # type: ignore[misc]
    """
    One-dimensional ndarray with axis labels...

    Parameters
    ----------
    data : array-like, Iterable, dict, or scalar value
        Contains data stored in Series...
    index : array-like or Index (1d)
        Values must be hashable and have the same...
    dtype : str, numpy.dtype, or ExtensionDtype, optional
        Data type for the output Series...
        See the :ref:`user guide <basics.dtypes>` ..
    name : Hashable, default None
        The name to give to the Series.
    copy : bool, default False
        Copy input data... See examples.

    Notes
    -----
    Please reference the :ref:`User Guide <basics.series>` for more information.

    Examples
    --------
    Constructing Series from a dictionary with an Index specified

    >>> d = {'a': 1, 'b': 2, 'c': 3}
    >>> ser = pd.Series(data=d, index=['a', 'b', 'c'])
    >>> ser
    a   1
    b   2
    c   3
    dtype: int64

    >>> r = np.array([1, 2])
    >>> ser = pd.Series(r, copy=False)
    >>> ser.iloc[0] = 999
    >>> r
    array([999,   2])
    >>> ser
    0    999
    1      2
    dtype: int64

    Due to input data type the Series has a `view` on
    the original data, so
    the data is changed as well.
    """

# module level doc-string
__doc__ = """
pandas - a powerful data analysis and manipulation library for Python
=====================================================================

**pandas** is a Python package providing fast, flexible, and expressive data
structures designed to make working with "relational" or "labeled" data both
easy and intuitive. It aims to be the fundamental high-level building block for
doing practical, **real world** data analysis in Python. Additionally, it has
the broader goal of becoming **the most powerful and flexible open source data
analysis / manipulation tool available in any language**. It is already well on
its way toward this goal.
...
'''

def add(a, b):
    '''
    >>> add(1, 2)
    3
    >>> add(5, 10)
    10
    '''

    return a * b


if __name__ == '__main__':
    import doctest
    doctest.testmod()

def foo():
    f"Not a docstring"

# foo.__doc__ is None
True