我想写出与下面的头一个函数：
def split_csv(file, sep=";", output_path=".", nrows=None, chunksize=None, low_memory=True, usecols=None):
正如你所看到的，我使用的是相同的参数，在几个发现pd.read_csv。我想知道（或做）是向前关于从read_csv这些参数，以我自己的功能，而无需复制/粘贴这些文档字符串。
编辑：据我所知，没有了这个盒子现有解决方案。因此，或许建设一个正在有序进行。我心里有：
some_new_fancy_library.get_doc(for_function = pandas.read_csv,for_parameters = ['sep','nrows'])将输出：
{'sep': 'doc as found in the docstring',
'nrows' : 'doc as found in the docstring', ...}
然后它会仅仅是一个插入字典的值到我自己的函数文档字符串的事
干杯
你可以用正则表达式解析文档字符串和匹配的参数返回到您的函数：
import re
pat = re.compile(r'([\w_+]+ :)') # capturing group for arguments
splitted = pat.split(pd.read_csv.__doc__)
# Compare the parsed docstring against your function's arguments and only extract the required docstrings
docstrings = '\n'.join([''.join(splitted[i: i+2]) for i, s in enumerate(splitted) if s.rstrip(" :") in split_csv.__code__.co_varnames])
split_csv.__doc__ = docstrings
help(split_csv)
# Help on function split_csv in module __main__:
#
# split_csv(file, sep=';', output_path='.', nrows=None, chunksize=None, low_memory=True, usecols=None)
# sep : str, default ','
# Delimiter to use. If sep is None, the C engine cannot automatically detect
# the separator, but the Python parsing engine can, meaning the latter will
# be used and automatically detect the separator by Python's builtin sniffer
# tool, ``csv.Sniffer``. In addition, separators longer than 1 character and
# different from ``'\s+'`` will be interpreted as regular expressions and
# will also force the use of the Python parsing engine. Note that regex
# delimiters are prone to ignoring quoted data. Regex example: ``'\r\t'``
#
# usecols : list-like or callable, default None
# Return a subset of the columns. If list-like, all elements must either
# be positional (i.e. integer indices into the document columns) or strings
# that correspond to column names provided either by the user in `names` or
# inferred from the document header row(s). For example, a valid list-like
# `usecols` parameter would be [0, 1, 2] or ['foo', 'bar', 'baz']. Element
# order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.
# To instantiate a DataFrame from ``data`` with element order preserved use
# ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns
# in ``['foo', 'bar']`` order or
# ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``
# for ``['bar', 'foo']`` order.
#
# If callable, the callable function will be evaluated against the column
# names, returning names where the callable function evaluates to True. An
# example of a valid callable argument would be ``lambda x: x.upper() in
# ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster
# parsing time and lower memory usage.
#
# nrows : int, default None
# Number of rows of file to read. Useful for reading pieces of large files
#
# chunksize : int, default None
# Return TextFileReader object for iteration.
# See the `IO Tools docs
# <http://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_
# for more information on ``iterator`` and ``chunksize``.
#
# low_memory : boolean, default True
# Internally process the file in chunks, resulting in lower memory use
# while parsing, but possibly mixed type inference. To ensure no mixed
# types either set False, or specify the type with the `dtype` parameter.
# Note that the entire file is read into a single DataFrame regardless,
# use the `chunksize` or `iterator` parameter to return the data in chunks.
# (Only valid with C parser)
但当然，这依赖于你有确切的参数名称复制功能。正如你所看到的，你将需要添加无与伦比的文档字符串你的自我（例如file，output_path）。