在我正在编写的函数中使用 pandas.read_csv 文档字符串

Question

我想用以下内容编写一个函数 header :

def split_csv(file, sep=";", output_path=".", nrows=None, chunksize=None, low_memory=True, usecols=None):

如您所见，我使用的参数与 pd.read_csv 中的几个参数相同。我想知道（或做）的是将有关这些参数的文档字符串从 read_csv 转发到我自己的函数，而不必 copy/paste 它们。

编辑：据我所知，没有开箱即用的现有解决方案。所以也许建造一个是有序的。我的想法:

some_new_fancy_library.get_doc(for_function = pandas.read_csv,for_parameters = ['sep','nrows']) 会输出：

{'sep': 'doc as found in the docstring', 'nrows' : 'doc as found in the docstring', ...}

然后只需将字典的值插入到我自己的函数的文档字符串中即可

干杯

Answer 1

您可以使用正则表达式解析文档字符串和return您的函数的匹配参数：

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)

当然，这取决于您对复制的函数具有准确的参数名称。如您所见，您需要自己添加不匹配的文档字符串（例如 file、output_path）。

在我正在编写的函数中使用 pandas.read_csv 文档字符串

Use pandas.read_csv docstring in the function I'm writing

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documentation

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