python - 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 header 编写一个函数:

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，而无需复制/粘贴它们。

编辑:据我所知，没有开箱即用的现有解决方案。所以也许 build 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': '在文档字符串中找到的文档', 'nrows' : '在文档字符串中找到的文档', ...}

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并将匹配的参数返回给您的函数:

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)

当然，这依赖于您拥有复制函数的确切参数名称。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)。