date_df["rank_num"] = date_df.groupby("issuer_id").report_date.agg("rank", **{"ascending": 1, "method": "min"})
  File "D:\python_virtualenv\es_env\lib\site-packages\pandas\core\groupby\groupby.py", line 3479, in aggregate
    return getattr(self, func_or_funcs)(*args, **kwargs)
  File "D:\python_virtualenv\es_env\lib\site-packages\pandas\core\groupby\groupby.py
 
", line 1906, in rank
    na_option=na_option, pct=pct, axis=axis)
  File "D:\python_virtualenv\es_env\lib\site-packages\pandas\core\groupby\groupby.py", line 1025, in _cython_transform
    **kwargs)
  File "D:\python_virtualenv\es_env\lib\site-packages\pandas\core\groupby\groupby.py", line 2630, in transform
    
 
return self._cython_operation('transform', values, how, axis, **kwargs)
  File "D:\python_virtualenv\es_env\lib\site-packages\pandas\core\groupby\groupby.py", line 2590, in _cython_operation
    **kwargs)
  File "D:\python_virtualenv\es_env\lib\site-packages\pandas\core\groupby\groupby.py", line 2664, in
 
 _transform
    transform_func(result, values, comp_ids, is_datetimelike, **kwargs)
  File "D:\python_virtualenv\es_env\lib\site-packages\pandas\core\groupby\groupby.py", line 2479, in wrapper
    return f(afunc, *args, **kwargs)
  File "D:\python_virtualenv\es_env\lib\site-packages\pandas\core\groupby\groupby.py", line 2430, in <lambda>
    kwargs.get('na_option', 'keep')
TypeError: 'NoneType' object is not callable

在使用pandas對一列日期進行分組排序時報錯，

1. 根據錯誤提示 File "D:\python_virtualenv\es_env\lib\site-packages\pandas\core\groupby\groupby.py", line 2430, in <lambda> kwargs.get('na_option', 'keep') 可知，是因為pandas模組的groupby.py檔案的下面程式碼中func函式傳入為None導致的。

'f': lambda func, a, b, c, d, **kwargs: func(
    a, b, c, d,
    kwargs.get('ties_method', 'average'),
    kwargs.get('ascending', True),
    kwargs.get('pct', False),
    kwargs.get('na_option', 'keep')
)

2. 根據錯誤提示
　　File "D:\python_virtualenv\es_env\lib\site-packages\pandas\core\groupby\groupby.py", line 2478, in wrapper return f(afunc, *args, **kwargs)
　　可知afunc就是傳入的函式，這個afunc是使用get_func函式一步步獲取的，最終是看_libs\groupby.py檔案下是否存在一個group_rank_object函式，但是檔案中沒有，所以獲得的是None。

def _get_cython_function(self, kind, how, values, is_numeric):
# 這一步檢視values中的資料型別，date無法識別，datetime識別為int
    dtype_str = values.dtype.name
    def get_func(fname):
        # see if there is a fused-type version of function
        # only valid for numeric
# 這一步看libgroupby中是不是有fname對應的函式
        f = getattr(libgroupby, fname, None)
        if f is not None and is_numeric:
            return f

        # otherwise find dtype-specific version, falling back to object
# 再看是不是有group_rank_object函式，因為沒有，所以最後返回的結果是None
        for dt in [dtype_str, 'object']:
            f = getattr(libgroupby, "%s_%s" % (fname, dtype_str), None)
            if f is not None:
                return f

    ftype = self._cython_functions[kind][how]

    if isinstance(ftype, dict):
# 這一步獲取傳入的函式afunc
        func = afunc = get_func(ftype['name'])
        # a sub-function
        f = ftype.get('f')
        if f is not None:

            def wrapper(*args, **kwargs):
                return f(afunc, *args, **kwargs)

            # need to curry our sub-function
            func = wrapper

3.結論
　　(1).0.23.4的pandas沒有對object的排序方式，只存在針對int和float的排序方式。
　　(2).0.23.4的pandas無法識別date型別，是作為object型別。但是可以識別datetime型別，會把datetime型別識別為int來處理。
　　(3).所以要對日期列進行排序，需要先轉換成時間才行。

0.23版本的pandas存在這個問題，但是0.22版本沒有這個問題。