5. 用 args 和 *kwargs 自定义聚合函数

#  用inspect模块查看groupby对象的agg方法的签名
 In[31]: college = pd.read_csv('data/college.csv')
         grouped = college.groupby(['STABBR', 'RELAFFIL'])
 In[32]: import inspect
         inspect.signature(grouped.agg)
Out[32]: <Signature (arg, *args, **kwargs)>

如何做

#  自定义一个返回去本科生人数在1000和3000之间的比例的函数
 In[33]: def pct_between_1_3k(s):
             return s.between(1000, 3000).mean()
#  用州和宗教分组，再聚合
 In[34]: college.groupby(['STABBR', 'RELAFFIL'])['UGDS'].agg(pct_between_1_3k).head(9)
Out[34]: 
STABBR  RELAFFIL
AK      0           0.142857
        1           0.000000
AL      0           0.236111
        1           0.333333
AR      0           0.279412
        1           0.111111
AS      0           1.000000
AZ      0           0.096774
        1           0.000000
Name: UGDS, dtype: float64

#  但是这个函数不能让用户自定义上下限，再新写一个函数
 In[35]: def pct_between(s, low, high):
             return s.between(low, high).mean()
#  使用这个自定义聚合函数，并传入最大和最小值
 In[36]: college.groupby(['STABBR', 'RELAFFIL'])['UGDS'].agg(pct_between, 1000, 10000).head(9)
Out[36]: 
STABBR  RELAFFIL
AK      0           0.428571
        1           0.000000
AL      0           0.458333
        1           0.375000
AR      0           0.397059
        1           0.166667
AS      0           1.000000
AZ      0           0.233871
        1           0.111111
Name: UGDS, dtype: float64

原理

#  显示指定最大和最小值
 In[37]: college.groupby(['STABBR', 'RELAFFIL'])['UGDS'].agg(pct_between, high=10000, low=1000).head(9)
Out[37]: 
STABBR  RELAFFIL
AK      0           0.428571
        1           0.000000
AL      0           0.458333
        1           0.375000
AR      0           0.397059
        1           0.166667
AS      0           1.000000
AZ      0           0.233871
        1           0.111111
Name: UGDS, dtype: float64

#  也可以关键字参数和非关键字参数混合使用，只要非关键字参数在后面
 In[38]: college.groupby(['STABBR', 'RELAFFIL'])['UGDS'].agg(pct_between, 1000, high=10000).head(9)
Out[38]: 
STABBR  RELAFFIL
AK      0           0.428571
        1           0.000000
AL      0           0.458333
        1           0.375000
AR      0           0.397059
        1           0.166667
AS      0           1.000000
AZ      0           0.233871
        1           0.111111
Name: UGDS, dtype: float64

更多

#  Pandas不支持多重聚合时，使用参数
 In[39]: college.groupby(['STABBR', 'RELAFFIL'])['UGDS'].agg(['mean', pct_between], low=100, high=1000)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-39-3e3e18919cf9> in <module>()
----> 1 college.groupby(['STABBR', 'RELAFFIL'])['UGDS'].agg(['mean', pct_between], low=100, high=1000)
/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/groupby.py in aggregate(self, func_or_funcs, *args, **kwargs)
   2871         if hasattr(func_or_funcs, '__iter__'):
   2872             ret = self._aggregate_multiple_funcs(func_or_funcs,
-> 2873                                                  (_level or 0) + 1)
   2874         else:
   2875             cyfunc = self._is_cython_func(func_or_funcs)
/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/groupby.py in _aggregate_multiple_funcs(self, arg, _level)
   2944                 obj._reset_cache()
   2945                 obj._selection = name
-> 2946             results[name] = obj.aggregate(func)
   2947 
   2948         if isinstance(list(compat.itervalues(results))[0],
/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/groupby.py in aggregate(self, func_or_funcs, *args, **kwargs)
   2878 
   2879             if self.grouper.nkeys > 1:
-> 2880                 return self._python_agg_general(func_or_funcs, *args, **kwargs)
   2881 
   2882             try:
/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/groupby.py in _python_agg_general(self, func, *args, **kwargs)
    852 
    853         if len(output) == 0:
--> 854             return self._python_apply_general(f)
    855 
    856         if self.grouper._filter_empty_groups:
/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/groupby.py in _python_apply_general(self, f)
    718     def _python_apply_general(self, f):
    719         keys, values, mutated = self.grouper.apply(f, self._selected_obj,
--> 720                                                    self.axis)
    721 
    722         return self._wrap_applied_output(
/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/groupby.py in apply(self, f, data, axis)
   1800             #  group might be modified
   1801             group_axes = _get_axes(group)
-> 1802             res = f(group)
   1803             if not _is_indexed_like(res, group_axes):
   1804                 mutated = True
/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/groupby.py in <lambda>(x)
    840     def _python_agg_general(self, func, *args, **kwargs):
    841         func = self._is_builtin_func(func)
--> 842         f = lambda x: func(x, *args, **kwargs)
    843 
    844         #  iterate through "columns" ex exclusions to populate output dict
TypeError: pct_between() missing 2 required positional arguments: 'low' and 'high'

#  用闭包自定义聚合函数
 In[40]: def make_agg_func(func, name, *args, **kwargs):
             def wrapper(x):
                 return func(x, *args, **kwargs)
             wrapper.__name__ = name
             return wrapper
         my_agg1 = make_agg_func(pct_between, 'pct_1_3k', low=1000, high=3000)
         my_agg2 = make_agg_func(pct_between, 'pct_10_30k', 10000, 30000)['UGDS'].agg(pct_between, 1000, high=10000).head(9)
Out[41]: