pandas 熊猫系列的 groupby 不起作用
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groupby for pandas Series not working
提问by andrew
I am unable to do a groupby on a pandas Series object. DataFrames are fine, but I cannot seem to do groupby with a Series. Has anyone been able to get this to work?
我无法对Pandas系列对象进行分组。DataFrames 很好，但我似乎无法使用 Series 进行 groupby。有没有人能够让这个工作？
>>> import pandas as pd
>>> a = pd.Series([1,2,3,4], index=[4,3,2,1])
>>> a
4 1
3 2
2 3
1 4
dtype: int64
>>> a.groupby()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/share/apps/install/anaconda/lib/python2.7/site-packages/pandas/core/generic.py", line 153, in groupby
sort=sort, group_keys=group_keys)
File "/share/apps/install/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py", line 537, in groupby
return klass(obj, by, **kwds)
File "/share/apps/install/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py", line 195, in __init__
level=level, sort=sort)
File "/share/apps/install/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py", line 1326, in _get_grouper
ping = Grouping(group_axis, gpr, name=name, level=level, sort=sort)
File "/share/apps/install/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py", line 1203, in __init__
self.grouper = self.index.map(self.grouper)
File "/share/apps/install/anaconda/lib/python2.7/site-packages/pandas/core/index.py", line 878, in map
return self._arrmap(self.values, mapper)
File "generated.pyx", line 2200, in pandas.algos.arrmap_int64 (pandas/algos.c:61221)
TypeError: 'NoneType' object is not callable
回答by Jeff
You need to pass a mapping of some kind (could be a dict/function/index)
您需要传递某种映射（可能是字典/函数/索引）
In [6]: a
Out[6]:
4 1
3 2
2 3
1 4
dtype: int64
In [7]: a.groupby(a.index).sum()
Out[7]:
1 4
2 3
3 2
4 1
dtype: int64
In [3]: a.groupby(lambda x: x % 2 == 0).sum()
Out[3]:
False 6
True 4
dtype: int64
回答by luca
if you need to groupby series' values:
如果您需要对系列的值进行分组：
grouped = a.groupby(a)
or
或者
grouped = a.groupby(lambda x: a[x])
回答by braunmagrin
Don't take the answer too seriously ;) I'm not saying this is a good idea.
不要太认真地回答这个问题；) 我不是说这是个好主意。
If you reallywant to do it inline, or in a "fluent" way, you could do something like this.
如果您真的想内联或以“流畅”的方式进行操作，您可以这样做。
def smart_groupby(self, by=None, *args, **kwargs):
if by is None:
return self.groupby(self, *args, **kwargs)
return self.groupby(by, *args, **kwargs)
import pandas as pd
ps.Series.groupby = smart_groupby
pd.Series(['a', 'a', 'a', 'b', 'b']).groupby().count()
and the result would be
结果是
a 3
b 2
dtype: int64
It should behave as usual, but with the added benefit that if you omit the byit groups based on itself.
它应该表现得像往常一样，但如果您省略by它基于自身的分组，则有额外的好处。
