我有一个这样的矩阵:
RCA = pd.DataFrame(
data=[
(1,0,0,0),
(1,1,1,0),
(0,0,1,0),
(0,1,0,1),
(1,0,1,0)],
columns=['ct1','ct2','ct3','ct4'],
index=['ind_1','ind_2','ind_3','ind_4','ind_5'])
我正在尝试计算:
norms = RCA.sum()
norm = np.maximum.outer(norms, norms)
我得到了这个错误:
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-9-4fd04a55ad8c> in <module>
4
5 norms = RCA.sum()
----> 6 norm = np.maximum.outer(norms, norms)
7 proximity = RCA.T.dot(RCA).div(norm)
8
~/opt/anaconda3/envs/py37/lib/python3.7/site-packages/pandas/core/series.py in __array_ufunc__(self, ufunc, method, *inputs, **kwargs)
746 return None
747 else:
--> 748 return construct_return(result)
749
750 def __array__(self, dtype=None) -> np.ndarray:
~/opt/anaconda3/envs/py37/lib/python3.7/site-packages/pandas/core/series.py in construct_return(result)
735 if method == "outer":
736 # GH#27198
--> 737 raise NotImplementedError
738 return result
739 return self._constructor(result, index=index, name=name, copy=False)
NotImplementedError:
这在Python 2.7中非常有效,但我需要在Python 3.x 中运行它
我需要想办法解决这个问题。非常感谢。
In [181]: RCA
Out[181]:
ct1 ct2 ct3 ct4
ind_1 1 0 0 0
ind_2 1 1 1 0
ind_3 0 0 1 0
ind_4 0 1 0 1
ind_5 1 0 1 0
In [182]: norms = RCA.sum()
In [183]: norms
Out[183]:
ct1 3
ct2 2
ct3 3
ct4 1
dtype: int64
In [184]: np.maximum.outer(norms,norms)
Traceback (most recent call last):
File "<ipython-input-184-d24a173874f6>", line 1, in <module>
np.maximum.outer(norms,norms)
File "/usr/local/lib/python3.8/dist-packages/pandas/core/generic.py", line 2032, in __array_ufunc__
return arraylike.array_ufunc(self, ufunc, method, *inputs, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pandas/core/arraylike.py", line 381, in array_ufunc
result = reconstruct(result)
File "/usr/local/lib/python3.8/dist-packages/pandas/core/arraylike.py", line 334, in reconstruct
raise NotImplementedError
NotImplementedError
有时,将数据帧(或序列(传递给numpy函数可以正常工作,但显然,在这里我们需要显式使用数组值:
In [185]: norms.values
Out[185]: array([3, 2, 3, 1])
In [186]: np.maximum.outer(norms.values,norms.values)
Out[186]:
array([[3, 3, 3, 3],
[3, 2, 3, 2],
[3, 3, 3, 3],
[3, 2, 3, 1]])
实际上,从回溯来看,显然pandas
使ufunc
适应了它自己的用途。np.maximum(norms,norms)
有效,但大熊猫显然没有适应outer
的方法。[186]是纯numpy
,返回一个数组。
普通np.maximum
返回一个系列:
In [192]: np.maximum(norms,norms)
Out[192]:
ct1 3
ct2 2
ct3 3
ct4 1
dtype: int64
outer
返回一个2d数组,在pandas
术语中,它将是一个数据帧,而不是一个系列。这可以解释熊猫为什么不执行outer
。