Question

DataFrame算法始终对齐索引和列名称。如果我有两个具有相同列数但列名不同的dfs，似乎我不能在它们之间进行算术运算：

Out[1]: 
length = pd.DataFrame(data=np.random.normal(size=[5,2]),index=range(5),columns=['length1','length2'])

length
Out[2]: 
    length1   length2
0 -0.430872  1.087211
1 -0.788218 -0.440801
2 -0.540136 -1.217191
3 -0.561248  0.305545
4  0.158832  0.075283

height = pd.DataFrame(data=np.random.normal(size=[5,2]),index=range(1,6),columns=['height1','height2'])

height
Out[3]: 
    height1   height2
1 -1.105751  1.089808
2 -0.360827 -0.803927
3  0.454469 -0.766144
4  0.476534 -0.855870
5 -0.007049  0.038307

length*height
Out[4]: 
   height1  height2  length1  length2
0      NaN      NaN      NaN      NaN
1      NaN      NaN      NaN      NaN
2      NaN      NaN      NaN      NaN
3      NaN      NaN      NaN      NaN
4      NaN      NaN      NaN      NaN
5      NaN      NaN      NaN      NaN

这可能是一种安全措施，可确保您仅对预期数据进行操作。但我仍然想知道有没有办法可以在两个DataFrame（具有相同列数）之间执行操作，但只能在索引轴上对齐？

编辑：原始示例过于简化，因为两个df具有相同的索引[0,1,2,3,4]。我将第二个df的索引移动了1，以使其成为一个更好的例子。

Answer 1

ans=pd.DataFrame(length.values * height.values)

将其转换为numpy数组并按照

进行乘法运算

          0         1
0  0.396724 -0.264562
1 -0.460419 -0.285086
2  0.126083 -0.494675
3 -0.272121  0.305155
4 -0.159292  0.444439

Answer 2

了解user3589054的用途，我认为此代码可能适合您：

height.multiply(length.values, axis = 0)

这是我的输出：

>>> length = pd.DataFrame(data=np.random.normal(size=[5,2]),index=range(5),columns=['length1','length2'])

>>> height = pd.DataFrame(data=np.random.normal(size=[5,2]),index=range(5),columns=['height1','height2'])

>>> length
        length1   length2
    0  1.000865 -0.758316
    1  0.285942 -2.000440
    2 -0.399625  0.686547
    3  0.809561  1.238211
    4  2.216696 -1.347227
>>> height
        height1   height2
    0  0.505477 -0.299634
    1 -0.234154 -2.490459
    2 -0.134534  1.063768
    3  0.010025  0.435895
    4  2.290053 -0.096494

 >>> height.multiply(length.values, axis = 0)
        height1   height2
    0  0.505915  0.227217
    1 -0.066954  4.982013
    2  0.053763  0.730326
    3  0.008116  0.539730
    4  5.076352  0.129999

Pandas DataFrame算术忽略列索引

2 个答案: