'Pandas DataFrame Arithmetic ignoring column index

DataFrame arithmetic always align both index and column names. If I have two dfs with same number of columns but different column names, it seems I can't do arithmetic operations between them:

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

This is probably a safety measure to make sure you are only operating on the intended data. But I'm still wondering is there a way I can perform operations between two DataFrames (with same number of columns) but only aligning on index axis?

Edit: original example was over-simplified in that the two df's have the same index [0,1,2,3,4]. I shifted the second df's index by 1 to make it a better example.

python pandas

Solution 1:^[1]

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

Converted it to a numpy array and do multiplication like that

          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

Solution 2:^[2]

Going of of what user3589054 did, I think this code might work for you:

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

Here is my output:

>>> 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

Solution 3:^[3]

Concise renaming of columns in order to preserve index alignment:

length * height.set_axis(length.columns, axis=1)

# output:
    length1   length2
0       NaN       NaN
1  0.010236 -0.144040
2 -0.342200 -1.320554
3 -0.223242 -0.545550
4 -4.178892  0.139534
5       NaN       NaN

Sources

This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.

Source: Stack Overflow

Solution	Source
Solution 1	NinjaGaiden
Solution 2	walker_4
Solution 3

'Pandas DataFrame Arithmetic ignoring column index

Solution 1:[1]

Solution 2:[2]

Solution 3:[3]

Sources

Related Questions

Solution 1:^[1]

Solution 2:^[2]

Solution 3:^[3]