基于pandas数据帧中的关键列减去列

Question

I have two dataframes looking like

df1:

   ID    A   B   C   D 
0 'ID1' 0.5 2.1 3.5 6.6
1 'ID2' 1.2 5.5 4.3 2.2
2 'ID1' 0.7 1.2 5.6 6.0 
3 'ID3' 1.1 7.2 10. 3.2

df2:

   ID    A   B   C   D 
0 'ID1' 1.0 2.0 3.3 4.4
1 'ID2' 1.5 5.0 4.0 2.2
2 'ID3' 0.6 1.2 5.9 6.2 
3 'ID4' 1.1 7.2 8.5 3.0

df1 can have multiple entries with the same ID whereas each ID occurs only once in df2. Also not all ID in df2 are necessarily present in df1. I can't solve this by using set_index() as multiple rows in df1 can have the same ID , and that the ID in df1 and df2 are not aligned.

I want to create a new dataframe where I subtract the values in df2[['A','B','C','D']] from df1[['A','B','C','D']] based on matching the ID.

The resulting dataframe would look like:

df_new:

   ID     A    B   C   D 
0 'ID1' -0.5  0.1 0.2 2.2
1 'ID2' -0.3  0.5 0.3 0.0
2 'ID1' -0.3 -0.8 2.3 1.6
3 'ID3'  0.5  6.0 1.5 0.2

I know how to do this with a loop, but since I'm dealing with huge data quantities this is not practical at all. What is the best way of approaching this with Pandas?

Answer 1

You just need set_index and subtract

(df1.set_index('ID')-df2.set_index('ID')).dropna(axis=0)
Out[174]: 
         A    B    C    D
ID                       
'ID1' -0.5  0.1  0.2  2.2
'ID1' -0.3 -0.8  2.3  1.6
'ID2' -0.3  0.5  0.3  0.0
'ID3'  0.5  6.0  4.1 -3.0

If the order matters add reindex for df2

(df1.set_index('ID')-df2.set_index('ID').reindex(df1.ID)).dropna(axis=0).reset_index()
Out[211]: 
      ID    A    B    C    D
0  'ID1' -0.5  0.1  0.2  2.2
1  'ID2' -0.3  0.5  0.3  0.0
2  'ID1' -0.3 -0.8  2.3  1.6
3  'ID3'  0.5  6.0  4.1 -3.0

Answer 2

Similarly to what Wen (who beat me to it) proposed, you can use pd.DataFrame.subtract :

df1.set_index('ID').subtract(df2.set_index('ID')).reset_index()

         A    B    C    D
ID                       
'ID1' -0.5  0.1  0.2  2.2
'ID1' -0.3 -0.8  2.3  1.6
'ID2' -0.3  0.5  0.3  0.0
'ID3'  0.5  6.0  4.1 -3.0

Answer 3

One method is to use numpy . We can extract the ordered indices required from df2 using numpy.searchsorted .

Then feed this into the construction of a new dataframe.

idx = np.searchsorted(df2['ID'], df1['ID'])

res = pd.DataFrame(df1.iloc[:, 1:].values - df2.iloc[:, 1:].values[idx],
                   index=df1['ID']).reset_index()

print(res)

      ID    0    1    2    3
0  'ID1' -0.5  0.1  0.2  2.2
1  'ID2' -0.3  0.5  0.3  0.0
2  'ID1' -0.3 -0.8  2.3  1.6
3  'ID3'  0.5  6.0  4.1 -3.0