I'm looking to replace values in a Dataframe with the values in a second Dataframe by matching the values in the first Dataframe with the columns from the second Dataframe.
Example:

import numpy as np
import pandas as pd
dt_index = pd.to_datetime(['2003-05-01', '2003-05-02', '2003-05-03', '2003-05-04'])
df = pd.DataFrame({'A':[1,1,3,12], 'B':[12,1,3,3], 'C':[3,12,12,1]}, index = dt_index)
df2 = pd.DataFrame({1:[1.4,4.2,1.3,5.6], 12:[2.3,7.3,9.5,0.4], 3:[8.8,0.1,8.7,2.4], 4:[9.6,9.8,5.5,1.8]}, index = dt_index)

df =             
             A   B   C
2003-05-01   1  12   3
2003-05-02   1   1  12
2003-05-03   3   3  12
2003-05-04  12   3   1

df2 = 
             1    12   3    4 
2003-05-01  1.4  2.3  8.8  9.6
2003-05-02  4.2  7.3  0.1  9.8
2003-05-03  1.3  9.5  8.7  5.5
2003-05-04  5.6  0.4  2.4  1.8

Expected output:

expect = pd.DataFrame({'A':[1.4,4.2,8.7,0.4], 'B':[2.3,4.2,8.7,2.4], 'C':[8.8,7.3,9.5,5.6]}, index = dt_index)

expect = 
              A    B    C
2003-05-01  1.4  2.3  8.8
2003-05-02  4.2  4.2  7.3
2003-05-03  8.7  8.7  9.5
2003-05-04  0.4  2.4  5.6

Attempt:

X = df.copy()
for i in np.unique(df):
    X.mask(df == i, df2[i], axis=0, inplace=True)

My attempt seems to work but I'm not sure if it has any pitfalls and how it would scale as the sizes of the Dataframe increase.
Are there better or faster solutions?

EDIT:

After cottontail's helpful answer, I realised I've made an oversimplification in my example. The values in df and columns of df and df2 cannot be assumed to be sequential.
I've now modified the example to reflect that.