Replace values in pandas dataframe based on other dataframe

Question

I have two dataframes, one in the form of:

#   X   Y   
1   2   0.0 
2   5   0.0 
3   10  0.0 
4   15  0.0 
5   17  0.0 
6   21  0.0 

and one in the form of:

A   B   C   
1   4   2   
2   5   3   
3   6   4   

I want to replace all the ABC values from the second dataframe, with the X values; so I want go over the ABC df and if the number matches the # of df1 to replace it with the X value

the end table should look:

A   B   C   
2   15  5   
5   17  10  
10  21  15   

is there a way I can do it?

Answer 1

IIUC replace

df1.replace(df.set_index('#').X)
Out[382]: 
    A   B   C
0   2  15   5
1   5  17  10
2  10  21  15

Answer 2

say your first DataFrame is a and your second is b , you can map b columns to ax values like this:

b.apply(lambda y: a.x[(y -1).tolist()].values)

The result is:

    A   B   C
0   2  15   5
1   5  17  10
2  10  21  15

Answer 3

Only you should use:

df1.set_index('#',inplace = True)
df=df.apply(lambda x: x.replace(df1.loc[x,'X']))

Example:

import pandas as pd
import numpy as np
df1=pd.DataFrame()
df1['#']=[1,2,3,4,5,6]
df1['X']=[2,5,10,15,17,21]
df1['Y']=[0,0,0,0,0,0]
df=pd.DataFrame()
df['A']=[1,2,3]
df['B']=[4,5,6]
df['C']=[2,3,4]
df1.set_index('#',inplace = True)
df=df.apply(lambda x: x.replace(df1.loc[x,'X']))
print(df)

Output:

    A   B   C
0   2  15   5
1   5  17  10
2  10  21  15

Note df1.set_index('#',inplace = True) set '#' column like index . if this column was already the index it is not necessary to execute it