How to replace rows with another ones in same dataframe with same index?

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I have a dataframe df1:

id1_a    id2_a   id3_a  id1_b    id2_b   id3_b
a1        a2       a3    b1        b2     b3
aa1       a2       a3    b12       b22    b23
ac1       c2       a3    bc2       bc2    bc3

I want to use it as a dictionary to replace values in columns id1_a, id2_a, id3_a in

this dataframe df2:

id1_a    id2_a   id3_a  
a1        a2       a3    
a1        a2       a3   
aa1       a2       a3    
ac1       c2       a3

so desired result is

id1_a    id2_a   id3_a  
b1        b2       b3   
b1        b2       b3
b12       b22      b23   
bc2       bc2      bc3

How could I do that? If there was inly one column I would turn it into dictionary and replace values, but what to do in three column case?

pandas dataframe python-3.x function python
1 Answers

If need replace all columns by dictionary created between _a and _b columns use:

df1.columns = df1.columns.str.split('_', expand=True)
df1 = df1.stack(0)

df2 = df2.replace(dict(zip(df1['a'], df1['b'])))
print (df2)

  id1_a id2_a id3_a
0    b1   b22   bc3
1    b1   b22   bc3
2   b12   b22   bc3
3   bc2   bc2   bc3

If need replace by match columns create Series for specify it first:

print (df1)
  id1_a id2_a id3_a id1_b id2_b id3_b
0    a1    a2    a3    b1    b2    b3
1   aa1    a1    a3   b12   b22   b23
2   ac1    c2    a3   bc2   bc2   bc3


print (df2)
  id1_a id2_a id3_a
0    b1   b22   bc3
1    b1    b2   bc3
2   b12    b2   bc3
3   bc2   bc2   bc3


df1.columns = df1.columns.str.split('_', expand=True)
s = (df1.stack(0)
        .groupby(level=1)
        .apply(lambda x: dict(zip(x['a'], x['b'])))
        .add_suffix('_a'))
print (s)
id1_a    {'a1': 'b1', 'aa1': 'b12', 'ac1': 'bc2'}
id2_a      {'a2': 'b2', 'a1': 'b22', 'c2': 'bc2'}
id3_a                               {'a3': 'bc3'}
dtype: object

df2 = df2.replace(s)
print (df2)
  id1_a id2_a id3_a
0    b1   b22   bc3
1    b1    b2   bc3
2   b12    b2   bc3
3   bc2   bc2   bc3
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