updating the columns in nested loops in python

I have two dataframes A and B as follows :

A
col1 col2 col3
A     B    V1
A     B    V2
A     C    V1
A     E    V2

B
Col1 Col2 Value1 Value2
A     B   nan     nan
A     D   nan     nan
A     C   nan     nan
A     G   nan     nan
A     E   nan     nan

I want to update the columns Value1 and Value2 in dataframe B on the basis of dataframe A, as if the combination of Col1 and Col 2 of A exists in B it will update columns Value1 Value2 i.e. the values from col3 in dataframe A.

I want the output as :

Col1 Col2 Value1 Value2
A     B   V1      V2
A     D   nan     nan
A     C   V1      nan
A     G   nan     nan
A     E   nan     V2

I tried the following code in python :

def update_b():
for x in b.index:
    for y in a.index:          
                  
            if ((a["col1"][y] == b["col1"][x]) & (a["col2"][y] == b["col2"][x])):
                               
                if (a["col3"][y] == "V1"):
                    b["value1"][x] = "V1"
                else:
                    b["value2"][x] = "V2"

update_b()

but it gives me an error
> ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Your dataframe(s) have bad design, you should revisit them, but to answer your initial question, we can leverage multindex & map

s = df.groupby(['col1','col2'])['col3'].agg(','.join).to_dict()
df2['Val'] = df2.set_index(['Col1','Col2']).index.map(s)
df2.loc[df2['Val'].str.contains(r'(v1)',case=False)==True,'Value1'] = 'V1'
df2.loc[df2['Val'].str.contains(r'(v2)',case=False)==True,'Value2'] = 'V2'
df2.pop('Val')

Result:

print(df2)
   Col1 Col2 Value1 Value2
0    A    B     V1     V2
1    A    D    NaN    NaN
2    A    C     V1    NaN
3    A    G    NaN    NaN
4    A    E    NaN     V2

As far as I understood the logic, you may try the below function, Note, the last row is different from your expected output (I couldnt figure how is that possible).

def return_updated_B(df):
    m=A.rename(columns=lambda x:x.capitalize()) #capitalize cols of A
    n=m.set_index(['Col1','Col2',m.groupby(['Col1','Col2'])
           .cumcount().add(1)]).unstack().droplevel(0,axis=1).add_prefix('Value')
    return n.reindex(pd.MultiIndex.from_arrays((B['Col1'],B['Col2']))).reset_index()

print(return_updated_B(A))

  Col1 Col2 Value1 Value2
0    A    B     V1     V2
1    A    D    NaN    NaN
2    A    C     V1    NaN
3    A    G    NaN    NaN
4    A    E     V2    NaN

You could try iterating over rows of the first dataframe and loc rows of the second one to assing values.

Something like the following:

for index, row in dfA.iterrows():
    dfB.loc[dfB.Col1 == row['col1'] & dfB.Col2 == row['col2'], "Value1"] = row['value1'] 

Writing answer from memory so there may be some syntax errors.