pandas, apply with args which are dataframe row entries

Question

I have a pandas dataframe 'df' with two columns 'A' and 'B', I have a function with two arguments

def myfunction(B, A):
    # do something here to get the result
    return result

and I would like to apply it row-by-row to df using the 'apply' function

df['C'] = df['B'].apply(myfunction, args=(df['A'],))

but I get the error

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

whats happening here, it seems it takes df['A'] as the whole series. not just the row entry from that series as required.

Answer 1

I think you need:

import pandas as pd
df = pd.DataFrame({'A':[1,2,3],
                   'B':[4,5,6]})

print (df)
   A  B
0  1  4
1  2  5
2  3  6

def myfunction(B, A):
    #some staff  
    result = B + A 
    # do something here to get the result
    return result

df['C'] = df.apply(lambda x: myfunction(x.B, x.A), axis=1)
print (df)
   A  B  C
0  1  4  5
1  2  5  7
2  3  6  9

Or:

def myfunction(x):

    result = x.B + x.A
    # do something here to get the result
    return result

df['C'] = df.apply(myfunction, axis=1)
print (df)
   A  B  C
0  1  4  5
1  2  5  7
2  3  6  9

Answer 2

I would add one more method, which could be useful when you need pass all columns to the function.

We consider that according to pandas.DataFrame.apply docs: Objects passed to the function are Series objects . So we convert pd.Series to list . Then unpack it using * operator, while our function call.

import pandas as pd
df = pd.DataFrame({'A':[1,2,3],
                   'B':[4,5,6]})

print (df)
   A  B
0  1  4
1  2  5
2  3  6

def myfunction(B, A):
    #some staff  
    result = B + A 
    # do something here to get the result
    return result

df['C'] = df.apply(lambda x: myfunction(*x.to_list()), axis=1)
print (df)
   A  B  C
0  1  4  5
1  2  5  7
2  3  6  9