Applying function with multiple arguments to create a new pandas column

You can go with @greenAfrican example, if it's possible for you to rewrite your function. But if you don't want to rewrite your function, you can wrap it into anonymous function inside apply, like this:

>>> def fxy(x, y):
...     return x * y

>>> df['newcolumn'] = df.apply(lambda x: fxy(x['A'], x['B']), axis=1)
>>> df
    A   B  newcolumn
0  10  20        200
1  20  30        600
2  30  10        300

Alternatively, you can use numpy underlying function:

>>> import numpy as np
>>> df = pd.DataFrame({"A": [10,20,30], "B": [20, 30, 10]})
>>> df['new_column'] = np.multiply(df['A'], df['B'])
>>> df
    A   B  new_column
0  10  20         200
1  20  30         600
2  30  10         300

or vectorize arbitrary function in general case:

>>> def fx(x, y):
...     return x*y
...
>>> df['new_column'] = np.vectorize(fx)(df['A'], df['B'])
>>> df
    A   B  new_column
0  10  20         200
1  20  30         600
2  30  10         300

This solves the problem:

df['newcolumn'] = df.A * df.B

You could also do:

def fab(row):
  return row['A'] * row['B']

df['newcolumn'] = df.apply(fab, axis=1)

If you need to create multiple columns at once:

1. Create the dataframe:

   import pandas as pd
   df = pd.DataFrame({"A": [10,20,30], "B": [20, 30, 10]})
   

2. Create the function:

   def fab(row):                                                  
       return row['A'] * row['B'], row['A'] + row['B']
   

3. Assign the new columns:

   df['newcolumn'], df['newcolumn2'] = zip(*df.apply(fab, axis=1))