Fix Python – Applying function with multiple arguments to create a new pandas column

Question

Asked By – Michael

I want to create a new column in a pandas data frame by applying a function to two existing columns. Following this answer I’ve been able to create a new column when I only need one column as an argument:

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

def fx(x):
    return x * x

print(df)
df['newcolumn'] = df.A.apply(fx)
print(df)

However, I cannot figure out how to do the same thing when the function requires multiple arguments. For example, how do I create a new column by passing column A and column B to the function below?

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

Now we will see solution for issue: Applying function with multiple arguments to create a new pandas column


Answer

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 question is answered By – alko

This answer is collected from stackoverflow and reviewed by FixPython community admins, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0