Applying function with multiple arguments to create a new pandas column

Question

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

Asked By: Michael

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Source

Answer 1

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)

Answered By: greenafrican

Answer 2

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

Answered By: Roman Pekar

Answer 3

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

Answered By: alko

Answer 4

One more dict style clean syntax:

df["new_column"] = df.apply(lambda x: x["A"] * x["B"], axis = 1)

or,

df["new_column"] = df["A"] * df["B"]

Answered By: Surya

Answer 5

If you need to create multiple columns at once:

Create the dataframe:

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

Create the function:

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

Assign the new columns:

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

Answered By: toto_tico

Answer 6

This will dynamically give you desired result. It works even if you have more than two arguments

df['anothercolumn'] = df[['A', 'B']].apply(lambda x: fxy(*x), axis=1)
print(df)


    A   B  newcolumn  anothercolumn
0  10  20        100            200
1  20  30        400            600
2  30  10        900            300

Answered By: Abiodun Mustapha

Answer 7

The answers focus on functions that takes the dataframe’s columns as inputs. More in general, if you want to use pandas .apply on a function with multiple arguments, some of which may not be columns, then you can specify them as keyword arguments inside .apply() call:

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

df['newcolumn'] = df.A.apply(fxy, y=df.B)
df['newcolumn1'] = df.A.apply(fxy, y=4)

Answered By: Luca Clissa

Applying function with multiple arguments to create a new pandas column

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