Return multiple columns from pandas apply()

Question

I have a pandas DataFrame, df_test. It contains a column \'size\' which represents size in bytes. I\'ve calculated KB, MB, and GB using the following code:

Answer 1

I believe the 1.1 version breaks the behavior suggested in the top answer here.

import pandas as pd
def test_func(row):
    row['c'] = str(row['a']) + str(row['b'])
    row['d'] = row['a'] + 1
    return row

df = pd.DataFrame({'a': [1, 2, 3], 'b': ['i', 'j', 'k']})
df.apply(test_func, axis=1)

The above code ran on pandas 1.1.0 returns:

   a  b   c  d
0  1  i  1i  2
1  1  i  1i  2
2  1  i  1i  2

While in pandas 1.0.5 it returned:

   a   b    c  d
0  1   i   1i  2
1  2   j   2j  3
2  3   k   3k  4

Which I think is what you'd expect.

Not sure how the release notes explain this behavior, however as explained here avoiding mutation of the original rows by copying them resurrects the old behavior. i.e.:

def test_func(row):
    row = row.copy()   #  <---- Avoid mutating the original reference
    row['c'] = str(row['a']) + str(row['b'])
    row['d'] = row['a'] + 1
    return row