How to apply multiple if/else conditions in a Dataframe using apply and lambda?

Question

I am trying to update certain rows in a column based on a set of conditions. For instance, in the given example below I am trying to update "Country" column names to a shorter version based on a few if statements and here is what I am using. Is there a better way to do this?

energy['Country'] = energy['Country'].apply(lambda x: 'South Korea' if x=='Republic of Korea' 
                        else('United States' if x=='United States of America20' 
                        else('United Kingdom' if x=='United Kingdom of Great Britain and Northern Ireland'
                        else('Hong Kong' if x=='China, Hong Kong Special Administrative Region' 
                             else x))))

It's almost always better to use a built-in instead of iterating and using apply. Rather than apply, use dataframe.replace with a dictionary of items to replace — G. Anderson
– G. Anderson, Commented Feb 11, 2020 at 21:07
stackoverflow.com/questions/19226488/…, stackoverflow.com/questions/34962104/… — AMC
– AMC, Commented Feb 11, 2020 at 21:50

Yuna A. · Accepted Answer · 2020-02-11 21:07:04Z

2

Using pd.Series.map

country_map = {'Republic of Korea': 'South Korea',
               'United States of America20': 'United States of America',
               'United Kingdom of Great Britain and Northern Ireland': 'United Kingdom',
               'China, Hong Kong Special Administrative Region': 'Hong Kong'}

energy['Country'] = energy['Country'].map(country_map)

answered Feb 11, 2020 at 21:07

Yuna A.

1496 bronze badges

Sign up to request clarification or add additional context in comments.

1 Comment

ssebastian Over a year ago

Thank you @Yuna A., this is helpful

Parfait · Accepted Answer · 2020-02-11 22:29:57Z

0

As much as you can, avoid DataFrame.apply which is a hidden loop. Consider vectorized processing such as numpy.select where you pass vectors (i.e., Numpy arrays or Pandas Series) into a method and not scalar elements one at a time:

energy['Country'] = np.select([energy['Country'] == 'South Korea', 
                               energy['Country'] == 'United States', 
                               energy['Country'] == 'United Kingdom', 
                               energy['Country'] == 'Hong Kong'],
                              ['Republic of Korea', 
                               'United States of America', 
                               'United Kingdom of Great Britain and Northern Ireland'
                               'China, Hong Kong Special Administrative Region'])

answered Feb 11, 2020 at 22:29

Parfait

108k19 gold badges103 silver badges138 bronze badges

1 Comment

ssebastian Over a year ago

Thanks all, really helpful suggestions

Collectives™ on Stack Overflow

How to apply multiple if/else conditions in a Dataframe using apply and lambda?

2 Answers 2

1 Comment

1 Comment

Your Answer

Linked

Hot Network Questions

Collectives™ on Stack Overflow

2 Answers 2

1 Comment

1 Comment

Your Answer

Sign up or log in

Post as a guest

Linked

Related