Create new pandas column based on categorical values in other column (python)

Question

I have a data frame with country and traffic columns:

Country    |   Traffic
  US            8687
  Italy         902834
  Germany       2343
  Brazil        4254
  France        23453

I want to add a third column called "Region" to this data frame. It would look like this:

 Country    |   Traffic   | Region
  US            8687         US
  Italy         902834       EU
  Germany       2343         EU
  Brazil        4254         LA
  France        23453        EU

The following code works if I have only two Regions. I am looking more for an if/else, map, or lambda statement:

df['Region'] = np.where(df['Country'] == 'US', 'US', 'EU')

Thank You.

score 3 · Answer 1 · answered Feb 08 '17 at 22:15

3

One simple approach is this:

dict ={'US':'US','Italy':'EU','Germany':'EU','Brazil':'LA','France':'EU'}

df['Region']=df['Country'].apply(lambda x : dict[x])

answered Feb 08 '17 at 22:15

Prashant_J

354
3
11

ASGM · Accepted Answer · 2017-02-09T18:24:58.560

2

You could use a dictionary:

region_from_country = {
    'US': 'US', 
    'Italy': 'EU',
    'Germany': 'EU',
    'Brazil': 'LA', 
    'France': 'EU',
}
df['Region'] = df['Country'].replace(region_from_country)

The keys in the dictionary are the countries and the values are the corresponding regions.

edited Feb 09 '17 at 18:24

answered Feb 08 '17 at 21:25

ASGM

11,051
1
32
53

Create new pandas column based on categorical values in other column (python)

2 Answers2