[FIXED] replace string under multiple if conditions in Pandas

Issue

If I have a dataframe as follows:

import pandas as pd
df = pd.DataFrame({
        'items': ['countryName', 'stateName', 'currencyName', 'companyName'],
        'code': ['NK', 'NK', 'NK', 'NK']
     })
print(df)

          items code
0   countryName   NK
1     stateName   NK
2  currencyName   NK
3   companyName   NK

How can transform NK under several conditions, for instance, if its items is "countryName", change NK to North Korea, if its items is "stateName", change NK to "North Kingstown" so on and so forth. Please note this is only part of dataframe. Thanks.

df = pd.DataFrame({
        'items': ['countryName', 'stateName', 'currencyName', 'companyName'],
        'code': ['North Korea', 'North Kingstown', 'Norwegian krone', 'Northrup-King']
     })
print(df)
          items             code
0   countryName      North Korea
1     stateName  North Kingstown
2  currencyName  Norwegian krone
3   companyName    Northrup-King

Solution

You can use np.where on the DF. It's a bit dirty and I'm sure someone else can give you a cleaner solution but it works.

df['code'] = np.where((df['code'] == 'NK') & (df['items'] == 'countryName'),
                      'North Korea',
                       df['code'])

df['code'] = np.where((df['code'] == 'NK') & (df['items'] == 'stateName'),
                      'North Kingstown',
                       df['code'])
... add the rest of the examples

How does it work:

• np.where((condition one) & (condition two) & (more conditions)...
• value to set for column 'code' if conditions are met, e.g. North Korea
• keep old value (NK) if conditions are not met

EDIT: Addition of simple dynamic version

replace_dict = {'North Korea':['NK','countryName'],'North Kingstown':['NK','stateName']}

for key in replace_dict.keys():
    df['code'] = np.where((df.code == replace_dict[key][0]) & (df['items'] == replace_dict[key][1]),
                          key,
                          df['code'])

Answered By - David