根据另一列中的条件填充新列

Question

I'm playing about with Python and pandas.

I have created a dataframe, I have a column (axis 1) called 'County' but I need to create a column called 'Region' and populate it like this (atleast I think):

If County column == 'Suffolk' or 'Norfolk' or 'Essex' then in Region column insert 'East Anglia'

If County column == 'Kent' or 'East Sussex' or 'West Sussex' then in Region Column insert 'South East'

If County column == 'Dorset' or 'Devon' or 'Cornwall' then in Region Column insert 'South West'

and so on...

So far I have this:

myDataFrame['Region'] = np.where(myDataFrame['County']=='Suffolk', 'East   Anglia', '')

But I suspect this won't work for any other counties

As I'm sure is obvious I am a beginner. I have tried googling and reading but only could find out about numpy where, which got me this far.

Answer 1

You'll definitely need df.isin and loc based indexing:

df['Region'] = np.nan
df.loc[df.County.isin(['Suffolk','Norfolk', 'Essex']), 'Region'] = 'East Anglia'
df.loc[df.County.isin(['Kent', 'East Sussex', 'West Sussex']), 'Region'] = 'South East'
df.loc[df.County.isin(['Dorset', 'Devon', 'Cornwall']), 'Region'] = 'South West'

---

You could also create a mapping of sorts and use df.map or df.replace :

mapping = { 'Suffolk' : 'East Anglia', 'Norfolk': 'East Anglia', ... 'Kent'  :'South East', ..., ... }
df['Region'] = df.County.map(mapping) 

I would prefer a map here because it would convert non-matches to NaN , which would be the ideal thing.