Changing abbreviated state names with full name

November 26, 2021

I have a dataframe with a column in it containing state names. The names are a mix of US states abbreviations and other countries state name. And, I only want to change the us state names and leave others as it is.

Data

   Country        State          
1  United States  MI
2  United States  PA
3  New Zealand    Auckland
4  France         Île-de-France
5  United States  FL

I have tried this code:

states = {
        'AK': 'Alaska',
        'AL': 'Alabama',
        'AR': 'Arkansas',
        'AS': 'American Samoa',
        'AZ': 'Arizona',
        'CA': 'California',
        'CO': 'Colorado',
        'CT': 'Connecticut',
        'DC': 'District of Columbia',
        'DE': 'Delaware',
         .
         .
         .
      }
states = {state: abbrev for state, abbrev in states.items()}
Data['State_full'] = Data['State'].map(states)

It replaces the US states as it should but it also replaces the other countries state name with None. What am i missing? Thanks in advance.

>Solution :

You can use Series.replace. It can take a dict, where the keys of the dict are values to find, and the values of the dict are the replacements. If a value isn’t found in the dict, it will be left as-is.

df['State'] = df['State'].replace(states)

Output:

>>> df
         Country          State
1  United States      Minnesota
2  United States   Pennsylvania
3    New Zealand       Auckland
4         France  Île-de-France
5  United States        Florida