pandas map column data based on value from another column using if to determine which dict to use

Question

I have the following dataframe:

df = pd.DataFrame([['Person1', 'CT', 2017],
               ['Person2', 'FL', 2017],
               ['Person3', 'TX', 2017],
              ['Person1', 'TX', 2016]], columns=['Name', 'State', 'Year'])

And two mapping tables below:

state_map = {'CT': 'Connecticut', 'FL': 'Florida', 'TX':'Texas'}
state_map2 = {'CT': 'ABC-CT', 'FL': 'BBC-Florida', 'TX':'CDA-TX'}

Here is what the data looks like:

    Name    State   Year
0   Person1   CT    2017
1   Person2   FL    2017
2   Person3   TX    2017
3   Person1   TX    2016

I would like to find a way to add a new column with values mapped using an if condition that determines whether to use values mapped from state_map or state_map2. So if df[df['Name']=='Person1'] then use state_map else use state_map2.

The final output should look like this:

    Name    State   Year   New_State_Name
0   Person1   CT    2017   Connecticut
1   Person2   FL    2017   BBC-Florida
2   Person3   TX    2017   CDA-TX
3   Person1   TX    2016   Texas

I tried the following code but it didn't work.

df['New_State_Name'] = [state_map[x] if df[df['Name'] == 'Person1'] else 
state_map2[x] for x in df['State']]

I got an error that says:

ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, 
a.bool(), a.item(), a.any() or a.all().

Answer 1

Use np.where:

df['New_State_Name'] = np.where(df['Name']=='Person1',df['State'].map(state_map),df['State'].map(state_map2))

Output:

      Name State  Year New_State_Name
0  Person1    CT  2017    Connecticut
1  Person2    FL  2017    BBC-Florida
2  Person3    TX  2017         CDA-TX
3  Person1    TX  2016          Texas