From Tuples To Multiple Columns In Pandas

How do I convert this dataframe location value 0 (Richmond, Virginia, nan, USA) 100

Solution 1:

new_col_list = ['city','state','regions','country']
for n,col in enumerate(new_col_list):
    df[col] = df['location'].apply(lambda location: location[n])

df = df.drop('location',axis=1)

Solution 2:

If you return a Series of the (split) location, you can merge (join to merge on index) the resulting DF directly with your value column.

addr = ['city', 'state', 'region', 'country']
df[['value']].join(df.location.apply(lambda loc: Series(loc, index=addr)))

   value           city     state  region country
0100       Richmond  Virginia     NaN     USA
1200  New York City  New York     NaN     USA

Solution 3:

I haven't timed this, but I would suggest this option:

df.loc[:,'city']=df.location.map(lambda x:x[0])
df.loc[:,'state']=df.location.map(lambda x:x[1])
df.loc[:,'regions']=df.location.map(lambda x:x[2])
df.loc[:,'country']=df.location.map(lambda x:x[3])

I'm guessing avoiding explicit for loop might lend itself to a SIMD instruction (certainly numpy looks for that, but perhaps not other libraries)