问题
How do I convert this dataframe
location value 0 (Richmond, Virginia, nan, USA) 100 1 (New York City, New York, nan, USA) 200
to this:
city state region country value 0 Richmond Virginia nan USA 100 1 New York City New York nan USA 200
Note that the location
column in the first dataframe contains tuples. I want to create four columns out of the location
column.
回答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)
回答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
0 100 Richmond Virginia NaN USA
1 200 New York City New York NaN USA
回答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)
来源:https://stackoverflow.com/questions/25559202/from-tuples-to-multiple-columns-in-pandas