将列值拆分为 2 个新列 - Python Pandas

Question

I have a dataframe that has column 'name'. With values like 'James Cameron'. I'd like to split it out into 2 new columns 'First_Name' and 'Last_Name', but there is no delimiter in the data so I am not quite sure how. I realize that 'James' is in position [0] and 'Cameron' is in position [1], but I am not sure you can recognize that without the delimiter

df = pd.DataFrame({'name':['James Cameron','Martin Sheen'],
               'Id':[1,2]})
df

EDIT:

Vaishali's answer below worked perfectly, for the dataframe I had provided. I created that dataframe as an example though. My real code looks like this"

data[['First_Name','Last_Name']] = data.director_name.str.split(' ', expand = True)

and that unfortunately, is throwing an error:

'Columns must be same length as key'

The column holds the same values as my example though. Any suggestions?

Thanks

Answer 1

You can split on space

df[['Name', 'Lastname']] = df.name.str.split(' ', expand = True)

    Id  name            Name    Lastname
0   1   James Cameron   James   Cameron
1   2   Martin Sheen    Martin  Sheen

EDIT: Handling the error 'Columns must be same length as key'. The data might have some names with more than one space, eg: George Martin Jr. In that case, one way is to split on space and use the first and the second string, ignoring third if it exists

df['First_Name'] = df.name.str.split(' ', expand = True)[0]
df['Last_Name'] = df.name.str.split(' ', expand = True)[1]

Answer 2

Slightly different way of doing this:

df[['first_name', 'last_name']] = df.apply(lambda row: row['name'].split(), axis=1)

df
   Id           name first_name last_name
0   1  James Cameron      James   Cameron
1   2   Martin Sheen     Martin     Sheen

Answer 3

I like this method... Not as quick as simply splitting but it drops in column names in a very convenient way.

df.join(df.name.str.extract('(?P<First>\S+)\s+(?P<Last>\S+)', expand=True))

   Id           name   First     Last
0   1  James Cameron   James  Cameron
1   2   Martin Sheen  Martin    Sheen