Extract words from dataframe formatted text column

Question

I need to create a new column from another one. The dataset is created by this code (I extracted only a few rows):

import pandas as pd

new_dataframe = pd.DataFrame({
    "Name": ['John', 'Lukas', 'Bridget', 'Carol','Madison'],
    "Notes": ["__ years old. NA", "__ years old. NA", 
        "__ years old. NA", "__ years old. Old account.", 
        "__ years old. New VIP account."], 
    "Status": [True, False, True, True, True]})

which generates the following

Name        Notes                           Status
John     23 years old. NA                    True
Lukas    52 years old. NA                    False
Bridget  64 years old. NA                    True
Carol    31 years old. Old account           True
Madison  54 years old. New VIP account.      True

I need to create two new columns that contain age information in the format:

1. __ years old (three words): eg 23 years old;
2. __ (only numbers): eg 23

At the end I should have

Name        Notes                           Status          L_Age           S_Age
    John     23 years old. NA                    True      23 years old       23
    Lukas    52 years old. NA                    False     52 years old       52
    Bridget  64 years old. NA                    True      64 years old       64
    Carol    31 years old. Old account           True      31 years old       31
    Madison  54 years old. New VIP account.      True      54 years old       54

I do not know how to extract the first three words, then only the first, to create new columns. I have tried with

new_dataframe.loc[new_dataframe.Notes == '', 'L_Age'] = new_dataframe.Notes.str.split()[:3]
new_dataframe.loc[new_dataframe.Notes == '', 'S_Age'] = new_dataframe.Notes.str.split()[0]

but it is wrong ( ValueError: Must have equal len keys and value when setting with an iterable ).

Help will be appreciated.

Answer 1

You can use this pattern to extract the information and join:

pattern = '^(?P<L_Age>(?P<S_Age>\d+) years? old)'

new_dataframe = new_dataframe.join(new_dataframe.Notes.str.extract(pattern))

Output:

      Name                           Notes  Status         L_Age S_Age
0     John                23 years old. NA    True  23 years old    23
1    Lukas                52 years old. NA   False  52 years old    52
2  Bridget                64 years old. NA    True  64 years old    64
3    Carol       31 years old. Old account    True  31 years old    31
4  Madison  54 years old. New VIP account.    True  54 years old    54

Answer 2

IIUC:

def get_first_n_words(txt, n):
    l = txt.split(' ')
    assert(len(l)>=n)
    return ' '.join(l[:n])

new_dataframe['L_Age'] = new_dataframe['Notes'].apply(lambda x: get_first_n_words(x, 3))
new_dataframe['S_Age'] = new_dataframe['Notes'].apply(lambda x: get_first_n_words(x, 1))