How to recognize order of values in column in Python Pandas Data Frame?

Question

I have Data Frame in Python Pandas like below:

import pandas as pd
import re
df = pd.DataFrame()
df["ADRESAT"] = ["Kowal Jan", "Nowak Adam PHU"]
df["NADAWCA"] = ["Jan Kowal", "Adam Nowak"]

And I had created 2 new columns:

• col1 - value from column "NADAWCA" which is in column "ADRESAT"

• col2 - rest of values (values from column "ADRESAT" beyon values which are also in column "NADAWCA")

  df["col2"] = df.apply(lambda r: re.sub(r["NADAWCA"], '', r["ADRESAT"], flags = re.IGNORECASE).strip(), axis=1) df["col1"] = df["NADAWCA"].str.title()

Nevertheless, as a result I have df like below. But as you can see in second row there is a mistake.

• In col1 is ok (value from column "NADAWCA" which are also in column "ADRESAT" but
• in col2 I need to have only PHU (means values from column "ADRESAT" beyond valyes which ares also in column "NADAWCA")

My question: How to modify my code so as to recognize that Adam Nowak and Nowak Adam is the same value ?

I need result as below :

Answer 1

As the order does matter, using set is not possible, So we need to check each word one by one:

# x[0] -> ADRESAT, x[1] -> NADAWCA
intersection = lambda x: ' '.join([x1 for x1 in x[1].split()
                             if x1.lower() in x[0].lower().split()])

difference = lambda x: ' '.join([x0 for x0 in x[0].split()
                           if not x0.lower() in x[1].lower().split()])

df['col1'] = df[['ADRESAT', 'NADAWCA']].apply(intersection, axis='columns')
df['col2'] = df[['ADRESAT', 'NADAWCA']].apply(difference, axis='columns')

>>> df
          ADRESAT     NADAWCA        col1 col2
0       Kowal Jan   Jan Kowal   Jan Kowal
1  Nowak Adam PHU  Adam Nowak  Adam Nowak  PHU