Look for value in another pandas dataframe and replace column value

Question

I have the following dataframe:

Country Employee ID Location
CZ        1        WAREHOUSE
CZ        2        Warehouse
CZ        3        PlaNt
CZ        4        Car
DK        5        Car
DK        7 
ES        7        *
ES        8       Técnico
ES        3       Rádio

I need to search for the values added in "Location" column in the dataframe below (not considering if the value is capslock or not and disconsidering special non english characters punctuation such as in the example) considering its country and:

• if the "Location" value exists in that country on second dataframe, replace for that value.
• if the "Location" value do not exist in that country on second dataframe, replace for the first value of the second dataframe
• Create a column named "Legacy Location" that, if "Location" was replaced, add previous Location Name to that column. if Location was not replaced, leave column blank. And, if "Location" was blank when doing the comparison, add the string "blank" to "Legacy Location" column.

This is the lookup dataframe:

Country Location name
CZ      Warehouse
CZ      Plant
DK      Car
DK      Plant
DK      Warehouse
ES      Tecnico
ES      Rádio

And that is the expected output:

Country Employee ID Location    Legacy Location
CZ         1        Warehouse   WAREHOUSE
CZ         2        Warehouse   
CZ         3        Plant       PlaNt
CZ         4        Warehouse   Car
DK         5        Car 
DK         7        Car         Blank
ES         7        Tecnico     *
ES         8        Tecnico     Técnico
ES         3        Rádio   

What is the best way to achieve this result?

Thank you so much!

Answer 1

Considering that df is the first dataframe and lookup_df is the lookup one, use this:

import numpy as np
import pandas as pd

out = df.merge(lookup_df, on='Country', how='left')
out['Location_v2'] = out.apply(lambda x: x['Location name'][0] if str(x['Location']).title() not in x['Location name'] else str(x['Location']).title(), axis=1)
out['Legacy Location'] = np.where(out['Location'].isna(), 'Blank',
                                  np.where(out['Location'] != out['Location_v2'], out['Location'], ''))
out = out.drop(['Location name', 'Location'], axis=1)
out.rename(columns = {'Location_v2':'Location'}, inplace = True)

>>> print(out)