How to find matching values between the columns of two dataframes?

• Use pandas Boolean Indexing the determine if 'Cust_id' of parent, is in 'Cust_id' of 'child'.
• Use .isin on a list of unique 'Cust_id' from 'child'.
  • Indexing with isin
  • child.Cust_id.unique() creates an array of all the unique values in 'Cust_id'

import pandas as pd

child = pd.DataFrame({'Cust_id': [1, 34, 45], 'Description': ['Good', 'Excellent', 'Bulk'], 'Detail': ['Regular', 'Normal', 'Buyer']})

parent = pd.DataFrame({'Name': ['xyz', 'abc', 'mno', 'pqr', 'rst', 'ert'], 'Cust_id': [1, 45, 56, 67, 34, 1], 'order': ['ice', 'bread', 'Butter', 'cookies', 'Rice', 'egg'],
                       'date': ['01-02-2019', '01-02-2019', '01-02-2019', '01-02-2019', '01-02-2019', '01-02-2019'], 'Payment': ['online', 'offline', 'offline', 'online', 'online', 'online']})

# mask using isin
mask = parent.Cust_id.isin(child.Cust_id.unique())

# return only the data from parent, where parent Cust_id isin child Cust_id
parent[mask]

# add a column to the parent dataframe
parent['in_child'] = mask

# display(parent)
  Name  Cust_id    order        date  Payment  in_child
0  xyz        1      ice  01-02-2019   online      True
1  abc       45    bread  01-02-2019  offline      True
2  mno       56   Butter  01-02-2019  offline     False
3  pqr       67  cookies  01-02-2019   online     False
4  rst       34     Rice  01-02-2019   online      True
5  ert        1      egg  01-02-2019   online      True

• pandas.DataFrame.merge can be used in various ways as well.
• The following solution uses an 'outer' merge with indicator=True
  • The '_merge' column indicates which dataframe the 'Cust_id' is in.
    • 'left_only' is the parent dataframe.
• .merge combines the information from both dataframes, and I'm not sure if that's the desired output.

merged = parent.merge(child, on='Cust_id', how='outer', indicator=True)

# display(merged)
  Name  Cust_id    order        date  Payment Description   Detail     _merge
0  xyz        1      ice  01-02-2019   online        Good  Regular       both
1  ert        1      egg  01-02-2019   online        Good  Regular       both
2  abc       45    bread  01-02-2019  offline        Bulk    Buyer       both
3  mno       56   Butter  01-02-2019  offline         NaN      NaN  left_only
4  pqr       67  cookies  01-02-2019   online         NaN      NaN  left_only
5  rst       34     Rice  01-02-2019   online   Excellent   Normal       both