pandas add value to new column based on condition

Question

I've been searching around but couldn't find the answer I was looking for, so I apologize for asking what I would imagine is a repetitive question.

I have two dataframes - df1 is a list of transaction data and df2 is a sort of key. df1['code'] references a column in df2.

If the code for the transaction found in df1 is in df2, I'd like to append a value to that df1 entry in a new column identifying that the transaction was valid. If the code is not in df2, I'd like to note the opposite in that same new column.

I understand how I might do this with a 'for' loop, but my understanding is I should learn how to use pandas without relying on that.

Thanks in advance for the help!

Answer 1

Use numpy.where() :

df1['new_col'] = numpy.where(df1['df1_code'].isin(df2['df2_code']), 'VALID', 'INVALID')

Sample DF

>>> import pandas as pd
>>> import numpy as np

>>> df1 = pd.DataFrame({'code':range(5,15), 'transaction':range(10)})
>>> df2 = pd.DataFrame({'code':range(12,22), 'transaction':range(7,17)})
>>> df1
   code  transaction
0     5            0
1     6            1
2     7            2
3     8            3
4     9            4
5    10            5
6    11            6
7    12            7
8    13            8
9    14            9
>>> df2
   code  transaction
0    12            7
1    13            8
2    14            9
3    15           10
4    16           11
5    17           12
6    18           13
7    19           14
8    20           15
9    21           16

>>> df1['new_col'] = np.where(df1['code'].isin(df2['code']), 'VALID', 'INVALID')
>>> df1
   code  transaction  new_col
0     5            0  INVALID
1     6            1  INVALID
2     7            2  INVALID
3     8            3  INVALID
4     9            4  INVALID
5    10            5  INVALID
6    11            6  INVALID
7    12            7    VALID
8    13            8    VALID
9    14            9    VALID