CODEWITHSUNDEEP
pandas
Zihs
Zihs

Reputation: 347

Pandas dataframe operations

I have the following dataframe,

df = pd.DataFrame({
'CARD_NO': [000, 001, 002, 002, 001, 111],
'request_code': [2400,2200,2400,3300,5500,6600],
'merch_id': [1, 2, 1, 3, 3, 5],
'resp_code': [0, 1, 0, 1, 1, 1]})

Based on this requirement, 

inquiries = df[(df.request_code == 2400) & (df.merch_id == 1) & (df.resp_code == 0)]

I need to flag records in df for which CARD_NO == CARD_NO where inquiries is True.
If inquiries returns:

[6 rows x 4 columns]
index    CARD_NO  merch_id  request_code  resp_code
0        0         1          2400          0
2        2         1          2400          0

Then df should look like so:

index     CARD_NO  merch_id  request_code  resp_code    flag
0        0         1          2400          0            N
1        1         2          2200          1            N
2        2         1          2400          0            N      
3        2         3          3300          1            Y
4        1         3          5500          1            N
5      111         5          6600          1            N

I've tried several merges, but cannot seem to get the result I want. Any help would be greatly appreciated. Thank you.

Upvotes: 0

Views: 257

Answers (2)

Jichao
Jichao

Reputation: 81

the following should work if I understand your question correctly, which is that you want to set the flag is ture only when the CARD_NO is in the filtered group but the row itself is not in the filtered group.

import numpy as np
filter = (df.request_code == 2400) & (df.merch_id == 1) & (df.resp_code == 0)
df['flag']=np.where(~filter & df.CARD_NO.isin(df.ix[filter, 'CARD_NO']), 'Y', 'N')

Upvotes: 1

filmor
filmor

Reputation: 32182

filtered = (df.request_code == 2400) & (df.merch_id == 1) & (df.resp_code == 0)
df["flag"] = filtered.map(lambda x: "Y" if x else "N")

Upvotes: 0

Related Questions