1

I have a dataframe like as below

df = pd.DataFrame(
    {'stud_id' : [101, 101, 101, 101, 
                  101, 101, 101, 101],
     'sub_code' : ['CSE01', 'CSE02', 'CSE03', 
                   'CSE06', 'CSE05', 'CSE04',
                   'CSE07', 'CSE08'],
     'marks' : ['A','B','C','D',
                'E','F','G','H']}
)

I would like to do the below

a) Filter my dataframe based on sub_code using a list of values

b) For the filtered/selected rows, replace their marks value by a constant value - FAIL

So, I tried the below but it doesn't work and results in NA for non-filtered rows. Instead of NA, I would like to see the actual value

sub_list = ['CSE01', 'CSE02', 'CSE03','CSE06', 'CSE05', 'CSE04']
df['marks'] = df[df['sub_code'].isin(sub_list)]['marks'].replace(r'^([A-Za-z])*$','FAIL', regex=True)

I expect my output to be like as below

enter image description here

The Great
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    You overcomplicate it, use `np.where`: `df['marks'] = np.where(df['sub_code'].isin(sub_list), 'FAIL', df['marks'])` or `loc`: `df.loc[df['sub_code'].isin(sub_list), 'marks'] = 'FAIL'` – mozway Jan 10 '23 at 05:47

1 Answers1

2

You can use pandas.loc

Code:

df.loc[df["sub_code"].isin(sub_list), "marks"] = "FAIL"

Output:

stud_id sub_code    marks
101 CSE01   FAIL
101 CSE02   FAIL
101 CSE03   FAIL
101 CSE06   FAIL
101 CSE05   FAIL
101 CSE04   FAIL
101 CSE07   G
101 CSE08   H
The6thSense
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