Iterate over specific csv rows and rerun code when new string detected after empty cell in first column

I want to create a report for the students with a script that I have created.
I want a logic that will iterate over the students and re-run my script when new students name detected.

student_name,gender,subject,marks
Mark,M,english,90
         biology,80
         chemistry,77
         physics,89 
George,M,french,81
         biology,66
         chemistry,82
         physics,79
         economic,57
Lisa,F,german,77
         biology,89
         chemistry,90
         physics,92
         economic,96

For example:
For Mark the script will run till 5th row i.e last subject.
For George the script will re-run till 10th row i.e last subject.
and so on for many students.

How can we achieve this and print the student name?

I have achieved this if the students details are present in csv separately in a directory for all students but How can I achieve if its in a single file.

Update

You can fix your csv file:

def read_csv(filename):
    with open('marks.csv') as fp:
        buf = io.StringIO()
        header = fp.readline()
        num_col = headers.count(',')
        buf.writelines(header)
        for row in fp:
            diff = num_col - row.count(',')
            buf.writelines(','*diff + row.lstrip())
        buf.seek(0)
    return pd.read_csv(buf).ffill()

df = read_csv('marks.csv')
# Use the loop with groupby

IIUC, you are looking for is groupby.

Suppose the following file marks.csv:

student_name,gender,subject,marks
Mark,M,english,90
Mark,M,biology,80
Mark,M,chemistry,77
Mark,M,physics,89
George,M,french,81
George,M,biology,66
George,M,chemistry,82
George,M,physics,79
George,M,economic,57
Lisa,F,german,77
Lisa,F,biology,89
Lisa,F,chemistry,90
Lisa,F,physics,92
Lisa,F,economic,96

You can do:

df = pd.read_csv('marks.csv')
for name, subdf in df.groupby('student_name', sort=False):
    print(f"[{name}]")
    print(subdf, end='\n\n')
    # Do stuff here

Output:

[Mark]
  student_name gender    subject  marks
0         Mark      M    english     90
1         Mark      M    biology     80
2         Mark      M  chemistry     77
3         Mark      M    physics     89

[George]
  student_name gender    subject  marks
4       George      M     french     81
5       George      M    biology     66
6       George      M  chemistry     82
7       George      M    physics     79
8       George      M   economic     57

[Lisa]
   student_name gender    subject  marks
9          Lisa      F     german     77
10         Lisa      F    biology     89
11         Lisa      F  chemistry     90
12         Lisa      F    physics     92
13         Lisa      F   economic     96

More on groupby: Group by: split-apply-combine