英文:
Iterate over specific csv rows and rerun code when new string detected after empty cell in first column
问题
我想创建一个为学生制作报告的脚本，我已经创建好了。
我想要一个逻辑，将遍历学生信息，并在检测到新的学生姓名时重新运行我的脚本。
例如：
对于Mark，脚本将运行到第5行，即最后一个科目。
对于George，脚本将重新运行到第10行，即最后一个科目。
以此类推，对于许多学生。
我们如何实现这个目标并打印学生姓名？
如果学生的详细信息分别存在于一个单独的CSV文件中，我已经实现了这一点，但如果它们存在于一个单一文件中，我该如何实现？
英文:
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,marksMark,M,english,90biology,80chemistry,77physics,89George,M,french,81biology,66chemistry,82physics,79economic,57Lisa,F,german,77biology,89chemistry,90physics,92economic,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.
答案1
得分: 2
更新
你可以修复你的CSV文件：
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')# 使用groupby循环
如果我理解正确，你正在寻找的是 groupby。
假设有以下文件 marks.csv：
student_name,gender,subject,marksMark,M,english,90Mark,M,biology,80Mark,M,chemistry,77Mark,M,physics,89George,M,french,81George,M,biology,66George,M,chemistry,82George,M,physics,79George,M,economic,57Lisa,F,german,77Lisa,F,biology,89Lisa,F,chemistry,90Lisa,F,physics,92Lisa,F,economic,96
你可以这样做：
df = pd.read_csv('marks.csv')for name, subdf in df.groupby('student_name', sort=False):print(f"[{name}]")print(subdf, end='\n\n')# 在这里执行操作
输出：
[Mark]student_name gender subject marks0 Mark M english 901 Mark M biology 802 Mark M chemistry 773 Mark M physics 89[George]student_name gender subject marks4 George M french 815 George M biology 666 George M chemistry 827 George M physics 798 George M economic 57[Lisa]student_name gender subject marks9 Lisa F german 7710 Lisa F biology 8911 Lisa F chemistry 9012 Lisa F physics 9213 Lisa F economic 96
更多关于 groupby 的信息：Group by: split-apply-combine
英文:
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,marksMark,M,english,90Mark,M,biology,80Mark,M,chemistry,77Mark,M,physics,89George,M,french,81George,M,biology,66George,M,chemistry,82George,M,physics,79George,M,economic,57Lisa,F,german,77Lisa,F,biology,89Lisa,F,chemistry,90Lisa,F,physics,92Lisa,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 marks0 Mark M english 901 Mark M biology 802 Mark M chemistry 773 Mark M physics 89[George]student_name gender subject marks4 George M french 815 George M biology 666 George M chemistry 827 George M physics 798 George M economic 57[Lisa]student_name gender subject marks9 Lisa F german 7710 Lisa F biology 8911 Lisa F chemistry 9012 Lisa F physics 9213 Lisa F economic 96
More on groupby: Group by: split-apply-combine
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