在 python/pandas dataframe 中索引不同值的实例

Question

I'm fairly new to python and I'm trying to automate a task to count instances of multiple values (student absence types) and then spit them back out on a single line per student. If I have a single value, I can accomplish that by:

import pandas as pd
df = pd.read_csv('attendanceUAnumbersLISTONLY.csv', header=0)
  
nf=df['StudentId'].value_counts()
print(nf)
nf.to_csv('studentua.csv', index=True, header=False)

The dataframe I'm pulling is a cognos report that simply shows a student ID number for each instance of an unexcused absence. The underlying dataset looks like:

StudentID	AbsenceType
123456	UA
123456	UA
654321	UA

I ultimately want the output to be:

StudentID	Count
123456	2
654321	1

That code above will do that. But if I want to pull values besides UA and put those into a different column of the output, that's where I'm stuck. So if I have values of P (present), I want to export them out in a new column that I can import into another system.

StudentID	UA	P
123456	2	7
654321	1	8

I can't get my head around how to do that.

Answer 1

What about first groupby using both columns, then size to get the number of occurences, unstack to pivot the level index level of AbsenceType and fillna to fill where no occurences are found:

df.groupby(['StudentId', 'AbsenceType']).size().unstack(level=1).fillna(0)