如何根据条件在 Pandas 中创建新列

Question

Quick silly question - I am sure this was asked before, but couldn't file detail. I have a dataframe df_students as below -

Student ID, Subjects ,  MArks_Received, Marks
222         English     3               90
222         Maths       3               80
222         Science     3               70
223         English     2               90
223         Maths       2               80
224         Maths       2               80

I am looking for below output based on Subjects and Received conditions, if no's of rows don't match for each student, will have to add extra Colum ( PENDING) or Received.

Student ID, Subjects ,  Expected_Rows, Marks, State
222         English     3               90    Received  
222         Maths       3               80    Received
222         Science     3               70    Received
223         English     2               90    Received
223         Maths       2               80    Received
224         Maths       2               80    PENDING

As I have Expected_Rows 2 for "224" , but received only 1 , I should mark this as "Pending".

I am able to aggregate sum of marks as below, but cant figure out how to add State. Any help is highlight appreciated.

Aggregate data frame

df_aggregate = df_students.groupby(['Student ', 'Marks'])['Marks'].agg(sum).reset_index()

Answer 1

There are many approaches, please see below if this helps:

Add a new column 'count' and then 'State' basis that:

df['Count'] = df.groupby('Student ID')['Student ID'].transform('count')
df['State'] = np.where(df['Count'] != df['MArks_Received'], 'PENDING','Received')

If you don't want to add a new column then use the following:

df['State'] = np.where(df.groupby('Student ID')['Student ID'].transform('count') != df['MArks_Received'], 'PENDING','Received')

It consider the rows where the count of 'Student ID' doesn't match with 'Expected Rows' .