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Pandas – compute previous custom quarter wise total revenue and reshape table

December 20, 2022

I have a dataframe like as below

df = pd.DataFrame(
    {'stud_id' : [101, 101, 101, 101, 
                  101, 101, 101, 101],
     'sub_code' : ['CSE01', 'CSE01', 'CSE01', 
                   'CSE01', 'CSE02', 'CSE02',
                   'CSE02', 'CSE02'],
     'ques_date' : ['10/11/2022', '06/06/2022','09/04/2022', '27/03/2022', 
                '13/05/2010',  '10/11/2021','11/1/2022', '27/02/2022'],
     'revenue' : [77, 86, 55, 90, 
                65, 90, 80, 67]}
)
df['ques_date'] = pd.to_datetime(df['ques_date'])

I would like to do the below

a) Compute custom quarter based on our organization FY calendar. Meaning, Oct-Dec is Q1, Jan -Mar is Q2,Apr – Jun is Q3 and July to Sep is Q4.

b) Group by stud_id

c) Compute sum of revenue from previous two quarters (from a specific date = 20/12/2022). For example, if we are in 2023Q1, I would like to get the sum of revenue for a customer from 2022Q4 and 2022Q3 seperately

So, I tried the below

df['custom_qtr'] = pd.to_datetime(df['ques_date'], dayfirst=True).dt.to_period('Q-SEP')
date_1 = pd.to_datetime('20-12-2022')
df['date_based_qtr']  = date_1.to_period('Q-SEP')
pat = '(Q(\d+))'
df['custom_qtr_number'] = df['custom_qtr'].astype(str).str.extract(pat, expand=False)[1]
df['date_qtr_number'] = df['date_based_qtr'].astype(str).str.extract(pat, expand=False)[1]

But am not sure, how to reshape the dataframe and get an output like below. You can see that we are 2023Q1 and I would like to get the sum of revenue from previous two quarters seperately. Meaning, revenue from 2022Q4 and 2022Q3 respectively

>Solution :

Use:

df['ques_date'] = pd.to_datetime(df['ques_date'])
df['custom_qtr'] = pd.to_datetime(df['ques_date'], dayfirst=True).dt.to_period('Q-SEP')
date_1 = pd.to_datetime('20-12-2022')
df['date_based_qtr']  = date_1.to_period('Q-SEP')

df = df[df['date_based_qtr'].sub(df['custom_qtr']).apply(lambda x: x.n).isin([1,2])]

df = df.pivot(['stud_id','date_based_qtr'],'custom_qtr','revenue')
print (df)
custom_qtr              2022Q3  2022Q4
stud_id date_based_qtr                
101     2023Q1              86      55