带有过滤条件的熊猫滚动窗口以删除一些最新数据

Question

This is a follow-up question of this . I would like to perform a rolling window of the last n days but I want to filter out the latest x days from each window (x is smaller than n)

Here is an example:

d = {'Name': ['Jack', 'Jim', 'Jack', 'Jim', 'Jack', 'Jack', 'Jim', 'Jack', 'Jane', 'Jane'],
     'Date': ['08/01/2021',
              '27/01/2021',
              '05/02/2021',
              '10/02/2021',
              '17/02/2021',
              '18/02/2021',
              '20/02/2021',
              '21/02/2021',
              '22/02/2021',
              '29/03/2021'],
     'Earning': [40, 10, 20, 20, 40, 50, 100, 70, 80, 90]}

df = pd.DataFrame(data=d)
df['Date'] = pd.to_datetime(df.Date, format='%d/%m/%Y')
df = df.sort_values('Date')

   Name       Date  Earning
0  Jack 2021-01-08       40
1   Jim 2021-01-27       10
2  Jack 2021-02-05       20
3   Jim 2021-02-10       20
4  Jack 2021-02-17       40
5  Jack 2021-02-18       50
6   Jim 2021-02-20      100
7  Jack 2021-02-21       70
8  Jane 2021-02-22       80
9  Jane 2021-03-29       90

I would like to

• For each row, take the last 30 days of the same Name - call it a window
• Remove the latest 20 days of each window (ie only take the earliest 10 days)
• Calculate the sum on the Earning column

Expected outcome: (The two columns Window_From and Window_To are not needed. I only use them to demonstrate the mock data)

   Name       Date  Earning Window_From  Window_To   Sum
0  Jack 2021-01-08       40  2020-12-09 2020-12-19   0.0
1   Jim 2021-01-27       10  2020-12-28 2021-01-07   0.0
2  Jack 2021-02-05       20  2021-01-06 2021-01-16  40.0
3   Jim 2021-02-10       20  2021-01-11 2021-01-21   0.0
4  Jack 2021-02-17       40  2021-01-18 2021-01-28   0.0
5  Jack 2021-02-18       50  2021-01-19 2021-01-29   0.0
6   Jim 2021-02-20      100  2021-01-21 2021-01-31  10.0
7  Jack 2021-02-21       70  2021-01-22 2021-02-01   0.0
8  Jane 2021-02-22       80  2021-01-23 2021-02-02   0.0
9  Jane 2021-03-29       90  2021-02-27 2021-03-09   0.0

Answer 1

Easy solution

Calculate 30 days and 20 days rolling sum then subtract 30 day sum from 20 day sum to get the effective rolling sum for first 10 days

s1 = df.groupby('Name').rolling('30d', on='Date')['Earning'].sum()
s2 = df.groupby('Name').rolling('20d', on='Date')['Earning'].sum()

df.merge(s1.sub(s2).reset_index(name='sum'), how='left')

---

   Name       Date  Earning   sum
0  Jack 2021-01-08       40   0.0
1   Jim 2021-01-27       10   0.0
2  Jack 2021-02-05       20  40.0
3   Jim 2021-02-10       20   0.0
4  Jack 2021-02-17       40   0.0
5  Jack 2021-02-18       50   0.0
6   Jim 2021-02-20      100  10.0
7  Jack 2021-02-21       70   0.0
8  Jane 2021-02-22       80   0.0
9  Jane 2021-03-29       90   0.0

Answer 2

An alternative to rolling (may be faster):

EDIT: actually slower with OP's dataset.

df['start'] = df['Date'] - pd.Timedelta(days=30)
df['end'] = df['start'] + pd.Timedelta(days=10) 
df = df.set_index(['Name', 'Date'])
df['Sum'] = [df.xs(n, level=0).loc[start:end, 'Earning'].sum() 
             for n, start, end in zip(df.index.get_level_values(0), df['start'], df['end'])]

print(df.reset_index().drop(columns=['start', 'end']))

   Name       Date  Earning  Sum
0  Jack 2021-01-08       40    0
1   Jim 2021-01-27       10    0
2  Jack 2021-02-05       20   40
3   Jim 2021-02-10       20    0
4  Jack 2021-02-17       40    0
5  Jack 2021-02-18       50    0
6   Jim 2021-02-20      100   10
7  Jack 2021-02-21       70    0
8  Jane 2021-02-22       80    0
9  Jane 2021-03-29       90    0