Moving Average Pandas Across Group - pandas

Question

My data has the following structure:
np.random.seed(25)
tdf = pd.DataFrame({'person_id' :[1,1,1,1,
2,2,
3,3,3,3,3,
4,4,4,
5,5,5,5,5,5,5,
6,
7,7,
8,8,8,8,8,8,8,
9,9,
10,10
],
'Date': ['2021-01-02','2021-01-05','2021-01-07','2021-01-09',
'2021-01-02','2021-01-05',
'2021-01-02','2021-01-05','2021-01-07','2021-01-09','2021-01-11',
'2021-01-02','2021-01-05','2021-01-07',
'2021-01-02','2021-01-05','2021-01-07','2021-01-09','2021-01-11','2021-01-13','2021-01-15',
'2021-01-02',
'2021-01-02','2021-01-05',
'2021-01-02','2021-01-05','2021-01-07','2021-01-09','2021-01-11','2021-01-13','2021-01-15',
'2021-01-02','2021-01-05',
'2021-01-02','2021-01-05'
],
'Quantity': np.floor(np.random.random(size=35)*100)
})
And I want to calculate moving average (2 periods) over Date. So, the final output looks like the following. For first MA, we are taking 2021-01-02 & 2021-01-05 across all observations & calculate the MA (50). Similarly for other dates. The output need not be in the structure I'm showing the report. I just need date & MA column in the final data.
Thanks!

Answer 1

IIUC, you can aggregate the similar dates first, getting the sum and count.
Then take the sum per rolling 2 dates (here it doesn't look like you want to take care of a defined period but rather raw successive values, so I am assuming here prior sorting).
Finally, perform the ratio of sum and count to get the mean:
g = tdf.groupby('Date')['Quantity']
out = g.sum().rolling(2).sum()/g.count().rolling(2).sum()
output:
Date
2021-01-02 NaN
2021-01-05 50.210526
2021-01-07 45.071429
2021-01-09 41.000000
2021-01-11 44.571429
2021-01-13 48.800000
2021-01-15 50.500000
Name: Quantity, dtype: float64
joining the original data:
g = tdf.groupby('Date')['Quantity']
s = g.sum().rolling(2).sum()/g.count().rolling(2).sum()
tdf.merge(s.rename('Quantity_MA(2)'), left_on='Date', right_index=True)
output:
person_id Date Quantity Quantity_MA(2)
0 1 2021-01-02 87.0 NaN
4 2 2021-01-02 41.0 NaN
6 3 2021-01-02 68.0 NaN
11 4 2021-01-02 11.0 NaN
14 5 2021-01-02 16.0 NaN
21 6 2021-01-02 51.0 NaN
22 7 2021-01-02 38.0 NaN
24 8 2021-01-02 51.0 NaN
31 9 2021-01-02 90.0 NaN
33 10 2021-01-02 45.0 NaN
1 1 2021-01-05 58.0 50.210526
5 2 2021-01-05 11.0 50.210526
7 3 2021-01-05 43.0 50.210526
12 4 2021-01-05 44.0 50.210526
15 5 2021-01-05 52.0 50.210526
23 7 2021-01-05 99.0 50.210526
25 8 2021-01-05 55.0 50.210526
32 9 2021-01-05 66.0 50.210526
34 10 2021-01-05 28.0 50.210526
2 1 2021-01-07 27.0 45.071429
8 3 2021-01-07 55.0 45.071429
13 4 2021-01-07 58.0 45.071429
16 5 2021-01-07 32.0 45.071429
26 8 2021-01-07 3.0 45.071429
3 1 2021-01-09 18.0 41.000000
9 3 2021-01-09 36.0 41.000000
17 5 2021-01-09 69.0 41.000000
27 8 2021-01-09 71.0 41.000000
10 3 2021-01-11 40.0 44.571429
18 5 2021-01-11 36.0 44.571429
28 8 2021-01-11 42.0 44.571429
19 5 2021-01-13 83.0 48.800000
29 8 2021-01-13 43.0 48.800000
20 5 2021-01-15 48.0 50.500000
30 8 2021-01-15 28.0 50.500000