我重新定义了一个类似的 Dataframe ，因为使用随机变量生成器会使调试变得困难。在每次测试中， Dataframe 都会随机变化。
除了保持简单，我将使用2和3移动平均线时期。

• • 启动 Dataframe **

Date  Store  Sales
9 2022-10-10      1   5347
8 2022-10-09      1   1561
7 2022-10-08      1   5648
6 2022-10-07      1   8123
5 2022-10-06      1   1401
4 2022-10-05      0   2745
3 2022-10-04      0   7848
2 2022-10-03      0   3151
1 2022-10-02      0   4296
0 2022-10-01      0   9028

它给出：

ex = pd.DataFrame({
  "Date": pd.date_range('2022-10-01', '2022-10-10'),
  "Store": [0]*5+[1]*5,
  "Sales": [9028, 4296, 3151, 7848, 2745, 1401, 8123, 5648, 1561, 5347],
})

ex.sort_values(['Store','Date'], ascending=False, inplace=True)

• • 拟议守则**

import pandas as pd
import numpy as np

ex = pd.DataFrame({
  "Date": pd.date_range('2022-10-01', '2022-10-10'),
  "Store": [0]*5+[1]*5,
  "Sales": [9028, 4296, 3151, 7848, 2745, 1401, 8123, 5648, 1561, 5347],
})

ex.sort_values(['Store','Date'], ascending=False, inplace=True)

periods=(2,3)

### STEP 1 -- Initialization : exhaustive Mean() Calculation
for per in periods:
    ex["Sales_mean_{0}_days".format(per)] = (
        ex.groupby(['Store'])['Sales']
          .apply(lambda g: g.shift(-1)
                            .rolling(per)
                            .mean()
                            .shift(-per+1))
     )

### STEP 2 -- New Row Insertion
def fmt_newRow(g, newRow, periods):
    return {
      "Date": pd.Timestamp(newRow[0]),
      "Store": newRow[1],
      "Sales": newRow[2],
      "Sales_mean_{0}_days".format(periods[0]): (g['Sales'].iloc[0:periods[0]-1].sum() + newRow[2]) / periods[0],
      "Sales_mean_{0}_days".format(periods[1]): (g['Sales'].iloc[0:periods[1]-1].sum() + newRow[2]) / periods[1],
    }    

def add2DF(ex, newRow):
    # g : sub-Store group
    g = (
        ex.loc[ex.Store==newRow[1]]
          .sort_values(['Store','Date'], ascending=False)
    )
    # Append newRow like a dictionnary and sort by ['Store','Date']
    ex = (
        ex.append(fmt_newRow(g, newRow, periods), ignore_index=True)
          .sort_values(['Store','Date'], ascending=False)
          .reset_index(drop=True)
    )
    #
    return ex

newRow = ['2022-10-11', 1, 2803] # [Date, Store, Sales]
ex = add2DF(ex, newRow)

print(ex)

• • 结果**

Date  Store  Sales  Sales_mean_2_days  Sales_mean_3_days
0  2022-10-11      1   2803             4075.0        3237.000000
1  2022-10-10      1   5347             3604.5        5110.666667
2  2022-10-09      1   1561             6885.5        5057.333333
3  2022-10-08      1   5648             4762.0                NaN
4  2022-10-07      1   8123                NaN                NaN
5  2022-10-06      1   1401                NaN                NaN
6  2022-10-05      0   2745             5499.5        5098.333333
7  2022-10-04      0   7848             3723.5        5491.666667
8  2022-10-03      0   3151             6662.0                NaN
9  2022-10-02      0   4296                NaN                NaN
10 2022-10-01      0   9028                NaN                NaN

• • 评论**
• 新行是一个列表，如下所示：[Date, Store, Sales]
• 每次需要将新行保存到dataframe时，都将其传递给fmt_newRow函数，该函数具有相应的子组g
• fmt_newRow以字典的形式返回一个新行，使用append Pandas函数将其集成到 Dataframe 中
• 无需重新计算所有平均值，因为只有per-last g值用于计算新的行平均值
• 检查周期2和3的移动平均值，结果正确。