在 python 中为组保留值

Question

I have a data which looks like below.

country item    week    stock   FCST
A        1       1       105    3
A        1       2       105    6
A        1       3       105    9
A        1       4       105    4
A        1       5       105    7
A        1       6       105    4
A        1       7       105    7

the task i wish to perform is assigning closing stock of the current week as the opening stock of next week. in the above table my stock qty was 105 at very beginning, based on the forecast(fcst column) it is decreasing and assigning as closing stock of the same week. now the closing stock should become opening stock for next coming week.

I have done the same in SAS using retain statement. I do not have any idea how replicate the same in python.

Also to make a note this operation to be performed for every country-Item combination. (We can not always shift the value to take as opening stock, AS new item might have different current stock).

Can anyone help me out on the same.

My Output should look like below table.

country item    week    stock   FCST    OPENING_STOCK   CLOSING_STK
A        1       1       105      3          105           102
A        1       2       105      6          102            96
A        1       3       105      9           96            87
A        1       4       105      4           87            83
A        1       5       105      7           83            76
A        1       6       105      4           76            72
A        1       7       105      7           72            65

Please click on above link to see the desired output.

Thanks in advance.

Answer 1

Now it works. combine_first is used to fill gaps in df.opening

import pandas as pd

df = pd.DataFrame({
'country': {0: 'A', 1: 'A', 2: 'A', 3: 'A', 4: 'A', 5: 'A', 6: 'A', 7: 'A', 8: 'A', 9: 'A', 10: 'A', 11: 'A', 12: 'A', 13: 'B', 14: 'B', 15: 'B', 16: 'B', 17: 'B', 18: 'B', 19: 'B', 20: 'B', 21: 'B', 22: 'B'}, 
'item': {0: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 1, 7: 2, 8: 1, 9: 2, 10: 2, 11: 1, 12: 1, 13: 1, 14: 1, 15: 1, 16: 2, 17: 3, 18: 2, 19: 1, 20: 2, 21: 1, 22: 3},
'week': {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 7, 7: 7, 8: 8, 9: 8, 10: 9, 11: 9, 12: 10, 13: 1, 14: 2, 15: 3, 16: 3, 17: 3, 18: 4, 19: 4, 20: 5, 21: 5, 22: 5},
'stock': {0: 105, 1: 105, 2: 105, 3: 105, 4: 105, 5: 105, 6: 105, 7: 94, 8: 105, 9: 94, 10: 94, 11: 105, 12: 105, 13: 100, 14: 100, 15: 100, 16: 200, 17: 300, 18: 200, 19: 100, 20: 200, 21: 100, 22: 300},
'FCST': {0: 3, 1: 6, 2: 9, 3: 4, 4: 7, 5: 4, 6: 7, 7: 2, 8: 1, 9: -5, 10: 2, 11: 8, 12: 6, 13: 2, 14: 6, 15: 8, 16: 3, 17: 7, 18: 8, 19: 9, 20: 3, 21: 5, 22: 6}
})

df_new = pd.DataFrame(columns=df.columns)
groups = df.groupby(["country", "item"])
df["closing"] = df.stock - groups.FCST.cumsum()
df["opening"] = groups.closing.shift(1)
df["opening"] = df["opening"].combine_first(df.stock)

Outputs:

   country  item  week  stock  FCST  closing  opening
0        A     1     1    105     3      102    105.0
1        A     1     2    105     6       96    102.0
2        A     1     3    105     9       87     96.0
3        A     1     4    105     4       83     87.0
4        A     1     5    105     7       76     83.0
5        A     1     6    105     4       72     76.0
6        A     1     7    105     7       65     72.0
7        A     2     7     94     2       92     94.0
8        A     1     8    105     1       64     65.0
9        A     2     8     94    -5       97     92.0
10       A     2     9     94     2       95     97.0
11       A     1     9    105     8       56     64.0
12       A     1    10    105     6       50     56.0
13       B     1     1    100     2       98    100.0
14       B     1     2    100     6       92     98.0
15       B     1     3    100     8       84     92.0
16       B     2     3    200     3      197    200.0
17       B     3     3    300     7      293    300.0
18       B     2     4    200     8      189    197.0
19       B     1     4    100     9       75     84.0
20       B     2     5    200     3      186    189.0
21       B     1     5    100     5       70     75.0
22       B     3     5    300     6      287    293.0

Answer 2

The code i have used to solve the issue is pasted below.

    df.sort_values(by=['ITM_CD','Country','WEEK'],inplace=True)
    df['CONCAT']=df['Country']+df['ITM_CD']

#CALCULATE BEGINING STOCK EVERY WEEK
    df['TMP1']=1
    grouper = (df["CONCAT"]!= df["CONCAT"].shift()).cumsum()   
    df["WEEK_NO"] = df.groupby(grouper)['TMP1'].cumsum() 
    df["FCST1"] = df.groupby(grouper)['FCST'].cumsum() 
 
    result = df.CURR_STCK_TOT - df.FCST1
    df["CLOSING"] = result
    df["CLOSING"] = np.where(df["CLOSING"]<0,0,df["CLOSING"])    
    df["OPENING"] = np.where(df["WEEK_NO"]==1,df["STOCK"],result.shift(1))
    df["OPENING"] = np.where(df["OPENING"]<0,0,df["OPENING"])  

Also i have done some extra manipulation like making all negative values 0.