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Question

I have a dataframe and I would like to get summing values for every [group] with one row Values before (‘subtract’ & ‘add’) and one row after (‘BEGUZE_UE’) the group. (A group can contain different strings, UE_1 doesn’t matter).
But I get some wrong results in ‘m_days’. Thanks for any help.

DataFrame:

data = {'ATEXT': ['', 'CT', 'RT', '', '', '', '', 'CT', 'CT', 'CT', 'RT', '', '', '', 'CTS', 'CT',
              '', 'CT', 'RT', 'RT', 'RT', 'CT', '', 'CT', 'RT', 'RT', '', '', 'CT', '', ''], 
    'BEGUZ_UE': [11.00, 23.00, 33.00, 15.00, 12.75, 19.75, 14.75, 23.00, 24.00, 24.00, 33.00, 15.00, 14.25, 13.00,
                 23.00, 24.00, 11.00, 23.00, 33.00, 24.00, 24.00, 24.00, 6.00, 23.00, 33.00, 24.00, 10.68,
                 21.00, 23.00, 12.75, 13.00],
    'subtract': [00.00, 00.00, 00.00, 00.00, 00.00, 3.57, 00.00, 00.00, 00.00, 00.00, 00.00, 00.08, 00.00, 00.00,
                 00.00, 00.00, 00.00, 00.00, 00.00, 00.00, 00.00, 00.00, 00.00, 00.00, 00.00, 00.00, 00.00,
                 6.08, 00.00, 00.00, 00.00], 
    'add': [3.92, 00.00, 00.00, 00.00, 4.95, 00.00, 2.95, 00.00, 00.00, 00.00, 00.00, 00.00, 2.62, 1.92, 00.00, 
            00.00, 3.92, 00.00, 00.00, 00.00, 00.00, 00.00, 12.00, 00.00, 00.00, 00.00, 2.95, 00.00, 00.00, 4.95,
            1.92],
    'UE_1': ['', '', '', '', 12.67, 24.7, 11.18, '', '', '', '', '', 14.17, 15.62, '', '',
             '', '', '', '', '', '', '', '', '', '', '', 23.95, '', '', 17.95]}

df = pd.DataFrame(data)
df

Used Code:

m = df['ATEXT'].eq("")
cond = (~m) & m.shift(-1)
df['UE_more_days'] = (df['BEGUZ_UE'].mask(m)
                      .groupby(m.cumsum()).cumsum()
                      .where(cond)
                     ).shift() # orginal ohne shift() hier
tmv = (df[['subtract', 'add']]
       .shift()
       .groupby(m.cumsum())
       .transform('max')
       .eval('add-subtract')
      )
df['m_days'] = (df.groupby(m[::-1].cumsum())['BEGUZ_UE']
                .transform('sum')
                .add(tmv)
                .where(cond)
                .shift()
               )

Result:

   ATEXT  BEGUZ_UE  subtract    add   UE_1  UE_more_days  m_days
0            11.00    [-0.00  +3.92                  NaN     NaN
1     CT     23.00      0.00   0.00                  NaN     NaN
2     RT     33.00      0.00   0.00                  NaN     NaN
3           +15.00]     0.00   0.00                 56.0   74.92 ok
4            12.75      0.00   4.95  12.67           NaN     NaN
5            19.75      3.57   0.00   24.7           NaN     NaN
6            14.75    [-0.00  +2.95  11.18           NaN     NaN
7     CT     23.00      0.00   0.00                  NaN     NaN
8     CT     24.00      0.00   0.00                  NaN     NaN
9     CT     24.00      0.00   0.00                  NaN     NaN
10    RT     33.00      0.00   0.00                  NaN     NaN
11          +15.00]     0.08   0.00                104.0  118.38 shold be 121.95
12           14.25      0.00   2.62  14.17           NaN     NaN
13           13.00    [-0.00  +1.92  15.62           NaN     NaN
14   CTS     23.00      0.00   0.00                  NaN     NaN
15    CT     24.00      0.00   0.00                  NaN     NaN
16          +11.00]   [-0.00  +3.92                 47.0   60.62 should be 59.92
17    CT     23.00      0.00   0.00                  NaN     NaN
18    RT     33.00      0.00   0.00                  NaN     NaN
19    RT     24.00      0.00   0.00                  NaN     NaN
20    RT     24.00      0.00   0.00                  NaN     NaN
21    CT     24.00      0.00   0.00                  NaN     NaN
22          + 6.00]   [-0.00 +12.00                128.0  137.92 ok
23    CT     23.00      0.00   0.00                  NaN     NaN
24    RT     33.00      0.00   0.00                  NaN     NaN
25    RT     24.00      0.00   0.00                  NaN     NaN
26          +10.68]     0.00   2.95                 80.0  102.68 ok
27           21.00    [-6.08  +0.00  23.95           NaN     NaN
28    CT     23.00      0.00   0.00                  NaN     NaN
29          +12.75]     0.00   4.95                 23.0   32.62 shold be 29.67
30           13.00      0.00   1.92  17.95           NaN     NaN

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Danh mục

sum groupby and add or sub row before to groupby and add row after to groupby