how to do forward rolling sum in pandas?

Question

I have this dataframe:

dates = pd.date_range(start='2016-01-01', periods=20, freq='d')
df = pd.DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8,
                   'B': np.concatenate((dates, dates)),
                   'C': np.arange(40)})

I sorted the data frame by Date:

df.sort_values('B',inplace=True)

I am looking to do a forward rolling sum on date. However, I can only do backward rolling sum using:

df.groupby('A').rolling(7, on='B',min_periods=0).C.sum()

A  B         
1  2016-01-01      0.0
   2016-01-02      1.0
   2016-01-03      3.0
   2016-01-04      6.0
   2016-01-05     10.0
   2016-01-06     15.0

I want to do forward rolling sum.

Asked By: Ahamed Moosa

||

Source

Answer 1

I believe need change ordering by iloc[::-1]:

df1 = (df.iloc[::-1]
        .groupby('A', sort=False)
        .rolling(7, on='B',min_periods=0).C
        .sum()
        .iloc[::-1])

Answered By: jezrael

Answer 2

I think you want

df["C"] = df["A"].cumsum()

See documentation here

Answered By: Toby Petty

Answer 3

Setup

dates = pd.date_range(start='2016-01-01', periods=20, freq='d')
df = pd.DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8,
                   'B': np.concatenate((dates, dates)),
                   'C': np.arange(40)})

Sort by `'B'` then when we roll, roll the reverse with `iloc[::-1]`

def rev_roll(x):
    return x.iloc[::-1].rolling(7, min_periods=0).sum().iloc[::-1]

df.assign(Roll=df.sort_values('B').groupby('A').C.transform(rev_roll))

Output

    A          B   C  Roll
0   1 2016-01-01   0    21
1   1 2016-01-02   1    28
2   1 2016-01-03   2    35
3   1 2016-01-04   3    42
4   1 2016-01-05   4    49
5   1 2016-01-06   5    56
6   1 2016-01-07   6    63
7   1 2016-01-08   7    70
8   1 2016-01-09   8    77
9   1 2016-01-10   9    84
10  1 2016-01-11  10    91
11  1 2016-01-12  11    98
12  1 2016-01-13  12   105
13  1 2016-01-14  13   112
14  1 2016-01-15  14    99
15  1 2016-01-16  15    85
16  1 2016-01-17  16    70
17  1 2016-01-18  17    54
18  1 2016-01-19  18    37
19  1 2016-01-20  19    19
20  2 2016-01-01  20   161
21  2 2016-01-02  21   168
22  2 2016-01-03  22   175
23  2 2016-01-04  23   182
24  2 2016-01-05  24   189
25  2 2016-01-06  25   196
26  2 2016-01-07  26   171
27  2 2016-01-08  27   145
28  2 2016-01-09  28   118
29  2 2016-01-10  29    90
30  2 2016-01-11  30    61
31  2 2016-01-12  31    31
32  3 2016-01-13  32   245
33  3 2016-01-14  33   252
34  3 2016-01-15  34   219
35  3 2016-01-16  35   185
36  3 2016-01-17  36   150
37  3 2016-01-18  37   114
38  3 2016-01-19  38    77
39  3 2016-01-20  39    39

Answered By: piRSquared

Answer 4

If your dates aren’t fully continuous (like you’ve got a missing day or two here and there) and you want a fixed N-Days window (and not N-records window), you can use:

def forward_rolling_mean(sub_df, col='units', days_ahead=7):
    rolling_data = [sub_df[sub_df['date'].between(date+pd.Timedelta(days=1), date+pd.Timedelta(days=1+days_ahead-1))][col].mean() for date in sub_df['date']]
    return pd.DataFrame({'%s_next%idays_mean' % (col, days_ahead): rolling_data}, index=sub_df['date'])

you can also change it to return a series instead of dataframe. later on you can join this to the original data.

Answered By: Idan Richman

Answer 5

Simple as:

df['B'] = df['A'].rolling(3).sum().shift(-3)

Answered By: Daniel Manso

Answer 6

indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=7)
df.groupby('A').rolling(window=indexer, on='B',min_periods=0).C.sum()

pandas doc

Answered By: eXcellme

how to do forward rolling sum in pandas?

Question:

Answers:

Setup

Sort by `'B'` then when we roll, roll the reverse with `iloc[::-1]`

Output

how to do forward rolling sum in pandas?

Question:

Answers:

Setup

Sort by 'B' then when we roll, roll the reverse with iloc[::-1]

Output

Sort by `'B'` then when we roll, roll the reverse with `iloc[::-1]`