If I have the dataframe:

   '|    |   time_index |   values |
    |---:|-------------:|---------:|
    |  0 |            1 |       21 |
    |  1 |            2 |        5 |
    |  2 |            3 |       25 |
    |  3 |            4 |        0 |
    |  4 |            5 |        4 |
    |  5 |            6 |       13 |
    |  6 |            7 |       20 |
    |  7 |            8 |        2 |
    |  8 |            9 |       15 |
    |  9 |           10 |       21 |'

I want to take the all the subsets of 3, in increments of one, so the first operation takes index [0,1,2], second iteration [1,2,3]. This logic implemented to the column values I would like to check if the value in the middle, is the max of the subset and flag it in another column.

Iterations:

1. Values: [21,5,25], max(values) == 5? False => ignore.
2. Values: [5,25,0], max(values) = 25? True => add flag in new column.

I have the feeling that this has to do with a rolling window but I am not sure how to go about it.

CodePudding user response：

To make rolling window calculations use the rolling method. Then you can apply the logic to each group using agg with a custom function.

# val.iat[1] stands for the middle value of each group
df['is_max'] = (
    df['values'].rolling(window=3, center=True)
                .agg(lambda vals: vals.iat[1] == vals.max())
                .astype('boolean')
)

>>> df

   time_index  values  is_max
0           1      21    <NA>
1           2       5   False
2           3      25    True
3           4       0   False
4           5       4   False
5           6      13   False
6           7      20    True
7           8       2   False
8           9      15   False
9          10      21    <NA>