Is there a way to identify and return monotonicity sections in a list in Python?-CodePudding

I am attempting to filter a pandas dataframe from the fastf1 package. I want to retrieve 'long runs', meaning that laptimes are similar over several laps.

In order to do this I want to find all monotonicity (of increasing by 1 each time) 'sections' within a dataframe after filtering.

I have filtered the LapTimes so most irrelevant laptimes are out of the dataframe, which gives:

print(fp_d1[['LapNumber', 'LapTime']])

output:

    LapNumber                LapTime
1           2 0 days 00:01:25.230000
3           4 0 days 00:01:44.087000
4           5 0 days 00:01:23.449000
6           7 0 days 00:01:23.234000
8           9 0 days 00:01:22.853000
9          10 0 days 00:01:33.581000
11         12 0 days 00:01:22.840000
12         13 0 days 00:01:40.480000
14         15 0 days 00:01:26.013000
15         16 0 days 00:01:25.739000
16         17 0 days 00:01:25.621000
17         18 0 days 00:01:25.750000
18         19 0 days 00:01:25.681000
19         20 0 days 00:01:25.556000
20         21 0 days 00:01:25.832000
21         22 0 days 00:01:25.669000
22         23 0 days 00:01:25.450000
23         24 0 days 00:01:25.408000
24         25 0 days 00:01:25.694000

From here I would like to make a function that in this case will only return laps 15-25.

Any help would be appreciated, if more information has to be given please let me know as well.

CodePudding user response：

Here is one possible solution.

I added the condition that there need to be more than 2 rows back to back to be present in the desired output:

condition = df['LapNumber'].diff().eq(1)
mask = condition.ne(condition.shift(-1)).cumsum()
print(mask)
out = df[df.groupby(mask)['LapNumber'].transform('count') > 2]
print(out)

Output:

    LapNumber                 LapTime
14         15  0 days 00:01:26.013000
15         16  0 days 00:01:25.739000
16         17  0 days 00:01:25.621000
17         18  0 days 00:01:25.750000
18         19  0 days 00:01:25.681000
19         20  0 days 00:01:25.556000
20         21  0 days 00:01:25.832000
21         22  0 days 00:01:25.669000
22         23  0 days 00:01:25.450000
23         24  0 days 00:01:25.408000