Check certain conditions looking back x hours (pandas)

Question

I have some data like this:

import pandas as pd

dates = ["12/25/2021 07:47:01", "12/25/2021 08:02:32", "12/25/2021 13:57:40", "12/25/2021 14:17:11", "12/25/2021 17:23:01", "12/25/2021 23:48:55", "12/26/2021 08:22:32", "12/26/2021 11:11:11", "12/26/2021 14:53:40",  "12/26/2021 16:07:07", "12/26/2021 23:56:07"]
is_manual = [0,0,0,0,1,1,0,0,0,0,1]
is_problem = [0,0,0,0,1,1,0,0,0,1,1]

df = pd.DataFrame({'dates':dates,
                   'manual_entry': is_manual,
                   'problem_entry': is_problem})

    dates               manual_entry    problem_entry
0   12/25/2021 07:47:01 0                0
1   12/25/2021 08:02:32 0                0
2   12/25/2021 13:57:40 0                0
3   12/25/2021 14:17:11 0                0
4   12/25/2021 17:23:01 1                1
5   12/25/2021 23:48:55 1                1
6   12/26/2021 08:22:32 0                0
7   12/26/2021 11:11:11 0                0
8   12/26/2021 14:53:40 0                0
9   12/26/2021 16:07:07 0                1
10  12/26/2021 23:56:07 1                1

What I would like to do is to take every row where problem_entry == 1 and examine if every row in the 24 hours prior to that row is manual_entry == 0

While I know you can create a rolling lookback window of a certain number of rows, each row is not spaced a normal time period apart, so wondering how to look back 24 hours and determine whether the criteria above is met.

Thanks in advance

EDIT: Expected output:

    dates               manual_entry    problem_entry
4   12/25/2021 17:23:01 1                1
10  12/26/2021 23:56:07 1                1

Asked By: Dr Wampa

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Source

Answer 1

Try the following.Extracted ‘manual_entry’ into a separate variable and collected the amounts in a sliding window of the day. If the current ‘manual_entry’ is equal to 1, then there were no other values during the day. Next, the dataframe is filtered where ‘problem_entry’, ‘manual_entry’ where are equal to 1.

df['dates'] = pd.to_datetime(df['dates'])
a = (df.rolling("86400s", on='dates', min_periods=1).sum()).loc[:, 'manual_entry']

print(df.loc[(df['problem_entry'] == 1) & (a == 1)])

Output:

                 dates  manual_entry  problem_entry
4  2021-12-25 17:23:01             1              1
10 2021-12-26 23:56:07             1              1

Answered By: inquirer

Check certain conditions looking back x hours (pandas)

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