Verifying timestamps in a time series

Question

I am working with time series data and I would like to know if there is a efficient & pythonic way to verify if the sequence of timestamps associated to the series is valid. In other words, I would like to know if the sequence of time stamps is in the correct ascending order without missing or duplicated values.

I suppose that verifying the correct order and the presence of duplicated values should be fairly straightforward but I am not so sure about the detection of missing timestamps.

Answer 1

numpy.diff can be used to find the difference between subsequent time stamps. These diffs can then be evaluated to determine if the timestamps look as expected:

import numpy as np
import datetime as dt

def errant_timestamps(ts, expected_time_step=None, tolerance=0.02):
    # get the time delta between subsequent time stamps
    ts_diffs = np.array([tsd.total_seconds() for tsd in np.diff(ts)])

    # get the expected delta
    if expected_time_step is None:
        expected_time_step = np.median(ts_diffs)

    # find the index of timestamps that don't match the spacing of the rest
    ts_slow_idx = np.where(ts_diffs < expected_time_step * (1-tolerance))[0] + 1
    ts_fast_idx = np.where(ts_diffs > expected_time_step * (1+tolerance))[0] + 1

    # find the errant timestamps
    ts_slow = ts[ts_slow_idx]
    ts_fast = ts[ts_fast_idx]

    # if the timestamps appear valid, return None
    if len(ts_slow) == 0 and len(ts_fast) == 0:
        return None

    # return any errant timestamps
    return ts_slow, ts_fast


sample_timestamps = np.array(
    [dt.datetime.strptime(sts, "%d%b%Y %H:%M:%S") for sts in (
        "05Jan2017 12:45:00",
        "05Jan2017 12:50:00",
        "05Jan2017 12:55:00",
        "05Jan2017 13:05:00",
        "05Jan2017 13:10:00",
        "05Jan2017 13:00:00",
    )]
)

print errant_timestamps(sample_timestamps)