[Python] How do you efficiently find gaps in one list of datetimes relative to another?

I have datasets from multiple instruments with differing, but hypothetically concurrent datetime stamps. If date from instrument A does not correspond to any data from instrument B within some interval, I want to flag those data for removal later.

The current way I handle this is to loop over A and test against B with a list comprehension. However, my datasets can get very large -- O(400k) -- which makes this looping on loops extremely slow and inefficient.

How can I reformulate the following "findGaps" method to be more efficient?

from datetime import datetime, timedelta
import time

start = datetime(2025,4,25,0,0,0)
stop = datetime(2025,4,25,2,0,0)
delta = timedelta(seconds=1)

# Main dataset:
dateTimeMain = []
while start <= stop:
start += delta
dateTimeMain.append(start)

# Test dataset with gaps:
dateTimeTest = dateTimeMain.copy()
del dateTimeTest[30:300]
del dateTimeTest[3000:3300]

def findGaps(dTM,dTT):
bTs = []
start = -1
i, index, stop = 0,0,0
tThreshold = timedelta(seconds=30)

for index, esTimeI in enumerate(dTM):

tDiff = [abs(x - esTimeI) for x in dTT]
if min(tDiff) > tThreshold:
i += 1
if start == -1:
start = index
stop = index
else:
if start != -1:
startstop = [dTM[start],dTM[stop]]
msg = f' Flag data from {startstop[0]} to {startstop[1]}'
print(msg)
bTs.append(startstop)
start = -1

if start != -1 and stop == index: # Records from a mid-point to the end are bad
startstop = [dTM[start],dTM[stop]]
bTs.append(startstop)

return bTs

tic = time.process_time()
badTimes = findGaps(dateTimeMain,dateTimeTest)
print(f'Loops: {len(dateTimeMain)} x {len(dateTimeTest)} = {len(dateTimeMain)*len(dateTimeTest)}')
print(f'Uncertainty Update Elapsed Time: {time.process_time() - tic:.3f} s')


Returns:

Flag data from 2025-04-25 00:01:01 to 2025-04-25 00:04:30
Flag data from 2025-04-25 00:55:01 to 2025-04-25 00:59:00
Loops: 7201 x 6631 = 47749831
Uncertainty Update Elapsed Time: 5.167 s

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