вы можете решить это решение, используя pandas и умножение матрицы
import pandas as pd
# Attempt #5: Use python and the pandas package
# create the pandas Data Frames (kind of like R data.frame)
myDataDF = pd.DataFrame({'Record':range(1,6), 'SomeValue':[10, 8, 14, 6, 2]})
linkTableDF = pd.DataFrame({'ValueOfInterest':['a', 'b', 'c'], 'LowerBound': [1, 4, 10],
'UpperBound':[3, 5, 16]})
# set the index of the linkTable (kind of like setting row names)
linkTableDF = linkTableDF.set_index('ValueOfInterest')
# now apply a function to each row of the linkTable
# this function checks if any of the values in myData are between the upper
# and lower bound of a specific row thus returning 5 values (length of myData)
mask = linkTableDF.apply(lambda r: myDataDF.SomeValue.between(r['LowerBound'],
r['UpperBound']), axis=1)
# mask is a 3 (length of linkTable) by 5 matrix of True/False values
# by transposing it we get the row names (the ValueOfInterest) as the column names
mask = mask.T
# we can then matrix multiply mask with its column names
myDataDF['ValueOfInterest'] = mask.dot(mask.columns)
Что в вашем случае вы можете использовать
mask = table.apply(lambda r: table2.AlarmInTimeStamp.between(r['start_time'],
r['End_time']), axis=1)
или, альтернативно, вы можете использовать SQL для ваши таблицы
источники: https://www.mango-solutions.com/in-between-a-rock-and-a-conditional-join/