Removing all rows with datetime value from listed multiple range in Pandas-CodePudding

I have tried to simplify the query to focus on main point only. I have a dataframe with one datetime column and one addtional column val1 as given below .

and I also have list of range of time as below

I need a way to remove all the records from the dataframe where "TIME" is between any one of the given ranges. My actual data has 100K records so I am struggling to find an optimized way to perform this action.

Please note, in actual case all the time Ranges are also derived on the go so cant use a logical operators to compare with fix set of time ranges.

Either filter out from the main dataframe or create a copy with required data only, I will be fine with both ways.

Copying above data below for reference.

Range table:

from,upto

'27-01-2020 00:41:00','27-01-2020 00:51:00'

'27-01-2020 00:56:35','27-01-2020 01:06:35'

'27-01-2020 01:32:00','27-01-2020 01:42:00'

'27-01-2020 01:49:50','27-01-2020 01:59:50'

Data table:

TIME,val1

'27-01-2020 00:35:45','15148'

'27-01-2020 00:35:50','20148'

'27-01-2020 00:35:55','25148'

'27-01-2020 00:47:45','4858'

'27-01-2020 00:48:49','8576'

'27-01-2020 00:50:53','3456'

'27-01-2020 00:52:05','1234'

'27-01-2020 00:52:10','4567'

'27-01-2020 00:58:35','5678'

'27-01-2020 00:59:40','4567'

'27-01-2020 01:04:35','3456'

'27-01-2020 01:10:23','4567'

'27-01-2020 01:11:24','7654'

'27-01-2020 01:15:35','4567'

CodePudding user response：

Use Series.between in list comprehension and np.logical_or.reduce for mask, last filter in inverted condition by ~:

df1['TIME'] = pd.to_datetime(df1['TIME'])
df2['upto'] = pd.to_datetime(df2['upto'])
df2['from'] = pd.to_datetime(df2['from'])

zipped = zip(df2['from'], df2['upto'])

m = np.logical_or.reduce([df1['TIME'].between(s, e) for s, e in zipped])

df = df1[~m]
print (df)
                  TIME     val1
0  2020-01-27 00:35:45  '15148'
1  2020-01-27 00:35:50  '20148'
2  2020-01-27 00:35:55  '25148'
6  2020-01-27 00:52:05   '1234'
7  2020-01-27 00:52:10   '4567'
11 2020-01-27 01:10:23   '4567'
12 2020-01-27 01:11:24   '7654'
13 2020-01-27 01:15:35   '4567'