We Keep Coding

pd.to_datetime doesn't work on values similar to '10/30/2022 7:00:00 AM +01:00'

I have a pandas dataframe with the following column:
import pandas as pd
df = pd.DataFrame(['10/30/2022 7:00:00 AM +01:00', '10/31/2022 12:00:00 AM +01:00',
'10/30/2022 3:00:00 PM +01:00', '10/30/2022 9:00:00 PM +01:00',
'10/30/2022 5:00:00 PM +01:00', '10/30/2022 10:00:00 PM +01:00',
'10/30/2022 3:00:00 AM +01:00', '10/30/2022 2:00:00 AM +02:00',
'10/30/2022 10:00:00 AM +01:00', '10/30/2022 4:00:00 PM +01:00',
'10/30/2022 1:00:00 AM +02:00'], columns = ['Date'])
I want to convert the date values so it looks like the following:
2022-10-30T00:00:00+02:00
I tried the following; df['Date'] = pd.to_datetime(df['Date']).dt.strftime('%Y-%m-%dT%H:%M:%S')
The code raises an error whenever the .dt is called:
AttributeError: Can only use .dt accessor with datetimelike values
Apparently the code pd.to_datetime() does not work.
Anyone knows how to fix this?

# convert to date
# convert to your format
df['Date']=df['Date'].apply(pd.to_datetime).apply(lambda x: x.strftime('%Y-%m-%dT%H:%M:%S%z'))
0 2022-10-30T07:00:00+0100
1 2022-10-31T00:00:00+0100
2 2022-10-30T15:00:00+0100
3 2022-10-30T21:00:00+0100
4 2022-10-30T17:00:00+0100
5 2022-10-30T22:00:00+0100
6 2022-10-30T03:00:00+0100
7 2022-10-30T02:00:00+0200
8 2022-10-30T10:00:00+0100
9 2022-10-30T16:00:00+0100
10 2022-10-30T01:00:00+0200
Name: Date, dtype: object

Add utc=True when converting to datetime. Try this:
import pandas as pd
df = pd.DataFrame(['10/30/2022 7:00:00 AM +01:00', '10/31/2022 12:00:00 AM +01:00',
'10/30/2022 3:00:00 PM +01:00', '10/30/2022 9:00:00 PM +01:00',
'10/30/2022 5:00:00 PM +01:00', '10/30/2022 10:00:00 PM +01:00',
'10/30/2022 3:00:00 AM +01:00', '10/30/2022 2:00:00 AM +02:00',
'10/30/2022 10:00:00 AM +01:00', '10/30/2022 4:00:00 PM +01:00',
'10/30/2022 1:00:00 AM +02:00'], columns = ['Date'])
df['Date'] =df['Date'] = pd.to_datetime(df['Date'], utc=True)
print(df['Date'].dt.strftime('%Y-%m-%dT%H:%M:%S'))
Output:
0 2022-10-30T06:00:00
1 2022-10-30T23:00:00
2 2022-10-30T14:00:00
3 2022-10-30T20:00:00
4 2022-10-30T16:00:00
5 2022-10-30T21:00:00
6 2022-10-30T02:00:00
7 2022-10-30T00:00:00
8 2022-10-30T09:00:00
9 2022-10-30T15:00:00
10 2022-10-29T23:00:00

Python extract rows from dataframe where data is between two rows of anothe dataframe

Dataset
df_one = ['2017-07-27 04:00:00', '2017-08-07 04:00:00', '2017-08-11 20:00:00', '2017-08-15 16:00:00', '2017-08-21 20:00:00', '2017-08-23 08:00:00', '2017-08-23 16:00:00', '2017-08-31 20:00:00', '2017-09-01 08:00:00', '2017-09-01 16:00:00', '2017-09-01 20:00:00', '2017-09-04 00:00:00', '2017-09-04 20:00:00', '2017-09-05 00:00:00', '2017-09-05 04:00:00', '2017-09-12 12:00:00', '2017-09-13 12:00:00', '2017-09-14 00:00:00', '2017-09-18 04:00:00', '2017-09-21 08:00:00', '2017-09-22 16:00:00', '2017-09-25 08:00:00', '2017-10-10 12:00:00', '2017-10-16 16:00:00', '2017-10-19 12:00:00', '2017-10-23 04:00:00', '2017-10-26 00:00:00', '2017-10-27 00:00:00', '2017-11-10 04:00:00', '2017-11-21 08:00:00', '2017-11-22 16:00:00', '2017-11-30 00:00:00', '2017-11-30 08:00:00', '2017-11-30 16:00:00', '2017-12-01 00:00:00', '2017-12-04 20:00:00', '2017-12-14 08:00:00', '2017-12-15 12:00:00', '2017-12-15 16:00:00', '2017-12-18 00:00:00', '2017-12-19 12:00:00', '2018-01-08 20:00:00', '2018-01-11 20:00:00', '2018-02-06 04:00:00', '2018-02-13 20:00:00', '2018-02-20 08:00:00', '2018-03-02 20:00:00', '2018-03-09 08:00:00', '2018-03-13 20:00:00', '2018-03-16 00:00:00', '2018-03-20 08:00:00', '2018-03-20 16:00:00', '2018-03-22 08:00:00', '2018-03-29 04:00:00', '2018-04-09 20:00:00', '2018-04-13 20:00:00', '2018-04-16 00:00:00', '2018-04-20 08:00:00', '2018-05-11 20:00:00', '2018-05-15 16:00:00', '2018-05-31 16:00:00', '2018-06-13 12:00:00', '2018-06-14 00:00:00', '2018-06-14 20:00:00', '2018-06-22 16:00:00', '2018-06-27 20:00:00', '2018-06-29 20:00:00', '2018-07-03 00:00:00', '2018-07-03 04:00:00', '2018-07-12 04:00:00', '2018-07-16 20:00:00', '2018-07-18 00:00:00', '2018-07-20 20:00:00', '2018-07-27 04:00:00', '2018-07-31 00:00:00', '2018-08-02 00:00:00', '2018-08-20 04:00:00', '2018-09-03 00:00:00', '2018-09-06 08:00:00', '2018-09-07 20:00:00', '2018-09-13 00:00:00', '2018-09-27 16:00:00', '2018-10-11 08:00:00', '2018-10-17 20:00:00', '2018-11-02 00:00:00', '2018-11-05 20:00:00', '2018-11-06 00:00:00', '2018-11-09 04:00:00', '2018-11-16 08:00:00', '2018-11-23 20:00:00', '2018-11-29 12:00:00', '2018-12-03 00:00:00', '2018-12-03 16:00:00', '2018-12-03 20:00:00', '2018-12-04 08:00:00', '2018-12-05 08:00:00', '2018-12-07 00:00:00', '2018-12-12 00:00:00', '2018-12-13 12:00:00', '2018-12-13 20:00:00', '2018-12-18 08:00:00', '2018-12-27 00:00:00', '2018-12-28 00:00:00', '2019-01-03 00:00:00', '2019-01-07 08:00:00', '2019-01-14 20:00:00', '2019-01-15 08:00:00', '2019-01-15 16:00:00', '2019-01-28 04:00:00', '2019-02-05 12:00:00', '2019-02-18 20:00:00', '2019-02-19 12:00:00', '2019-02-20 00:00:00', '2019-03-04 16:00:00', '2019-03-13 00:00:00', '2019-03-22 20:00:00', '2019-04-08 20:00:00', '2019-04-18 16:00:00', '2019-04-30 16:00:00', '2019-05-03 00:00:00', '2019-05-07 04:00:00', '2019-05-08 00:00:00', '2019-05-08 12:00:00', '2019-05-09 08:00:00', '2019-05-09 12:00:00', '2019-05-09 16:00:00', '2019-05-09 20:00:00', '2019-05-15 04:00:00', '2019-05-24 08:00:00', '2019-05-29 00:00:00', '2019-06-03 08:00:00', '2019-06-14 00:00:00', '2019-06-20 12:00:00', '2019-07-01 16:00:00', '2019-07-11 12:00:00', '2019-07-16 16:00:00', '2019-07-19 04:00:00', '2019-07-22 00:00:00', '2019-08-05 16:00:00', '2019-08-14 04:00:00', '2019-08-26 04:00:00', '2019-08-27 12:00:00', '2019-08-27 16:00:00', '2019-08-28 00:00:00', '2019-09-05 08:00:00', '2019-09-11 20:00:00', '2019-09-13 04:00:00', '2019-09-17 00:00:00', '2019-09-18 04:00:00', '2019-09-19 08:00:00', '2019-09-19 16:00:00', '2019-09-20 20:00:00', '2019-10-03 04:00:00', '2019-10-09 08:00:00', '2019-10-09 16:00:00', '2019-10-25 08:00:00', '2019-10-30 08:00:00', '2019-11-05 12:00:00', '2019-11-18 00:00:00', '2019-11-25 00:00:00', '2019-12-02 20:00:00', '2019-12-09 08:00:00', '2019-12-10 16:00:00', '2019-12-19 00:00:00', '2019-12-19 16:00:00', '2019-12-19 20:00:00', '2019-12-27 08:00:00', '2020-01-03 16:00:00', '2020-01-06 16:00:00', '2020-01-08 00:00:00', '2020-01-14 12:00:00', '2020-01-14 20:00:00', '2020-01-15 20:00:00', '2020-01-17 16:00:00', '2020-01-31 20:00:00', '2020-02-05 04:00:00', '2020-02-24 04:00:00', '2020-02-24 12:00:00', '2020-02-25 00:00:00', '2020-03-12 20:00:00', '2020-03-26 04:00:00', '2020-04-01 20:00:00', '2020-04-08 04:00:00', '2020-04-08 08:00:00', '2020-04-09 20:00:00', '2020-04-16 04:00:00', '2020-04-27 20:00:00', '2020-04-28 12:00:00', '2020-04-28 16:00:00', '2020-05-05 16:00:00', '2020-05-13 04:00:00', '2020-05-14 04:00:00', '2020-05-19 00:00:00', '2020-05-25 00:00:00', '2020-05-26 16:00:00', '2020-06-15 00:00:00', '2020-06-16 08:00:00', '2020-06-17 04:00:00', '2020-06-23 08:00:00', '2020-06-25 12:00:00', '2020-06-29 20:00:00', '2020-06-30 00:00:00', '2020-07-02 04:00:00', '2020-07-03 12:00:00', '2020-07-06 08:00:00', '2020-07-10 16:00:00', '2020-07-10 20:00:00', '2020-08-10 04:00:00', '2020-08-13 08:00:00', '2020-08-20 16:00:00', '2020-08-21 08:00:00', '2020-08-21 16:00:00', '2020-08-27 12:00:00', '2020-08-28 00:00:00', '2020-08-28 12:00:00', '2020-09-02 20:00:00', '2020-09-10 20:00:00', '2020-09-17 04:00:00', '2020-09-18 12:00:00', '2020-09-21 16:00:00', '2020-09-30 00:00:00', '2020-10-14 00:00:00', '2020-10-20 00:00:00', '2020-10-28 04:00:00', '2020-11-05 04:00:00', '2020-11-11 20:00:00', '2020-11-13 00:00:00', '2020-11-24 08:00:00', '2020-11-24 16:00:00', '2020-12-10 08:00:00', '2020-12-10 16:00:00', '2020-12-23 04:00:00', '2020-12-24 12:00:00', '2020-12-24 16:00:00', '2020-12-28 12:00:00', '2021-01-08 08:00:00', '2021-01-21 20:00:00', '2021-01-26 12:00:00', '2021-01-27 00:00:00', '2021-01-27 16:00:00', '2021-02-09 04:00:00', '2021-02-17 08:00:00', '2021-02-19 16:00:00', '2021-02-26 20:00:00', '2021-03-11 20:00:00', '2021-03-12 20:00:00', '2021-03-15 04:00:00', '2021-03-15 12:00:00', '2021-03-18 04:00:00', '2021-03-19 04:00:00', '2021-03-23 04:00:00', '2021-03-23 16:00:00', '2021-04-02 16:00:00', '2021-04-05 00:00:00', '2021-04-06 00:00:00', '2021-05-03 00:00:00', '2021-05-07 04:00:00', '2021-05-13 04:00:00', '2021-05-14 20:00:00', '2021-05-27 08:00:00', '2021-06-01 00:00:00', '2021-06-02 16:00:00', '2021-06-03 08:00:00', '2021-06-03 12:00:00']
df_two = ['2017-08-11 23:59', '2017-09-14 23:59', '2017-10-10 23:59', '2017-10-12 23:59', '2017-10-16 23:59', '2017-10-25 23:59', '2018-04-23 23:59', '2018-07-09 23:59', '2018-07-31 23:59', '2018-08-30 23:59', '2018-09-05 23:59', '2018-09-28 23:59', '2018-11-20 23:59', '2019-01-03 23:59', '2019-01-16 23:59', '2019-01-29 23:59', '2019-02-06 23:59', '2019-04-18 23:59', '2019-05-10 23:59', '2019-06-04 23:59', '2019-06-05 23:59', '2019-07-03 23:59', '2019-07-10 23:59', '2019-07-16 23:59', '2019-08-05 23:59', '2019-10-15 23:59', '2019-10-29 23:59', '2019-12-10 23:59', '2019-12-26 23:59', '2020-01-08 23:59', '2020-01-14 23:59', '2020-01-20 23:59', '2020-02-03 23:59', '2020-03-30 23:59', '2020-05-01 23:59', '2020-05-19 23:59', '2020-10-02 23:59', '2020-10-05 23:59', '2020-10-14 23:59', '2020-11-11 23:59', '2021-01-19 23:59', '2021-01-20 23:59', '2021-02-02 23:59', '2021-02-12 23:59', '2021-02-19 23:59', '2021-02-22 23:59', '2021-03-02 23:59', '2021-04-14 23:59', '2021-04-16 23:59', '2021-05-05 23:59', '2021-05-06 23:59']
I'm looking to find those previous & current rows from df_one where df_two date is in-between 2 consecutive rows of df_one
The sort of logic I'm looking to write is something on the lines below
for each row in df_two:
for each row in df_one:
if df_two > df_one_previous_row & df_two < df_one_current_row:
print(df_one_previous_row & df_one_current_row)
Expected Output
2017-08-11 20:00:00 - 2017-08-11 23:59 - 2017-08-15 16:00:00
Found
2017-09-14 00:00:00 - 2017-09-14 23:59 - 2017-09-18 04:00:00
Found
2017-10-10 12:00:00 - 2017-10-10 23:59 - 2017-10-16 16:00:00
Found
2017-10-10 12:00:00 - 2017-10-12 23:59 - 2017-10-16 16:00:00
Found
2017-10-16 16:00:00 - 2017-10-16 23:59 - 2017-10-19 12:00:00
Found
2017-10-23 04:00:00 - 2017-10-25 23:59 - 2017-10-26 00:00:00
Found
2018-04-20 08:00:00 - 2018-04-23 23:59 - 2018-05-11 20:00:00
Found
2018-07-03 04:00:00 - 2018-07-09 23:59 - 2018-07-12 04:00:00
Found
2018-07-31 00:00:00 - 2018-07-31 23:59 - 2018-08-02 00:00:00
Found
2018-08-20 04:00:00 - 2018-08-30 23:59 - 2018-09-03 00:00:00
Found
2018-09-03 00:00:00 - 2018-09-05 23:59 - 2018-09-06 08:00:00
Found
2018-09-27 16:00:00 - 2018-09-28 23:59 - 2018-10-11 08:00:00
Found
2018-11-16 08:00:00 - 2018-11-20 23:59 - 2018-11-23 20:00:00
Found
2019-01-03 00:00:00 - 2019-01-03 23:59 - 2019-01-07 08:00:00
Found
2019-01-15 16:00:00 - 2019-01-16 23:59 - 2019-01-28 04:00:00
Found
2019-01-28 04:00:00 - 2019-01-29 23:59 - 2019-02-05 12:00:00
Found
2019-02-05 12:00:00 - 2019-02-06 23:59 - 2019-02-18 20:00:00
Found
2019-04-18 16:00:00 - 2019-04-18 23:59 - 2019-04-30 16:00:00
Found
2019-05-09 20:00:00 - 2019-05-10 23:59 - 2019-05-15 04:00:00
Found
2019-06-03 08:00:00 - 2019-06-04 23:59 - 2019-06-14 00:00:00
Found
2019-06-03 08:00:00 - 2019-06-05 23:59 - 2019-06-14 00:00:00
Found
2019-07-01 16:00:00 - 2019-07-03 23:59 - 2019-07-11 12:00:00
Found
2019-07-01 16:00:00 - 2019-07-10 23:59 - 2019-07-11 12:00:00
Found
2019-07-16 16:00:00 - 2019-07-16 23:59 - 2019-07-19 04:00:00
Found
2019-08-05 16:00:00 - 2019-08-05 23:59 - 2019-08-14 04:00:00
Found
2019-10-09 16:00:00 - 2019-10-15 23:59 - 2019-10-25 08:00:00
Found
2019-10-25 08:00:00 - 2019-10-29 23:59 - 2019-10-30 08:00:00
Found
2019-12-10 16:00:00 - 2019-12-10 23:59 - 2019-12-19 00:00:00
Found
2019-12-19 20:00:00 - 2019-12-26 23:59 - 2019-12-27 08:00:00
Found
2020-01-08 00:00:00 - 2020-01-08 23:59 - 2020-01-14 12:00:00
Found
2020-01-14 20:00:00 - 2020-01-14 23:59 - 2020-01-15 20:00:00
Found
2020-01-17 16:00:00 - 2020-01-20 23:59 - 2020-01-31 20:00:00
Found
2020-01-31 20:00:00 - 2020-02-03 23:59 - 2020-02-05 04:00:00
Found
2020-03-26 04:00:00 - 2020-03-30 23:59 - 2020-04-01 20:00:00
Found
2020-04-28 16:00:00 - 2020-05-01 23:59 - 2020-05-05 16:00:00
Found
2020-05-19 00:00:00 - 2020-05-19 23:59 - 2020-05-25 00:00:00
Found
2020-09-30 00:00:00 - 2020-10-02 23:59 - 2020-10-14 00:00:00
Found
2020-09-30 00:00:00 - 2020-10-05 23:59 - 2020-10-14 00:00:00
Found
2020-10-14 00:00:00 - 2020-10-14 23:59 - 2020-10-20 00:00:00
Found
2020-11-11 20:00:00 - 2020-11-11 23:59 - 2020-11-13 00:00:00
Found
2021-01-08 08:00:00 - 2021-01-19 23:59 - 2021-01-21 20:00:00
Found
2021-01-08 08:00:00 - 2021-01-20 23:59 - 2021-01-21 20:00:00
Found
2021-01-27 16:00:00 - 2021-02-02 23:59 - 2021-02-09 04:00:00
Found
2021-02-09 04:00:00 - 2021-02-12 23:59 - 2021-02-17 08:00:00
Found
2021-02-19 16:00:00 - 2021-02-19 23:59 - 2021-02-26 20:00:00
Found
2021-02-19 16:00:00 - 2021-02-22 23:59 - 2021-02-26 20:00:00
Found
2021-02-26 20:00:00 - 2021-03-02 23:59 - 2021-03-11 20:00:00
Found
2021-04-06 00:00:00 - 2021-04-14 23:59 - 2021-05-03 00:00:00
Found
2021-04-06 00:00:00 - 2021-04-16 23:59 - 2021-05-03 00:00:00
Found
2021-05-03 00:00:00 - 2021-05-05 23:59 - 2021-05-07 04:00:00
Found
2021-05-03 00:00:00 - 2021-05-06 23:59 - 2021-05-07 04:00:00
Found
Looping with a for or while is not so performance effective from the looks of it. Please could I get help to write a piece of code for this?

We can use np.searchsorted to find the indices in df_one for the corresponding timestamps in df_two which satisfy the condition of inclusion. Note: The timestamps in df_one must be sorted in order for searchsorted to work properly
one = pd.to_datetime(df_one)
two = pd.to_datetime(df_two)
i = np.searchsorted(one, two)
m = ~np.isin(i, [0, len(one)])
df = pd.DataFrame({'df_two': two})
df.loc[m, 'df_one_prev'] = one[i[m] - 1]
df.loc[m, 'df_one_curr'] = one[i[m]]
df['found'] = np.where(m, 'found', 'not found')
df_two df_one_prev df_one_curr found
0 2017-08-11 23:59:00 2017-08-11 20:00:00 2017-08-15 16:00:00 found
1 2017-09-14 23:59:00 2017-09-14 00:00:00 2017-09-18 04:00:00 found
2 2017-10-10 23:59:00 2017-10-10 12:00:00 2017-10-16 16:00:00 found
3 2017-10-12 23:59:00 2017-10-10 12:00:00 2017-10-16 16:00:00 found
4 2017-10-16 23:59:00 2017-10-16 16:00:00 2017-10-19 12:00:00 found
5 2017-10-25 23:59:00 2017-10-23 04:00:00 2017-10-26 00:00:00 found
6 2018-04-23 23:59:00 2018-04-20 08:00:00 2018-05-11 20:00:00 found
7 2018-07-09 23:59:00 2018-07-03 04:00:00 2018-07-12 04:00:00 found
8 2018-07-31 23:59:00 2018-07-31 00:00:00 2018-08-02 00:00:00 found
9 2018-08-30 23:59:00 2018-08-20 04:00:00 2018-09-03 00:00:00 found
10 2018-09-05 23:59:00 2018-09-03 00:00:00 2018-09-06 08:00:00 found
11 2018-09-28 23:59:00 2018-09-27 16:00:00 2018-10-11 08:00:00 found
12 2018-11-20 23:59:00 2018-11-16 08:00:00 2018-11-23 20:00:00 found
13 2019-01-03 23:59:00 2019-01-03 00:00:00 2019-01-07 08:00:00 found
14 2019-01-16 23:59:00 2019-01-15 16:00:00 2019-01-28 04:00:00 found
15 2019-01-29 23:59:00 2019-01-28 04:00:00 2019-02-05 12:00:00 found
16 2019-02-06 23:59:00 2019-02-05 12:00:00 2019-02-18 20:00:00 found
17 2019-04-18 23:59:00 2019-04-18 16:00:00 2019-04-30 16:00:00 found
18 2019-05-10 23:59:00 2019-05-09 20:00:00 2019-05-15 04:00:00 found
19 2019-06-04 23:59:00 2019-06-03 08:00:00 2019-06-14 00:00:00 found
20 2019-06-05 23:59:00 2019-06-03 08:00:00 2019-06-14 00:00:00 found
21 2019-07-03 23:59:00 2019-07-01 16:00:00 2019-07-11 12:00:00 found
22 2019-07-10 23:59:00 2019-07-01 16:00:00 2019-07-11 12:00:00 found
23 2019-07-16 23:59:00 2019-07-16 16:00:00 2019-07-19 04:00:00 found
24 2019-08-05 23:59:00 2019-08-05 16:00:00 2019-08-14 04:00:00 found
25 2019-10-15 23:59:00 2019-10-09 16:00:00 2019-10-25 08:00:00 found
26 2019-10-29 23:59:00 2019-10-25 08:00:00 2019-10-30 08:00:00 found
27 2019-12-10 23:59:00 2019-12-10 16:00:00 2019-12-19 00:00:00 found
28 2019-12-26 23:59:00 2019-12-19 20:00:00 2019-12-27 08:00:00 found
29 2020-01-08 23:59:00 2020-01-08 00:00:00 2020-01-14 12:00:00 found
30 2020-01-14 23:59:00 2020-01-14 20:00:00 2020-01-15 20:00:00 found
31 2020-01-20 23:59:00 2020-01-17 16:00:00 2020-01-31 20:00:00 found
32 2020-02-03 23:59:00 2020-01-31 20:00:00 2020-02-05 04:00:00 found
33 2020-03-30 23:59:00 2020-03-26 04:00:00 2020-04-01 20:00:00 found
34 2020-05-01 23:59:00 2020-04-28 16:00:00 2020-05-05 16:00:00 found
35 2020-05-19 23:59:00 2020-05-19 00:00:00 2020-05-25 00:00:00 found
36 2020-10-02 23:59:00 2020-09-30 00:00:00 2020-10-14 00:00:00 found
37 2020-10-05 23:59:00 2020-09-30 00:00:00 2020-10-14 00:00:00 found
38 2020-10-14 23:59:00 2020-10-14 00:00:00 2020-10-20 00:00:00 found
39 2020-11-11 23:59:00 2020-11-11 20:00:00 2020-11-13 00:00:00 found
40 2021-01-19 23:59:00 2021-01-08 08:00:00 2021-01-21 20:00:00 found
41 2021-01-20 23:59:00 2021-01-08 08:00:00 2021-01-21 20:00:00 found
42 2021-02-02 23:59:00 2021-01-27 16:00:00 2021-02-09 04:00:00 found
43 2021-02-12 23:59:00 2021-02-09 04:00:00 2021-02-17 08:00:00 found
44 2021-02-19 23:59:00 2021-02-19 16:00:00 2021-02-26 20:00:00 found
45 2021-02-22 23:59:00 2021-02-19 16:00:00 2021-02-26 20:00:00 found
46 2021-03-02 23:59:00 2021-02-26 20:00:00 2021-03-11 20:00:00 found
47 2021-04-14 23:59:00 2021-04-06 00:00:00 2021-05-03 00:00:00 found
48 2021-04-16 23:59:00 2021-04-06 00:00:00 2021-05-03 00:00:00 found
49 2021-05-05 23:59:00 2021-05-03 00:00:00 2021-05-07 04:00:00 found
50 2021-05-06 23:59:00 2021-05-03 00:00:00 2021-05-07 04:00:00 found

Sorting a list of time string ranges

I have the following list which I need to sort in ascending order:
tlist = ['10:10 AM - 10:20 AM', '10:20 AM - 10:30 AM', '10:30 AM - 10:40 AM', '10:40 AM - 10:50 AM', '10:50 AM - 11:00 AM', '11:00 AM - 11:10 AM', '11:10 AM - 11:20 AM', '11:20 AM - 11:30 AM', '11:30 AM - 11:40 AM', '11:40 AM - 11:50 AM', '11:50 AM - 12:00 PM', '12:00 PM - 12:10 PM', '12:10 PM - 12:20 PM', '12:20 PM - 12:30 PM', '12:30 PM - 12:40 PM', '12:40 PM - 12:50 PM', '12:50 PM - 1:00 PM', '1:00 PM - 1:10 PM', '1:10 PM - 1:20 PM', '1:20 PM - 1:30 PM', '1:30 PM - 1:40 PM', '1:40 PM - 1:50 PM', '1:50 PM - 2:00 PM', '2:00 PM - 2:10 PM', '2:10 PM - 2:20 PM', '2:20 PM - 2:30 PM', '2:30 PM - 2:40 PM', '2:40 PM - 2:50 PM', '2:50 PM - 3:00 PM', '3:00 PM - 3:10 PM', '3:10 PM - 3:20 PM', '3:20 PM - 3:30 PM', '3:30 PM - 3:40 PM', '3:40 PM - 3:50 PM', '3:50 PM - 4:00 PM', '4:00 PM - 4:10 PM', '4:10 PM - 4:20 PM', '4:20 PM - 4:30 PM', '4:30 PM - 4:40 PM', '4:40 PM - 4:50 PM', '4:50 PM - 5:00 PM', '5:00 PM - 5:10 PM', '5:10 PM - 5:20 PM', '5:20 PM - 5:30 PM', '5:30 PM - 5:40 PM', '5:40 PM - 5:50 PM', '5:50 PM - 6:00 PM', '6:00 PM - 6:10 PM', '6:10 PM - 6:20 PM', '6:20 PM - 6:30 PM', '6:30 PM - 6:40 PM', '6:40 PM - 6:50 PM', '6:50 PM - 7:00 PM', '7:00 PM - 7:10 PM', '7:10 AM - 7:20 AM', '7:10 PM - 7:20 PM', '7:20 AM - 7:30 AM', '7:20 PM - 7:30 PM', '7:30 AM - 7:40 AM', '7:30 PM - 7:40 PM', '7:40 AM - 7:50 AM', '7:40 PM - 7:50 PM', '7:50 AM - 8:00 AM', '7:50 PM - 8:00 PM', '8:00 AM - 8:10 AM', '8:00 PM - 8:10 PM', '8:10 AM - 8:20 AM', '8:10 PM - 8:20 PM', '8:20 AM - 8:30 AM', '8:20 PM - 8:30 PM', '8:30 AM - 8:40 AM', '8:30 PM - 8:40 PM', '8:40 AM - 8:50 AM', '8:40 PM - 8:50 PM', '8:50 AM - 9:00 AM', '8:50 PM - 9:00 PM', '9:00 AM - 9:10 AM', '9:00 PM - 9:10 PM', '9:10 AM - 9:20 AM', '9:10 PM - 9:20 PM', '9:20 AM - 9:30 AM', '9:20 PM - 9:30 PM', '9:30 AM - 9:40 AM', '9:40 AM - 9:50 AM', '9:50 AM - 10:00 AM']
While attempting to do that, I had written an iterator to list each time string as a time object, but failing in conversion.
import time
tlist = ['10:10 AM - 10:20 AM', '10:20 AM - 10:30 AM', '10:30 AM - 10:40 AM', '10:40 AM - 10:50 AM', '10:50 AM - 11:00 AM', '11:00 AM - 11:10 AM', '11:10 AM - 11:20 AM', '11:20 AM - 11:30 AM', '11:30 AM - 11:40 AM', '11:40 AM - 11:50 AM', '11:50 AM - 12:00 PM', '12:00 PM - 12:10 PM', '12:10 PM - 12:20 PM', '12:20 PM - 12:30 PM', '12:30 PM - 12:40 PM', '12:40 PM - 12:50 PM', '12:50 PM - 1:00 PM', '1:00 PM - 1:10 PM', '1:10 PM - 1:20 PM', '1:20 PM - 1:30 PM', '1:30 PM - 1:40 PM', '1:40 PM - 1:50 PM', '1:50 PM - 2:00 PM', '2:00 PM - 2:10 PM', '2:10 PM - 2:20 PM', '2:20 PM - 2:30 PM', '2:30 PM - 2:40 PM', '2:40 PM - 2:50 PM', '2:50 PM - 3:00 PM', '3:00 PM - 3:10 PM', '3:10 PM - 3:20 PM', '3:20 PM - 3:30 PM', '3:30 PM - 3:40 PM', '3:40 PM - 3:50 PM', '3:50 PM - 4:00 PM', '4:00 PM - 4:10 PM', '4:10 PM - 4:20 PM', '4:20 PM - 4:30 PM', '4:30 PM - 4:40 PM', '4:40 PM - 4:50 PM', '4:50 PM - 5:00 PM', '5:00 PM - 5:10 PM', '5:10 PM - 5:20 PM', '5:20 PM - 5:30 PM', '5:30 PM - 5:40 PM', '5:40 PM - 5:50 PM', '5:50 PM - 6:00 PM', '6:00 PM - 6:10 PM', '6:10 PM - 6:20 PM', '6:20 PM - 6:30 PM', '6:30 PM - 6:40 PM', '6:40 PM - 6:50 PM', '6:50 PM - 7:00 PM', '7:00 PM - 7:10 PM', '7:10 AM - 7:20 AM', '7:10 PM - 7:20 PM', '7:20 AM - 7:30 AM', '7:20 PM - 7:30 PM', '7:30 AM - 7:40 AM', '7:30 PM - 7:40 PM', '7:40 AM - 7:50 AM', '7:40 PM - 7:50 PM', '7:50 AM - 8:00 AM', '7:50 PM - 8:00 PM', '8:00 AM - 8:10 AM', '8:00 PM - 8:10 PM', '8:10 AM - 8:20 AM', '8:10 PM - 8:20 PM', '8:20 AM - 8:30 AM', '8:20 PM - 8:30 PM', '8:30 AM - 8:40 AM', '8:30 PM - 8:40 PM', '8:40 AM - 8:50 AM', '8:40 PM - 8:50 PM', '8:50 AM - 9:00 AM', '8:50 PM - 9:00 PM', '9:00 AM - 9:10 AM', '9:00 PM - 9:10 PM', '9:10 AM - 9:20 AM', '9:10 PM - 9:20 PM', '9:20 AM - 9:30 AM', '9:20 PM - 9:30 PM', '9:30 AM - 9:40 AM', '9:40 AM - 9:50 AM', '9:50 AM - 10:00 AM']
for t in tlist:
f = t.split('-')[0]
print(f)
ft = time.strptime(f, "%I:%M %p")
print(f, ft)
I'm getting an error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-19-0a2d8195df22> in <module>()
4 f = t.split('-')[0]
5 print(f)
----> 6 ft = time.strptime(f, "%I:%M %p")
7 print(f, ft)
/usr/lib/python3.6/_strptime.py in _strptime_time(data_string, format)
557 """Return a time struct based on the input string and the
558 format string."""
--> 559 tt = _strptime(data_string, format)[0]
560 return time.struct_time(tt[:time._STRUCT_TM_ITEMS])
561
/usr/lib/python3.6/_strptime.py in _strptime(data_string, format)
363 if len(data_string) != found.end():
364 raise ValueError("unconverted data remains: %s" %
--> 365 data_string[found.end():])
366
367 iso_year = year = None
ValueError: unconverted data remains:
How can I fix this error? Is there an easier technique of sorting these other than the tedious method of looping over the list and transferring to an intermediary list?

By doing
f = t.split('-')[0]
ft = time.strptime(f, "%I:%M %p")
you end up with a space before and after each date string (eg '10:10 AM - 10:20 AM' becomes '10:10 AM ' and ' 10:20 AM').
This is also what the error message is saying:
ValueError: unconverted data remains:
strptime tried to apply the format %I:%M %p to f, but it got a leftover whitespace it did not know what to do with.
The solution is to either
split on ' - ': f = t.split(' - ')[0]
or
use strip (f = t.split('-')[0].strip()) (probably the better solution as it is a bit more generic)
You could also include the whitespace into the format (time.strptime(f, "%I:%M %p ")) but this will be a de-facto fix just waiting to break again in the future.

Change
f = t.split('-')[0]
To
f = t.split('-')[0].strip()
After split('-'), you will get 2 value exp: '10:10 AM ' and ' 10:20 AM'. So, It need to remove space in these value.

Using sorted
Ex:
import time
tlist = ['10:10 AM - 10:20 AM', '10:20 AM - 10:30 AM', '10:30 AM - 10:40 AM', '10:40 AM - 10:50 AM', '10:50 AM - 11:00 AM', '11:00 AM - 11:10 AM', '11:10 AM - 11:20 AM', '11:20 AM - 11:30 AM', '11:30 AM - 11:40 AM', '11:40 AM - 11:50 AM', '11:50 AM - 12:00 PM', '12:00 PM - 12:10 PM', '12:10 PM - 12:20 PM', '12:20 PM - 12:30 PM', '12:30 PM - 12:40 PM', '12:40 PM - 12:50 PM', '12:50 PM - 1:00 PM', '1:00 PM - 1:10 PM', '1:10 PM - 1:20 PM', '1:20 PM - 1:30 PM', '1:30 PM - 1:40 PM', '1:40 PM - 1:50 PM', '1:50 PM - 2:00 PM', '2:00 PM - 2:10 PM', '2:10 PM - 2:20 PM', '2:20 PM - 2:30 PM', '2:30 PM - 2:40 PM', '2:40 PM - 2:50 PM', '2:50 PM - 3:00 PM', '3:00 PM - 3:10 PM', '3:10 PM - 3:20 PM', '3:20 PM - 3:30 PM', '3:30 PM - 3:40 PM', '3:40 PM - 3:50 PM', '3:50 PM - 4:00 PM', '4:00 PM - 4:10 PM', '4:10 PM - 4:20 PM', '4:20 PM - 4:30 PM', '4:30 PM - 4:40 PM', '4:40 PM - 4:50 PM', '4:50 PM - 5:00 PM', '5:00 PM - 5:10 PM', '5:10 PM - 5:20 PM', '5:20 PM - 5:30 PM', '5:30 PM - 5:40 PM', '5:40 PM - 5:50 PM', '5:50 PM - 6:00 PM', '6:00 PM - 6:10 PM', '6:10 PM - 6:20 PM', '6:20 PM - 6:30 PM', '6:30 PM - 6:40 PM', '6:40 PM - 6:50 PM', '6:50 PM - 7:00 PM', '7:00 PM - 7:10 PM', '7:10 AM - 7:20 AM', '7:10 PM - 7:20 PM', '7:20 AM - 7:30 AM', '7:20 PM - 7:30 PM', '7:30 AM - 7:40 AM', '7:30 PM - 7:40 PM', '7:40 AM - 7:50 AM', '7:40 PM - 7:50 PM', '7:50 AM - 8:00 AM', '7:50 PM - 8:00 PM', '8:00 AM - 8:10 AM', '8:00 PM - 8:10 PM', '8:10 AM - 8:20 AM', '8:10 PM - 8:20 PM', '8:20 AM - 8:30 AM', '8:20 PM - 8:30 PM', '8:30 AM - 8:40 AM', '8:30 PM - 8:40 PM', '8:40 AM - 8:50 AM', '8:40 PM - 8:50 PM', '8:50 AM - 9:00 AM', '8:50 PM - 9:00 PM', '9:00 AM - 9:10 AM', '9:00 PM - 9:10 PM', '9:10 AM - 9:20 AM', '9:10 PM - 9:20 PM', '9:20 AM - 9:30 AM', '9:20 PM - 9:30 PM', '9:30 AM - 9:40 AM', '9:40 AM - 9:50 AM', '9:50 AM - 10:00 AM']
print(sorted(tlist, key=lambda x: time.strptime(x.split("-")[0].strip(), "%I:%M %p")))
Output:
['7:10 AM - 7:20 AM', '7:20 AM - 7:30 AM', '7:30 AM - 7:40 AM', '7:40 AM - 7:50 AM', '7:50 AM - 8:00 AM', '8:00 AM - 8:10 AM', '8:10 AM - 8:20 AM', '8:20 AM - 8:30 AM', '8:30 AM - 8:40 AM', '8:40 AM - 8:50 AM', '8:50 AM - 9:00 AM', '9:00 AM - 9:10 AM', '9:10 AM - 9:20 AM', '9:20 AM - 9:30 AM', '9:30 AM - 9:40 AM', '9:40 AM - 9:50 AM', '9:50 AM - 10:00 AM', '10:10 AM - 10:20 AM', '10:20 AM - 10:30 AM', '10:30 AM - 10:40 AM', '10:40 AM - 10:50 AM', '10:50 AM - 11:00 AM', '11:00 AM - 11:10 AM', '11:10 AM - 11:20 AM', '11:20 AM - 11:30 AM', '11:30 AM - 11:40 AM', '11:40 AM - 11:50 AM', '11:50 AM - 12:00 PM', '12:00 PM - 12:10 PM', '12:10 PM - 12:20 PM', '12:20 PM - 12:30 PM', '12:30 PM - 12:40 PM', '12:40 PM - 12:50 PM', '12:50 PM - 1:00 PM', '1:00 PM - 1:10 PM', '1:10 PM - 1:20 PM', '1:20 PM - 1:30 PM', '1:30 PM - 1:40 PM', '1:40 PM - 1:50 PM', '1:50 PM - 2:00 PM', '2:00 PM - 2:10 PM', '2:10 PM - 2:20 PM', '2:20 PM - 2:30 PM', '2:30 PM - 2:40 PM', '2:40 PM - 2:50 PM', '2:50 PM - 3:00 PM', '3:00 PM - 3:10 PM', '3:10 PM - 3:20 PM', '3:20 PM - 3:30 PM', '3:30 PM - 3:40 PM', '3:40 PM - 3:50 PM', '3:50 PM - 4:00 PM', '4:00 PM - 4:10 PM', '4:10 PM - 4:20 PM', '4:20 PM - 4:30 PM', '4:30 PM - 4:40 PM', '4:40 PM - 4:50 PM', '4:50 PM - 5:00 PM', '5:00 PM - 5:10 PM', '5:10 PM - 5:20 PM', '5:20 PM - 5:30 PM', '5:30 PM - 5:40 PM', '5:40 PM - 5:50 PM', '5:50 PM - 6:00 PM', '6:00 PM - 6:10 PM', '6:10 PM - 6:20 PM', '6:20 PM - 6:30 PM', '6:30 PM - 6:40 PM', '6:40 PM - 6:50 PM', '6:50 PM - 7:00 PM', '7:00 PM - 7:10 PM', '7:10 PM - 7:20 PM', '7:20 PM - 7:30 PM', '7:30 PM - 7:40 PM', '7:40 PM - 7:50 PM', '7:50 PM - 8:00 PM', '8:00 PM - 8:10 PM', '8:10 PM - 8:20 PM', '8:20 PM - 8:30 PM', '8:30 PM - 8:40 PM', '8:40 PM - 8:50 PM', '8:50 PM - 9:00 PM', '9:00 PM - 9:10 PM', '9:10 PM - 9:20 PM', '9:20 PM - 9:30 PM']

Why isn't Django rendering my template?

I'm working on a Django application and I'm just trying to push data up to the front-end to display.
In my views.py here's what I have:
def index(request):
...
context = RequestContext(request)
rooms = dict(db.studybug.find_one())
timeRange = [room.encode('utf-8') for room in rooms['timeRange']]
return render_to_response('studybug/index.html', timeRange, context)
Here, timeRange is a list that contains the following:
timeRange = ['Room 203A 10:00 AM \xc2\xa0', 'Room 203A 10:30 AM \xc2\xa0', 'Room 203A 11:00 AM \xc2\xa0', 'Room 203A 11:30 AM \xc2\xa0', 'Room 203A 12:00 PM \xc2\xa0', 'Room 203A 12:30 PM \xc2\xa0', 'Room 203A 3:00 PM \xc2\xa0', 'Room 203A 3:30 PM \xc2\xa0', 'Room 203A 4:00 PM \xc2\xa0', 'Room 203A 4:30 PM \xc2\xa0', 'Room 203A 5:00 PM \xc2\xa0', 'Room 203A 5:30 PM \xc2\xa0', 'Room 203A 6:00 PM \xc2\xa0', 'Room 203A 6:30 PM \xc2\xa0', 'Room 203A 7:00 PM \xc2\xa0', 'Room 203A 7:30 PM \xc2\xa0', 'Room 203A 8:00 PM \xc2\xa0', 'Room 203A 8:30 PM \xc2\xa0', 'Room 203A 9:00 PM \xc2\xa0', 'Room 203A 9:30 PM \xc2\xa0', 'Room 203A 10:00 PM \xc2\xa0', 'Room 203A 10:30 PM \xc2\xa0', 'Room 203A 11:00 PM \xc2\xa0', 'Room 203A 11:30 PM \xc2\xa0']
And then in my template (index.html), I have the following loop:
<div class="row">
...
<ul>
{% for item in timeRange %}
<li>{{ item }}</li>
{% endfor %}
</ul>
</div>
However, despite the list being generated in the backend, nothing is being displayed on the webpage. I know the list exists, but Django's rendering engine won't display it.
Am I missing something obvious here?
Thanks,
G

render_to_response second parameter, should be a dict containing your data, you are passing a list.
your render_to_response should looks like this:
return render_to_response('studybug/index.html', {'timeRange':timeRange}, context)

Getting the closest date to a given date

Given this base date:
base_date = "10/29 06:58 AM"
I want to find a tuple within the list that contains the closest date to the base_date, but it must not be an earlier date.
list_date = [('10/30 02:18 PM', '-103', '-107'), ('10/30 02:17 PM', '+100', '-110'), \
('10/29 02:15 AM', '-101', '-109')
so here the output should be ('10/30 02:17 PM', '+100', '-110') (it can't be the 3rd tuple because the date there happened earlier than the base date)
My question is, does it exist any module for such date comparison? I tried to first change the data all to AM format and then compare but my code gets ugly with lots of slicing.
#edit:
Big list to test:
[('10/30 02:18 PM', '+13 -103', '-13 -107'), ('10/30 02:17 PM', '+13 +100', '-13 -110'), ('10/30 02:15 PM', '+13 -101', '-13 -109'), ('10/30 02:14 PM', '+13 -103', '-13 -107'), ('10/30 01:59 PM', '+13 -105', '-13 -105'), ('10/30 01:46 PM', '+13 -106', '-13 -104'), ('10/30 01:37 PM', '+13 -105', '-13 -105'), ('10/30 01:24 PM', '+13 -107', '-13 -103'), ('10/30 01:23 PM', '+13 -106', '-13 -104'), ('10/30 01:05 PM', '+13 -103', '-13 -107'), ('10/30 01:02 PM', '+13 -104', '-13 -106'), ('10/30 12:55 PM', '+13 -103', '-13 -107'), ('10/30 12:51 PM', '+13.5 -110', '-13.5 +100'), ('10/30 12:44 PM', '+13.5 -108', '-13.5 -102'), ('10/30 12:38 PM', '+13.5 -107', '-13.5 -103'), ('10/30 12:35 PM', '+13 -102', '-13 -108'), ('10/30 12:34 PM', '+13 -103', '-13 -107'), ('10/30 12:06 PM', '+13.5 -110', '-13.5 +100'), ('10/30 11:57 AM', '+13.5 -108', '-13.5 -102'), ('10/30 11:36 AM', '+13.5 -107', '-13.5 -103'), ('10/30 09:01 AM', '+13.5 -110', '-13.5 +100'), ('10/30 08:59 AM', '+13.5 -108', '-13.5 -102'), ('10/30 08:13 AM', '+13.5 -105', '-13.5 -105'), ('10/30 06:11 AM', '+13.5 +100', '-13.5 -110'), ('10/30 06:09 AM', '+13.5 -105', '-13.5 -105'), ('10/30 06:04 AM', '+13.5 -110', '-13.5 +100'), ('10/30 05:32 AM', '+13.5 -105', '-13.5 -105'), ('10/30 04:48 AM', '+13.5 -107', '-13.5 -103'), ('10/30 12:51 AM', '+13.5 -110', '-13.5 +100'), ('10/29 01:31 PM', '+13.5 -105', '-13.5 -105'), ('10/29 01:31 PM', '+13 +103', '-13 -113'), ('10/29 01:28 PM', '+13 -102', '-13 -108'), ('10/29 07:59 AM', '+13 -105', '-13 -105'), ('10/29 07:20 AM', '+13 -103', '-13 -107'), ('10/29 07:14 AM', '+13 -105', '-13 -105'), ('10/29 04:47 AM', '+13 +100', '-13 -110'), ('10/29 04:14 AM', '+13 -105', '-13 -105'), ('10/28 08:17 PM', '+12.5 +100', '-12.5 -110'), ('10/28 12:52 PM', '+12.5 -105', '-12.5 -105')]
Big list to test2:
[('10/30 04:30 PM', '+1.5 -111', '-1.5 +101'), ('10/30 04:24 PM', '+1.5 -110', '-1.5 +100'), ('10/30 04:21 PM', '+1.5 -111', '-1.5 +101'), ('10/30 04:15 PM', '+1.5 -112', '-1.5 +102'), ('10/30 04:14 PM', '+1.5 -110', '-1.5 +100'), ('10/30 03:57 PM', '+1.5 -111', '-1.5 +101'), ('10/30 03:40 PM', '+1.5 -110', '-1.5 +100'), ('10/30 03:31 PM', '+1.5 -111', '-1.5 +101'), ('10/30 03:30 PM', '+1.5 -109', '-1.5 -101'), ('10/30 03:25 PM', '+1.5 -107', '-1.5 -103'), ('10/30 03:24 PM', '+1.5 -110', '-1.5 +100'), ('10/30 03:23 PM', '+1.5 -108', '-1.5 -102'), ('10/30 03:22 PM', '+1.5 -106', '-1.5 -104'), ('10/30 02:14 PM', '+1.5 -104', '-1.5 -106'), ('10/30 01:41 PM', '+1.5 -105', '-1.5 -105'), ('10/30 01:37 PM', '+1.5 -107', '-1.5 -103'), ('10/30 01:36 PM', '+1.5 -105', '-1.5 -105'), ('10/30 01:06 PM', '+1.5 -103', '-1.5 -107'), ('10/30 12:56 PM', '+2 -111', '-2 +101'), ('10/30 12:53 PM', '+2 -110', '-2 +100'), ('10/30 12:50 PM', '+2 -113', '-2 +103'), ('10/30 12:49 PM', '+2 -112', '-2 +102'), ('10/30 12:46 PM', '+2 -113', '-2 +103'), ('10/30 12:45 PM', '+2 -110', '-2 +100'), ('10/30 12:43 PM', '+2 -108', '-2 -102'), ('10/30 12:38 PM', '+2.5 -116', '-2.5 +106'), ('10/30 12:38 PM', '+2.5 -113', '-2.5 +103'), ('10/30 12:37 PM', '+2.5 -110', '-2.5 +100'), ('10/30 10:30 AM', '+2.5 -105', '-2.5 -105'), ('10/30 10:07 AM', '+3 -113', '-3 +103'), ('10/30 09:55 AM', '+3 -112', '-3 +102'), ('10/30 09:51 AM', '+3 -110', '-3 +100'), ('10/30 09:32 AM', '+3 -109', '-3 -101'), ('10/30 06:04 AM', '+3 -110', '-3 +100'), ('10/30 03:16 AM', '+3 -107', '-3 -103'), ('10/30 03:14 AM', '+3.5 -116', '-3.5 +106'), ('10/30 01:03 AM', '+3.5 -115', '-3.5 +105'), ('10/30 12:17 AM', '+3.5 -110', '-3.5 +100'), ('10/29 08:52 PM', '+3.5 -108', '-3.5 -102'), ('10/29 01:31 PM', '+3.5 -105', '-3.5 -105'), ('10/29 06:48 AM', '+3.5 -110', '-3.5 +100'), ('10/29 06:47 AM', '+3.5 -109', '-3.5 -101'), ('10/29 05:39 AM', '+3.5 -113', '-3.5 +103'), ('10/29 03:34 AM', '+3.5 -108', '-3.5 -102'), ('10/29 12:44 AM', '+3.5 -110', '-3.5 +100'), ('10/29 12:41 AM', '+3.5 -107', '-3.5 -103'), ('10/29 12:40 AM', '+3.5 -105', '-3.5 -105'), ('10/28 12:52 PM', '+4 -105', '-4 -105')]

This can be done using datetime module, which is able to parse date string into datetime object, which supports comparison and arithmetic with dates:
from datetime import datetime
# function for parsing strings using specific format
get_datetime = lambda s: datetime.strptime(s, "%m/%d %I:%M %p")
base = get_datetime(base_date)
later = filter(lambda d: get_datetime(d[0]) > base, list_date)
closest_date = min(later, key = lambda d: get_datetime(d[0]))

>>> from datetime import timedelta, datetime
>>> base_date = "10/29 06:58 AM"
>>> b_d = datetime.strptime(base_date, "%m/%d %I:%M %p")
def func(x):
d = datetime.strptime(x[0], "%m/%d %I:%M %p")
delta = d - b_d if d > b_d else timedelta.max
return delta
...
>>> min(list_date, key = func)
('10/30 02:17 PM', '+100', '-110')
datetime.strptime converts the date to a datetime object, so b_d now looks something like this :
>>> b_d
datetime.datetime(1900, 10, 29, 6, 58)
Now we can write a function that can be passed to key parameter of min:
delta = d - b_d if d > b_d else timedelta.max
if d > b_d i.e if the date passed to min is greater than base_date then assign their difference to delta else assign timedelta.max to it.
>>> timedelta.max
datetime.timedelta(999999999, 86399, 999999)
Update:
>>> from datetime import timedelta, datetime
>>> base_date = '10/29 06:59 AM'
>>> b_d = datetime.strptime(base_date, "%m/%d %I:%M %p")
>>> def func(x):
... d = datetime.strptime(x[0], "%m/%d %I:%M %p")
... delta = d - b_d if d > b_d else timedelta.max
... return delta
...
>>> lis2 = [('10/30 04:30 PM', '+1.5 -111', '-1.5 +101'), ('10/30 04:24 PM', '+1.5 -110', '-1.5 +100'), ('10/30 04:21 PM', '+1.5 -111', '-1.5 +101'), ('10/30 04:15 PM', '+1.5 -112', '-1.5 +102'), ('10/30 04:14 PM', '+1.5 -110', '-1.5 +100'), ('10/30 03:57 PM', '+1.5 -111', '-1.5 +101'), ('10/30 03:40 PM', '+1.5 -110', '-1.5 +100'), ('10/30 03:31 PM', '+1.5 -111', '-1.5 +101'), ('10/30 03:30 PM', '+1.5 -109', '-1.5 -101'), ('10/30 03:25 PM', '+1.5 -107', '-1.5 -103'), ('10/30 03:24 PM', '+1.5 -110', '-1.5 +100'), ('10/30 03:23 PM', '+1.5 -108', '-1.5 -102'), ('10/30 03:22 PM', '+1.5 -106', '-1.5 -104'), ('10/30 02:14 PM', '+1.5 -104', '-1.5 -106'), ('10/30 01:41 PM', '+1.5 -105', '-1.5 -105'), ('10/30 01:37 PM', '+1.5 -107', '-1.5 -103'), ('10/30 01:36 PM', '+1.5 -105', '-1.5 -105'), ('10/30 01:06 PM', '+1.5 -103', '-1.5 -107'), ('10/30 12:56 PM', '+2 -111', '-2 +101'), ('10/30 12:53 PM', '+2 -110', '-2 +100'), ('10/30 12:50 PM', '+2 -113', '-2 +103'), ('10/30 12:49 PM', '+2 -112', '-2 +102'), ('10/30 12:46 PM', '+2 -113', '-2 +103'), ('10/30 12:45 PM', '+2 -110', '-2 +100'), ('10/30 12:43 PM', '+2 -108', '-2 -102'), ('10/30 12:38 PM', '+2.5 -116', '-2.5 +106'), ('10/30 12:38 PM', '+2.5 -113', '-2.5 +103'), ('10/30 12:37 PM', '+2.5 -110', '-2.5 +100'), ('10/30 10:30 AM', '+2.5 -105', '-2.5 -105'), ('10/30 10:07 AM', '+3 -113', '-3 +103'), ('10/30 09:55 AM', '+3 -112', '-3 +102'), ('10/30 09:51 AM', '+3 -110', '-3 +100'), ('10/30 09:32 AM', '+3 -109', '-3 -101'), ('10/30 06:04 AM', '+3 -110', '-3 +100'), ('10/30 03:16 AM', '+3 -107', '-3 -103'), ('10/30 03:14 AM', '+3.5 -116', '-3.5 +106'), ('10/30 01:03 AM', '+3.5 -115', '-3.5 +105'), ('10/30 12:17 AM', '+3.5 -110', '-3.5 +100'), ('10/29 08:52 PM', '+3.5 -108', '-3.5 -102'), ('10/29 01:31 PM', '+3.5 -105', '-3.5 -105'), ('10/29 06:48 AM', '+3.5 -110', '-3.5 +100'), ('10/29 06:47 AM', '+3.5 -109', '-3.5 -101'), ('10/29 05:39 AM', '+3.5 -113', '-3.5 +103'), ('10/29 03:34 AM', '+3.5 -108', '-3.5 -102'), ('10/29 12:44 AM', '+3.5 -110', '-3.5 +100'), ('10/29 12:41 AM', '+3.5 -107', '-3.5 -103'), ('10/29 12:40 AM', '+3.5 -105', '-3.5 -105'), ('10/28 12:52 PM', '+4 -105', '-4 -105')]
>>> min(lis2, key = func)
('10/29 01:31 PM', '+3.5 -105', '-3.5 -105')
Timing comparisons:
Script:
from datetime import datetime, timedelta
import sys
import time
list_date = [('10/30 04:30 PM', '+1.5 -111', '-1.5 +101'), ('10/30 04:24 PM', '+1.5 -110', '-1.5 +100'), ('10/30 04:21 PM', '+1.5 -111', '-1.5 +101'), ('10/30 04:15 PM', '+1.5 -112', '-1.5 +102'), ('10/30 04:14 PM', '+1.5 -110', '-1.5 +100'), ('10/30 03:57 PM', '+1.5 -111', '-1.5 +101'), ('10/30 03:40 PM', '+1.5 -110', '-1.5 +100'), ('10/30 03:31 PM', '+1.5 -111', '-1.5 +101'), ('10/30 03:30 PM', '+1.5 -109', '-1.5 -101'), ('10/30 03:25 PM', '+1.5 -107', '-1.5 -103'), ('10/30 03:24 PM', '+1.5 -110', '-1.5 +100'), ('10/30 03:23 PM', '+1.5 -108', '-1.5 -102'), ('10/30 03:22 PM', '+1.5 -106', '-1.5 -104'), ('10/30 02:14 PM', '+1.5 -104', '-1.5 -106'), ('10/30 01:41 PM', '+1.5 -105', '-1.5 -105'), ('10/30 01:37 PM', '+1.5 -107', '-1.5 -103'), ('10/30 01:36 PM', '+1.5 -105', '-1.5 -105'), ('10/30 01:06 PM', '+1.5 -103', '-1.5 -107'), ('10/30 12:56 PM', '+2 -111', '-2 +101'), ('10/30 12:53 PM', '+2 -110', '-2 +100'), ('10/30 12:50 PM', '+2 -113', '-2 +103'), ('10/30 12:49 PM', '+2 -112', '-2 +102'), ('10/30 12:46 PM', '+2 -113', '-2 +103'), ('10/30 12:45 PM', '+2 -110', '-2 +100'), ('10/30 12:43 PM', '+2 -108', '-2 -102'), ('10/30 12:38 PM', '+2.5 -116', '-2.5 +106'), ('10/30 12:38 PM', '+2.5 -113', '-2.5 +103'), ('10/30 12:37 PM', '+2.5 -110', '-2.5 +100'), ('10/30 10:30 AM', '+2.5 -105', '-2.5 -105'), ('10/30 10:07 AM', '+3 -113', '-3 +103'), ('10/30 09:55 AM', '+3 -112', '-3 +102'), ('10/30 09:51 AM', '+3 -110', '-3 +100'), ('10/30 09:32 AM', '+3 -109', '-3 -101'), ('10/30 06:04 AM', '+3 -110', '-3 +100'), ('10/30 03:16 AM', '+3 -107', '-3 -103'), ('10/30 03:14 AM', '+3.5 -116', '-3.5 +106'), ('10/30 01:03 AM', '+3.5 -115', '-3.5 +105'), ('10/30 12:17 AM', '+3.5 -110', '-3.5 +100'), ('10/29 08:52 PM', '+3.5 -108', '-3.5 -102'), ('10/29 01:31 PM', '+3.5 -105', '-3.5 -105'), ('10/29 06:48 AM', '+3.5 -110', '-3.5 +100'), ('10/29 06:47 AM', '+3.5 -109', '-3.5 -101'), ('10/29 05:39 AM', '+3.5 -113', '-3.5 +103'), ('10/29 03:34 AM', '+3.5 -108', '-3.5 -102'), ('10/29 12:44 AM', '+3.5 -110', '-3.5 +100'), ('10/29 12:41 AM', '+3.5 -107', '-3.5 -103'), ('10/29 12:40 AM', '+3.5 -105', '-3.5 -105'), ('10/28 12:52 PM', '+4 -105', '-4 -105')]
base_date = "10/29 06:58 AM"
def func1(list_date):
#http://stackoverflow.com/a/17249420/846892
get_datetime = lambda s: datetime.strptime(s, "%m/%d %I:%M %p")
base = get_datetime(base_date)
later = filter(lambda d: get_datetime(d[0]) > base, list_date)
return min(later, key = lambda d: get_datetime(d[0]))
def func2(list_date):
#http://stackoverflow.com/a/17249470/846892
b_d = datetime.strptime(base_date, "%m/%d %I:%M %p")
def func(x):
d = datetime.strptime(x[0], "%m/%d %I:%M %p")
delta = d - b_d if d > b_d else timedelta.max
return delta
return min(list_date, key = func)
def func3(list_date):
#http://stackoverflow.com/a/17249529/846892
fmt = '%m/%d %I:%M %p'
d = datetime.strptime(base_date, fmt)
def foo(x):
return (datetime.strptime(x[0],fmt)-d).total_seconds() > 0
return sorted(list_date, key=foo)[-1]
def func4(list_date):
#http://stackoverflow.com/a/17249441/846892
fmt = '%m/%d %I:%M %p'
base_d = datetime.strptime(base_date, fmt)
candidates = ((datetime.strptime(d, fmt), d, x, y) for d, x, y in list_date)
candidates = min((dt, d, x, y) for dt, d, x, y in candidates if dt > base_d)
return candidates[1:]
Results:
>>> from so import *
#check output irst
>>> func1(list_date)
('10/29 01:31 PM', '+3.5 -105', '-3.5 -105')
>>> func2(list_date)
('10/29 01:31 PM', '+3.5 -105', '-3.5 -105')
>>> func3(list_date)
('10/29 01:31 PM', '+3.5 -105', '-3.5 -105')
>>> func4(list_date)
('10/29 01:31 PM', '+3.5 -105', '-3.5 -105')
>>> %timeit func1(list_date)
100 loops, best of 3: 3.07 ms per loop
>>> %timeit func2(list_date)
100 loops, best of 3: 1.59 ms per loop #winner
>>> %timeit func3(list_date)
100 loops, best of 3: 1.91 ms per loop
>>> %timeit func4(list_date)
1000 loops, best of 3: 2.02 ms per loop
#increase the input size
>>> list_date = list_date *10**3
>>> len(list_date)
48000
>>> %timeit func1(list_date)
1 loops, best of 3: 3.6 s per loop
>>> %timeit func2(list_date) #winner
1 loops, best of 3: 1.99 s per loop
>>> %timeit func3(list_date)
1 loops, best of 3: 2.09 s per loop
>>> %timeit func4(list_date)
1 loops, best of 3: 2.02 s per loop
#increase the input size again
>>> list_date = list_date *10
>>> len(list_date)
480000
>>> %timeit func1(list_date)
1 loops, best of 3: 36.4 s per loop
>>> %timeit func2(list_date) #winner
1 loops, best of 3: 20.2 s per loop
>>> %timeit func3(list_date)
1 loops, best of 3: 22.8 s per loop
>>> %timeit func4(list_date)
1 loops, best of 3: 22.7 s per loop

decorate, filter, find the closest date, undecorate
>>> base_date = "10/29 06:58 AM"
>>> list_date = [
... ('10/30 02:18 PM', '-103', '-107'),
... ('10/30 02:17 PM', '+100', '-110'),
... ('10/29 02:15 AM', '-101', '-109')
... ]
>>> import datetime
>>> fmt = '%m/%d %H:%M %p'
>>> base_d = datetime.datetime.strptime(base_date, fmt)
>>> candidates = ((datetime.datetime.strptime(d, fmt), d, x, y) for d, x, y in list_date)
>>> candidates = min((dt, d, x, y) for dt, d, x, y in candidates if dt > base_d)
>>> print candidates[1:]
('10/30 02:17 PM', '+100', '-110')

You can consider putting the dates list into a Pandas index and then use 'truncate' or 'get_loc' function.
import pandas as pd
##Initial inputs
list_date = [('10/30 02:18 PM', '-103', '-107'),('10/29 02:15 AM', '-101', '-109') , ('10/30 02:17 PM', '+100', '-110'), \
] # reordered to show the method is input order insensitive
base_date = "10/29 06:58 AM"
##Make a data frame with data
df=pd.DataFrame(list_date)
df.columns=['date','val1','val2']
dateIndex=pd.to_datetime(df['date'], format='%m/%d %I:%M %p')
df=df.set_index(dateIndex)
df=df.sort_index(ascending=False) #earliest comes on top
##Find the result
base_dateObj=pd.to_datetime(base_date, format='%m/%d %I:%M %p')
result=df.truncate(after=base_dateObj).iloc[-1] #take the bottom value, or the 1st after the base date
(result['date'],result['val1'], result['val2']) # result is ('10/30 02:17 PM', '+100', '-110')
Reference: this link

Linear search?
import sys
import time
base_date = "10/29 06:58 AM"
def str_to_my_time(my_str):
return time.mktime(time.strptime(my_str, "%m/%d %I:%M %p"))
# assume year 1900...
base_dt = str_to_my_time(base_date)
list_date = [('10/30 02:18 PM', '-103', '-107'),
('10/30 02:17 PM', '+100', '-110'),
('10/29 02:15 AM', '-101', '-109')]
best_delta = sys.maxint
best_match = None
for t in list_date:
the_dt = str_to_my_time(t[0])
delta_sec = the_dt - base_dt
if (delta_sec >= 0) and (delta_sec < best_delta):
best_delta = delta_sec
best_match = t
print best_match, best_delta
Producing:
('10/30 02:17 PM', '+100', '-110') 112740.0

import time
import sys
#The Function
def to_sec(date_string):
return time.mktime(time.strptime(date_string, '%m/%d %I:%M %p'))
#The Test
base_date = "10/29 06:58 AM"
base_date_sec = to_sec(base_date)
result = None
difference = sys.maxint
list_date = [
('10/30 02:18 PM', '-103', '-107'),
('10/30 02:17 PM', '+100', '-110'),
('10/29 02:15 AM', '-101', '-109') ]
for date_str in list_date:
diff_sec = to_sec(date_str[0])-base_date_sec
if diff_sec >= 0 and diff_sec < difference:
result = date_str
difference = diff_sec
print result

import datetime
fmt = '%m/%d %H:%M %p'
d = datetime.datetime.strptime(base_date, fmt)
def foo(x):
return (datetime.datetime.strptime(x[0],fmt)-d).total_seconds() > 0
sorted(list_date, key=foo)[-1]

I was looking up this problem and found some answers, most of which check all elements.
I have my dates sorted (and assume most people do), so if you do as well, use numpy:
import numpy as np
// dates is a numpy array of np.datetime64 objects
dates = np.array([date1, date2, date3, ...], dtype=np.datetime64)
timestamp = np.datetime64('Your date')
np.searchsorted(dates, timestamp)
searchsorted uses binary search, which uses the fact the dates are sorted, and is thus very efficient.
If you use pandas, this is possible:
dates = df.index # df is a DatetimeIndex-ed dataframe
timestamp = pd.to_datetime('your date here', format='its format')
np.searchsorted(dates, timestamp)
The function returns the index of the closest date (if the searched date is included in dates, its index is returned [if that isn't wanted, use side='right' as an argument into the function]), so to get the date do this:
dates[np.searchsorted(dates, timestamp)]