Python Pandas - How to perform floor operation on the DateTimeIndex with hourly frequency

To perform floor operation on the DateTimeIndex with hourly frequency, use the DateTimeIndex.floor() method. For hourly frequency, use the freq parameter with value ‘H’.

At first, import the required libraries −

import pandas as pd

Create a DatetimeIndex with period 5 and frequency as min i.e. minutes −

datetimeindex = pd.date_range('2021-09-29 07:20:32.261811624', periods=5,
tz='Australia/Adelaide', freq='20min')

Display DateTimeInde −

print("DateTimeIndex...\n", datetimeindex)

Floor operation on DateTimeIndex date with hourly frequency, For hourly frequency, we have used 'H' −

print("\nPerforming floor operation with hourly frequency...\n",
datetimeindex.floor(freq='H'))

Example

Following is the code −

import pandas as pd

# DatetimeIndex with period 5 and frequency as min i.e. minutes
# timezone is Australia/Adelaide
datetimeindex = pd.date_range('2021-09-29 07:20:32.261811624', periods=5,
tz='Australia/Adelaide', freq='20min')

# display DateTimeIndex
print("DateTimeIndex...\n", datetimeindex)

# display DateTimeIndex frequency
print("DateTimeIndex frequency...\n", datetimeindex.freq)

# Floor operation on DateTimeIndex date with hourly frequency
# For hourly frequency, we have used 'H'
print("\nPerforming floor operation with hourly frequency...\n",
datetimeindex.floor(freq='H'))

Output

This will produce the following code −

DateTimeIndex...
DatetimeIndex(['2021-09-29 07:20:32.261811624+09:30',
'2021-09-29 07:40:32.261811624+09:30',
'2021-09-29 08:00:32.261811624+09:30',
'2021-09-29 08:20:32.261811624+09:30',
'2021-09-29 08:40:32.261811624+09:30'],
dtype='datetime64[ns, Australia/Adelaide]', freq='20T')
DateTimeIndex frequency...
<20 * Minutes>

Performing floor operation with hourly frequency...
DatetimeIndex(['2021-09-29 07:00:00+09:30', '2021-09-29 07:00:00+09:30',
'2021-09-29 08:00:00+09:30', '2021-09-29 08:00:00+09:30',
'2021-09-29 08:00:00+09:30'],
dtype='datetime64[ns, Australia/Adelaide]', freq=None)