Python Pandas - How to Round the DateTimeIndex with seconds frequency

To round the DateTimeIndex with seconds frequency, use the DateTimeIndex.round() method. For seconds frequency, use the freq parameter with value 'S'.

Creating a DateTimeIndex

First, let's create a DateTimeIndex with nanosecond precision ?

import pandas as pd

# Create DatetimeIndex with period 5 and frequency as 28 seconds
# timezone is Australia/Adelaide
datetimeindex = pd.date_range('2021-09-29 07:20:32.261811624', periods=5,
                             tz='Australia/Adelaide', freq='28s')

print("Original DateTimeIndex...")
print(datetimeindex)
print("\nDateTimeIndex frequency:", datetimeindex.freq)

Original DateTimeIndex...
DatetimeIndex(['2021-09-29 07:20:32.261811624+09:30',
               '2021-09-29 07:21:00.261811624+09:30',
               '2021-09-29 07:21:28.261811624+09:30',
               '2021-09-29 07:21:56.261811624+09:30',
               '2021-09-29 07:22:24.261811624+09:30'],
              dtype='datetime64[ns, Australia/Adelaide]', freq='28S')

DateTimeIndex frequency: <28 * Seconds>

Rounding to Seconds Frequency

Use the round() method with freq='S' to round timestamps to the nearest second ?

import pandas as pd

datetimeindex = pd.date_range('2021-09-29 07:20:32.261811624', periods=5,
                             tz='Australia/Adelaide', freq='28s')

# Round operation on DateTimeIndex with seconds frequency
rounded_index = datetimeindex.round(freq='S')

print("After rounding to seconds frequency...")
print(rounded_index)

After rounding to seconds frequency...
DatetimeIndex(['2021-09-29 07:20:32+09:30', '2021-09-29 07:21:00+09:30',
               '2021-09-29 07:21:28+09:30', '2021-09-29 07:21:56+09:30',
               '2021-09-29 07:22:24+09:30'],
              dtype='datetime64[ns, Australia/Adelaide]', freq=None)

Extracting Seconds Component

You can extract the seconds component from the DateTimeIndex to see the values before rounding ?

import pandas as pd

datetimeindex = pd.date_range('2021-09-29 07:20:32.261811624', periods=5,
                             tz='Australia/Adelaide', freq='28s')

# Extract the second component
seconds = datetimeindex.second
print("Seconds from DateTimeIndex:", seconds)

# Round and compare
rounded_index = datetimeindex.round(freq='S')
print("\nOriginal timestamps:")
for ts in datetimeindex:
    print(ts)
    
print("\nRounded timestamps:")
for ts in rounded_index:
    print(ts)

Seconds from DateTimeIndex: Int64Index([32, 0, 28, 56, 24], dtype='int64')

Original timestamps:
2021-09-29 07:20:32.261811624+09:30
2021-09-29 07:21:00.261811624+09:30
2021-09-29 07:21:28.261811624+09:30
2021-09-29 07:21:56.261811624+09:30
2021-09-29 07:22:24.261811624+09:30

Rounded timestamps:
2021-09-29 07:20:32+09:30
2021-09-29 07:21:00+09:30
2021-09-29 07:21:28+09:30
2021-09-29 07:21:56+09:30
2021-09-29 07:22:24+09:30

Other Rounding Frequencies

You can round to different time frequencies using various freq parameter values ?

import pandas as pd

datetimeindex = pd.date_range('2021-09-29 07:20:32.261811624', periods=3,
                             tz='Australia/Adelaide', freq='28s')

print("Original:", datetimeindex[0])
print("Round to second:", datetimeindex.round('S')[0])
print("Round to minute:", datetimeindex.round('T')[0])
print("Round to hour:", datetimeindex.round('H')[0])

Original: 2021-09-29 07:20:32.261811624+09:30
Round to second: 2021-09-29 07:20:32+09:30
Round to minute: 2021-09-29 07:21:00+09:30
Round to hour: 2021-09-29 07:00:00+09:30

Conclusion

Use DateTimeIndex.round(freq='S') to round timestamps to the nearest second, removing sub-second precision. The method preserves timezone information while eliminating nanosecond and microsecond components.