Pandas Series dt.floor() | Round DateTime Values to Nearest Frequency

Last Updated : 11 Jul, 2025

The dt.floor() method performs floor operation on the data to the specified frequency.

This is useful when we want to round down the DateTime data to a specific frequency level, such as hourly (‘H’), daily (‘D’), monthly (‘M’), etc.

Example

Python3 
import pandas as pd
sr = pd.Series(['2012-12-31 08:45', '2019-1-1 12:30', '2008-02-2 10:30',
               '2010-1-1 09:25', '2019-12-31 00:00'])

idx = ['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5']
sr.index = idx
sr = pd.to_datetime(sr)
result = sr.dt.floor(freq = 'D')
print(result)

Output:

dt.floor method output

Syntax

Syntax: Series.dt.floor(floor) 

Parameter 

  • freq : The frequency level to floor the index to

Returns: DatetimeIndex, TimedeltaIndex, or Series

How to Round Down DateTime Objects to a Specified Frequency

To round down DateTime objects in Pandas Series to a specified frequency we use the Series.dt.floor method of the Pandas library in Python.

Let us understand it better with an example:

Example:

Use the Series dt.floor() function to floor the DateTime data of the given Series object to the specified frequency.

Python3 
# importing pandas as pd
import pandas as pd

# Creating the Series
sr = pd.Series(pd.date_range('2012-12-31 09:45', periods = 5, freq = 'T',
                            tz = 'Asia / Calcutta'))

# Creating the index
idx = ['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5']

# set the index
sr.index = idx

# Print the series
print(sr)

Output

datetime series created

Python3 
# floor to hourly frequency
result = sr.dt.floor(freq = 'H')

# print the result
print(result)

Output :

datetime data after dt.floor method

As we can see in the output, the Series.dt.floor() function has successfully floored the DateTime values in the given series object to the specified frequency.

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