Series.dt can be used to access the values of the series as datetimelike and return several properties. Pandas Series.dt.ceil() function perform ceil operation on the data to the specified freq.
Syntax: Series.dt.ceil(*args, **kwargs)
Parameter :
freq : The frequency level to ceil the index toReturns : DatetimeIndex, TimedeltaIndex, or Series
Example #1: Use Series.dt.ceil() function to ceil the datetime data of the given series object to the specified frequency.
# importing pandas as pd import pandas as pd # Creating the Series 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']) # Creating the index idx = ['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'] # set the index sr.index = idx # Convert the underlying data to datetime sr = pd.to_datetime(sr) # Print the series print(sr) |
Output :
Now we will use Series.dt.ceil() function to ceil the datetime values in the given series object to Daily frequency.
# ceil to daily frequency result = sr.dt.ceil(freq = 'D') # print the result print(result) |
Output :
As we can see in the output, the Series.dt.ceil() function has successfully ceiled the datetime values in the given series object to the specified frequency.
Example #2 : Use Series.dt.ceil() function to ceil the datetime data of the given series object to the specified frequency.
# 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 :
Now we will use Series.dt.ceil() function to ceil the datetime values in the given series object to Hourly frequency.
# ceil to hourly frequency result = sr.dt.ceil(freq = 'H') # print the result print(result) |
Output :
As we can see in the output, the Series.dt.ceil() function has successfully ceiled the datetime values in the given series object to the specified frequency.