Python | Pandas Timestamp.ceil
Last Updated :
08 Jan, 2019
Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas Timestamp.ceil() function return a new Timestamp ceiled to this resolution. The function takes the desired time series frequency as an input.
Syntax : Timestamp.ceil()
Parameters :
freq : a freq string indicating the ceiling resolution
Return : Timestamp
Example #1: Use Timestamp.ceil() function to ceil the given Timestamp object to Daily time series frequency.
import pandas as pd
ts = pd.Timestamp(year = 2011, month = 11, day = 21,
hour = 10, second = 49, tz = 'US/Central')
print(ts)
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Output :
Now we will use the Timestamp.ceil() function to ceil the ts object to Daily frequency.
Output :
As we can see in the output, the Timestamp.ceil() function has ceiled the time series frequency of the given Timestamp object to the input frequency.
Example #2: Use Timestamp.ceil() function to ceil the given Timestamp object to minutely time series frequency.
import pandas as pd
ts = pd.Timestamp(year = 2009, month = 5, day = 31,
hour = 4, second = 49, tz = 'Europe/Berlin')
print(ts)
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Output :
Now we will use the Timestamp.ceil() function to ceil the ts object to minutely frequency.
Output :
As we can see in the output, the Timestamp.ceil() function has ceiled the time series frequency of the given Timestamp object to the input frequency.
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