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Python | Pandas Timestamp.ceil

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  • 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.




# importing pandas as pd
import pandas as pd
  
# Create the Timestamp object
ts = pd.Timestamp(year = 2011,  month = 11, day = 21,
           hour = 10, second = 49, tz = 'US/Central')
  
# Print the Timestamp object
print(ts)

Output :

Now we will use the Timestamp.ceil() function to ceil the ts object to Daily frequency.




# ceil the given object to daily frequency
ts.ceil(freq ='D')

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.




# importing pandas as pd
import pandas as pd
  
# Create the Timestamp object
ts = pd.Timestamp(year = 2009,  month = 5, day = 31,
        hour = 4, second = 49, tz = 'Europe/Berlin')
  
# Print the Timestamp object
print(ts)

Output :

Now we will use the Timestamp.ceil() function to ceil the ts object to minutely frequency.




# ceil the given object to minutely frequency
ts.ceil(freq ='T')

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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