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 resolutionReturn : 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.
