Python | Pandas Timestamp.to_datetime64
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.to_datetime64()
function return a numpy.datetime64 object with ‘ns’ precision for the given Timestamp object.
Syntax :Timestamp.to_datetime64()
Parameters : None
Return : numpy.datetime64 object
Example #1: Use Timestamp.to_datetime64()
function to return a numpy.datetime64 object for the given Timestamp object.
# 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.to_datetime64()
function to return a numpy.datetime64 object for the given Timestamp.
# return numpy.datetime64 object ts.to_datetime64() |
Output :
As we can see in the output, the Timestamp.to_datetime64()
function has returned a numpy.datetime64 object for the given Timestamp object with ‘ns’ precision.
Example #2: Use Timestamp.to_datetime64()
function to return a numpy.datetime64 object for the given Timestamp object.
# 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.to_datetime64()
function to return a numpy.datetime64 object for the given Timestamp.
# return numpy.datetime64 object ts.to_datetime64() |
Output :
As we can see in the output, the Timestamp.to_datetime64()
function has returned a numpy.datetime64 object for the given Timestamp object with ‘ns’ precision.
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