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.replace() function is used to replace the member values of the given Timestamp. The function implements datetime.replace, and it also handles nanoseconds.
Syntax :Timestamp.replace()
Parameters :
year : int
month : int
day : int
hour : int
minute : int
second : int
microsecond : int
nanosecond : int
tzinfo : int
fold : intReturn : Timestamp with fields replaced
Example #1: Use Timestamp.replace() function to replace the year value in the given Timestamp.
# importing pandas as pdimport pandas as pd # Create the Timestamp objectts = pd.Timestamp(year = 2011, month = 11, day = 21, hour = 10, second = 49, tz = 'US/Central') # Print the Timestamp objectprint(ts) |
Output :
Now we will use the Timestamp.replace() function to replace the current year in the object with 2019.
# replace yearts.replace(year = 2019) |
Output :
As we can see in the output, the Timestamp.replace() function has returned a Timestamp object with year value equal to 2019.
Example #2: Use Timestamp.replace() function to replace the year, month and hour value in the given Timestamp.
# importing pandas as pdimport pandas as pd # Create the Timestamp objectts = pd.Timestamp(year = 2009, month = 5, day = 31, hour = 4, second = 49, tz = 'Europe/Berlin') # Print the Timestamp objectprint(ts) |
Output :
Now we will use the Timestamp.replace() function to replace the current year, month and hour value in the object.
# replace year, month and hour valuets.replace(year = 2019, month = 12, hour = 1) |
Output :
As we can see in the output, the Timestamp.replace() function has returned a Timestamp object with year value equal to 2019, month value equal to 12 and hour value equal to 1.
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