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 TimedeltaIndex.to_native_types() function format the specified values of self (TimedeltaIndex object) and return the object in its native format.
Syntax : TimedeltaIndex.to_native_types(slicer=None, **kwargs)
Parameters :
slicer : An indexer into self that specifies which values are used in the formatting process.(int, array-like)
kwargs : Options for specifying how the values should be formatted.(dict)Return : Array object
Example #1: Use TimedeltaIndex.to_native_types() function to format the given TimedeltaIndex object to its native format.
# importing pandas as pd import pandas as pd # Create the TimedeltaIndex object tidx = pd.TimedeltaIndex(data=['06:05:01.000030','+23:59:59.999999', '22 day 2 min 3us 10ns','+23:29:59.999999', '+12:19:59.999999']) # Print the TimedeltaIndex object print(tidx) |
Output :
Now we will use the TimedeltaIndex.to_native_types() function to format tidx to its native type.
# format tidx tidx.to_native_types() |
Output :
As we can see in the output, the TimedeltaIndex.to_native_types() function has returned an array object containing elements of dtype ‘<U23′.
Example #2: Use TimedeltaIndex.to_native_types() function to format the given TimedeltaIndex object to its native format.
# importing pandas as pd import pandas as pd # Create the TimedeltaIndex object tidx = pd.TimedeltaIndex(start ='1 days 02:00:12.001124', periods = 5, freq ='D', name ='Koala') # Print the TimedeltaIndex object print(tidx) |
Output :
Now we will use the TimedeltaIndex.to_native_types() function to format tidx to its native type.
# format tidx tidx.to_native_types() |
Output :
As we can see in the output, the TimedeltaIndex.to_native_types() function has returned an array object containing elements of dtype ‘<U22′.
