Python | Pandas TimedeltaIndex.get_duplicates Last Updated : 28 Dec, 2018 Comments Improve Suggest changes Like Article Like Report 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.get_duplicates() function return a TimedeltaIndex object which contain all the duplicate values in the given TimedeltaIndex object. Syntax : TimedeltaIndex.get_duplicates() Parameters : None Return : TimedeltaIndex object Example #1: Use TimedeltaIndex.get_duplicates() function to find all the duplicate values in the given TimedeltaIndex object. Python3 # importing pandas as pd import pandas as pd # Create the TimedeltaIndex object tidx = pd.TimedeltaIndex(data =['3 days 06:05:01.000030', '1 days 06:05:01.000030', '3 days 06:05:01.000030', '1 days 02:00:00', '21 days 06:15:01.000030']) # Print the TimedeltaIndex object print(tidx) Output : Now we will use the TimedeltaIndex.get_duplicates() function to find all the duplicate values in tidx Python3 # find duplicates tidx.get_duplicates() Output : As we can see in the output, the TimedeltaIndex.get_duplicates() function has returned an object which contains all the duplicate values present in tidx. Example #2: Use TimedeltaIndex.get_duplicates() function to find all the duplicate values in the given TimedeltaIndex object. Python3 # importing pandas as pd import pandas as pd # Create the TimedeltaIndex object tidx = pd.TimedeltaIndex(data =['06:05:01.000030', '3 days 06:05:01.000030', '22 day 2 min 3us 10ns', '+23:59:59.999999', '3 days 06:05:01.000030', '+12:19:59.999999']) # Print the TimedeltaIndex object print(tidx) Output : Now we will use the TimedeltaIndex.get_duplicates() function to find all the duplicate values in tidx Python3 # find duplicates tidx.get_duplicates() Output : As we can see in the output, the TimedeltaIndex.get_duplicates() function has returned an object which contains all the duplicate values present in tidx. Comment More infoAdvertise with us Next Article Python | Pandas TimedeltaIndex.get_duplicates S Shubham__Ranjan Follow Improve Article Tags : Technical Scripter Python Python-pandas Python pandas-TimedeltaIndex Practice Tags : python Similar Reads Python | Pandas TimedeltaIndex.has_duplicates 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.has_duplicates attribute return a boolean value. It return True 2 min read Python | Pandas TimedeltaIndex.duplicated 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.duplicated() function detects duplicate values in the given Time 2 min read Python | Pandas TimedeltaIndex.drop_duplicates 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.drop_duplicates() function return Index with duplicate values re 2 min read Python | Pandas TimedeltaIndex.delete 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.delete() function make a new DatetimeIndex with passed location 2 min read Python | Pandas TimedeltaIndex.insert 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.insert() function return the underlying data as an ndarray of th 2 min read Like