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.notna() function detect all the non-missing values from the given TimedeltaIndex object. The functionality of this function is opposite of TimedeltaIndex.isna().
Syntax : TimedeltaIndex.notna()
Parameters : None
Return : a boolean array of whether the values are not NA
Example #1: Use TimedeltaIndex.notna() function to detect all the non-missing values from the given TimedeltaIndex object.
# importing pandas as pd import pandas as pd   # Create the TimedeltaIndex object tidx = pd.TimedeltaIndex(data =['06:05:01.000030', None, '22 day 2 min 3us 10ns',                                    '+23:59:59.999999', None, '+12:19:59.999999'])   # Print the TimedeltaIndex object print(tidx) |
Output :
Now we will use the TimedeltaIndex.notna() function to detect all the non-missing values from the tidx object.
# find all non-missing values tidx.notna() |
Output :
As we can see in the output, the TimedeltaIndex.notna() function has returned a boolean array which contains True value corresponding to the non-missing values and False value corresponding to the missing values.
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Example #2: Use TimedeltaIndex.notna() function to detect all the non-missing values from the given TimedeltaIndex object.
# importing pandas as pd import pandas as pd   # Create the TimedeltaIndex object tidx = pd.TimedeltaIndex(data =[None, '1 days 06:05:01.000030', None,                        '1 days 02:00:00', '21 days 06:15:01.000030'])   # Print the TimedeltaIndex object print(tidx) |
Output :
Now we will use the TimedeltaIndex.notna() function to detect all the non-missing values from the tidx object.
# find all non-missing values tidx.notna() |
Output :
As we can see in the output, the TimedeltaIndex.notna() function has returned a boolean array which contains True value corresponding to the non-missing values and False value corresponding to the missing values.
