Python | Pandas TimedeltaIndex.notna

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.

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# 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)

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Output :

Now we will use the TimedeltaIndex.notna() function to detect all the non-missing values from the tidx object.

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# find all non-missing values
tidx.notna()

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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.
 
Example #2: Use TimedeltaIndex.notna() function to detect all the non-missing values from the given TimedeltaIndex object.

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# 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)

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Output :

Now we will use the TimedeltaIndex.notna() function to detect all the non-missing values from the tidx object.

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# find all non-missing values
tidx.notna()

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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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