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  2. Python | Pandas Index.isna ()

Python | Pandas Index.isna ()

Index.isna() Pandas Index.isna() detects missing values. It returns a boolean of the same size indicating whether the values ​​are NA. NA values ​​such as None, numpy.NaN, or pd.NaT map to True. Everything else is matched against false values. Characters such as empty strings & # 39; & # 39; or numpy.inf are not considered NA values ​​(unless you set pandas.options.mode.use_inf_as_na = True).

Syntax: Index.isna ()

Parameters: Doesn`t take any parameter.

Returns: A boolean array of whether my values ​​are NA

Example # 1: Use Index.isna () to check if any value in the index is NaN .

# import pandas as pd

import pandas as pd

 
# Create an index

idx = pd.Index ([ `Labrador` , None , `Beagle` , `Mastiff` , 

  `Lhasa` , None , `Husky` , ` Beagle` ])

 
# Print index
idx

Output:

Now we will check the missing values ​​in the index .

# checks for missing characters readings.
idx.isna ()

Output:

Function returned an array object of the same size as the index.  True means there was no index mark, and False means there was an index mark.

Example # 2: Use Index.isna () to check if missing Datetime indexes count as NaN values ​​or not.

# import pandas as pd

import pandas as pd

 
# Create date and time index

idx = pd.DatetimeIndex ([pd.Timestamp ( `2015-02-11` ), 

  None , pd.Timestamp (``), pd.NaT])

 
# Print date and time index
idx

Exit:

Now we check if there are tags in the Datetime index .

# check if past Datetime has passed
# Index marks are missing or not.
idx.isna ()

Output:

As we can see from the output, the function returned an array object whose size matches with the size of the Datetime index.  True means there is no index mark, and False means no index mark is missing.