Python | Pandas Index.is_categorical()

pandas.isna() function in Python

Last Updated : 14 Aug, 2020

This method is used to detect missing values for an array-like object. This function takes a scalar or array-like object and indicates whether values are missing (``NaN`` in numeric arrays, ``None`` or ``NaN`` in object arrays, ``NaT`` in datetimelike).

Syntax : pandas.isna(obj)

Argument :

obj : scalar or array-like, Object to check for null or missing values.

Below is the implementation of the above method with some examples :

Example 1 :

Python3

# importing package
import numpy
import pandas

# string "deep" is not nan value
print(pandas.isna("deep"))

# numpy.nan represents a nan value
print(pandas.isna(numpy.nan))

Output :

False
True

Example 2 :

Python3

# importing package
import numpy
import pandas

# create and view data
array = numpy.array([[1, numpy.nan, 3], 
                     [4, 5, numpy.nan]])

print(array)

# numpy.nan represents a nan value
print(pandas.isna(array))

Output :

[[ 1. nan  3.]
 [ 4.  5. nan]]
[[False  True False]
 [False False  True]]

Python | Pandas Index.is_categorical()

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