pandas.eval() function in Python

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]]

pandas.eval() function in Python

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