Check if numpy array contains any NaN value

In this article, we will see how to check whether  the numpy array contains NaN value or not.

Introduction

numpy stands for numeric python which is used to perform mathematical operations on arrays.

It is a module in which we have to import from the python. 

Syntax to import:

import numpy

We can also use alias for the module 

For example,

import numpy as np

We can directly use np to call the numpy module.

Array

An array is an one dimensional data structure used to store single data type data.

I.E It will only store all integer data or all string type data.or all float type data.

We can create an numpy array by using array() function.

 

Syntax:

numpy.array(elements)

where, elements are the input data elements.

NaN is not a number. We can create NaN by using numpy.nan.

Method 1 : Using isnan() from numpy module

We can check whether the numpy array contains NaN values or not by using any() operator through isnan() operator(available in numpy module).

We have to check this by using an condition.any() will check if there are any NaN values

Syntax:

if(numpy.isnan(array_input).any()):
    print("Has NaN values")
else:
    print("No NaN values")

Example:

In this example, we will create an numpy array with 10 elements (3 NaN among 10 elements) and check the NaN values.

#import numpy module
import numpy

#create an numpy  array with 10 elements that includes three NaN values
array_input=numpy.array([12,34,56,78,numpy.nan,numpy.nan,10,10,numpy.nan,123])

#display array
print(array_input)

#check the array has NaN values or not.
if(numpy.isnan(array_input).any()):
    print("Has NaN values")
else:
    print("No NaN values")

Output:

It contains three NaN values, so it printed NaN values are present(if block is executed).

[ 12.  34.  56.  78.  nan  nan  10.  10.  nan 123.]
Has NaN values

Method 2 : Using isna() from pandas module

We can check whether the numpy array contains NaN values or not by using any() operator through isna() operator(available in pandas module).

We have to check this by using an condition.any() will check if there are any NaN values

Syntax:

if(pandas.isna(array_input).any()):
    print("Has NaN values")
else:
    print("No NaN values")

Example:

In this example, we will create an numpy array with 10 elements (3 NaN among 10 elements) and check the NaN values.

#import numpy module
import numpy

#import pandas module
import pandas

#create an numpy  array with 10 elements that includes three NaN values
array_input=numpy.array([12,34,56,78,numpy.nan,numpy.nan,10,10,numpy.nan,123])

#display array
print(array_input)

#check the array has NaN values or not.
if(pandas.isna(array_input).any()):
    print("Has NaN values")
else:
    print("No NaN values")

Output:

It contains three NaN values, so it printed NaN values are present(if block is executed).

[ 12.  34.  56.  78.  nan  nan  10.  10.  nan 123.]
Has NaN values