# Test array values for NaN in Numpy

To test array for NaN, use the numpy.isnan() method in Python Numpy. Returns True where x is NaN, false otherwise. This is a scalar if x is a scalar. The condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.

NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity.

## Steps

At first, import the required library −

import numpy as np

Create an array with some nan values −

arr = np.array([1, 2, 10, 50, -np.nan, 0., np.nan])


Display the array −

print("Array...", arr)

Get the type of the array −

print("Our Array type...", arr.dtype)


Get the dimensions of the Array −

print("Our Array Dimensions...",arr.ndim)

Get the number of elements in the Array −

print("Number of elements...", arr.size)


To test array for NaN, use the numpy.isnan() method in Python Numpy −

print("Test array for NaN...",np.isnan(arr))

## Example

import numpy as np

# Create an array with some nan values
arr = np.array([1, 2, 10, 50, -np.nan, 0., np.nan])

# Display the array
print("Array...", arr)

# Get the type of the array
print("Our Array type...", arr.dtype)

# Get the dimensions of the Array
print("Our Array Dimensions...",arr.ndim)

# Get the number of elements in the Array
print("Number of elements...", arr.size)

# To test array for NaN, use the numpy.isnan() method in Python Numpy
print("Test array for NaN...",np.isnan(arr))

## Output

Array...
[ 1. 2. 10. 50. nan 0. nan]

Our Array type...
float64

Our Array Dimensions...
1

Number of elements...
7

Test array for NaN...
[False False False False True False True]