Return True if two Numpy arrays are element-wise equal within a tolerance

To return True if two arrays are element-wise equal within a tolerance, use the ma.allclose() method in Python Numpy. This function is equivalent to allclose except that masked values are treated as equal (default) or unequal, depending on the masked_equal argument. The "masked_values" parameter is used to set the masked values in both the arrays are considered equal (True) or not (False).

Returns True if the two arrays are equal within the given tolerance, False otherwise. If either array contains NaN, then False is returned.

A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans that determines for each element of the associated array whether the value is valid or not

Steps

At first, import the required library −

import numpy as np

Creating a 3x3 array with int elements using the numpy.arange() method −

arr1 = np.arange(9).reshape((3,3))
print("Array1...
", arr1)
print("
Array type...
", arr1.dtype)

Create a masked array1 −

arr1 = ma.array(arr1)

Mask Array1 −

arr1[0, 1] = ma.masked
arr1[1, 1] = ma.masked

Display Masked Array 1 −

print("
Masked Array1...
",arr1)

Creating another 3x3 array with int elements using the numpy.arange() method

arr2 = np.arange(9).reshape((3,3))
print("
Array2...
", arr2)
print("
Array type...
", arr2.dtype)

Create masked array2 −

arr2 = ma.array(arr2)

Mask Array2 −

arr2[2, 0] = ma.masked
arr2[2, 2] = ma.masked

Display Masked Array 2 −

print("
Masked Array2...
",arr2)

To return True if two arrays are element-wise equal within a tolerance, use the ma.allclose() method in Python Numpy −

print("
Result...
",ma.allclose(arr1, arr2))

Example

import numpy as np
import numpy.ma as ma

# Array 1
# Creating a 3x3 array with int elements using the numpy.arange() method
arr1 = np.arange(9).reshape((3,3))
print("Array1...
", arr1)
print("
Array type...
", arr1.dtype)

# Get the dimensions of the Array
print("
Array Dimensions...
",arr1.ndim)

# Get the shape of the Array
print("
Our Array Shape...
",arr1.shape)

# Get the number of elements of the Array
print("
Elements in the Array...
",arr1.size)

# Create a masked array
arr1 = ma.array(arr1)

# Mask Array1
arr1[0, 1] = ma.masked
arr1[1, 1] = ma.masked

# Display Masked Array 1
print("
Masked Array1...
",arr1)

# Array 2
# Creating another 3x3 array with int elements using the numpy.arange() method
arr2 = np.arange(9).reshape((3,3))
print("
Array2...
", arr2)
print("
Array type...
", arr2.dtype)

# Get the dimensions of the Array
print("
Array Dimensions...
",arr2.ndim)

# Get the shape of the Array
print("
Our Array Shape...
",arr2.shape)

# Get the number of elements of the Array
print("
Elements in the Array...
",arr2.size)

# Create a masked array
arr2 = ma.array(arr2)

# Mask Array2
arr2[2, 0] = ma.masked
arr2[2, 2] = ma.masked

# Display Masked Array 2
print("
Masked Array2...
",arr2)

# To Return True if two arrays are element-wise equal within a tolerance, use the ma.allclose() method in Python Numpy
print("
Result...
",ma.allclose(arr1, arr2))

Output

Array1...
[[0 1 2]
[3 4 5]
[6 7 8]]

Array type...
int64

Array Dimensions...
2

Our Array Shape...
(3, 3)

Elements in the Array...
9

Masked Array1...
[[0 -- 2]
[3 -- 5]
[6 7 8]]

Array2...
[[0 1 2]
[3 4 5]
[6 7 8]]

Array type...
int64

Array Dimensions...
2

Our Array Shape...
(3, 3)

Elements in the Array...
9

Masked Array2...
[[0 1 2]
[3 4 5]
[-- 7 --]]

Result...
True