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

NumpyServer Side ProgrammingProgramming

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...\n", arr1)
print("\nArray type...\n", arr1.dtype)

arr1 = ma.array(arr1)

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

print("\nMasked Array1...\n",arr1)

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

arr2 = np.arange(9).reshape((3,3))
print("\nArray2...\n", arr2)
print("\nArray type...\n", arr2.dtype)

arr2 = ma.array(arr2)

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

print("\nMasked Array2...\n",arr2)

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

print("\nResult...\n",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...\n", arr1)
print("\nArray type...\n", arr1.dtype)

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

# Get the shape of the Array
print("\nOur Array Shape...\n",arr1.shape)

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

arr1 = ma.array(arr1)

# Array 2
# Creating another 3x3 array with int elements using the numpy.arange() method
arr2 = np.arange(9).reshape((3,3))
print("\nArray2...\n", arr2)
print("\nArray type...\n", arr2.dtype)

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

# Get the shape of the Array
print("\nOur Array Shape...\n",arr2.shape)

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

arr2 = ma.array(arr2)

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

[[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

True