Reduce a mask to nomask when possible in Numpy

To reduce a mask to nomask when possible, use the np.ma.shrink_mask() method in Numpy. 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.

NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. It supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.

Steps

At first, import the required library −

import numpy as np
import numpy.ma as ma

Create an array with int elements using the numpy.array() method −

arr = np.array([[65, 68, 81], [93, 33, 39], [73, 88, 51], [62, 45, 67]])
print("Array...
", arr)
print("\nArray type...
", arr.dtype)

Get the dimensions of the Array −

print("\nArray Dimensions...
",arr.ndim)

Create a masked array and mask some of them as invalid −

maskArr = ma.masked_array(arr, mask =[[0, 0, 0], [ 0, 0, 0], [0, 0, 0], [0, 0, 0]])
print("\nOur Masked Array mask
", maskArr.mask)
print("\nOur Masked Array type...
", maskArr.dtype)

To reduce a mask to nomask when possible, use the np.ma.shrink_mask() −

print("\nResult...
",maskArr.shrink_mask())

Example

import numpy as np
import numpy.ma as ma

# Create an array with int elements using the numpy.array() method
arr = np.array([[65, 68, 81], [93, 33, 39], [73, 88, 51], [62, 45, 67]])
print("Array...
", arr)
print("\nArray type...
", arr.dtype)

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

# Create a masked array and mask some of them as invalid
maskArr = ma.masked_array(arr, mask =[[0, 0, 0], [ 0, 0, 0], [0, 0, 0], [0, 0, 0]])
print("\nOur Masked Array mask
", maskArr.mask)
print("\nOur Masked Array type...
", maskArr.dtype)

# To reduce a mask to nomask when possible, use the np.ma.shrink_mask() method in Numpy
print("\nResult...
",maskArr.shrink_mask())

Output

Array...
[[65 68 81]
[93 33 39]
[73 88 51]
[62 45 67]]

Array type...
int64

Array Dimensions...
2

Our Masked Array mask
[[False False False]
[False False False]
[False False False]
[False False False]]

Our Masked Array type...
int64

Result...
[[65 68 81]
[93 33 39]
[73 88 51]
[62 45 67]]