Divide masked array elements by a given scalar element and return arrays with Quotient and Remainder in NumPy

To divide masked array elements by a given scalar element and return arrays with Quotient and Remainder, use the ma.MaskedArray.__divmod__() method in Python 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([[85, 68, 81, 84], [67, 33, 39, 53], [29, 88, 51, 37], [56, 45, 67, 85]])
print("Array...
", arr)
print("
Array type...
", arr.dtype)

Get the dimensions of the Array −

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

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

maskArr = ma.masked_array(arr, mask =[[1, 1, 0, 0], [ 0, 0, 1, 0], [0, 0, 0, 1], [0, 1, 0, 0]])
print("
Our Masked Array
", maskArr)
print("
Our Masked Array type...
", maskArr.dtype)

Get the dimensions of the Masked Array −

print("
Our Masked Array Dimensions...
",maskArr.ndim)

Get the shape of the Masked Array −

print("
Our Masked Array Shape...
",maskArr.shape)

Get the number of elements of the Masked Array −

print("
Elements in the Masked Array...
",maskArr.size)

The scalar −

val = 5
print("
The given value...
",val)

To divide masked array elements by a given scalar element and return arrays with Quotient and Remainder, use the ma.MaskedArray.__divmod__() method −

print("
Resultant Arrays...
",maskArr.__divmod__(val))

Example

import numpy as np
import numpy.ma as ma

# Create an array with int elements using the numpy.array() method
arr = np.array([[85, 68, 81, 84], [67, 33, 39, 53], [29, 88, 51, 37], [56, 45, 67, 85]])
print("Array...
", arr)
print("
Array type...
", arr.dtype)

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

# Create a masked array and mask some of them as invalid
maskArr = ma.masked_array(arr, mask =[[1, 1, 0, 0], [ 0, 0, 1, 0], [0, 0, 0, 1], [0, 1, 0, 0]])
print("
Our Masked Array
", maskArr)
print("
Our Masked Array type...
", maskArr.dtype)

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

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

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

# The scalar
val = 5
print("
The given value...
",val)

# To divide masked array elements by a given scalar element and return arrays with Quotient and Remainder,
# use the ma.MaskedArray.__divmod__() method
print("
Resultant Arrays...
",maskArr.__divmod__(val))

Output

Array...
[[85 68 81 84]
[67 33 39 53]
[29 88 51 37]
[56 45 67 85]]

Array type...
int64

Array Dimensions...
2

Our Masked Array
[[-- -- 81 84]
[67 33 -- 53]
[29 88 51 --]
[56 -- 67 85]]

Our Masked Array type...
int64

Our Masked Array Dimensions...
2

Our Masked Array Shape...
(4, 4)

Elements in the Masked Array...
16

The given value...
5

Resultant Arrays...
(masked_array(
data=[[--, --, 16, 16],
   [13, 6, --, 10],
   [5, 17, 10, --],
   [11, --, 13, 17]],
mask=[[ True, True, False, False],
   [False, False, True, False],
   [False, False, False, True],
   [False, True, False, False]],
fill_value=999999), masked_array(
data=[[--, --, 1, 4],
   [2, 3, --, 3],
   [4, 3, 1, --],
   [1, --, 2, 0]],
mask=[[ True, True, False, False],
   [False, False, True, False],
   [False, False, False, True],
   [False, True, False, False]],
fill_value=999999))