Return the absolute value of a masked Array in NumPy

To return the absolute value of a masked Array, use the ma.MaskedArray.__abs__() method. If the element is negative, the abs() method negates it and returns. 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([[55, 85, -68, 84], [67, -33, -39, -53], [29, 88, -51, 37], [-56, -45, 67, 85]])
print("Array...\n", arr)
print("\nArray type...\n", arr.dtype)

Get the dimensions of the Array −

print("Array Dimensions...\n",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("\nOur Masked Array\n", maskArr)
print("\nOur Masked Array type...\n", maskArr.dtype)

Get the dimensions of the Masked Array −

print("\nOur Masked Array Dimensions...\n",maskArr.ndim)

Get the shape of the Masked Array −

print("\nOur Masked Array Shape...\n",maskArr.shape)

Get the number of elements of the Masked Array −

print("\nElements in the Masked Array...\n",maskArr.size)

To return the absolute value of a masked Array, use the ma.MaskedArray.__abs__() method. If the element is negative, the abs() method negates it and returns. If the element is positive, the abs() method returns the same value −

print("\nReturning the absolute value of the elements of a masked array...\n", maskArr.__abs__())

Example

import numpy as np
import numpy.ma as ma

# Create an array with int elements using the numpy.array() method
arr = np.array([[55, 85, -68, 84], [67, -33, -39, -53], [29, 88, -51, 37], [-56, -45, 67, 85]])
print("Array...\n", arr)
print("\nArray type...\n", arr.dtype)

# Get the dimensions of the Array
print("\nArray Dimensions...\n",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("\nOur Masked Array\n", maskArr)
print("\nOur Masked Array type...\n", maskArr.dtype)

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

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

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

# To return the absolute value of a masked Array, use the ma.MaskedArray.__abs__() method
# If the element is negative, the abs() method negates it and returns
# If the element is positive, the abs() method returns the same value
print("\nReturning the absolute value of the elements of a masked array...\n",
maskArr.__abs__())

Output

Array...
[[ 55 85 -68 84]
[ 67 -33 -39 -53]
[ 29 88 -51 37]
[-56 -45 67 85]]

Array type...
int64

Array Dimensions...
2

Our Masked Array
[[-- -- -68 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

Returning the absolute value of the elements of a masked array...
[[-- -- 68 84]
[67 33 -- 53]
[29 88 51 --]
[56 -- 67 85]]

AmitDiwan

Updated on 05-Feb-2022 07:00:46

• Related Questions & Answers
• Return a copy of the masked array in NumPy
• Return the mask of a masked array in Numpy
• Return the transpose of the masked array in NumPy
• Return the length of the masked array in Numpy
• Return the average of the masked array elements in Numpy
• Return the variance of the masked array elements in Numpy
• Return the pickle of the masked array as a string in NumPy
• Divide a scalar value into every element of a masked Array and return the floor value in NumPy
• Divide every element of a masked Array into a scalar value and return the floor value in NumPy
• Return each element of the masked array rounded in Numpy
• Return array of indices of the maximum values from a masked array in NumPy
• Return array of indices of the minimum values from a masked array in NumPy
• Return the default fill value for a masked array with float datatype in Numpy
• Return the default fill value for a masked array with complex datatype in Numpy
• Return the common filling value of two masked arrays in Numpy