Check which element in a masked array is equal to a given value in NumPy

NumpyServer Side ProgrammingProgramming

To check which element in a masked array is equal to a given value, use the ma.MaskedArray.__eq__() method. True is returned for every array element equal to a given value val. 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)

The value to be compared −

val = 67
print("\nThe given value to be compared with the masked array elements...\n",val)

To check which element in a masked array is equal to a given value, use the ma.MaskedArray.__eq__() method. Returns with boolean type i.e. True and False. True is returned for every array element equal to a given value val −

print("\nDisplay True for each element equal to a given value val...\n", maskArr.__eq__(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([[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)

# The value to be compared
val = 67
print("\nThe given value to be compared with the masked array elements...\n",val)

# To check which element in a masked array is equal to a given value, use the ma.MaskedArray.__eq__() method
# Returns with boolean type i.e. True and False.
# True is returned for every array element equal to a given value val
print("\nDisplay True for each element equal to a given value val...\n",
maskArr.__eq__(val))

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

The given value to be compared with the masked array elements...
67

Display True for each element equal to a given value val...
[[-- -- False False]
[True False -- False]
[False False False --]
[False -- True False]]
raja
Updated on 05-Feb-2022 06:55:54

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