Mask using floating point equality in Numpy


To mask using floating point equality, use the numpy.ma.masked_values() method in Python Numpy. Return a MaskedArray, masked where the data in array x are approximately equal to value, determined using isclose. The default tolerances for masked_values are the same as those for isclose.

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
import numpy.ma as ma

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

arr = np.array([[71, 55, 82.8], [82.8, 33, 39], [73, 82.3, 51], [90, 77, 82.8]])
print("Array...
", arr)

Get the type pf array −

print("
Array type...
", arr.dtype)

Get the dimensions of the Array −

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

Get the shape of the Array −

print("
Our Array Shape...
",arr.shape)

Get the number of elements of the Array −

print("
Number of Elements in the Array...
",arr.size)

To mask using floating point equality, use the numpy.ma.masked_values() method in Python Numpy −

print("
Result...
",np.ma.masked_values(arr, 82.8))

Example

import numpy as np
import numpy.ma as ma

# Create an array with int elements using the numpy.array() method
arr = np.array([[71, 55, 82.8], [82.8, 33, 39], [73, 82.3, 51], [90, 77, 82.8]])
print("Array...
", arr) # Get the type pf array print("
Array type...
", arr.dtype) # Get the dimensions of the Array print("
Array Dimensions...
",arr.ndim) # Get the shape of the Array print("
Our Array Shape...
",arr.shape) # Get the number of elements of the Array print("
Number of Elements in the Array...
",arr.size) # To mask using floating point equality, use the numpy.ma.masked_values() method in Python Numpy print("
Result...
",np.ma.masked_values(arr, 82.8))

Output

Array...
[[71. 55. 82.8]
[82.8 33. 39. ]
[73. 82.3 51. ]
[90. 77. 82.8]]

Array type...
float64

Array Dimensions...
2

Our Array Shape...
(4, 3)

Number of Elements in the Array...
12

Result...
[[71.0 55.0 --]
[-- 33.0 39.0]
[73.0 82.3 51.0]
[90.0 77.0 --]]

Updated on: 04-Feb-2022

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