Get the number of elements of the Masked Array in Numpy


To get the number of elements of the Masked Array, use the ma.MaskedArray.size attribute in Numpy. The array.size returns a standard arbitrary precision Python integer. This may not be the case with other methods of obtaining the same value, which returns an instance of np.int_), and may be relevant if the value is used further in calculations that may overflow a fixed size integer type.

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 using the numpy.array() method −

arr = np.array([[35, 85], [67, 33]])
print("Array...
", arr) print("
Array type...
", arr.dtype) print("
Array itemsize...
", arr.itemsize)

Get the dimensions of the Array −

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

Get the total bytes consumed −

print("Array nbytes...
",arr.nbytes)

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

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

Get the itemsize of the Masked Array −

print("
Our Masked Array itemsize...
", maskArr.itemsize)

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, use the ma.MaskedArray.size attribute in Numpy −

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

Example

import numpy as np
import numpy.ma as ma

arr = np.array([[35, 85], [67, 33]])
print("Array...
", arr) print("
Array type...
", arr.dtype) print("
Array itemsize...
", arr.itemsize) # Get the dimensions of the Array print("Array Dimensions...
",arr.ndim) # Get the total bytes consumed print("Array nbytes...
",arr.nbytes) # Create a masked array and mask some of them as invalid maskArr = ma.masked_array(arr, mask =[[0, 0], [ 0, 1]]) print("
Our Masked Array
", maskArr) print("
Our Masked Array type...
", maskArr.dtype) # Get the itemsize of the Masked Array print("
Our Masked Array itemsize...
", maskArr.itemsize) #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) # To get the number of elements of the Masked Array, use the ma.MaskedArray.size attribute in Numpy print("
Elements in the Masked Array...
",maskArr.size)

Output

Array...
[[35 85]
[67 33]]

Array type...
int64

Array itemsize...
8
Array Dimensions...
2
Array nbytes...
32

Our Masked Array
[[35 85]
[67 --]]

Our Masked Array type...
int64

Our Masked Array itemsize...
8

Our Masked Array Dimensions...
2

Our Masked Array Shape...
(2, 2)

Elements in the Masked Array...
4

Updated on: 17-Feb-2022

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