Return an empty masked array of the given shape where all the data are masked in Numpy

To return an empty masked array of the given shape and dtype where all the data are masked, use the ma.masked_all() method in Python Numpy. The 1st parameter sets the shape of the required MaskedArray.

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

Return an empty masked array of the given shape and dtype where all the data are masked using the ma.masked_all() method −

arr = ma.masked_all((5, 5))


Displaying our array −

print("Array...",arr)

Get the datatype −

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

Example

# Python ma.MaskedArray - Return an empty masked array of the given shape where all the data are masked

import numpy as np
import numpy.ma as ma

# To return an empty masked array of the given shape and dtype where all the data are masked, use the ma.masked_all() method in Python Numpy
# The 1st parameter sets the shape of the required MaskedArray

# Displaying our array
print("Array...",arr)

# Get the datatype
print("Array datatype...",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("Elements in the Array...",arr.size)

Output

Array...
[[-- -- -- -- --]
[-- -- -- -- --]
[-- -- -- -- --]
[-- -- -- -- --]
[-- -- -- -- --]]

Array datatype...
float64

Array Dimensions...
2

Our Array Shape...
(5, 5)

Elements in the Array...
25