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Return the mask of a masked array in Numpy
To return the mask of a masked array, use the ma.getmask() method in Python Numpy. Returns the mask of a as an ndarray if a is a MaskedArray and the mask is not nomask, else return nomask. To guarantee a full array of booleans of the same shape as a, use getmaskarray.
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
Creating a 4x4 array with int elements using the numpy.arange() method −
arr = np.arange(16).reshape((4,4))
print("Array...
", arr)
print("
Array type...
", arr.dtype)Get the dimensions of the Array −
print("
Array Dimensions...
",arr.ndim)
print("
Our Array type...
", arr.dtype)Get the shape of the Array −
print("
Our Masked Array Shape...
",arr.shape)
Get the number of elements of the Array −
print("
Elements in the Masked Array...
",arr.size)Create a masked array −
arr = ma.array(arr) arr[0, 1] = ma.masked arr[1, 1] = ma.masked arr[2, 1] = ma.masked arr[2, 2] = ma.masked arr[3, 0] = ma.masked arr[3, 2] = ma.masked arr[3, 3] = ma.masked
Count the number of masked elements along specific axis −
print("
The number of masked elements...
",ma.count_masked(arr, axis = 1))
To return the mask of a masked array, use the ma.getmask() method in Python Numpy −
print("
Result (mask of a masked array)...
",ma.getmask(arr))Example
import numpy as np
import numpy.ma as ma
# Creating a 4x4 array with int elements using the numpy.arange() method
arr = np.arange(16).reshape((4,4))
print("Array...
", arr)
print("
Array type...
", arr.dtype)
# Get the dimensions of the Array
print("
Array Dimensions...
",arr.ndim)
print("
Our Array type...
", arr.dtype)
# Get the shape of the Array
print("
Our Masked Array Shape...
",arr.shape)
# Get the number of elements of the Array
print("
Elements in the Masked Array...
",arr.size)
# Create a masked array
arr = ma.array(arr)
arr[0, 1] = ma.masked
arr[1, 1] = ma.masked
arr[2, 1] = ma.masked
arr[2, 2] = ma.masked
arr[3, 0] = ma.masked
arr[3, 2] = ma.masked
arr[3, 3] = ma.masked
# Count the number of masked elements along specific axis
print("
The number of masked elements...
",ma.count_masked(arr, axis = 1))
# To return the mask of a masked array, use the ma.getmask() method in Python Numpy
print("
Result (mask of a masked array)...
",ma.getmask(arr))Output
Array... [[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11] [12 13 14 15]] Array type... int64 Array Dimensions... 2 Our Array type... int64 Our Masked Array Shape... (4, 4) Elements in the Masked Array... 16 The number of masked elements... [1 1 2 3] Result (mask of a masked array)... [[False True False False] [False True False False] [False True True False] [ True False True True]]
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