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Determine whether input has masked values
To determine whether input has masked values, use the ma.is_masked() method in Python Numpy. Accepts any object as input, but always returns False unless the input is a MaskedArray containing masked values. Returns True if the array is a MaskedArray with masked values, False otherwise.
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)
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))
Return the mask of a masked array −
print("
The mask of a masked array)...
",ma.getmask(arr))Return the data of a masked array as an ndarray −
print("
Data of a masked array as an ndarray...
",ma.getdata(arr))
To determine whether input has masked values, use the ma.is_masked() method in Python Numpy −
print("
Whether input has masked values?
",ma.is_masked(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))
# Return the mask of a masked array
print("
The mask of a masked array)...
",ma.getmask(arr))
# Return the data of a masked array as an ndarray
print("
Data of a masked array as an ndarray...
",ma.getdata(arr))
# To determine whether input has masked values, use the ma.is_masked() method in Python Numpy
print("
Whether input has masked values?
",ma.is_masked(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] The mask of a masked array)... [[False True False False] [False True False False] [False True True False] [ True False True True]] Data of a masked array as an ndarray... [[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11] [12 13 14 15]] Whether input has masked values? True
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