Transform a masked array into a flexibletype array in Numpy

To transform a masked array into a flexible-type array, use the ma.MaskedArray.toflex() method in Numpy. The flexible type array that is returned will have two fields: the _data field stores the _data part of the array.

The method returns a new flexible-type ndarray with two fields: the first element containing a value, the second element containing the corresponding mask boolean. The returned record shape matches self.shape.

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([[49, 85, 45], [67, 33, 59]])
print("Array...
", arr)
print("\nArray type...
", arr.dtype)

Get the dimensions of the Array −

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

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

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

Get the dimensions of the Masked Array −

print("\nOur Masked Array Dimensions...
",maskArr.ndim)

Get the shape of the Masked Array −

print("\nOur Masked Array Shape...
",maskArr.shape)

Get the number of elements of the Masked Array −

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

Transform a masked array into a flexible-type array, use the ma.MaskedArray.toflex() method in Numpy. The flexible type array that is returned will have two fields: the _data field stores the _data part of the array −

print("\nResult of the transformation...
",maskArr.toflex())

Example

# Python ma.MaskedArray - Transform a masked array into a flexibletype array

import numpy as np
import numpy.ma as ma

# Create an array with int elements using the numpy.array() method
arr = np.array([[49, 85, 45], [67, 33, 59]])
print("Array...
", arr)
print("\nArray type...
", arr.dtype)

# Get the dimensions of the Array
print("\nArray Dimensions...
",arr.ndim)

# Create a masked array and mask some of them as invalid
maskArr = ma.masked_array(arr, mask =[[0, 0, 1], [ 0, 1, 0]])
print("\nOur Masked Array
", maskArr)
print("\nOur Masked Array type...
", maskArr.dtype)

# Get the dimensions of the Masked Array
print("\nOur Masked Array Dimensions...
",maskArr.ndim)

# Get the shape of the Masked Array
print("\nOur Masked Array Shape...
",maskArr.shape)

# Get the number of elements of the Masked Array
print("\nElements in the Masked Array...
",maskArr.size)

# To transform a masked array into a flexible-type array, use the ma.MaskedArray.toflex() method in Numpy

# The flexible type array that is returned will have two fields: the _data field stores the _data part of the array.

#, the _mask field stores the _mask part of the array.
print("\nResult of the transformation...
",maskArr.toflex())

Output

Array...
[[49 85 45]
[67 33 59]]

Array type...
int64

Array Dimensions...
2

Our Masked Array
[[49 85 --]
[67 -- 59]]

Our Masked Array type...
int64

Our Masked Array Dimensions...
2

Our Masked Array Shape...
(2, 3)

Elements in the Masked Array...
6

Result of the transformation...
[[(49, False) (85, False) (45, True)]
[(67, False) (33, True) (59, False)]]