Return the copy of a masked array cast to a specified type in Numpy


To return the copy of the array, cast to a specified type, use the ma.MaskedArray.astype() method in Numpy. The parameter is the data-type to which the array is cast. Another parameter, order controls the memory layout order of the result. ‘C’ means C order, ‘F’ means Fortran order, ‘A’ means ‘F’ order if all the arrays are Fortran contiguous, ‘C’ order otherwise, and ‘K’ means as close to the order the array elements appear in memory as possible. Default is ‘K’.

Casting between a simple data type and a structured one is possible only for “unsafe” casting.

Casting to multiple fields is allowed, but casting from multiple fields is not.

Returns the ndarray, unless copy is False and the other conditions for returning the input array are satisfied, arr_t is a new array of the same shape as the input array, with dtype, order given by dtype, order.

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([[35, 85, 45], [67, 33, 59]])
print("Array...
", arr) print("
Array 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("
Our Masked Array
", maskArr) print("
Our Masked Array type...
", maskArr.dtype)

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 −

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

Return the copy of the array, cast to a specified type, use the ma.MaskedArray.astype() method in Numpy −

print("
Copy of the array cast to float type...
",maskArr.astype(float))

Example

# Python ma.MaskedArray - Copy of the array, cast to a specified type

import numpy as np
import numpy.ma as ma

# Create an array with int elements using the numpy.array() method
arr = np.array([[35, 85, 45], [67, 33, 59]])
print("Array...
", arr) print("
Array 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("
Our Masked Array
", maskArr) print("
Our Masked Array type...
", maskArr.dtype) # 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 print("
Elements in the Masked Array...
",maskArr.size) # To return the copy of the array, cast to a specified type, use the ma.MaskedArray.astype() method in Numpy # Here, the parameter is the data-type to which the array is cast print("
Copy of the array cast to float type...
",maskArr.astype(float))

Output

Array...
[[35 85 45]
[67 33 59]]

Array type...
int32

Array Dimensions...
2

Our Masked Array
[[35 85 --]
[67 -- 59]]

Our Masked Array type...
int32

Our Masked Array Dimensions...
2

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

Elements in the Masked Array...
6

Copy of the array cast to float type...
[[35.0 85.0 --]
[67.0 -- 59.0]]

Updated on: 02-Feb-2022

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