# Return a new array when dtype is different from the current dtype in Numpy

NumpyServer Side ProgrammingProgramming

To return a new array when dtype is different from the current dtype, use the ma.MaskedArray.__array__(dtype) method in Python Numpy. We have set the dtype parameter to be float. 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.

NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. It supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.

## 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([[65, 68, 81], [93, 33, 39], [73, 88, 51], [62, 45, 67]])
print("Array...\n", arr)
print("\nArray type...\n", arr.dtype)

Get the dimensions of the Array −

print("\nArray Dimensions...\n",arr.ndim)


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

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

Get the dimensions of the Masked Array −

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


Get the shape of the Masked Array −

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

Get the number of elements of the Masked Array −

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


To return a new array when dtype is different from the current dtype, use the ma.MaskedArray.__array__(dtype) method. We have set the dtype parameter to be float −

print("\nResult...\n",maskArr.__array__(float))

## Example

import numpy as np
import numpy.ma as ma

# Create an array with int elements using the numpy.array() method
arr = np.array([[65, 68, 81], [93, 33, 39], [73, 88, 51], [62, 45, 67]])
print("Array...\n", arr)
print("\nArray type...\n", arr.dtype)

# Get the dimensions of the Array
print("\nArray Dimensions...\n",arr.ndim)
# Create a masked array and mask some of them as invalid
maskArr = ma.masked_array(arr, mask =[[1, 1, 0], [ 1, 0, 0], [0, 1, 0], [0, 1, 0]])

# Get the dimensions of the Masked Array

# Get the shape of the Masked Array
# Get the number of elements of the Masked Array

# To return a new array when dtype is different from the current dtype, use the ma.MaskedArray.__array__(dtype) method
# We have set the dtype parameter to be float
print("\nResult...\n",maskArr.__array__(float))

## Output

Array...
[[65 68 81]
[93 33 39]
[73 88 51]
[62 45 67]]

Array type...
int64

Array Dimensions...
2

[[-- -- 81]
[-- 33 39]
[73 -- 51]
[62 -- 67]]

int64

2

[62. 45. 67.]]