# Get the datatype of a masked array in NumPy

To get the datatype of the masked array, use the ma.MaskedArray.dtype attribute in Numpy. The data type object describes how the bytes in the fixed-size block of memory corresponding to an array item should be interpreted.

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.

Masked arrays are arrays that may have missing or invalid entries. The numpy.ma module provides a nearly work-alike replacement for numpy that supports data arrays with masks.

## Steps

At first, import the required library −

import numpy as np
import numpy.ma as ma

Create an array with using the numpy.array() method −

arr = np.array([[35, 85], [67, 33]])
print("Our Array...", arr)

Get the datatype of the array −

print("Our Array type...", arr.dtype)

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

maskArr = ma.masked_array(arr, mask =[[0, 0], [ 0, 1]])
print("Our Masked Array", maskArr)

To get the datatype of the masked array, use the ma.MaskedArray.dtype attribute in Numpy −

print("Our Masked Array type...", maskArr.dtype)

## Example

import numpy as np
import numpy.ma as ma

# Create a numpy array using the numpy.array() method
arr = np.array([[35, 85], [67, 33]])
print("Our Array...", arr)

# Get the datatype of the arrat
print("Our Array type...", arr.dtype)

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

# To get the datatype of the masked array, use the ma.MaskedArray.dtype attribute in Numpy
print("Our Masked Array type...", maskArr.dtype)

## Output

Our Array...
[[35 85]
[67 33]]

Our Array type...
int64

int64