# Get the current shape of the Masked Array in Numpy

To get the shape of the Masked Array, use the ma.MaskedArray.shape attribute in Numpy. The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array dimensions to it.

As with numpy.reshape, one of the new shape dimensions can be -1, in which case its value is inferred from the size of the array and the remaining dimensions. Reshaping an array in-place will fail if a copy is required.

## Steps

At first, import the required library −

import numpy as np
import numpy.ma as ma

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

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

Get the dimensions of the Array −

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


Get the total bytes consumed −

print("Array nbytes...",arr.nbytes)

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 type...", maskArr.dtype)

Get the itemsize of the Masked Array −

print("Our Masked Array itemsize...", maskArr.itemsize)


Get the dimensions of the Masked Array −

print("Our Masked Array Dimensions...",maskArr.ndim)

Get the shape of the Masked Array, use the ma.MaskedArray.shape attribute in Numpy −

print("Our Masked Array Shape...",maskArr.shape)


## Example

import numpy as np
import numpy.ma as ma
arr = np.array([[35, 85], [67, 33]])
print("Array...", arr)
print("Array type...", arr.dtype)
print("Array itemsize...", arr.itemsize)

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

# Get the total bytes consumed
print("Array nbytes...",arr.nbytes)

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

# Get the itemsize of the Masked Array

# Get the dimensions of the Masked Array

# To get the shape of the Masked Array, use the ma.MaskedArray.shape attribute in Numpy
print("Our Masked Array Shape...",maskArr.shape)

## Output

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

Array type...
int64

Array itemsize...
8
Array Dimensions...
2
Array nbytes...
32

[[35 85]
[67 --]]

(2, 2)