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# Return the data of a masked array as an ndarray

To return the data of a masked array as an ndarray, use the **ma.getdata()** method in Python Numpy. Returns the data of a (if any) as an ndarray if a is a MaskedArray, else return a as a ndarray or subclass if not.

The subok parameter suggest whether to force the output to be a pure ndarray (False) or to return a subclass of ndarray if appropriate (True, default).

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

Creating a 4x4 array with int elements using the numpy.arange() method −

arr = np.arange(16).reshape((4,4)) print("Array...

", arr) print("

Array type...

", arr.dtype)

Get the dimensions of the Array −

print("

Array Dimensions...

",arr.ndim)

Get the shape of the Array −

print("

Our Masked Array Shape...

",arr.shape)

Get the number of elements of the Array −

print("

Elements in the Masked Array...

",arr.size)

Create a masked array −

arr = ma.array(arr) arr[0, 1] = ma.masked arr[1, 1] = ma.masked arr[2, 1] = ma.masked arr[2, 2] = ma.masked arr[3, 0] = ma.masked arr[3, 2] = ma.masked arr[3, 3] = ma.masked

Count the number of masked elements along specific axis −

print("

The number of masked elements...

",ma.count_masked(arr, axis = 1))

Return the mask of a masked array −

print("

The mask of a masked array)...

",ma.getmask(arr))

To return the data of a masked array as an ndarray, use the ma.getdata() method in Python Numpy −

print("

Result (data of a masked array as an ndarray)...

",ma.getdata(arr))

## Example

import numpy as np import numpy.ma as ma # Creating a 4x4 array with int elements using the numpy.arange() method arr = np.arange(16).reshape((4,4)) print("Array...

", arr) print("

Array type...

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

Array Dimensions...

",arr.ndim) print("

Our Array type...

", arr.dtype) # Get the shape of the Array print("

Our Masked Array Shape...

",arr.shape) # Get the number of elements of the Array print("

Elements in the Masked Array...

",arr.size) # Create a masked array arr = ma.array(arr) arr[0, 1] = ma.masked arr[1, 1] = ma.masked arr[2, 1] = ma.masked arr[2, 2] = ma.masked arr[3, 0] = ma.masked arr[3, 2] = ma.masked arr[3, 3] = ma.masked # Count the number of masked elements along specific axis print("

The number of masked elements...

",ma.count_masked(arr, axis = 1)) # Return the mask of a masked array print("

The mask of a masked array)...

",ma.getmask(arr)) # To return the data of a masked array as an ndarray, use the ma.getdata() method in Python Numpy print("

Result (data of a masked array as an ndarray)...

",ma.getdata(arr))

## Output

Array... [[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11] [12 13 14 15]] Array type... int64 Array Dimensions... 2 Our Array type... int64 Our Masked Array Shape... (4, 4) Elements in the Masked Array... 16 The number of masked elements... [1 1 2 3] The mask of a masked array)... [[False True False False] [False True False False] [False True True False] [ True False True True]] Result (data of a masked array as an ndarray)... [[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11] [12 13 14 15]]

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