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# Return a copy of the masked array collapsed into one dimension in the order the elements occur in memory in Numpy

To return a copy of the array collapsed into one dimension, use the **ma.MaskedArray.flatten()** method in Numpy. The "**order**" parameter is used to flatter in order. The ‘K’ order means to flatten
in the order the elements occur in memory.
The order ‘C’ means to flatten in row-major (C-style) order. ‘F’ means to flatten in column-major
(Fortran- style) order. ‘A’ means to flatten in column-major order if a is Fortran contiguous in
memory, row-major order otherwise. ‘K’ means to flatten a in the order the elements occur in
memory. The default is ‘C’.

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

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

arr = np.array([[49, 85, 45], [67, 33, 59]]) print("Array...\n", arr) print("\nArray type...\n", arr.dtype)

Get the dimensions of the Array −

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

Return a copy of the array collapsed into one dimension, use the ma.MaskedArray.flatten() method in Numpy. The "order" parameter is used to flatter in order. The ‘K’ order means to flatten in the order the elements occur in memory −

print("\nResult...\n",maskArr.flatten(order = 'K'))

## Example

# Python ma.MaskedArray - Return a copy of the array collapsed into one dimension # in the order the elements occur in memory import numpy as np import numpy.ma as ma # Create an array with int elements using the numpy.array() method arr = np.array([[78, 85, 51], [56, 33, 97]]) 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 =[[0, 1, 0], [ 0, 0, 0]]) print("\nOur Masked Array\n", maskArr) 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 copy of the array collapsed into one dimension, use the ma.MaskedArray.flatten() method in Numpy # The "order" parameter is used to flatter in order # The ‘K’ order means to flatten in the order the elements occur in memory. print("\nResult...\n",maskArr.flatten(order = 'K'))

## Output

Array... [[78 85 51] [56 33 97]] Array type... int64 Array Dimensions... 2 Our Masked Array [[78 -- 51] [56 33 97]] Our Masked Array type... int64 Our Masked Array Dimensions... 2 Our Masked Array Shape... (2, 3) Elements in the Masked Array... 6 Result... [78 -- 51 56 33 97]

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