# Return a masked array containing the same data but with a new shape viewed as column-major order in Numpy

To return a masked array containing the same data, but with a new shape, use the ma.MaskedArray.reshape() method in Numpy. Give a new shape to the array without changing its data. The order is set using the "order" parameter. The 'F' order determines whether the array data should be viewed as in FORTRAN i.e. F (column-major).

The new shape should be compatible with the original shape. If an integer is supplied, then the result will be a 1-D array of that length.

The order determines whether the array data should be viewed as in C (row-major) or FORTRAN (column-major) order. Returns a masked array containing the same data, but with a new shape. The result is a view on the original array; if this is not possible, a ValueError is raised.

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

Get the dimensions of the Array −

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

Get the dimensions of the Masked Array −

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


Get the shape of the Masked Array −

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

Get the number of elements of the Masked Array −

print("Elements in the Masked Array...",maskArr.size)


Return a masked array containing the same data, but with a new shape, use the ma.MaskedArray.reshape() method. Give a new shape to the array without changing its data. The new shape of the masked array is set to 6x1 as a parameter. The new shape should be compatible with the original shape. If an integer is supplied, then the result will be a 1-D array of that length. The order is set using the "order" parameter. The 'F' order determines whether the array data should be viewed as in FORTRAN i.e. F (column-major) −

print("Result...",maskArr.reshape((6,1),order='F'))

## Example

# Python ma.MaskedArray - Return a masked array containing the same data but with a new shape
# viewed as column-major order

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

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

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

# The masked array is 1x6

# 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 masked array containing the same data, but with a new shape, use the ma.MaskedArray.reshape() method in Numpy
# Give a new shape to the array without changing its data
# The new shape of the masked array is set to 6x1 as a parameter
# The new shape should be compatible with the original shape.
# If an integer is supplied, then the result will be a 1-D array of that length
# The order is set using the "order" parameter
# The 'F' order determines whether the array data should be viewed as in FORTRAN i.e F (column-major)
print("Result...",maskArr.reshape((6,1),order='F'))

## Output

Array...
[[78 85 51]
[56 33 97]]

Array type...
int64

Array Dimensions...
2

[[78 -- 51]
[56 33 --]]

int64

2

(2, 3)

6

Result...
[[78]
[56]
[--]
[33]
[51]
[--]]

Updated on: 04-Feb-2022

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