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# Numpy MaskedArray.reshape() function | Python

`numpy.MaskedArray.reshape()` function is used to give a new shape to the masked array without changing its data.It 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.

Syntax : `numpy.ma.reshape(shape, order)`

Parameters:

shape:[ int or tuple of ints] The new shape should be compatible with the original shape.
order : [‘C’, ‘F’, ‘A’, ‘K’, optional] By default, ‘C’ index order is used.
–> The elements of a are read using this index order.
–> ‘C’ means to index the elements in C-like order, with the last axis index changing fastest, back to the first axis index changing slowest.
–> ‘F’ means to index the elements in Fortran-like index order, with the first index changing fastest, and the last index changing slowest.
–> ‘A’ means to read the elements in Fortran-like index order if m is Fortran contiguous in memory, C-like order otherwise.
–> ‘K’ means to read the elements in the order they occur in memory, except for reversing the data when strides are negative.

Return : [ reshaped_array] A new view on the array.

Code #1 :

 `# Python program explaining``# numpy.MaskedArray.reshape() method ``   ` `# importing numpy as geek  ``# and numpy.ma module as ma ``import` `numpy as geek ``import` `numpy.ma as ma ``   ` `# creating input array  ``in_arr ``=` `geek.array([``1``, ``2``, ``3``, ``-``1``]) ``print` `(``"Input array : "``, in_arr) ``   ` `# Now we are creating a masked array. ``# by making third entry as invalid.  ``mask_arr ``=` `ma.masked_array(in_arr, mask ``=``[``1``, ``0``, ``1``, ``0``]) ``print` `(``"Masked array : "``, mask_arr) ``   ` `# applying MaskedArray.reshape methods to make  ``# it a 2d masked array``out_arr ``=` `mask_arr.reshape(``2``, ``2``) ``print` `(``"Output 2D masked array : "``, out_arr) `

Output:

```Input array :  [ 1  2  3 -1]
Masked array :  [-- 2 -- -1]
Output 2D masked array :  [[-- 2]
[-- -1]]
```

Code #2 :

 `# Python program explaining``# numpy.MaskedArray.reshape() method ``   ` `# importing numpy as geek  ``# and numpy.ma module as ma ``import` `numpy as geek ``import` `numpy.ma as ma ``   ` `# creating input array ``in_arr ``=` `geek.array([[[ ``2e8``, ``3e``-``5``]], [[ ``-``45.0``, ``2e5``]]])``print` `(``"Input array : "``, in_arr)``    ` `# Now we are creating a masked array. ``# by making one entry as invalid.  ``mask_arr ``=` `ma.masked_array(in_arr, mask ``=``[[[ ``1``, ``0``]], [[ ``0``, ``0``]]]) ``print` `(``"3D Masked array : "``, mask_arr) ``   ` `# applying MaskedArray.reshape methods to make  ``# it a 2d masked array``out_arr ``=` `mask_arr.reshape(``1``, ``4``) ``print` `(``"Output 2D masked array : "``, out_arr) ``print``()`` ` `# applying MaskedArray.reshape methods to make  ``# it a 1d masked array``out_arr ``=` `mask_arr.reshape(``4``, ) ``print` `(``"Output 1D masked array : "``, out_arr)  `

Output:

```Input array :  [[[ 2.0e+08  3.0e-05]]

[[-4.5e+01  2.0e+05]]]
3D Masked array :  [[[-- 3e-05]]

[[-45.0 200000.0]]]
Output 2D masked array :  [[-- 3e-05 -45.0 200000.0]]

Output 1D masked array :  [-- 3e-05 -45.0 200000.0]
```

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