Numpy MaskedArray.reshape () Function | python

numpy.MaskedArray.reshape() is used to reshape a masked array without changing its data. Returns a masked array containing the same data but with a new shape. The result is a representation of 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.
– & gt; The elements of a are read using this index order.
– & gt; `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.
– & gt; `F` means to index the elements in Fortran-like index order, with the first index changing fastest, and the last index changing slowest.
– & gt; `A` means to read the elements in Fortran-like index order if m is Fortran contiguous in memory, C-like order otherwise.
– & gt; `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

 
# import numy as geek
# and numpy.ma module as ma

import numpy as geek 

import numpy.ma as ma 

 
# create input array

in_arr = < / code> geek.array ([ 1 , 2 , 3 , - 1 ]) 

print ( " Input array: " , in_arr) 

 
# Now we create a masked array.
# making the third entry invalid.

mask_arr = ma.masked_array (in_arr, mask = [ 1 , 0 , 1 , 0 ]) 

print ( " Masked array: " , mask_arr) 

 
# using MaskedArray.reshape methods to create
# this is 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

 
# import numy as geek
# and numpy.ma module as ma

import numpy as geek 

import numpy.ma as ma 

 
# create input array

in_arr = geek.array ([[[ 2e8 , 3e - 5 ]], [[ - 45.0 , 2e5 ]]])

print ( "Input array:" , in_arr)

 
# Now we create a masked array.
# invalidating one record.

mask_arr = ma.masked_array (in_arr, mask = [[[ 1 , 0 ]], [[ 0 , 0 ]]]) 

print ( "3D Masked array:" , mask_arr) 

 
# using MaskedArray.reshape methods to create
# this is a 2d masked array

out_arr = mask_arr.reshape ( 1 , 4

print ( "Output 2D masked array:" , out_arr) 

print ()

 
# applying MaskedArray.reshape methods to creation
# this is a 1d 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]