Numpy MaskedArray.reshape() function | Python

03 Oct, 2019
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 arrayout_arr = mask_arr.reshape(2, 2) print ("Output 2D masked array : ", out_arr) 
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 arrayout_arr = mask_arr.reshape(1, 4) print ("Output 2D masked array : ", out_arr) print()  # applying MaskedArray.reshape methods to make  # it a 1d masked arrayout_arr = mask_arr.reshape(4, ) print ("Output 1D masked array : ", out_arr)  
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]

Python numpy-arrayManipulation
Python-numpy
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