Numpy MaskedArray.reshape() function | Python

Last Updated : 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 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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