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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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