Numpy MaskedArray.reshape() function | Python
Last Updated :
03 Oct, 2019
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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:
Code #2 :
Input array : [ 1 2 3 -1] Masked array : [-- 2 -- -1] Output 2D masked array : [[-- 2] [-- -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([[[ 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]
