Numpy MaskedArray.reshape() function | Python (original) (raw)

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 :

import numpy as geek

import numpy.ma as ma

in_arr = geek.array([ 1 , 2 , 3 , - 1 ])

print ( "Input array : " , in_arr)

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

print ( "Masked array : " , mask_arr)

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 :

import numpy as geek

import numpy.ma as ma

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

print ( "Input array : " , in_arr)

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

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

out_arr = mask_arr.reshape( 1 , 4 )

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

print ()

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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• Get the QR factorization of a given NumPy array In this article, we will discuss QR decomposition or QR factorization of a matrix. QR factorization of a matrix is the decomposition of a matrix say 'A' into 'A=QR' where Q is orthogonal and R is an upper-triangular matrix. We factorize the matrix using numpy.linalg.qr() function. Syntax : numpy.lin 2 min read

• How to get the magnitude of a vector in NumPy? The fundamental feature of linear algebra are vectors, these are the objects having both direction and magnitude. In Python, NumPy arrays can be used to depict a vector. There are mainly two ways of getting the magnitude of vector: By defining an explicit function which computes the magnitude of a g 3 min read

• How to compute the eigenvalues and right eigenvectors of a given square array using NumPY? In this article, we will discuss how to compute the eigenvalues and right eigenvectors of a given square array using NumPy library. Example: Suppose we have a matrix as: [[1,2], [2,3]] Eigenvalue we get from this matrix or square array is: [-0.23606798 4.23606798] Eigenvectors of this matrix are: [[ 2 min read