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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.

# Python program explaining# numpy.MaskedArray.reshape() method# importing numpy as geek# and numpy.ma module as maimportnumpyasgeekimportnumpy.maasma# creating input arrayin_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]]

# Python program explaining# numpy.MaskedArray.reshape() method# importing numpy as geek# and numpy.ma module as maimportnumpyasgeekimportnumpy.maasma# creating input arrayin_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 matrix.squeeze()

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TheÂ 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.Â There are mainly two ways of getting the magnitude of vector: By defining an explicit function which computes the magnitude of a

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.Â Example: Suppose we have a matrix as:Â [[1,2], [2,3]]Â Eigenvalue we get from this matrix or square array is:Â [-0.23606798 4.23606798] Eigenvectors of this matrix are

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