Numpy.reshape() function with example in python

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In this article, you will learn, How to reshape numpy arrays in python using numpy.reshape() function.

Before going further into article, first learn about numpy.reshape() function syntax and it’s parameters.

Syntax: numpy.reshape(a, newshape, order=’C’)
This function helps to get a new shape to an array without changing its data.

Parameters:
a : array_like
Array to be reshaped.

newshape : int or tuple of ints
The new shape should be compatible with the original shape. If an integer, then the result will be a 1-D array of that length. One shape dimension can be -1. In this case, the value is inferred from the length of the array and remaining dimensions.

order : {‘C’, ‘F’, ‘A’}, optional
Read the elements of a using this index order, and place the elements into the reshaped array using this index order. ‘C’ means to read / write the elements using C-like index order, with the last axis index changing fastest, back to the first axis index changing slowest. ‘F’ means to read / write the elements using Fortran-like index order, with the first index changing fastest, and the last index changing slowest. Note that the ‘C’ and ‘F’ options take no account of the memory layout of the underlying array, and only refer to the order of indexing. ‘A’ means to read / write the elements in Fortran-like index order if a is Fortran contiguous in memory, C-like order otherwise.

Returns:
reshaped_array : ndarray
This will be a new view object if possible; otherwise, it will be a copy. Note there is no guarantee of the memory layout (C- or Fortran- contiguous) of the returned array.

You can practice the code here with changing numbers. Practice more and learn more.

#python program to reshare arrays.

import numpy as practice array = practice.arange(10) print("Original array : \n", array) # shape array with 1 D array array = practice.arange(10).reshape(1, 10) print("\nshape array with 1 D array \n", array) # shape array with 2 rows and 5 columns (2d dimensions) array = practice.arange(10).reshape(2, 5) print("\narray reshaped with 2 rows and 4 columns : \n", array) # shape array with 5 rows and 2 columns (2d dimensions) array = practice.arange(10).reshape(5 ,2) print("\nshape array with 5 rows and 2 columns : \n", array) # Constructs 3D array array = practice.arange(8).reshape(2,2,2) print("\nOriginal array reshaped to 3D : \n", array)

Output:

Original array : 
 [0 1 2 3 4 5 6 7 8 9]

shape array with 1 D array 
 [[0 1 2 3 4 5 6 7 8 9]]

array reshaped with 2 rows and 4 columns : 
 [[0 1 2 3 4]
 [5 6 7 8 9]]

shape array with 5 rows and 2 columns : 
 [[0 1]
 [2 3]
 [4 5]
 [6 7]
 [8 9]]

Original array reshaped to 3D : 
 [[[0 1]
  [2 3]]

 [[4 5]
  [6 7]]]

 

 

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