{"id":7056,"date":"2023-11-01T15:27:08","date_gmt":"2023-11-01T09:57:08","guid":{"rendered":"https:\/\/python-programs.com\/?p=7056"},"modified":"2023-11-10T12:11:43","modified_gmt":"2023-11-10T06:41:43","slug":"python-numpy-reshape-function-tutorial-with-examples","status":"publish","type":"post","link":"https:\/\/python-programs.com\/python-numpy-reshape-function-tutorial-with-examples\/","title":{"rendered":"Python: numpy.reshape() function Tutorial with examples"},"content":{"rendered":"<h2>Understanding numpy.reshape() function Tutorial with examples<\/h2>\n<p>In this article we will see how we can use numpy.reshape() function to change the shape of a numpy array.<\/p>\n<h3><u>numpy.reshape<\/u>() :<\/h3>\n<pre><u>Syntax<\/u>:- numpy.reshape(a, newshape, order='C')<\/pre>\n<p>where,<\/p>\n<ul>\n<li><strong>a :<\/strong>\u00a0Array, list or list of lists which need to be reshaped.<\/li>\n<li><strong>newshape :<\/strong>\u00a0New shape which is a tuple or a int. (Pass tuple for converting a 2D or 3D array and Pass integer for creating array of 1D shape.)<\/li>\n<\/ul>\n<ul>\n<li><strong>order :<\/strong>\u00a0Order in which items from given array will be used. (&#8216;<strong>C<\/strong>&#8216;: Read items from array in row-wise manner, &#8216;<strong>F<\/strong>&#8216;: Read items from array in column-wise manner, &#8216;<strong>A<\/strong>&#8216;: Read items from array based on memory order of items)<\/li>\n<\/ul>\n<h3>Converting shapes of Numpy arrays using numpy.reshape() :<\/h3>\n<h4>Use numpy.reshape() to convert a 1D numpy array to a 2D Numpy array :<\/h4>\n<p>To pass 1D numpy array to 2D numpy array we will pass array and tuple i.e. (3&#215;3) as numpy to <code>reshape()<\/code> function.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import numpy as sc\r\n# Produce a 1D Numpy array from a given list\r\nnumArr = sc.array([10, 20, 30, 40, 50, 60, 70, 80, 92])\r\nprint('Original Numpy array:')\r\nprint(numArr)\r\n# Convert the 1D Numpy array to a 2D Numpy array\r\narr_twoD = sc.reshape(numArr, (3,3))\r\nprint('2D Numpy array:')\r\nprint(arr_twoD)\r\n<\/pre>\n<pre>Output :\r\nOriginal Numpy array:\r\n[10 20 30 40 50 60 70 80 92]\r\n2D Numpy array:\r\n[[10 20 30]\r\n [40 50 60]\r\n\u00a0[70 80 90]]<\/pre>\n<h3>New shape must be compatible with the original shape :<\/h3>\n<p>The new shape formed must be compatible with the shape of array passed i.e. if rows denoted by &#8216;R&#8217;, columns by &#8216;C&#8217;, total no. of items by &#8216;N&#8217; then new shape must satisfy the relation <em>R*C=N <\/em>otherwise it will give rise to error.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import numpy as sc\r\n# Produce a 1D Numpy array from a given list\r\nnumArr = sc.array([10, 20, 30, 40, 50, 60, 70, 80, 92])\r\nprint('Original Numpy array:')\r\nprint(numArr)\r\n# convert the 1D Numpy array to a 2D Numpy array\r\narr_twoD = sc.reshape(numArr, (3,2))\r\nprint('2D Numpy array:')\r\nprint(arr_twoD)\r\n<\/pre>\n<pre>Output :\r\nValueError: total size of new array must be unchanged\r\n\r\n<\/pre>\n<h4><strong>Using numpy.reshape() to convert a 1D numpy array to a 3D Numpy array :<\/strong><\/h4>\n<p>We can convert a 1D numpy array into 3D numpy array passing array and shape of 3D array as tuple to reshape() function.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import numpy as sc\r\n# Produce a 1D Numpy array from a given list\r\nnumArr = sc.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 91, 95, 99])\r\nprint('Original Numpy array:')\r\nprint(numArr)\r\n# Convert the 1D Numpy array to a 3D Numpy array\r\narr_threeD = sc.reshape(numArr, (3,2,2))\r\nprint('3D Numpy array:')\r\nprint(arr_threeD)\r\n<\/pre>\n<pre>Output :\r\nOriginal Numpy array:\r\n[10 20 30 40 50 60 70 80 90 91 95 99]\r\n3D Numpy array:\r\n[[[10 20]\r\n  [30 40]]\r\n [[50 60]\r\n  [70 80]]\r\n [[90 91]\r\n\u00a0 [95 99]]]<\/pre>\n<h3>Use numpy.reshape() to convert a 3D numpy array to a 2D Numpy array :<\/h3>\n<p>We can also even convert a 3D numpy array to 2D numpy array.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import numpy as sc\r\n# Create a 3D numpy array\r\narr_threeD = sc.array([[[10, 20],\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 [30, 40],\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 [50, 60]],\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 [[70, 80],\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 [90, 91],\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 [95, 99]]])\r\nprint('3D Numpy array:')\r\nprint(arr_threeD)\r\n# Converting 3D numpy array to numpy array of size 2x6\r\narr_twoD = sc.reshape(arr_threeD, (2,6))\r\nprint('2D Numpy Array:')\r\nprint(arr_twoD)<\/pre>\n<pre>Output :\r\n3D Numpy array:\r\n[[[10 20]\r\n  [30 40]\r\n  [50 60]]\r\n [[70 80]\r\n  [90 91]\r\n  [95 99]]]\r\n2D Numpy Array:\r\n[[10 20 30 40 50 60]\r\n\u00a0[70 80 90 91 95 99]]<\/pre>\n<h3>Use numpy.reshape() to convert a 2D numpy array to a 1D Numpy array :<\/h3>\n<p>If we pass a numpy array and &#8216;-1&#8217; to reshape() then it will get convert into array of any shape to a flat array.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import numpy as sc\r\narr_twoD = sc.array([[10, 20, 30],\r\n                [30, 40, 50],\r\n                [60, 70, 82]])     \r\n# Covert numpy array of any shape to 1D array\r\nflatn_arr = sc.reshape(arr_twoD, -1)\r\nprint('1D Numpy array:')\r\nprint(flatn_arr) \r\n<\/pre>\n<pre>Output :\r\n1D Numpy array:\r\n[10 20 30 30 40 50 60 70 82]<\/pre>\n<h3>numpy.reshape() returns a new view object if possible :<\/h3>\n<p>If possible in some scenarios reshape() function returns a view of the passed object. If we modify anything in view object it will reflect in main objet and vice-versa.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import numpy as sc\r\n# create a 1D Numpy array\r\nnum_arr = sc.array([10, 20, 30, 40, 50, 60, 70, 80, 92])  \r\n# Get a View object of any shape \r\narr_twoD = sc.reshape(num_arr, (3,3))\r\nprint('Original array:')\r\nprint(arr_twoD)\r\n# Modify the 5th element of the original array \r\n# Modification will also be visible in view object\r\nnum_arr[4] = 9\r\nprint('Modified 1D Numpy array:')\r\nprint(num_arr)\r\nprint('2D Numpy array:')\r\nprint(arr_twoD)\r\n<\/pre>\n<pre>Output :\r\nOriginal array:\r\n[[10 20 30]\r\n [40 50 60]\r\n [70 80 92]]\r\nModified 1D Numpy array:\r\n[10 20 30 40\u00a0 9 60 70 80 90]\r\n2D Numpy array:\r\n[[10 20 30]\r\n [40\u00a0 9 60]\r\n\u00a0[70 80 90]]<\/pre>\n<h3>How to check if reshape() returned a view object ?<\/h3>\n<p>In some scenarios reshape() function may not return a view object. We can check what object reshape() returns by seeing its base attribute if it is view or not.<\/p>\n<p>If base attribute is <em>None <\/em>then it is not a view object, else it is a view object i.e. base attribute point to original array object.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import numpy as sc\r\n# create a 1D Numpy array\r\nnum_arr = sc.array([10, 20, 30, 40, 50, 60, 70, 80, 90])\r\narr_twoD = sc.reshape(num_arr, (3,3))\r\nif arr_twoD.base is not None:\r\n    print('arr_twoD is a view of original array')\r\n    print('base array : ', arr_twoD.base)\r\n<\/pre>\n<pre>Output :\r\narr_twoD is a view of original array\r\nbase array :\u00a0 [10 20 30 40 50 60 70 80 90]<\/pre>\n<h3>numpy.reshape() &amp; different type of order parameters :<\/h3>\n<p>We can also pass order parameter whose value can be &#8216;C&#8217; or &#8216;F&#8217; or &#8216;A&#8217;. This parameter will decide in which order the elements of given array will be used. Default value of order parameter is &#8216;C&#8217;.<\/p>\n<h3><strong>Convert 1D to 2D array row wise with order \u2018C\u2019 :<\/strong><\/h3>\n<p><strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/strong>By passing order paramter &#8216;C&#8217; in reshape() function the given array will be read row wise.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import numpy as sc\r\n# create a 1D Numpy array\r\nnum_arr = sc.array([10, 20, 30, 40, 50, 60, 70, 80, 92])\r\nprint('original array:')\r\nprint(num_arr)\r\n# Covert 1D numpy array to 2D by reading array in row wise manner\r\narr_twoD = sc.reshape(num_arr, (3, 3), order = 'C')\r\nprint('2D array being read in row wise manner:')\r\nprint(arr_twoD)\r\n<\/pre>\n<pre>Output :\r\noriginal array:\r\n[10 20 30 40 50 60 70 80 92]\r\n2D array being read in row wise manner:\r\n[[10 20 30]\r\n [40 50 60]\r\n\u00a0[70 80 90]]<\/pre>\n<h4>Convert 1D to 2D array column wise with order \u2018F\u2019 :<\/h4>\n<p><strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/strong>By passing order parameter &#8216;C&#8217; in reshape() function the given array will be read row wise.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import numpy as sc\r\n# create a 1D Numpy array\r\nnum_arr = sc.array([10, 20, 30, 40, 50, 60, 70, 80, 92])\r\nprint('original array:')\r\nprint(num_arr)\r\n# Covert 1D numpy array to 2D by reading array in column wise manner\r\narr_twoD = sc.reshape(num_arr, (3, 3), order = 'F')\r\nprint('2D array being read in column wise manner:')\r\nprint(arr_twoD)\r\n<\/pre>\n<pre>Output :\r\noriginal array:\r\n[10 20 30 40 50 60 70 80 92]\r\n2D array being read in column wise manner:\r\n[[10 40 70]\r\n [20 50 80]\r\n\u00a0[30 60 90]]<\/pre>\n<h4>Convert 1D to 2D array by memory layout with parameter order \u201cA\u201d :<\/h4>\n<p><strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/strong>If we pass order as &#8216;A&#8217; in reshape() function, then items of input array will be read\u00a0 basis on internal memory unit.<\/p>\n<p>Here it will read elements based on memory layout of original given array and it does not consider the current view of input array<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import numpy as sc\r\n# create a 1D Numpy array\r\nnum_arr = sc.array([10, 20, 30, 40, 50, 60, 70, 80, 90])\r\nprint('Original 1D array: ',num_arr)\r\n# Create a 2D view object and get transpose view of it\r\narr_twoD = sc.reshape(num_arr, (3, 3)).T\r\nprint('2D transposed View:')\r\nprint(arr_twoD)\r\n# Read elements in row wise from memory layout of original 1D array\r\nflatn_arr = sc.reshape(arr_twoD, 9, order='A')\r\nprint('Flattened 1D array')\r\nprint(flatn_arr)\r\n<\/pre>\n<pre>Output :\r\nOriginal 1D array:\u00a0 [10 20 30 40 50 60 70 80 90]\r\n2D transposed View:\r\n[[10 40 70]\r\n [20 50 80]\r\n [30 60 90]]\r\nFlattened 1D array\r\n[10 20 30 40 50 60 70 80 90]<\/pre>\n<h3>Convert the shape of a list using numpy.reshape() :<\/h3>\n<ul>\n<li>In reshape() function we can pass list or list of list instead of array.<\/li>\n<\/ul>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import numpy as sc\r\nnum_list = [10,20,30,40,50,60,70,80,90]\r\n# To convert a list to 2D Numpy array\r\narr_twoD = sc.reshape(num_list, (3,3))\r\nprint('2D Numpy array:')\r\nprint(arr_twoD)\r\n<\/pre>\n<pre>Output :\r\n2D Numpy array:\r\n[[10 20 30]\r\n [40 50 60]\r\n\u00a0[70 80 90]]<\/pre>\n<ul>\n<li>We can also convert a 2D numpy array to list of list<\/li>\n<\/ul>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">import numpy as sc\r\nnum_list = [10,20,30,40,50,60,70,80,90]\r\n# Convert a given list to 2D Numpy array\r\narr_twoD = sc.reshape(num_list, (3,3))\r\nprint('2D Numpy array:')\r\nprint(arr_twoD)\r\n# Convert the 2D Numpy array to list of list\r\nlist_list = [ list(elem) for elem in arr_twoD]\r\nprint('List of List: ')\r\nprint(list_list)\r\n<\/pre>\n<pre>Output :\r\n2D Numpy array:\r\n[[10 20 30]\r\n [40 50 60]\r\n [70 80 90]]\r\nList of List:\r\n[[10, 20, 30], [40, 50, 60], [70, 80, 90]]<\/pre>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Understanding numpy.reshape() function Tutorial with examples In this article we will see how we can use numpy.reshape() function to change the shape of a numpy array. numpy.reshape() : Syntax:- numpy.reshape(a, newshape, order=&#8217;C&#8217;) where, a :\u00a0Array, list or list of lists which need to be reshaped. newshape :\u00a0New shape which is a tuple or a int. &hellip;<\/p>\n<p class=\"read-more\"> <a class=\"\" href=\"https:\/\/python-programs.com\/python-numpy-reshape-function-tutorial-with-examples\/\"> <span class=\"screen-reader-text\">Python: numpy.reshape() function 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