{"id":19654,"date":"2021-06-29T08:05:12","date_gmt":"2021-06-29T08:05:12","guid":{"rendered":"https:\/\/www.askpython.com\/?p=19654"},"modified":"2021-06-30T09:10:28","modified_gmt":"2021-06-30T09:10:28","slug":"numpy-vstack","status":"publish","type":"post","link":"https:\/\/www.askpython.com\/python-modules\/numpy-vstack","title":{"rendered":"Numpy vstack() method – A Complete Overview"},"content":{"rendered":"\n

Hello everyone! In this tutorial, we will learn what the Numpy vstack()<\/code> method is and how to use it in Python. So let’s get started.<\/p>\n\n\n\n

What is numpy.vstack() method ?<\/h2>\n\n\n\n

Numpy.vstack()<\/code> is a function in Python that takes a tuple<\/a> of arrays and concatenates them vertically along the first dimension to make them a single array. <\/p>\n\n\n\n

It’s syntax is:<\/p>\n\n\n

\nnumpy.vstack(tup)\n<\/pre><\/div>\n\n\n
The parameter it takes is a tuple which is a sequence of ndarrays that we want to concatenate. The arrays must have the same shape along all axis except the first axis.<\/p>\n\n\n\n
The method returns a ndarray which is formed by stacking the arrays given in the input. The returned array will have at least 2 dimensions.<\/p>\n\n\n\n
Examples of Numpy vstack()<\/h2>\n\n\n\n
For linear 1-D arrays, all the arrays are stacked vertically to form a 2-D array. All the input arrays must have the same length.<\/p>\n\n\n
\nimport numpy\n\na = numpy.array([1, 2, 3, 4, 5])\nb = numpy.array([6, 7, 8, 9, 10])\nc = numpy.array([11, 12, 13, 14, 15])\n\nprint("Shape of array A:", a.shape)\nprint("Shape of array B:", b.shape)\nprint("Shape of array C:", c.shape)\nprint()\n\nstack = numpy.vstack((a, b, c))\nprint("Shape of new stacked array:", stack.shape)\nprint("Stacked array is")\nprint(stack)\n<\/pre><\/div>\n\n
\nShape of array A: (5,)\nShape of array B: (5,)\nShape of array C: (5,)\n\nShape of new stacked array: (3, 5)\nStacked array is\n[[ 1  2  3  4  5]\n [ 6  7  8  9 10]\n [11 12 13 14 15]]\n<\/pre><\/div>\n\n\n
For N-dimensional arrays, arrays are stacked along the first dimensions as shown in the following example.<\/p>\n\n\n
\nimport numpy\n\na = numpy.array([ [1, 2, 3], [4, 5, 6] ])\nb = numpy.array([ [7, 8, 9], [10, 11, 12] ])\n\nprint("Shape of array A:", a.shape)\nprint("Shape of array B:", b.shape)\nprint()\n\nstack = numpy.vstack((a, b))\nprint("Shape of new stacked array:", stack.shape)\nprint("Array is")\nprint(stack)\n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\nShape of array A: (2, 3)\nShape of array B: (2, 3)\n\nShape of new stacked array: (4, 3)\nArray is\n[[ 1  2  3]\n [ 4  5  6]\n [ 7  8  9]\n [10 11 12]]\n<\/pre><\/div>\n\n\n
For N-dimensional arrays, the shape of the arrays must be the same along all dimensions except the first dimension as shown below.<\/p>\n\n\n
\nimport numpy\n\na = numpy.array([ [1, 2], [3, 4] ])\nb = numpy.array([ [5, 6], [7, 8], [9, 10] ])\n\nprint("Shape of array A:", a.shape)\nprint("Shape of array B:", b.shape)\nprint()\n\nstack = numpy.vstack((a, b))\nprint("Shape of new stacked array:", stack.shape)\nprint("Array is")\nprint(stack)\n<\/pre><\/div>\n\n
\nShape of array A: (2, 2)\nShape of array B: (3, 2)\n\nShape of new stacked array: (5, 2)\nArray is\n[[ 1  2]\n [ 3  4]\n [ 5  6]\n [ 7  8]\n [ 9 10]]\n<\/pre><\/div>\n\n\n
Conclusion<\/h2>\n\n\n\n
In this Python tutorial, we learned about vstack()<\/code> method present in the NumPy module. This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r\/g\/b channels (third axis).<\/p>\n\n\n\n
Thanks for reading!!<\/p>\n","protected":false},"excerpt":{"rendered":"
Hello everyone! In this tutorial, we will learn what the Numpy vstack() method is and how to use it in Python. So let’s get started. What is numpy.vstack() method ? Numpy.vstack() is a function in Python that takes a tuple of arrays and concatenates them vertically along the first dimension to make them a single […]<\/p>\n","protected":false},"author":31,"featured_media":19656,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-19654","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python-modules"],"blocksy_meta":[],"_links":{"self":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/posts\/19654","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/users\/31"}],"replies":[{"embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/comments?post=19654"}],"version-history":[{"count":0,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/posts\/19654\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/media\/19656"}],"wp:attachment":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/media?parent=19654"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/categories?post=19654"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/tags?post=19654"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}