{"id":4515,"date":"2022-08-15T09:08:14","date_gmt":"2022-08-15T09:08:14","guid":{"rendered":"https:\/\/www.pythontutorial.net\/?page_id=4515"},"modified":"2022-08-16T05:05:03","modified_gmt":"2022-08-16T05:05:03","slug":"numpy-stack","status":"publish","type":"page","link":"https:\/\/www.pythontutorial.net\/python-numpy\/numpy-stack\/","title":{"rendered":"NumPy stack()"},"content":{"rendered":"\n

Summary<\/strong>: in this tutorial, you’ll learn how to use the NumPy stack()<\/code> function to join two or more arrays into a single array.<\/p>\n\n\n\n

Introduction to the NumPy stack() function #<\/a><\/h2>\n\n\n\n

The stack()<\/code> function two or more arrays<\/a> into a single array. Unlike the concatenate()<\/a><\/code> function, the stack() function joins 1D arrays to be one 2D array and joins 2D arrays to be one 3D array.<\/p>\n\n\n\n

The following shows the syntax of the stack()<\/code> function:<\/p>\n\n\n

numpy.stack((a1,a2,...),axis=0<\/span>)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
In this syntax, the (a1, a2, …) is a sequence of arrays with ndarray<\/code> type or array-like objects. All arrays a1, a2, .. must have the same shape.<\/p>\n\n\n\n
The axis<\/code> parameter specifies the axis in the result array along which the function stacks the input arrays. By default, the axis is zero which joins the input arrays vertically.<\/p>\n\n\n\n
NumPy stack() function examples #<\/a><\/h2>\n\n\n\n
Let’s take some examples of using the stack()<\/code> function.<\/p>\n\n\n\n
1) Using stack() function to join 1D arrays #<\/a><\/h3>\n\n\n\n
The following example uses the stack()<\/code> function to join two 1D arrays:<\/p>\n\n\n
import<\/span> numpy as<\/span> np\n\na = np.array([1<\/span>, 2<\/span>])\nb = np.array([3<\/span>, 4<\/span>])\n\nc = np.stack((a, b))\nprint(c)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Output:<\/p>\n\n\n
[[1<\/span> 2<\/span>] \n [3<\/span> 4<\/span>]]<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
\"numpy<\/figure>\n\n\n\n
The following example uses the stack()<\/code> function to join two 1D arrays horizontally by using axis 1:<\/p>\n\n\n
import<\/span> numpy as<\/span> np\n\na = np.array([1<\/span>, 2<\/span>])\nb = np.array([3<\/span>, 4<\/span>])\n\nc = np.stack((a, b), axis=1<\/span>)\nprint(c)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Output:<\/p>\n\n\n
[[1<\/span> 3<\/span>] \n [2<\/span> 4<\/span>]]<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
2) Using numpy stack() function to join 2D arrays #<\/a><\/h3>\n\n\n\n
The following example uses the stack()<\/code> function to join elements of two 2D arrays. The result is a 3D array:<\/p>\n\n\n
import<\/span> numpy as<\/span> np\n\na = np.array([\n    [1<\/span>, 2<\/span>],\n    [3<\/span>, 4<\/span>]\n])\nb = np.array([\n    [5<\/span>, 6<\/span>],\n    [7<\/span>, 8<\/span>]\n])\n\nc = np.stack((a, b))\nprint(c)\nprint(c.shape)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
\"numpy<\/figure>\n\n\n\n
Output:<\/p>\n\n\n
[[[1<\/span> 2<\/span>]\n  [3<\/span> 4<\/span>]]\n [[5<\/span> 6<\/span>]\n  [7<\/span> 8<\/span>]]]\n(2<\/span>, 2<\/span>, 2<\/span>)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
NumPy stack() vs. concatenate() #<\/a><\/h2>\n\n\n\n
The following example illustrates the difference between stack()<\/code> and concatenate()<\/code> functions:<\/p>\n\n\n
a = np.array<\/span>([1<\/span>,2<\/span>])\nb = np.array<\/span>([3<\/span>,4<\/span>])\n\nc = np.concatenate((a,b)) # return 1-D array                    <\/span>\nd = np.stack((a,b)) # return 2-D array<\/span>\nprint<\/span>(c)\nprint<\/span>(d)<\/code><\/span>Code language:<\/span> PHP<\/span> (<\/span>php<\/span>)<\/span><\/small><\/pre>\n\n\n
Output:<\/p>\n\n\n
[1<\/span> 2<\/span> 3<\/span> 4<\/span>]\n[[1<\/span> 2<\/span>]\n [3<\/span> 4<\/span>]]<\/code><\/span>Code language:<\/span> JSON \/ JSON with Comments<\/span> (<\/span>json<\/span>)<\/span><\/small><\/pre>\n\n\n
\"numpy<\/figure>\n\n\n\n
In this example, the concatenate() function joins elements of two arrays along an existing axis<\/em> while the stack()<\/code> function joins the two arrays along a new axis<\/em>.<\/p>\n\n\n\n
Summary #<\/a><\/h2>\n\n\n\n
  • Use the numpy stack()<\/code> function to join two or more arrays into one.<\/li><\/ul>\n
    \n\t
    \n\t\t
    Was this tutorial helpful ?<\/div>\n\t\t
    \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t<\/path>\n\t\t\t\t<\/svg>\n\t\t\t\t Yes <\/span>\n\t\t\t<\/button>\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t<\/path>\n\t\t\t\t<\/svg>\n\t\t\t\t No <\/span>\n\t\t\t<\/button>\n\t\t<\/div>\n\t<\/header>\n\n\t
    \n\t\t
    \n\t\t\t
    <\/div>\n\t\t\t
Besides the stack()<\/code> function, NumPy also has vstack()<\/a><\/code> function that joins two or more arrays vertically and hstack()<\/a><\/code> function that joins two or more arrays horizontally.<\/p>\n\n\n\n