{"id":4379,"date":"2022-08-10T04:07:22","date_gmt":"2022-08-10T04:07:22","guid":{"rendered":"https:\/\/www.pythontutorial.net\/?page_id=4379"},"modified":"2022-08-10T04:11:17","modified_gmt":"2022-08-10T04:11:17","slug":"numpy-any","status":"publish","type":"page","link":"https:\/\/www.pythontutorial.net\/python-numpy\/numpy-any\/","title":{"rendered":"NumPy any()"},"content":{"rendered":"\n

Summary<\/strong>: in this tutorial, you’ll learn how to use the numpy any()<\/code> function that returns True<\/code> if any element in an array evaluates True<\/code>.<\/p>\n\n\n\n

Introduction to the numpy any() function #<\/a><\/h2>\n\n\n\n

The numpy any()<\/code> function returns True<\/code> if any element in an array (or along a given axis) evaluates to True<\/code>. <\/p>\n\n\n\n

Here’s the syntax of the any<\/code> function:<\/p>\n\n\n

numpy.any(a, axis=None<\/span>, out=None<\/span>, keepdims=<no value>, *, where=<no value>)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Typically, the input array contains numbers. In the boolean context, all non-zero numbers evaluate to True<\/code> while zero evaluates to False<\/code>. Therefore, the any()<\/code> function returns True<\/code> if any number in the array is nonzero or False<\/code> if all numbers are zero.<\/p>\n\n\n\n
NumPy any() function examples #<\/a><\/h2>\n\n\n\n
Let’s take some examples of using the any()<\/code> function.<\/p>\n\n\n\n
1) Using numpy any() function on 1-D array examples #<\/a><\/h3>\n\n\n\n
The following example uses the any()<\/code> function to test whether any number in an array are non-zero:<\/p>\n\n\n
import<\/span> numpy as<\/span> np\n\nresult = np.any([0<\/span>, 1<\/span>, 2<\/span>, 3<\/span>])\nprint(result)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Output:<\/p>\n\n\n
True<\/span><\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
The result is True<\/code> because the array of three non-zero numbers.<\/p>\n\n\n
import<\/span> numpy as<\/span> np\n\n\nresult = np.any(np.array([0<\/span>, 0<\/span>]))\nprint(result)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Output:<\/p>\n\n\n
False<\/span><\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
This example returns False<\/code> because all numbers in the array are zero. In fact, you can pass any object that can be converted into a list to the any()<\/code> function. For example:<\/p>\n\n\n
import<\/span> numpy as<\/span> np\n\n\nresult = np.any([0<\/span>, 0<\/span>])\nprint(result)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Output:<\/p>\n\n\n
False<\/span><\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
2) Using numpy any() function with a multidimensional array example #<\/a><\/h3>\n\n\n\n
The following example uses the any()<\/code> function to test if any elements of a multidimensional array evaluate to True<\/code>:<\/p>\n\n\n
import<\/span> numpy as<\/span> np\n\na = np.array([[0<\/span>, 1<\/span>], [2<\/span>, 3<\/span>]])\nresult = np.any(a)\nprint(result)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Output:<\/p>\n\n\n
True<\/span><\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Also, you can evaluate elements along an axis by passing the axis<\/code> argument like this:<\/p>\n\n\n
import<\/span> numpy as<\/span> np\n\na = np.array([\n    [0<\/span>, 0<\/span>],\n    [0<\/span>, 1<\/span>]\n])\nresult = np.any(a, axis=0<\/span>)\nprint(result)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Output:<\/p>\n\n\n
[False<\/span>  True<\/span>]<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
And axis-1:<\/p>\n\n\n
import<\/span> numpy as<\/span> np\n\na = np.array([\n    [0<\/span>, 0<\/span>],\n    [0<\/span>, 1<\/span>]\n])\nresult = np.any(a, axis=1<\/span>)\nprint(result)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Output:<\/p>\n\n\n
[False<\/span>  True<\/span>]<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Summary #<\/a><\/h2>\n\n\n\n
  • Use the numpy any<\/code> function to test whether any element in an array or along an axis evaluates to True<\/code>.<\/li><\/ul>\n
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In this syntax, a<\/code> is a numpy array<\/a> or any object that can be converted to an array e.g., a list<\/a>. <\/p>\n\n\n\n