{"id":30162,"date":"2024-05-31T15:09:14","date_gmt":"2024-05-31T09:39:14","guid":{"rendered":"https:\/\/codeforgeek.com\/?p=30162"},"modified":"2024-05-31T15:09:15","modified_gmt":"2024-05-31T09:39:15","slug":"numpy-hypot-in-python","status":"publish","type":"post","link":"https:\/\/codeforgeek.com\/numpy-hypot-in-python\/","title":{"rendered":"Understanding numpy.hypot() in Python"},"content":{"rendered":"\n

Have you ever needed to find the distance between two points in space? In this guide, we will solve this problem using the NumPy hypot()<\/strong> function. By using hypot, we can accurately measure the straight-line distance between two points in space, typically known as the hypotenuse. Let us look at this function in detail.<\/p>\n\n\n\n

What is Euclidean Distance?<\/h2>\n\n\n\n

Euclidean<\/strong> Distance<\/strong> measures a straight-line distance between two points in space<\/strong>. <\/p>\n\n\n\n

Let me illustrate this with an example. Consider a number line (a one-dimensional spatial). If you have two points such as 3 and 7<\/strong>, it simply means that their Euclidean Distance is the absolute difference between those two points.<\/p>\n\n\n\n

\"Euclidean<\/figure>\n\n\n\n

Now consider a flat map or plane<\/strong> that represents a two-dimensional space. You want to determine the distance covered by moving from position (1,2) to position (4,6). To find the Euclidean Distance, use the Pythagorean theorem:<\/p>\n\n\n\n

\"Pythagorean<\/figure>\n\n\n\n

Now that we are speaking of the Pythagorean theorem, let’s first talk about hypotenuse<\/strong>. When we draw a right-angled triangle<\/strong>, the side just opposite the right angle is what we call hypotenuse, and it is also the longest side<\/strong> of that triangle. In a 2D plane, the Euclidean distance between two points (1, 2) and (4, 6) is the length of the hypotenuse of a right triangle formed by these points.<\/p>\n\n\n\n

\"Hypotenuse\"<\/figure>\n\n\n\n

Introducing numpy.hypot() Function<\/h2>\n\n\n\n

The numpy.hypot<\/strong> function in the NumPy library calculates the Euclidean distance between two points in a 2D space. It finds the length of the hypotenuse<\/strong> of a right-angled triangle when given the lengths of the other two sides. This function is useful for measuring distances in machine learning, computer vision, and physics.<\/p>\n\n\n\n

Syntax:<\/h3>\n\n\n\n

Let’s look at the format of this function.<\/p>\n\n\n

\nnumpy.hypot(x1, x2, \/, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, **kwargs)\n<\/pre><\/div>\n\n\n
Here is what each parameter<\/strong> means.<\/p>\n\n\n\n