To **calculate** the **euclidean** **distance** in **Python**, use the **math.dist() **function. The **math.dist()** is a built-in **method** that returns the **Euclidean** **distance** **between** two points (x and y), where x and y are the coordinates of that point.

Euclidean distance is the widely used technique for finding the distance between two points. This formula can calculate the distance between two points with n coordinates.

**Syntax**

`math.dist(x, y)`

**Arguments**

x | Required. Specifies point 1 |

y | Required. Specifies point 2 |

**Example**

```
import math
x = (2, 3)
y = (3, 5)
distance = math.dist(x, y)
print(distance)
```

**Output**

`2.23606797749979`

This math module has a function called dist. This dist() function is used for calculating the distance between two points. In addition, the dist() function performs the Euclidian distance calculation.

**Euclidian Distance Formula:**

This formula can be used to find the distance between two points. This is the basic Euclidian formula.

**Python Program for calculating Euclidean distance**

```
import math
x = input()
y = input()
x1, y1 = x.split(',')
x2, y2 = y.split(',')
x1 = int(x1)
x2 = int(x2)
y1 = int(y1)
y2 = int(y2)
distance = math.sqrt(((x2 - x1) ** 2) + (y2 - y1) ** 2)
print("The Euclidean distance between the points using formula is : ", str(distance))
```

**Output**

```
2,3
3,5
The Euclidean distance between the points using formula is : 2.23606797749979
```

We imported the math function to perform the square root operation in this program. We have got the input coordinates, and we have found the distance between two points by applying the values to the Euclidian distance formula.

**Using np.linalg.norm() function**

To **calculate** **Euclidean** **distance** using the **numpy** **library**, use the **np.linalg.norm()** function. The **np.linalg.norm() **function is used to calculate one of the eight different matrix norms or one of the vector norms.

```
import numpy as np
x = np.array((2, 3))
y = np.array((3, 5))
distance = np.linalg.norm(x - y)
print(distance)
```

**Output**

`2.23606797749979`

In this program, we used the numpy package. We created two numpy arrays. Then we have passed these numpy arrays as an argument to the **np.linalg.norm()** function. The norm() function is present inside the module called the linalg. This norm function is used to calculate the Euclidian distance between two points.

**Calculating Euclidean distance using np.dot() function**

The **np.dot()** function calculates the dot product of two arrays. It can handle 2D arrays but considers them as matrix and will perform matrix multiplication.

```
import numpy as np
x = np.array((2, 3))
y = np.array((3, 5))
d = x - y
distance = np.dot(d.T, d)
distance = np.sqrt(distance)
print(distance)
```

**Output**

`2.23606797749979`

In this program, we found the difference between two arrays and we have stored it in d. After finding the distance between two arrays we have made a transpose.

Then, we multiply the transpose and the normal array d using the **np.dot()** function. Then, we found the **np.sqrt()** of the distance found from the dot product. That is the Euclidian distance.

That’s it for this tutorial.

**Related posts**

How to Perform Matrix Multiplication in Python

Krunal Lathiya is a Software Engineer with over eight years of experience. He has developed a strong foundation in computer science principles and a passion for problem-solving. In addition, Krunal has excellent knowledge of Data Science and Machine Learning, and he is an expert in R Language. Krunal has experience with various programming languages and technologies, including PHP, Python, and JavaScript. He is comfortable working in front-end and back-end development.