To work with an array in Python, use the numpy library. The numpy provides an important data type called a **nd****array.**

**np.arange**

To create an array based on numerical routines in Python, use the **np.arange()** function. The **numpy.arange()** function returns an instance of **ndarray** with evenly spaced values.

```
import numpy as np
arr = np.arange(start=0, stop=10, step=2)
print(arr)
```

**Output**

`[0 2 4 6 8]`

In this example, np.arange() accepts three arguments.

**start****stop****step**

The **start **parameter specifies from where you want to start the range of your array.

The **stop **argument specifies what is the endpoint of your array.

The **step** argument specifies the spacing between every two consecutive values in the array. The step cannot be 0. Otherwise, you’ll get a **ZeroDivisionError**.

In our code, we started with 0 up to 10 and the spacing is 2 that means first value is 0 and then space of 2 which means the next value is 0 + 2 = 2 and then 2 + 2 = 4 and then 4 + 2 = 6 and 6 + 2 = 8.

The endpoint is **10,** which is not included in the output because the **np.arange()** function does not include the endpoint.

**np.arange include endpoint**

To **include endpoint** in **np.arange()** function, increase your upper bound by one increment. By default, the np.arange() function does not allow us to include an endpoint, but by increasing your upper value, you can get your endpoint.

For example, in the above code, if you want to include 10 in your output, then you have to set the upper value or stop value to 11. That way, it will include 10 in your output.

```
import numpy as np
arr = np.arange(start=0, stop=11, step=2)
print(arr)
```

**Output**

`[ 0 2 4 6 8 10]`

Woohoo! We get the endpoint in the output as expected in the **numpy arange()** function.

That’s it for **np.arange include endpoint** tutorial.

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.