Arrays are not native data types in Python. To use an array in Python, we need to use the **numpy **library. For large arrays, a vectorized **numpy** operation is the fastest.

**np.arange**

The **np.arange()** function returns an array with evenly spaced elements as per the interval. To use the **np.arange()** method in your Python script, you have to import a Numpy library.

`import numpy as np`

Let’s see the syntax of numpy arange() method.

**Syntax**

`numpy.arange(start, stop, step, dtype=None)`

**Example**

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

**Output**

`[ 0 2 4 6 8 10]`

You can see that we got the evenly spaced array elements in the output.

The **np.arange()** method **includes** the start value but **excludes** the endpoint value. To include the endpoint in np.arange()’s output, set the upper limit by **+1** that way, it will include your desired value.

**range**

To create a sequence of numbers, starting from 0 by default, and increments by 1, use the built-in Python range() function.

**Syntax**

`range(start, stop, step)`

**Example**

```
data = range(6)
for i in data:
print(i)
```

**Output**

```
0
1
2
3
4
5
```

In this example, we called the **range()** function with only one argument that returns a sequence of numbers from 0 to 5. Here too, if you want to include the endpoint then you have to set the upper limit by +1, and that way, it will include your desired endpoint in the output.

**np.arange vs range**

- The main difference between
**range**and**np.arange**is that the**range()**function returns an iterator instead of a**list**and**np.arange()**function gives a**numpy array**that consists of evenly spaced values within a given interval. - The range() function generates a sequence of
**integer**values lying between a certain range. - The
**range()**is a built-in function whereas**arange()**is a**numpy**library function. - The
**range()**function is more convenient when you need to iterate values using the**for loop**. The**np.arange()**function is more useful when you are working with arrays and you need to generate an array based on a specific sequence.

That’s it for np.arange vs range comparison article.

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.