In this tutorial, we will look at the syntax and usage of the numpy arange() function with the help of some examples.

## What does numpy arange() do?

The numpy arange() function is used to create an array of evenly spaced values in a given interval with a uniform step size. The following is the syntax:

import numpy as np # np.arange with all the default paramters arr = np.arange(start=0, stop, step=1, dtype=None, like=None) # mostly you'll be using only these paramters arr = np.arange(start, stop, step)

It returns a numpy array of values that are evenly spaced. Note that the values are generated in the half-open interval `[start, stop)`

. That is the interval including *start *but excluding *stop*.

Also, this function is similar to the numpy linspace() function which also generates evenly spaced values in linear space. More on the difference between the two at the end of this tutorial.

## Examples

Let’s look at some examples of using the numpy arange() function.

### 1. Generate numbers from 0

If you only pass a single number to the `np.arange()`

function, it will return an array of values starting from 0 with a step-size of 1 till (but not including) the number you passed. For example, to generate values from 0 to 4 –

import numpy as np # generate values from 0 to 4 arr = np.arange(5) # display the returned array print(arr)

Output:

[0 1 2 3 4]

Here, we only pass one value to the function, 5 which is treated as the value for the parameter *stop.* For paramters *start *and *step*, their default values – 0 and 1 are used respectively. Thus we get an array of values from 0 to 5 (5 not included) with a step-size of 1.

**Data Science Programs By Skill Level**

**Introductory** ⭐

- Harvard University Data Science: Learn R Basics for Data Science
- Standford University Data Science: Introduction to Machine Learning
- UC Davis Data Science: Learn SQL Basics for Data Science
- IBM Data Science: Professional Certificate in Data Science
- IBM Data Analysis: Professional Certificate in Data Analytics
- Google Data Analysis: Professional Certificate in Data Analytics
- IBM Data Science: Professional Certificate in Python Data Science
- IBM Data Engineering Fundamentals: Python Basics for Data Science

**Intermediate ⭐⭐⭐**

- Harvard University Learning Python for Data Science: Introduction to Data Science with Python
- Harvard University Computer Science Courses: Using Python for Research
- IBM Python Data Science: Visualizing Data with Python
- DeepLearning.AI Data Science and Machine Learning: Deep Learning Specialization

**Advanced ⭐⭐⭐⭐⭐**

- UC San Diego Data Science: Python for Data Science
- UC San Diego Data Science: Probability and Statistics in Data Science using Python
- Google Data Analysis: Professional Certificate in Advanced Data Analytics
- MIT Statistics and Data Science: Machine Learning with Python - from Linear Models to Deep Learning
- MIT Statistics and Data Science: MicroMasters® Program in Statistics and Data Science

**🔎 Find Data Science Programs 👨💻 111,889 already enrolled**

Disclaimer: Data Science Parichay is reader supported. When you purchase a course through a link on this site, we may earn a small commission at no additional cost to you. Earned commissions help support this website and its team of writers.

### 2. Evenly spaced values between two numbers

Let’s now generate values between two custom numbers. For example, values between 2 and 9 –

# generate values between 2 and 9 arr = np.arange(2, 9) # display the returned array print(arr)

Output:

[2 3 4 5 6 7 8]

Here, 2 is the start value and 9 is the stop value. Since we have not specified the step-size, its default value of 1 is used.

### 3. Using a custom step-size

Let’s now generate values between 2 and 9 with a step-size of 2. For this, pass the desired step-size to the `step`

parameter.

# generate values between 2 and 9 arr = np.arange(2, 9, step=2) # display the returned array print(arr)

Output:

[2 4 6 8]

You can see that the resulting values are still evenly spaced but now have a step-size of two.

For more on the numpy arange() function, refer to its documentation.

## Numpy linspace() vs arange()

Both the numpy linspace() and arange() functions are used to generate evenly spaced values in a given interval but there are some differences between the two –

- By default, the
`np.linspace()`

function generates values in the range`[start, stop]`

where as the`np.arange()`

generates values in the half-open interval`[start, stop)`

. - You can specify the number of values you want to generate with
`np.linspace()`

but not with`np.arange()`

- You can specify the step-size with
`np.arange()`

but not with`np.linspace()`

Thus if you want to generate evenly-spaced values between two numbers and want a spcific step-size use `np.arange()`

or if you want to generate a fixed number of values that are evenly-spaced between two numbers use `np.linspace()`

.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having numpy version 1.18.5

**Subscribe to our newsletter for more informative guides and tutorials. ****We do not spam and you can opt out any time.**