# np.arange() | NumPy Arange Function in Python

By | September 27, 2021

## What is numpy.arrange() Function?

The Python NumPy library comes with many inbuilt functions and `arange()`is one of those.

The Python numpy `arange()`function is similar to the Python range() function. As the Python `range()` function return a `range()` iterable object of elements similarly the numpy `arange()` function returns a numpy `ndarray`object containing elements with evenly spaced intervals.

Let’s say you want to create a numpy array of 100 elements values from o to 99, so instead of writing all the values in a list and converting it to a numpy array or using a for a loop. We can simply use the Python NumPy `arange()`function and create an array of 100 elements with a single statement.

#### NumPy arrage() Syntax

`np.arange(start, stop, step, dtype=none)`

#### Parameters

• start, represent the starting number from where the element values of the array should start.
• end represents the excluded ending point up to which the arrange function should put numbers.
• step represents the gap or interval between the elements, by default its value is 1.
• dtype represent the data type of all the elements.

### How to use numpy.arange() function?

While using the `arange()` function all the other parameters are optional except the `end`.

#### Example 1: np.arange() function with end parameter

```>>> import numpy as np
>>> arr = np.arange(9)
>>> print(arr)
[0 1 2 3 4 5 6 7 8]```

The end parameter value does not include in the array.

#### Example 2: np.arange() function with start and end parameter

```>>> import numpy as np
>>> arr = np.arange(1, 20)
>>> print(arr)
[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]```

start =1 (include), and end = 20 (excluded)

#### Example 3: np.arange() function with start, end, and step parameter

```>>> import numpy as np
>>> arr = np.arange(1, 20, 2)
>>> print(arr)
[ 1 3 5 7 9 11 13 15 17 19]```

#### Example 4: np.arange() function with start, end, step and type parameter

```>>> import numpy as np
>>> arr = np.arange(1, 20, 2, float)
>>> print(arr)
[ 1. 3. 5. 7. 9. 11. 13. 15. 17. 19.]```

## Summary

• The Python numpy `arange()`function is used to create a numpy array of elements with equally spaced intervals.
• It can accept 4 parameters start, end, steps, and type.
• Only end parameter is mandatory and the other 3 are optional.
• The `arange()` function includes the start value but excludes the end value for the array.