np arange with Example

In this python tutorial. we will learn how to use NumPy arrange method. The np.arange() method builds a very basic array based on a user-supplied numerical range.

NumPy is a Python library that is commonly regarded as the most significant for numerical computation. The np array is used to generate numerical ranges.

Difference between inbuilt range and np.arrange() Method

Both functions accept the start, stop, and step arguments, which is one important commonality. However, range() has an important limitation – it can only work with integers! If you pass in any other data type, you will get a TypeError.

The Syntax:
numpy.arange([start, ]stop, [step, ], dtype=None) -> numpy.ndarray

The parameters are:

• start: This is the number (integer or decimal) that defines the first value in the array.
• stop: This is the number that defines the end of the array and isn’t included in the array.
• step: This is the number that defines the spacing (difference) between each two consecutive values in the array and defaults to 1.
• dtype: This is the type of the elements of the output array and defaults to None.

How To Import NumPy You must first import the NumPy library into your Python script before using the np.arange() method. This can be done with the following code:

import numpy as np

Simple Example Using np arrange

The sample code to generate some numerical array:

np.arange(0,6)
np.arange(-3,2)
np.arange(0,0)

Output:

array([0, 1, 2, 3, 4, 5, 6])

array([-3, -2, -1,  0,  1,  2])

array([], dtype=int64)

np.arange() method using a single argument

Let’s create a python array using np.arrange() by passing a single parameter:

np.arange(1)
np.arange(5)

Output:

array([0])

array([0, 1, 2, 3, 4])

np.arange() Method’s step Argument

The np.arange() method in NumPy takes an optional third argument, step, which specifies how much space should be between each element of the array it returns. The step is set to one by default.

Let’s create an array that had a space of 5 integers between them, we could specify step=5:

np.arange(0,10, step=5)

Output:

array([0, 5])