# NumPy arange : Implementation with Steps

NumPy arrange functions allow you to create an array with equal or evenly spaced values. You can consider it as a built-in python function range() but it returns array instead a list return by range() function. In this tutorial you will implement numpy arrange() function with steps.

## Syntax of Numpy arrange function

The syntax of the arrange() function is below.

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

Here’s what each argument means:

• start (optional): The start of the interval. If not specified, it defaults to 0.
• stop: The end of the interval. The generated values are up to, but not including, this value.
• step (optional): Spacing between values. If not specified, it defaults to 1.
• dtype (optional): The data type of the output array. If not specified, the data type is inferred from the other input arguments.

## Steps to Implement Numpy arrange Function

### Step 1: Import NumPy

The first step is to import the NumPy package. You should make sure you have NumPy installed, and then import it into your Python script or Jupyter Notebook.

``import numpy as np``

### Step 2: Use arange function

The `arange` function takes three arguments: `start`, `stop`, and `step`. It generates values from `start` to `stop - step` with a step size of `step`. Use the lines below of code to generate the numpy array.

``````   start_value = 0
stop_value = 20
step_size = 4

result_array = np.arange(start_value, stop_value, step_size)``````

In this example, `result_array` will be `[0, 4, 8, 12, 16 ]`.

If you omit the `step` argument, it defaults to 1

``   result_array_default_step = np.arange(start_value, stop_value)``

Output

``[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19]``

### Step 3: Print or display the result

Now you can print the resulting array or use it in further calculations and manipulation.

``   print(result_array)``

You can also use the array for various mathematical operations or as needed in your application.

Below is the complete code.

``````import numpy as np

# Define parameters
start_value = 0
stop_value = 20
step_size = 4

# Use arange function
result_array = np.arange(start_value, stop_value, step_size)

# Print the result
print(result_array)``````

Output

``[ 0  4  8 12 16]``

You can Adjust the `start_value`, `stop_value`, and `step_size` according to your requirements.

## Conclusion

Numpy arange function is the quick method to create a numpy array whose difference is the same. In this tutorial you have learned the syntax of the numpy arange() function. The above steps allow you to implement the numpy arange function.

Hi, I am CodeTheBest. Here you will learn the best coding tutorials on the latest technologies like a flutter, react js, python, Julia, and many more in a single place.

## SPECIAL OFFER!

This Offer is Limited! Grab your Discount!
15000 ChatGPT Prompts
Offer Expires In: 