Advance Slicing And Indexing + Numpy Array Walkthrough

Author(s): Adam Ross Nelson

Originally published on Towards AI.

Exploring advanced slicing techniques with Numpy, skimage, + Python

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When working with NumPy arrays, mastering advanced slicing techniques can greatly enhance your data manipulation capabilities. In this article, we’ll walk through common indexing and slicing approaches.

The first portion of this article will demonstrate with simple on dimensional arrays, while the second portion of this article will demonstrate an image (which can also be represented as an array and the skimage.io module.

The final portion of this article also provides a careful point-by-point look at the advanced slicing techniques and syntax discussed here.

The Last Item

To begin, let’s create a NumPy array. If you want to grab just the last item in the array, you can use the negative index -1. This will return the last element.

import numpy as nparr = np.array([1, 2, 3, 4, 5])# Grab the last itemlast_item = arr[-1]print(last_item) # Output: 5

Every Other Item

If you need to access every other element in an array, you can use the slicing notation ::2. This will skip elements and select every second item in the array.

# Get every other itemevery_other_item = arr[::2]print(every_other_item) # Output: [1 3 5]

Reversals

Reversing an array is straightforward using the ::-1 slicing notation. This flips the array in… Read the full blog for free on Medium.

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Published via Towards AI