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1D Array Indexing, Slicing & Step Strides0%
1D Array Indexing, Slicing & Step Strides
Beginner12 min read•Updated: Jul 11, 2026
1D Array Indexing, Slicing & Step Strides
Indexing and slicing allow you to extract individual elements, sub-ranges, and strided patterns from arrays. While 1D NumPy slicing closely resembles Python list slicing, it introduces fundamental differences in memory handling and performance.
1. Zero-Based & Negative Indexing
Like standard Python, NumPy uses zero-based indexing for forward navigation and negative integers to count backwards from the end:
import numpy as np
arr = np.array([10, 20, 30, 40, 50, 60, 70])
print(arr[0]) # 10 (First element)
print(arr[2]) # 30 (Third element)
print(arr[-1]) # 70 (Last element)
print(arr[-3]) # 50 (Third from the end)
Click to Zoom2. Basic Slicing Syntax: [start : stop : step]
A slice extracts a range of elements defined by three parameters separated by colons:
# Syntax: arr[start:stop:step]
# - start: Index where slice begins (INCLUSIVE, defaults to 0)
# - stop: Index where slice ends (EXCLUSIVE, defaults to len)
# - step: Interval stride count (defaults to 1)
data = np.array([100, 200, 300, 400, 500, 600, 700, 800])
# Slice from index 2 up to index 6 (not including 6)
print(data[2:6]) # [300 400 500 600]
# Slice from beginning up to index 4
print(data[:4]) # [100 200 300 400]
# Slice from index 4 to the very end
print(data[4:]) # [500 600 700 800]
# Extract every 2nd element across the entire array
print(data[::2]) # [100 300 500 700]
# Negative step: Reverse the entire array effortlessly!
print(data[::-1]) # [800 700 600 500 400 300 200 100]
3. The Power of In-Place Mutation via Slices
In pure Python lists, assigning to a slice copies new pointers. In NumPy, assigning a scalar to a slice performs an instant vectorized broadcast assignment in contiguous RAM:
numbers = np.zeros(8, dtype=int)
print(numbers) # [0 0 0 0 0 0 0 0]
# Set elements from index 2 to 5 to the number 99
numbers[2:6] = 99
print(numbers) # [ 0 0 99 99 99 99 0 0]
# Set every even index to -1
numbers[::2] = -1
print(numbers) # [-1 0 -1 99 -1 99 -1 0]
Interactive Quiz
Multiple Choice Questions
Question 1
What will 'arr[1:5]' extract from 'arr = np.array([10, 20, 30, 40, 50, 60])'?
Question 2
What is the most concise, idiomatic NumPy slice expression to invert the order of an array backwards?
Question 3
What happens when you execute 'arr[2:5] = 0' on a NumPy array?
Question 4
What will 'np.arange(10)[::3]' return?
Question 5
In the slice syntax 'arr[start:stop:step]', which boundary is EXCLUDED from the returned array?
Next Lesson
Multi-Dimensional Array Slicing (Row, Column & Matrix Slices)
Continue learning with hands-on practice, examples, and exercises in the upcoming topic.
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Practice Quiz
Test your understanding of this lesson with 5 questions. Each question has one correct answer.