Array Indexing and Slicing

Array indexing and slicing are essential operations for accessing and manipulating elements within NumPy arrays. These techniques provide flexibility in extracting specific subsets of data or modifying array values.

Basic Indexing

Single Element Access:

Python

import numpy as np

arr = np.array([1, 2, 3, 4, 5])

# Access the first element
element = arr[0]

Multidimensional Indexing:

Python

arr2d = np.array([[1, 2, 3], [4, 5, 6]])

# Access the element at the second row, first column
element = arr2d[1, 0]

Slicing

Extracting Subarrays:

Python

# Extract elements from the second to fourth position
slice = arr[1:4]

# Extract the first two rows and columns
slice2d = arr2d[:2, :2]

Negative Indexing:

Python

# Access the last element
last_element = arr[-1]

# Extract the last two elements
last_two = arr[-2:]

Step Size:

Python

# Extract every other element
every_other = arr[::2]

Advanced Indexing

Boolean Indexing:

Python

mask = arr > 3

# Extract elements where the mask is True
result = arr[mask]

Fancy Indexing:

Python

indices = np.array([0, 2, 4])

# Extract elements at the specified indices
result = arr[indices]

Modifying Array Elements

Assignment:

Python

arr[0] = 10
arr2d[1, 2] = 20

In-place Operations:

Python

arr += 1  # Increment all elements by 1

By mastering array indexing and slicing, you can efficiently extract, modify, and manipulate elements within NumPy arrays, enabling a wide range of data analysis and manipulation tasks.