Learn Slicing Array

NumPy slicing is a powerful feature that allows you to extract sub-arrays or specific elements from multi-dimensional arrays (ndarrays). Slicing in NumPy follows a similar syntax to slicing in Python lists, but it extends to multiple dimensions.

Basic Slicing

• You can use the colon : operator to specify slicing ranges
• Syntax: array[start:stop:step]
• If start is omitted, slicing starts from the beginning.
• If stop is omitted, slicing continues until the end.
• If step is omitted, slicing proceeds with a step of 1.

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
# Slicing the array
print(arr[1:4]) # Output: [2 3 4]

Multi-dimensional Slicing:

• For multi-dimensional arrays, you can slice along each dimension separately by separating slices with commas.
• Syntax: array[start:stop:step, start:stop:step, ...]

arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
# Slicing rows and columns
print(arr_2d[:2, 1:]) # Output: [[2 3]
# [5 6]]

Negative Slicing

• Negative indices can be used to slice from the end of the array.

# Negative slicing
print(arr[-3:]) # Output: [3 4 5]

Strided Slicing:

• You can specify a step value to select elements at regular intervals.
• Syntax: array[start:stop:step]

# Strided slicing
print(arr[::2]) # Output: [1 3 5]