Array Indexing in NumPy

Indexing in NumPy allows you to access or modify specific elements in an array. It works similarly to Python lists but supports multi-dimensional indexing.

Indexing in 1D Arrays

A 1D NumPy array behaves like a Python list.

Accessing Elements

import numpy as np

# Creating a 1D array
arr = np.array([10, 20, 30, 40, 50])

print(arr[0])  # First element
print(arr[2])  # Third element
print(arr[-1]) # Last element

Output:

10
30
50

Negative indexing works just like in lists (-1 is the last element).

Indexing in 2D Arrays

For multi-dimensional arrays, we specify row and column indices.

Accessing Elements in a 2D Array

arr = np.array([[10, 20, 30], 
                [40, 50, 60], 
                [70, 80, 90]])

print(arr[0, 0])  # First row, first column
print(arr[1, 2])  # Second row, third column
print(arr[-1, -1]) # Last row, last column

Output:

10
60
90

Syntax: arr[row, column]

Indexing in 3D Arrays

A 3D array consists of multiple 2D arrays (matrices).
The indexing follows this pattern:
arr[depth, row, column]

Accessing Elements in a 3D Array

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

                [[7, 8, 9], 
                 [10, 11, 12]]])

print(arr[0, 1, 2])  # First matrix, second row, third column
print(arr[1, 0, 1])  # Second matrix, first row, second column

Output:

6
8

Visualizing the 3D Array:

Matrix 1:      Matrix 2:
[[1, 2, 3]     [[7, 8, 9]
 [4, 5, 6]]     [10,11,12]]

Modifying Elements Using Indexing

You can update elements using their index positions.

Modifying Array Elements

arr = np.array([10, 20, 30])
arr[1] = 50  # Change second element to 50
print(arr)

Output:

[10 50 30]

Indexing Using Boolean Conditions

You can filter elements using boolean indexing.

# Get Elements Greater Than 20
arr = np.array([10, 20, 30, 40, 50])
filtered_arr = arr[arr > 20]  # Get elements greater than 20
print(filtered_arr)

Output:

[30 40 50]

Use Case: This is useful for filtering datasets in data science.

Summary

• 1D Indexing: Use simple indices (arr[2])
• 2D Indexing: Use row and column indices (arr[1, 2])
• 3D Indexing: Use depth, row, column (arr[0,1,2])
• Modifying Elements: Assign new values using indices
• Boolean Indexing: Filter elements based on conditions