Basic Indexing: Accessing Individual Elements
The simplest form of indexing is accessing individual elements using their row and column indices (for 2D arrays) or their index (for 1D arrays). Indices in NumPy start at 0.
import numpy as np
arr_1d = np.array([10, 20, 30, 40, 50])
print(arr_1d[0]) # Output: 10
print(arr_1d[2]) # Output: 30
arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(arr_2d[0, 0]) # Output: 1 (first row, first column)
print(arr_2d[1, 2]) # Output: 6 (second row, third column)Slicing: Extracting Subarrays
Slicing allows you to extract portions of an array. It follows the [start:stop:step] notation, where start is inclusive, stop is exclusive, and step determines the increment. Omitting any part uses the default values (0 for start, the array’s size for stop, and 1 for step).
print(arr_1d[1:4]) # Output: [20 30 40]
print(arr_1d[:3]) # Output: [10 20 30]
print(arr_1d[::2]) # Output: [10 30 50] (every other element)
print(arr_2d[0:2, 1:3]) # Output: [[2 3] [5 6]] (rows 0 and 1, columns 1 and 2)
print(arr_2d[:, 0]) # Output: [1 4 7] (all rows, first column)Boolean Indexing: Selecting Elements Based on Conditions
Boolean indexing allows you to select elements based on a boolean condition. This is incredibly useful for filtering data.
bool_idx = arr_1d > 20
print(arr_1d[bool_idx]) # Output: [30 40 50]
bool_idx_2d = arr_2d % 2 == 0
print(arr_2d[bool_idx_2d]) # Output: [2 4 6 8] (Note: it flattens the array)Fancy Indexing: Selecting Elements Using Integer Arrays
Fancy indexing uses integer arrays to specify the indices you want to select. This is more flexible than slicing for selecting arbitrary elements.
print(arr_1d[[0, 2, 4]]) # Output: [10 30 50]
rows = np.array([0, 1, 2])
cols = np.array([0, 1, 2])
print(arr_2d[rows, cols]) # Output: [1 5 9] (diagonal elements)Multi-dimensional Fancy Indexing
Fancy indexing extends to multiple dimensions, allowing for complex element selection patterns.
rows = np.array([[0, 1], [2, 0]])
cols = np.array([[0, 2], [1, 0]])
print(arr_2d[rows, cols]) # Output: [[1 3] [8 4]]These examples demonstrate the flexibility and power of NumPy array indexing. By mastering these techniques, you can significantly enhance the efficiency and readability of your numerical Python code. Further exploration into advanced indexing techniques will unlock even greater capabilities within the NumPy library.