NumPy — Array Indexing

Integer indexing

import numpy as np

arr = np.array([10, 20, 30, 40, 50])
indices = [0, 2, 4]
print(arr[indices])  # [10, 30, 50]

2D integer indexing

arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
rows = [0, 1, 2]
cols = [0, 1, 2]
print(arr[rows, cols])  # [1, 5, 9]

Boolean indexing

arr = np.array([1, 2, 3, 4, 5])
mask = arr > 3
print(arr[mask])  # [4, 5]

Multiple conditions

arr = np.array([1, 2, 3, 4, 5])
mask = (arr > 2) & (arr < 5)
print(arr[mask])  # [3, 4]

np.where

arr = np.array([1, 2, 3, 4, 5])
result = np.where(arr > 3, 1, 0)
print(result)  # [0, 0, 0, 1, 1]

argwhere

arr = np.array([1, 2, 3, 4, 5])
indices = np.argwhere(arr > 3)
print(indices)  # [[3], [4]]

Mini Practice

1. Use integer indexing
2. Apply boolean masks
3. Combine conditions
4. Use np.where and argwhere

Up Next

Continue with Structured Arrays - Custom dtypes.