๐ข NumPy Arrays
NumPy arrays are faster and more compact than Python lists. They are the foundation for scientific computing.
๐ก Quick Tip:
Mastering this concept will significantly boost your Python data science skills!
๐ป Code Example:
import numpy as np # 1. Create arrays arr = np.array([10, 20, 30, 40, 50]) matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # 2. Broadcasting โ apply operations without loops print(arr * 2) # [20 40 60 80 100] print(arr + 100) # [110 120 130 140 150] # 3. Slicing and indexing print(matrix[1, :]) # Second row โ [4 5 6] print(matrix[:, 2]) # Third column โ [3 6 9] print(matrix[0:2, 1:]) # Sub-matrix # 4. Statistical operations data = np.random.randint(1, 100, size=(5, 4)) print("Mean:", data.mean()) print("Std :", data.std()) print("Max :", data.max(axis=0)) # Per column max # 5. Reshape and stack flat = np.arange(12) reshaped = flat.reshape(3, 4) print(reshaped) # 6. Linear algebra A = np.array([[2, 1], [1, 3]]) b = np.array([8, 13]) x = np.linalg.solve(A, b) # Solve Ax = b print("Solution:", x) # [3. 2.] # 7. Boolean mask filtering scores = np.array([45, 82, 91, 33, 77, 60]) passing = scores[scores >= 60] print("Passing scores:", passing) # [82 91 77 60]
Keep exploring and happy coding! ๐ป
