NumPy Array Operations

import numpy as np

a = np.array([10, 20, 30, 40, 50])
b = np.array([2, 4, 5, 8, 10])

print(a + b)     # [12 24 35 48 60]
print(a - b)     # [8 16 25 32 40]
print(a * b)     # [20 80 150 320 500]
print(a / b)     # [5. 5. 6. 5. 5.]
print(a ** 2)    # [100 400 900 1600 2500]
print(a % 3)     # [1 2 0 1 2]

# Scalar operations (broadcast to all elements)
print(a + 100)   # [110 120 130 140 150]
print(a * 1.1)   # [11. 22. 33. 44. 55.]

A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])

# Element-wise multiplication
print(A * B)          # [[5 12],[21 32]]

# Matrix multiplication (dot product)
print(A @ B)          # [[19 22],[43 50]]
print(np.dot(A, B))   # Same result

# Transpose
print(A.T)            # [[1 3],[2 4]]

# Determinant and inverse
print(np.linalg.det(A))   # -2.0
print(np.linalg.inv(A))   # [[-2. 1.],[1.5 -0.5]]

scores = np.array([85, 92, 78, 96, 67, 88, 74, 91])

print(f"Mean:    {np.mean(scores):.2f}")    # 83.88
print(f"Median:  {np.median(scores):.2f}") # 86.5
print(f"Std Dev: {np.std(scores):.2f}")    # 9.43
print(f"Variance:{np.var(scores):.2f}")    # 88.86
print(f"Min:     {np.min(scores)}")        # 67
print(f"Max:     {np.max(scores)}")        # 96
print(f"Sum:     {np.sum(scores)}")        # 671
print(f"Cumsum:  {np.cumsum(scores)}")     # Running total

# Axis-based operations on 2D arrays
matrix = np.array([[1, 2, 3],
                   [4, 5, 6],
                   [7, 8, 9]])

print(np.sum(matrix, axis=0))    # Column sums: [12 15 18]
print(np.sum(matrix, axis=1))    # Row sums: [6 15 24]
print(np.mean(matrix, axis=0))   # Column means: [4. 5. 6.]

arr = np.array([3, 1, 4, 1, 5, 9, 2, 6])

print(np.sort(arr))            # [1 1 2 3 4 5 6 9]
print(np.argsort(arr))         # Indices that would sort: [1 3 6 0 2 4 7 5]
print(np.argmax(arr))          # Index of max: 5
print(np.argmin(arr))          # Index of min: 1
print(np.where(arr > 4))       # Indices where condition true: (array([4,5,7]),)
print(arr[arr > 4])            # Values where True: [5 9 6]

a = np.array([[1, 2, 3],
              [4, 5, 6]])
b = np.array([[7, 8, 9]])

# Stack rows (axis=0)
vstack = np.vstack([a, b])      # shape (3,3)
print(vstack)

# Stack columns (axis=1)
c = np.array([[10], [11]])
hstack = np.hstack([a, c])      # shape (2,4)
print(hstack)

# Split
arr = np.arange(12)
halves = np.array_split(arr, 3)  # Split into 3 equal parts
for h in halves:
    print(h)  # [0,1,2,3], [4,5,6,7], [8,9,10,11]

import numpy as np

# Student marks: 5 subjects, 6 students
marks = np.array([
    [85, 92, 78, 88, 91],  # Alice
    [72, 68, 75, 80, 70],  # Bob
    [95, 97, 92, 94, 96],  # Carol
    [60, 55, 62, 58, 65],  # David
    [88, 84, 90, 87, 85],  # Eve
    [76, 79, 73, 81, 77],  # Frank
])

names = np.array(['Alice', 'Bob', 'Carol', 'David', 'Eve', 'Frank'])
subjects = np.array(['Math', 'Science', 'English', 'History', 'CS'])

totals = np.sum(marks, axis=1)
averages = np.mean(marks, axis=1)
ranks = np.argsort(averages)[::-1]  # Descending order

print("=" * 55)
print(f"{&#039;Rank':<6}{'Name':<10}{'Total':<8}{'Average':<10}{'Grade'}")
print("=" * 55)
for rank, idx in enumerate(ranks, 1):
    avg = averages[idx]
    grade = &#039;A+' if avg >= 90 else 'A' if avg >= 80 else 'B' if avg >= 70 else 'C'
    print(f"{rank:<6}{names[idx]:<10}{totals[idx]:<8}{avg:<10.1f}{grade}")

print("\nSubject Averages:")
for subj, avg in zip(subjects, np.mean(marks, axis=0)):
    print(f"  {subj}: {avg:.1f}")

print(f"\nClass Top Score: {np.max(marks)}")
print(f"Class Low Score: {np.min(marks)}")
print(f"Pass rate (avg>=60): {np.mean(averages >= 60)*100:.0f}%")