11. NumPy Operations

Element-wise Operations

NumPy applies operations to every element:

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

print(arr + 10)     # [11, 12, 13, 14, 15]
print(arr * 2)      # [2, 4, 6, 8, 10]
print(arr ** 2)     # [1, 4, 9, 16, 25]
print(arr / 2)      # [0.5, 1.0, 1.5, 2.0, 2.5]

Array-to-Array Operations

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

print(a + b)   # [5, 7, 9]
print(a * b)   # [4, 10, 18]
print(a - b)   # [-3, -3, -3]

Reduction Operations

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

print(arr.sum())    # 15
print(arr.mean())   # 3.0
print(arr.std())    # 1.414... (population std)
print(arr.min())    # 1
print(arr.max())    # 5
print(arr.prod())   # 120 (1*2*3*4*5)

Axis Operations

For 2D arrays, specify the axis:

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

print(matrix.sum(axis=0))  # [5, 7, 9]  (sum columns)
print(matrix.sum(axis=1))  # [6, 15]    (sum rows)
print(matrix.mean(axis=1)) # [2.0, 5.0]

Comparison and Masking

arr = np.array([1, 5, 3, 8, 2])

mask = arr > 3
print(mask)          # [False, True, False, True, False]
print(arr[mask])      # [5, 8] (fancy indexing)

# Combined
print(arr[arr % 2 == 0])  # [1, 3, 2] -> even values only

Local verification checkpoint

Run the smallest example from this chapter in a local workspace and record the package version, runtime, data path, and observed output. If the result depends on model size, vector count, CPU/GPU backend, or available memory, note that constraint beside the exercise so the lesson remains reproducible.

Local verification checkpoint

Run the smallest example from this chapter in a local workspace and record the package version, runtime, data path, and observed output. If the result depends on model size, vector count, CPU/GPU backend, or available memory, note that constraint beside the exercise so the lesson remains reproducible.