Python practice: NumPy Arrays & Vectorization

Import numpy as np. Create array prices = np.array([100.0, 200.0, 300.0]). Multiply by 1.18 to compute taxed, and print taxed:

Given 2D array matrix = np.array([[10, 20, 30], [40, 50, 60]]), print matrix.shape on line 1 and matrix.size on line 2:

Given units = np.array([50, 80, 100]) and returns_arr = np.array([5, 10, 12]), compute net = units - returns_arr and print net:

Given sales = np.array([1200, 3400, 850, 4100]), print sales.sum() on line 1, and sales.mean() on line 2:

Given 2D array grid = np.array([[10, 20], [30, 40]]), compute column-wise sum using grid.sum(axis=0) and print the result:

Given 2D array grid = np.array([[10, 20], [30, 40]]), compute row-wise sum using grid.sum(axis=1) and print the result:

Given mat = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), slice rows 0:2 and columns 1:3 using mat[0:2, 1:3] and print:

Given units = np.array([10, 25, 40]), multiply by unit cost 45.0 to calculate costs and print costs: