4.1 Arrays and Shape

By Ravindra Bagale Last updated: 30 September 2026

import numpy as np

qty = np.array([2, 1, 2, 1, 1, 3])
print(qty.shape, qty.dtype)   # (6,) int64

prices = np.array([32.0, 90.0, 120.0, 58.0, 1299.0, 65.0])
line_total = qty * prices     # vectorised
print(line_total)
print(line_total.sum())

2-D example — fictional city × day order counts:

# rows: Pune, Nashik, Nagpur; cols: three days
counts = np.array([
    [210, 230, 900],   # Diwali spike on day 3
    [120, 130, 400],
    [150, 160, 450],
], dtype=np.int32)
print(counts.shape)       # (3, 2) wait — (3, 3)
print(counts.mean(axis=1))  # mean per city

Steps in Jupyter

import numpy as np
Build qty and prices as above.
Compute line_total = qty * prices and line_total.mean().
Print counts.sum(axis=0) (totals per day).

What you should see. Element-wise multiply without a Python for loop. Shape (3, 3) for the city-day grid.