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Python Mini Projects for Beginners: 5 Practice Projects Before Learning pandas

P13.4 Python Project 4: Delivery Time Report with NumPy

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Why. Operations teams ask: how fast are we, how many orders are late, which hub is slowest?

What. We store delivery minutes per hub in NumPy arrays and compute averages, late counts and the slowest hub.

How.

Build the report

  1. Store minutes per hub in a dictionary of arrays.
  2. For each hub compute mean, median and late % (> 30 min).
  3. Find the slowest hub by mean.
  4. Print a neat table.

Worked example A — Hub report.

Step 1 — What we do. Three delivery hubs in Maharashtra.

Step 2 — Type and run this code.

import numpy as np
hubs = {
    "Pune": np.array([18, 22, 35, 19, 25, 41]),
    "Mumbai": np.array([28, 33, 31, 26, 38, 29]),
    "Nashik": np.array([15, 17, 21, 19, 16, 23]),
}
print(f"{'Hub':<8}{'Mean':>6}{'Median':>8}{'Late%':>7}")
for hub, m in hubs.items():
    late = (m > 30).mean() * 100
    print(f"{hub:<8}{m.mean():>6.1f}{np.median(m):>8.1f}{late:>6.0f}%")
slowest = max(hubs, key=lambda h: hubs[h].mean())
print("Slowest hub:", slowest)

Step 3 — Output you should see.

Hub       Mean  Median  Late%
Pune      26.7    23.5    33%
Mumbai    30.8    30.0    50%
Nashik    18.5    18.0     0%
Slowest hub: Mumbai

Step 4 — What it means. (m > 30).mean() is the share of True values — a quick percentage of late orders.

Ravindra Bagale's Tip

The trick (condition).mean() gives a percentage in one step. You will use it in pandas too.