Step 4: Best sellers and share of revenue

Which products matter most? Use collections.Counter to total units per product (its most_common method sorts for you), and a defaultdict(float) to total revenue. Then compute each top product's share of all revenue with the % format.

Examplepython

import csv, io

RAW = """date,region,product,units,price
2026-01-05,North,Notebook,10,4.50
2026-01-09,South,Pen,50,1.20
2026-01-12,North,Backpack,3,39.90
2026-01-15, East ,Notebook,8,4.50
2026-02-01,South,Headphones,2,59.00
2026-02-03,West,Pen,n/a,1.20
2026-02-10,North,Pen,40,1.20
2026-02-14,East,Backpack,5,39.90
2026-02-20,,Mouse,4,19.50
2026-03-02,South,Notebook,12,4.50
2026-03-09,West,Headphones,3,59.00
2026-03-15,North,Mouse,6,19.50
2026-03-21,East,Pen,30,1.20"""

def clean(raw_rows):
    good = []
    for r in raw_rows:
        try:
            units, price = int(r["units"]), float(r["price"])
        except ValueError:
            continue
        if not r["region"].strip():
            continue
        good.append({"date": r["date"], "region": r["region"].strip(), "product": r["product"].strip(),
                     "units": units, "price": price, "revenue": round(units * price, 2)})
    return good

sales = clean(csv.DictReader(io.StringIO(RAW)))

from collections import Counter, defaultdict

units_by_product = Counter()
for s in sales:
    units_by_product[s["product"]] += s["units"]
print("best sellers by units:", units_by_product.most_common(3))

revenue_by_product = defaultdict(float)
for s in sales:
    revenue_by_product[s["product"]] += s["revenue"]
top = sorted(revenue_by_product.items(), key=lambda kv: kv[1], reverse=True)[:3]
total = sum(revenue_by_product.values())
for product, revenue in top:
    print(f"{product:<11} {revenue:>8.2f}  {revenue / total:5.1%}")

by_month = defaultdict(float)
for s in sales:
    by_month[s["date"][:7]] += s["revenue"]
print(dict(sorted(by_month.items())))

Open in editor »

Units and revenue tell different stories: the cheap pen sells the most units but headphones and backpacks earn more per item. A good analysis reports both. The last line groups revenue by month using the first 7 characters of the date (2026-01).

Your turn: write the code

Use Counter to count how often each word appears in the text, and print the two most common words as a list of tuples.

Quick check

What does Counter.most_common(3) return?

The three most frequent items with their countsThe three largest numbersThree random items

Finish the quick check (and any code exercise) to complete this lesson, or mark it done yourself.

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