Totals and Counts

We have rows. Now we want numbers about those rows: how much in total, how many sales, the smallest, the largest, the average. This is the part people actually ask for.

First, fix the strings

Remember from the last phase: every value the CSV gives us is a string. '120.50' is text. Before we can do math, we convert the amount column to a float.

float("120.50") gives 120.5. We do that for each row. The cleanest move is a list comprehension that pulls out only the numbers we care about:

import csv
import io

CSV_TEXT = """date,region,product,amount
2026-01-03,North,Widget,120.50
2026-01-05,South,Gadget,89.00
2026-01-08,North,Gadget,210.00
2026-01-11,East,Widget,55.50
2026-01-14,South,Widget,134.00
2026-01-19,East,Gadget,410.00"""

rows = list(csv.DictReader(io.StringIO(CSV_TEXT)))

amounts = [float(row["amount"]) for row in rows]
print(amounts)

Now they're real numbers - no quotes - and we can add, compare, and average them.

The five numbers

We want five things about the whole dataset: how many amounts there are, their total, the smallest, the largest, and the average. Python's built-ins can get you there without much code.

Your turn. This one's the point of the phase, so give it a shot before you read on. Fill in summarize and hit Run - the checks underneath tell you whether it works. My version is in the next block whenever you want it.

def summarize(amounts):
    # Given a list of numbers, return a dict with:
    #   "count"    - how many numbers
    #   "total"    - their sum
    #   "smallest" - the minimum
    #   "largest"  - the maximum
    #   "average"  - total / count
    pass


# --- checks: fix your function until this prints "All good." ---
stats = summarize([120.50, 89.00, 210.00, 55.50, 134.00, 410.00])
assert isinstance(stats, dict), f"summarize should return a dict, got: {stats!r}"
assert stats["count"] == 6, f"count should be 6, got: {stats.get('count')}"
assert stats["total"] == 1019.0, f"total should be 1019.0, got: {stats.get('total')}"
assert stats["smallest"] == 55.5, f"smallest should be 55.5, got: {stats.get('smallest')}"
assert stats["largest"] == 410.0, f"largest should be 410.0, got: {stats.get('largest')}"
assert abs(stats["average"] - 169.8333333333333) < 0.0001, f"average should be about 169.83, got: {stats.get('average')}"
print("All good.")

Stuck on one of the five? Each is a single built-in call away - the question is which built-in fits.

One way to write it

There's no special "average" built-in - it's the sum divided by the count, which you already have. Python's built-ins do the rest:

import csv
import io

CSV_TEXT = """date,region,product,amount
2026-01-03,North,Widget,120.50
2026-01-05,South,Gadget,89.00
2026-01-08,North,Gadget,210.00
2026-01-11,East,Widget,55.50
2026-01-14,South,Widget,134.00
2026-01-19,East,Gadget,410.00"""

rows = list(csv.DictReader(io.StringIO(CSV_TEXT)))
amounts = [float(row["amount"]) for row in rows]


def summarize(amounts):
    return {
        "count": len(amounts),
        "total": sum(amounts),
        "smallest": min(amounts),
        "largest": max(amounts),
        "average": sum(amounts) / len(amounts),
    }


stats = summarize(amounts)
print(f"Count:    {stats['count']}")
print(f"Total:    {stats['total']:.2f}")
print(f"Smallest: {stats['smallest']:.2f}")
print(f"Largest:  {stats['largest']:.2f}")
print(f"Average:  {stats['average']:.2f}")

The :.2f in the f-string rounds to two decimal places - so 157.041666... prints as 157.04. Money never wants fifteen digits after the dot.

The one bug waiting to happen

Look at average = total / count. If count is zero - an empty CSV, or one with only a header - that line blows up with ZeroDivisionError. It's worth one guard.

Before you run this, guess what the average line prints when there are no data rows at all. Then check.

import csv
import io

# An empty file: header only, no data rows.
CSV_TEXT = "date,region,product,amount"

rows = list(csv.DictReader(io.StringIO(CSV_TEXT)))
amounts = [float(row["amount"]) for row in rows]

count = len(amounts)
total = sum(amounts)
average = total / count if count else 0.0

print(f"Count:   {count}")
print(f"Total:   {total:.2f}")
print(f"Average: {average:.2f}")

total / count if count else 0.0 reads as "divide if there's anything, otherwise zero." sum([]) is already 0 and len([]) is 0, so those two are fine on their own - it's the division that needs the guard. One small condition saves you a crash on the inevitable empty file.

Which row was the biggest?

max(amounts) tells you the biggest number, but often you want the whole row - what product, what region. For that, give max a key so it compares rows by their amount and hands back the row itself.

Before you run it, guess which row prints as the biggest sale - the region and product, not just the number.

import csv
import io

CSV_TEXT = """date,region,product,amount
2026-01-03,North,Widget,120.50
2026-01-05,South,Gadget,89.00
2026-01-08,North,Gadget,210.00
2026-01-11,East,Widget,55.50
2026-01-14,South,Widget,134.00
2026-01-19,East,Gadget,410.00"""

rows = list(csv.DictReader(io.StringIO(CSV_TEXT)))

biggest = max(rows, key=lambda r: float(r["amount"]))
print("Biggest sale:")
print(f"  {biggest['date']}  {biggest['region']}  {biggest['product']}  {biggest['amount']}")

key=lambda r: float(r["amount"]) tells max how to rank the rows. Without the float, it would compare the amounts as strings - and '89.00' sorts higher than '410.00' alphabetically, which would be wrong. The conversion matters everywhere you compare.

What you've got

The headline numbers for the whole dataset: count, total, min, max, average - plus the single biggest row. That's already a useful summary. But a real report breaks the totals out by category - total per region, per product - so you can see where the money's actually coming from.

That's grouping, and it's the next phase. The pattern there builds straight on what you have: convert to a number, then accumulate. We'll do it per group instead of all at once.