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

Python's built-ins do almost all the work here:

There's no special "average" built-in - average is the sum divided by the count, which you already have. Let's compute all five and print them.

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]

count = len(amounts)
total = sum(amounts)
smallest = min(amounts)
largest = max(amounts)
average = total / count

print(f"Count:    {count}")
print(f"Total:    {total:.2f}")
print(f"Smallest: {smallest:.2f}")
print(f"Largest:  {largest:.2f}")
print(f"Average:  {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:

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:

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.