1 The Problem
We want an expense tracker that records each spend with an amount, category, and date, then reports: total spent, spending per category, and spending per month. It builds on summarising by adding dates and grouped reporting — closer to a real finance tool.
Where this shows up: accounting software, expense reports, budgeting apps, any analytics grouped by time. Grouping records by date and category is exactly how dashboards and financial reports are built.
2 How to Think About It
Think about the reporting, before any code:
The plan — in plain English
1. Each expense has an amount, a category, and a date. → 2. Total = sum of all amounts. → 3. By category = group and sum per category. → 4. By month = take the year-month part of each date and group by that. → 5. Persist to a file.
3 The Build — explained part by part
Here is the complete tracker. Each part is explained below.
Pythonexpenses.py
import json
import os
from collections import defaultdict
FILE = "expenses.json"
def load():
if os.path.exists(FILE):
with open(FILE) as f:
return json.load(f)
return []
def save(expenses):
with open(FILE, "w") as f:
json.dump(expenses, f)
def total(expenses):
return sum(e["amount"] for e in expenses)
def by_category(expenses):
totals = defaultdict(float)
for e in expenses:
totals[e["category"]] += e["amount"]
return dict(totals)
def by_month(expenses):
totals = defaultdict(float)
for e in expenses:
month = e["date"][:7] # "2026-06-24" -> "2026-06"
totals[month] += e["amount"]
return dict(totals)
if __name__ == "__main__":
expenses = load()
expenses.append({"amount": 25.0, "category": "food", "date": "2026-06-24"})
save(expenses)
print("Total:", total(expenses))
print("By category:", by_category(expenses))
print("By month:", by_month(expenses))
What each part does — in plain words
each expense = {amount, category, date} — a record with three fields. The date is stored as text like "2026-06-24".total(expenses) — sum every amount in one line.
by_category — the same grouping you learned in the budget tracker: a
defaultdict totals each category.e["date"][:7] — the new trick: take the first 7 characters of the date (
"2026-06-24" → "2026-06") to get the year-month. Grouping by that gives monthly totals.by_month — group amounts by that year-month string, so you see spending per month without any date library.
Common mistakes — and how to avoid them
✗ Slicing the date wrong —
[:4] gives only the year, [:10] the whole day.✓ Use
[:7] to get year-month like 2026-06.✗ Storing dates in inconsistent formats — then slicing fails.
✓ Always store dates as
YYYY-MM-DD so slicing is reliable.✗ Re-checking
if category in totals — unnecessary with defaultdict.✓ Use
defaultdict(float) so keys start at 0 automatically.4 Test & Prove Each Part
We test the three reports — total, by category, by month — with known expenses.
The total sums all amounts
Category grouping totals each category
Month grouping uses the year-month of the date
Pythontest_expenses.py
from collections import defaultdict
def total(expenses):
return sum(e["amount"] for e in expenses)
def by_category(expenses):
totals = defaultdict(float)
for e in expenses:
totals[e["category"]] += e["amount"]
return dict(totals)
def by_month(expenses):
totals = defaultdict(float)
for e in expenses:
totals[e["date"][:7]] += e["amount"]
return dict(totals)
EXPENSES = [
{"amount": 10, "category": "food", "date": "2026-06-01"},
{"amount": 20, "category": "food", "date": "2026-07-01"},
{"amount": 5, "category": "travel", "date": "2026-06-15"},
]
def test_total():
"""Total is 35."""
assert total(EXPENSES) == 35
def test_by_category():
"""Food totals 30, travel 5."""
result = by_category(EXPENSES)
assert result["food"] == 30
assert result["travel"] == 5
def test_by_month():
"""June totals 15, July 20."""
result = by_month(EXPENSES)
assert result["2026-06"] == 15
assert result["2026-07"] == 20
Run with pytest -v. Each report is a small function tested with a known set of expenses. The month test proves the date-slicing trick groups correctly.
5 The Interface
ADDamount, category, dateone expense
What it expects
{"amount": 25, "category": "food", "date": "2026-06-24"}REPORTtotalsoverall, by category, by month
What it returns
Total: 25.0
By category: {'food': 25.0}
By month: {'2026-06': 25.0}6 Run It & Automate It
Run it locally
python3 expenses.pyLog expenses and view totals by category and month. Saved in
expenses.json.Jenkins runs the tests automatically — each line explained below.
What you should see when it works
Terminala real run
Total: 25.0
By category: {'food': 25.0}
By month: {'2026-06': 25.0}If it breaks — how to fix it
🚨 Monthly grouping looks wrong.
Check your dates are
YYYY-MM-DD and you slice [:7].🚨
KeyError in a report.Use
defaultdict(float) instead of a plain dict.GroovyJenkinsfile
// Jenkinsfile — automated tests on every change.
pipeline {
agent any
stages {
stage('Get the code') { steps { checkout scm } } // download the code
stage('Install') { steps { sh 'python3 -m venv venv && . venv/bin/activate && pip install pytest' } } // test tool
stage('Test') { steps { sh '. venv/bin/activate && pytest -v' } } // run every test
}
post {
success { echo 'All tests passed.' }
failure { echo 'A test failed — look above.' }
}
}
🎯 Try this next — make it yours
- Budget alerts. Warn when a category exceeds a monthly limit. (Teaches: comparing totals to limits.)
- Date range filter. Report only expenses between two dates. (Teaches: filtering by date.)
- Use real dates. Switch to the
datetimemodule for proper date maths. (Teaches: date handling.)
What you learned
You learned grouped reporting over time: summing totals, grouping by category, and grouping by month using a simple date-string slice. Time-based grouping is the engine behind every financial dashboard. Related: collections, String Methods.