Build a Budget App with Python

Install Dependencies

pip install pandas matplotlib

The BudgetTracker Class

We build a class that saves transactions to a JSON file, supports income and expenses with categories, and generates summaries.

import pandas as pd
from datetime import datetime
import json

class BudgetTracker:
    def __init__(self, filename="budget.json"):
        self.filename = filename
        self.transactions = self._load()

    def _load(self):
        try:
            with open(self.filename, "r") as f:
                return json.load(f)
        except FileNotFoundError:
            return []

    def _save(self):
        with open(self.filename, "w") as f:
            json.dump(self.transactions, f, indent=2)

    def add_income(self, amount: float, description: str, category: str = "Salary"):
        self.transactions.append({
            "date": datetime.now().strftime("%Y-%m-%d"),
            "type": "income",
            "amount": amount,
            "description": description,
            "category": category
        })
        self._save()
        print(f"Added income: £{amount:.2f} ({description})")

    def add_expense(self, amount: float, description: str, category: str):
        self.transactions.append({
            "date": datetime.now().strftime("%Y-%m-%d"),
            "type": "expense",
            "amount": amount,
            "description": description,
            "category": category
        })
        self._save()
        print(f"Added expense: £{amount:.2f} ({description})")

Summary and Usage

    def summary(self) -> dict:
        df = pd.DataFrame(self.transactions)
        if df.empty:
            return {"income": 0, "expenses": 0, "balance": 0}

        income = df[df["type"] == "income"]["amount"].sum()
        expenses = df[df["type"] == "expense"]["amount"].sum()

        return {
            "income": income,
            "expenses": expenses,
            "balance": income - expenses,
            "by_category": df[df["type"] == "expense"].groupby("category")["amount"].sum().to_dict()
        }

# Usage
budget = BudgetTracker()
budget.add_income(3500, "Monthly salary")
budget.add_expense(1200, "Rent", "Housing")
budget.add_expense(300, "Groceries", "Food")
budget.add_expense(80, "Electric bill", "Utilities")
budget.add_expense(50, "Netflix + Spotify", "Entertainment")
budget.add_expense(150, "Petrol", "Transport")

report = budget.summary()
print(f"\nIncome: £{report['income']:.2f}")
print(f"Expenses: £{report['expenses']:.2f}")
print(f"Balance: £{report['balance']:.2f}")
print(f"\nSpending by category:")
for cat, amount in report['by_category'].items():
    print(f"  {cat}: £{amount:.2f}")

Visualise Your Budget

# Visualise spending
import matplotlib.pyplot as plt

categories = list(report["by_category"].keys())
amounts = list(report["by_category"].values())

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

# Pie chart
ax1.pie(amounts, labels=categories, autopct="%1.0f%%", startangle=90)
ax1.set_title("Spending Breakdown")

# Income vs Expenses bar
ax2.bar(["Income", "Expenses", "Savings"],
        [report["income"], report["expenses"], report["balance"]],
        color=["green", "red", "blue"])
ax2.set_title("Monthly Summary")
ax2.set_ylabel("£")

plt.tight_layout()
plt.savefig("budget_report.png", dpi=150)
plt.show()