Capstone Project: Personal Finance Tracker

Overview

The Personal Finance Tracker project is designed to help users take control of their money by tracking their incomes and expenses. It simulates a real-world use case, where data is collected, stored, analyzed, and visualized — all using Python. This project brings together everything you’ve learned so far: user input, file handling, conditionals, data structures, and simple data visualization. It also serves as a stepping stone to working with external files and basic data analytics.


Core Features

  • Input Transactions: Users can add income or expense entries along with categories such as “Food”, “Rent”, or “Salary”.
  • Persistent Storage: Save each transaction to a CSV file or database so that data isn’t lost when the program closes.
  • Summary Calculations: Compute overall balance, total income, total expense, and show a breakdown by category.
  • Visual Reporting: Use charts like pie charts or bar graphs to represent expenses visually.
  • Optional Filters: Add filters to show summaries for a specific date range or category.

Tools and Concepts You’ll Use

  • File Handling: Read from and write to CSV files using Python’s built-in csv module.
  • Date and Time: Utilize datetime to timestamp transactions automatically.
  • Dictionaries & Lists: Use these core data structures to organize and manipulate financial data.
  • Pandas (Optional): Efficiently analyze large amounts of financial data.
  • Matplotlib: Turn your raw data into meaningful visual insights with simple graphs.
  • SQLite (Optional): For those looking to expand further, use a lightweight database to manage transactions more securely.

Step-by-Step Project Breakdown

1. 📥 Collect User Input

amount = float(input("Enter amount: "))
category = input("Enter category (e.g. Food, Rent, Salary): ")
type_ = input("Is this an income or expense? ").lower()

Prompt the user to enter transaction details. Ensure the type is either income or expense.


2. 💾 Save to File

from datetime import datetime
import csv

entry = [datetime.now().strftime("%Y-%m-%d"), type_, category, amount]

with open("finance_data.csv", mode="a", newline="") as file:
    writer = csv.writer(file)
    writer.writerow(entry)

Every transaction is saved with the current date, type, category, and amount.


3. 📊 Analyze and Summarize Transactions

import pandas as pd

df = pd.read_csv("finance_data.csv", names=["Date", "Type", "Category", "Amount"])
income = df[df["Type"] == "income"]["Amount"].sum()
expense = df[df["Type"] == "expense"]["Amount"].sum()
balance = income - expense

print(f"Total Income: ₹{income}")
print(f"Total Expenses: ₹{expense}")
print(f"Current Balance: ₹{balance}")

Read from the CSV, calculate totals, and present an easy-to-read summary in the terminal.


4. 📈 Visualize Expense Breakdown

import matplotlib.pyplot as plt

expense_data = df[df["Type"] == "expense"].groupby("Category")["Amount"].sum()
expense_data.plot(kind='pie', autopct='%1.1f%%', startangle=90)
plt.title("Expenses by Category")
plt.ylabel("")
plt.show()

Visual feedback helps users quickly understand where most of their money goes.


Sample Output

  • CSV Sample:
  2025-03-30,expense,Food,500
  2025-03-30,income,Salary,15000
  • Terminal Output:
  Total Income: ₹15000.0
  Total Expenses: ₹500.0
  Current Balance: ₹14500.0
  • Pie Chart Example:
    A colorful chart showing how much was spent in each category like Food, Rent, Shopping, etc.

Bonus Enhancements (Optional Ideas)

  • GUI Interface: Build a simple user interface using tkinter to make the tracker more user-friendly.
  • Database Storage: Replace CSV with SQLite using the sqlite3 module for more structured data storage.
  • Monthly Filters: Allow users to choose a specific month/year and generate custom reports.
  • Export Report: Automatically generate PDF or Excel reports using pandas or xlsxwriter.

What You’ll Learn

  • How to take user input and store data persistently
  • How to work with CSV files and analyze data with Pandas
  • How to generate basic visual reports using matplotlib
  • How to write modular, real-world Python programs

Next Project: [Simple Chatbot – Let’s Build an Interactive Assistant!]