Python Personal Finance Tracker Project

Introduction

• Aimed at mastering Python skills.
• Covers Python syntax, advanced features, and popular modules.
• Builds a personal finance tracker to log transactions, organize them, get summaries, and plot data.

Project Overview

• Functions: Add transactions, view summaries, visualize data.
• Data stored in CSV files for easy import to Excel/Google Sheets.
• Example features include date-based entries, viewing summaries, and plotting income/expenses.

Setting Up

• Create a folder personal_finance_tracker_tutorial and open it in Visual Studio Code.
• Install necessary modules: matplotlib and pandas.
• Use pip install matplotlib and pip install pandas.
• Requirements file available for necessary modules.

Files Structure

• Main files: main.py and data_entry.py.
• CSV file as the database.
• Functions in data_entry.py for user data collection.

Implementing Features

Initialize CSV

• Class CSV with methods to handle CSV operations.
• Method: initialize_csv(): Check and create CSV file if not found. Defines columns: Date, Amount, Category, Description.

Adding Entries

• Method: add_entry(date, amount, category, description).
• Uses CSV writer to append entries.

User Input Functions

• Data collection functions in data_entry.py.
• Functions: get_date(), get_amount(), get_category(), get_description().
• Each function uses input validation.

Main Flow

• Import data collection functions in main.py.
• Define main functions to handle user choices and call respective methods for adding/viewing transactions.
• User choices:
  1. Add new transaction
  2. View transactions within a date range
  3. Exit

Viewing Transactions

• Method: get_transactions(start_date, end_date).
• Reads CSV and filters data based on date range.
• Generates and prints transaction summaries including total income, expenses, and net savings.

Plotting Data

• Method: plot_transactions(df).
• Uses matplotlib to visualize data.
• Steps: Resample data, sum amounts, reindex, plot data.
• Configurable plot features like labels, title, and grid.

Final Integration

• Main loop in main() to handle user input and display choices interactively.
• Calling plot_transactions based on user preference to visualize data.
• Testing with sample data to ensure functionality.

Advanced Features and Closing

• Introduced advanced Python features and modules like Matplotlib and Pandas.
• Demonstrated practical application development.
• Code available online, suitable for extending into a full-feature project.
• Encouraged to experiment and build upon the foundation.