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Debugging and Tools

Introduction

This folder contains materials for learning how to debug code, diagnose errors, and use essential developer tools effectively. You will progress from reading error messages through systematic debugging strategies to profiling and automated quality checks.

Target Audience: Beginner developers who want to move beyond "it doesn't work" and learn to methodically find and fix bugs.

Learning Roadmap

[Foundations]              [Strategy]               [Tooling]
    |                         |                        |
    v                         v                        v
Reading Errors -------> Debugging Strategy ----> Linters & Formatters
    |                         |                        |
    v                         v                        v
Print Debugging -------> Logging ------------> Type Checking
    |                         |                        |
    v                         v                        v
Using a Debugger         Testing Basics ------> Profiling Basics
    |                                                  |
    v                                                  v
Common Bug Patterns      VCS for Debugging ---> Debugging Workflow

What You'll Learn

  • How to read Python tracebacks and decode error messages
  • Strategic use of print statements and the logging module
  • Hands-on use of pdb, breakpoint(), and IDE debuggers
  • Recognition of common bug patterns (off-by-one, mutable defaults, scope issues)
  • Systematic debugging strategies (binary search, minimal reproduction)
  • Writing tests with assert, unittest, and pytest
  • Using linters (pylint, flake8, ruff) and formatters (black)
  • Adding type hints and checking them with mypy
  • Profiling code with cProfile, timeit, and memory profilers
  • Using git bisect, git blame, and git diff for debugging
  • Combining all techniques into a real-world debugging workflow

Prerequisites

  • Basic Python programming (variables, functions, loops, classes)
  • Basic terminal/command-line usage
  • Basic Git knowledge (helpful for Lesson 11)

File List

Filename Difficulty Key Topics
01_Reading_Error_Messages.md ⭐ Traceback anatomy, common error types, reading stack traces
02_Print_Debugging.md ⭐ Strategic print, f-string tricks, temporary vs permanent debug output
03_Using_a_Debugger.md ⭐ pdb, breakpoint(), stepping, IDE debugger
04_Common_Bug_Patterns.md ⭐⭐ Off-by-one, mutable defaults, scope, None handling
05_Debugging_Strategy.md ⭐⭐ Binary search, reproduction, minimal examples, rubber duck
06_Logging.md ⭐⭐ logging module, levels, formatters, handlers, configuration
07_Testing_Basics.md ⭐⭐ assert, unittest, pytest, test-driven debugging
08_Linters_and_Formatters.md ⭐⭐ pylint, flake8, black, ruff, pre-commit hooks
09_Type_Checking.md ⭐⭐ Type hints, mypy, gradual typing, common type errors
10_Profiling_Basics.md ⭐⭐⭐ cProfile, timeit, memory_profiler, line_profiler
11_Version_Control_for_Debugging.md ⭐⭐⭐ git bisect, git blame, git diff, debugging with VCS
12_Debugging_Workflow.md ⭐⭐⭐ End-to-end case studies, combining all techniques

Recommended Learning Path

Stage 1: Error Reading and Basic Debugging

  1. Reading Error Messages -> Print Debugging -> Using a Debugger

Stage 2: Patterns and Strategy

  1. Common Bug Patterns -> Debugging Strategy

Stage 3: Logging and Testing

  1. Logging -> Testing Basics

Stage 4: Automated Tools

  1. Linters and Formatters -> Type Checking -> Profiling Basics

Stage 5: Advanced Workflows

  1. Version Control for Debugging -> Debugging Workflow

Quick Start

Try the Debugger

# Save this as buggy.py and debug it
def average(numbers):
    total = 0
    for i in range(len(numbers)):
        total += numbers[i]
    return total / len(numbers)

# This will crash -- can you find why?
result = average([])
print(f"Average: {result}")
# Run with debugger
python -m pdb buggy.py

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