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Python Debugging Techniques

Master Python debugging — print debugging, pdb debugger, VS Code debugger, logging strategies, unit testing with pytest, profiling, and systematic debugging workflows for finding and fixing bugs quickly.

Debugging is the process of finding and fixing bugs in your code. Professional developers spend significant time debugging — mastering these techniques will make you dramatically more productive.

The Debugging Mindset

Before diving into tools, adopt this mindset:

Reproduce the bug — can you consistently trigger it?
Isolate the problem — narrow down which code causes it
Understand what's happening — don't guess; verify your assumptions
Fix one thing at a time — avoid multiple simultaneous changes
Verify the fix — confirm it works and didn't break anything else

Print Debugging (Quick and Easy)

Example

# Basic print debugging def calculate_total(prices, discount): print(f"DEBUG: prices = {prices}") # Check input print(f"DEBUG: discount = {discount}") subtotal = sum(prices) print(f"DEBUG: subtotal = {subtotal}") # Check intermediate total = subtotal * (1 - discount) print(f"DEBUG: total = {total}") # Check output return total # Better: Use a debug flag DEBUG = True def debug_print(*args, **kwargs): if DEBUG: import inspect frame = inspect.currentframe().f_back filename = frame.f_code.co_filename.split('/')[-1] lineno = frame.f_lineno print(f"[DEBUG {filename}:{lineno}]", *args, **kwargs) debug_print("Price list:", prices) debug_print(f"Calculating: {a} + {b} = {a+b}")

Using breakpoint() and pdb

Python's built-in debugger pdb lets you step through code interactively:

python example

def process_orders(orders):
    total = 0
    for i, order in enumerate(orders):
        breakpoint()  # Pause execution here (Python 3.7+)
        # Old way: import pdb; pdb.set_trace()
        
        amount = order.get('amount', 0)
        total += amount
    return total

PDB Commands Reference

Code example

pdb Commands:
  n (next)        — Execute current line, go to next
  s (step)        — Step INTO function calls
  c (continue)    — Continue until next breakpoint
  q (quit)        — Quit debugger
  p expr          — Print the value of expression
  pp expr         — Pretty print the value
  l (list)        — Show current code context (11 lines)
  ll (longlist)   — Show full function source
  w (where)       — Show call stack
  u (up)          — Move up the call stack
  d (down)        — Move down the call stack
  b 25            — Set breakpoint at line 25
  b func_name     — Set breakpoint at function
  cl              — Clear all breakpoints
  h (help)        — Show help
  h command       — Help for specific command
  
  ! expr          — Execute any Python expression
  !x = 5          — Change variable value during debugging

Example

# Example debugging session def buggy_function(data): import pdb; pdb.set_trace() # Old style (still works) # In the debugger: # (Pdb) p data → print data value # (Pdb) pp data → pretty print data # (Pdb) n → next line # (Pdb) !data['key'] = 'fixed' → modify variable # (Pdb) c → continue running result = [item for item in data if item > 0] return sum(result) # Run with pdb from command line # python -m pdb my_script.py # python -m pdb -c continue my_script.py # Continue until first breakpoint

Conditional Breakpoints

python example

# Set a breakpoint only when a condition is true
def process_items(items):
    for i, item in enumerate(items):
        if item < 0:
            breakpoint()  # Only stops when negative item found
        process(item)

# In pdb — set conditional breakpoint
# (Pdb) b 45, x > 100   ← Break at line 45 only when x > 100
# (Pdb) b my_func, n > 50  ← Break in my_func when n > 50

The icecream Library (Better print debugging)

bash example

pip install icecream

python example

from icecream import ic

def calculate(a, b):
    ic(a, b)                       # ic| a: 5, b: 3
    result = a + b
    ic(result)                     # ic| result: 8
    ic(a + b * 2, a ** 2)         # ic| a + b * 2: 11, a ** 2: 25
    return result

# Auto-includes file, line, and variable names
# ic| my_script.py:6 in calculate()- result: 8

# Disable for production
ic.disable()
# ic.enable()  # Re-enable

# Configure prefix
ic.configureOutput(prefix='🐛 Debug: ')

Logging-Based Debugging

Example

import logging # Configure detailed logging for debugging logging.basicConfig( level=logging.DEBUG, format='%(asctime)s | %(levelname)-8s | %(name)s:%(lineno)d | %(message)s', handlers=[ logging.FileHandler('debug.log'), logging.StreamHandler() ] ) logger = logging.getLogger(__name__) def process_data(data: list) -> dict: logger.debug(f"process_data called with {len(data)} items") result = {} for i, item in enumerate(data): logger.debug(f"Processing item {i}: {item!r}") try: key = item['id'] result[key] = item logger.debug(f" → Stored as key '{key}'") except KeyError as e: logger.warning(f" ⚠ Item {i} missing key {e}: {item}") logger.info(f"Processed {len(result)}/{len(data)} items successfully") return result # Using different log levels for debugging logger.debug("Detailed execution trace") # Only in development logger.info("Process completed successfully") # General info logger.warning("Unexpected but handled") # Something to note logger.error("Something went wrong") # Actual error logger.exception("Error with traceback") # Includes full traceback

Assertions for Debugging

python example

def calculate_percentage(value, total):
    """Calculate what percentage value is of total."""
    assert total != 0, f"Total cannot be zero! Got total={total}"
    assert isinstance(value, (int, float)), f"value must be numeric, got {type(value)}"
    assert isinstance(total, (int, float)), f"total must be numeric, got {type(total)}"
    assert 0 <= value <= total, f"value ({value}) must be between 0 and total ({total})"
    
    return (value / total) * 100

# Assertions can be disabled in production with:
# python -O my_script.py  (optimization flag disables assert)

# Use assert for:
# - Checking function preconditions
# - Verifying invariants
# - Documenting assumptions

# Don't use assert for:
# - Input validation from users (use if/raise instead)
# - Security checks (can be disabled!)

VS Code Debugging

Configure launch.json in .vscode/:

Example

{ "version": "0.2.0", "configurations": [ { "name": "Python: Current File", "type": "debugpy", "request": "launch", "program": "${file}", "console": "integratedTerminal", "justMyCode": true }, { "name": "Python: Flask", "type": "debugpy", "request": "launch", "module": "flask", "env": { "FLASK_APP": "app.py", "FLASK_DEBUG": "1" }, "args": ["run", "--no-debugger"] }, { "name": "Python: Debug Tests", "type": "debugpy", "request": "launch", "module": "pytest", "args": ["${file}", "-v"], "console": "integratedTerminal" }, { "name": "Python: Attach to Process", "type": "debugpy", "request": "attach", "connect": {"host": "localhost", "port": 5678} } ] }

VS Code debugging features:

Breakpoints — Click the gutter (line numbers) to set
Conditional breakpoints — Right-click breakpoint → Edit condition
Watch variables — Add expressions to watch panel
Call stack — See how you got to current line
Debug console — Evaluate expressions while paused

Debugging with pytest

Example

# test_calculator.py import pytest from calculator import Calculator class TestCalculator: def setup_method(self): self.calc = Calculator() def test_add_positive(self): result = self.calc.add(2, 3) assert result == 5, f"Expected 5, got {result}" def test_divide_by_zero(self): with pytest.raises(ZeroDivisionError): self.calc.divide(10, 0) def test_complex_calculation(self): # Arrange inputs = [1, 2, 3, 4, 5] # Act result = self.calc.sum_list(inputs) # Assert — use descriptive messages assert result == 15, f"Sum of {inputs} should be 15, got {result}" # Run specific test: pytest test_calculator.py::TestCalculator::test_add_positive # Run with output: pytest -s (shows print statements) # Run with verbose: pytest -v # Run with pdb on failure: pytest --pdb # Run and stop at first failure: pytest -x # Show locals on failure: pytest -l # Generate HTML report: pytest --html=report.html

Performance Debugging (Profiling)

python example

# Method 1: Simple time measurement
import time

start = time.perf_counter()
# ... your code ...
end = time.perf_counter()
print(f"Elapsed: {end - start:.4f}s")

# Method 2: timeit for microbenchmarks
import timeit

# Benchmark a single expression
t = timeit.timeit('sum(range(1000))', number=10000)
print(f"Average: {t/10000*1000:.4f}ms")

# Method 3: cProfile — function-level profiling
import cProfile
import pstats

# Profile a function
cProfile.run('my_function()', 'profile_stats')

# Analyze results
stats = pstats.Stats('profile_stats')
stats.sort_stats('cumulative')  # Sort by cumulative time
stats.print_stats(20)           # Show top 20 functions

# Or profile from command line:
# python -m cProfile -s cumulative my_script.py

# Method 4: line_profiler — line-by-line
# pip install line_profiler
# @profile decorator (from line_profiler)
# kernprof -l -v my_script.py

# Method 5: memory_profiler
# pip install memory_profiler
# @profile decorator
# python -m memory_profiler my_script.py

Debugging Patterns

Bisect Debugging

python example

# When you don't know where the bug is, use divide and conquer
# Add checkpoints to narrow down the problem

def process_large_dataset(data):
    # Step 1: Check input
    print(f"CHECKPOINT 1: Input has {len(data)} rows")
    assert all('id' in item for item in data), "Some items missing 'id'"
    
    # Step 2: After first transformation
    transformed = transform_data(data)
    print(f"CHECKPOINT 2: After transform: {len(transformed)} rows")
    assert len(transformed) == len(data), "Transform changed row count!"
    
    # Step 3: After filter
    filtered = filter_data(transformed)
    print(f"CHECKPOINT 3: After filter: {len(filtered)} rows")
    
    # Step 4: Final result
    result = aggregate(filtered)
    print(f"CHECKPOINT 4: Final result: {result}")
    return result

Rubber Duck Debugging

Simply explain your code out loud (or to a rubber duck):

Describe what the code is supposed to do
Describe what it actually does
Often you'll find the bug while explaining!

Minimal Reproducible Example

Example

# When you have a complex bug, strip it down to the minimal case # Original complex code: # result = process(data, config, filter_fn, transform_fn, logger) # Minimal reproduction: import pandas as pd # Bug: KeyError when merging DataFrames df1 = pd.DataFrame({'id': [1, 2, 3], 'name': ['a', 'b', 'c']}) df2 = pd.DataFrame({'ID': [1, 2, 4], 'value': [10, 20, 30]}) # Note: 'ID' not 'id'! try: result = df1.merge(df2, on='id') except KeyError as e: print(f"Error: {e}") print(f"df1 columns: {df1.columns.tolist()}") print(f"df2 columns: {df2.columns.tolist()}") # Found it! Column name mismatch result = df1.merge(df2, left_on='id', right_on='ID')

Debugging Flask Applications

Example

# flask_debug.py from flask import Flask import logging app = Flask(__name__) # Enable Flask debugger app.config['DEBUG'] = True # Flask's built-in debug tools: # 1. Auto-reloader — reloads on code changes # 2. Debugger — interactive debugger in browser # 3. Error pages — detailed error info # Add request/response logging @app.before_request def log_request(): from flask import request app.logger.debug(f"Request: {request.method} {request.path}") app.logger.debug(f"Headers: {dict(request.headers)}") @app.after_request def log_response(response): app.logger.debug(f"Response: {response.status_code}") return response # Set breakpoint in route @app.route('/debug-test') def debug_route(): data = {'key': 'value'} breakpoint() # Will pause here when route is hit return str(data) # Run: flask --app flask_debug run --debug

Summary

Debugging techniques covered:

✅ Print debugging with debug_print helper
✅ pdb and breakpoint() for interactive debugging
✅ PDB command reference (n, s, c, p, w, b)
✅ icecream for enhanced print debugging
✅ Logging strategies for production debugging
✅ Assertions for verifying assumptions
✅ VS Code debugger configuration
✅ pytest debugging flags (--pdb, -s, -v)
✅ Performance profiling (cProfile, timeit)
✅ Systematic debugging patterns (bisect, minimal repro)

*Next: Learning Resources →*

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ResourcesPython Best PracticesMaster Python best practices and coding standards — PEP 8 style guide, clean code principles, naming conventions, documentation, type hints, logging, testing, virtual environments, and professional Python patterns.ResourcesCommon Python Errors and SolutionsMaster Python error handling by understanding the most common errors — SyntaxError, IndentationError, NameError, TypeError, AttributeError, ImportError, ValueError, KeyError, IndexError, and how to fix them.ResourcesPython Learning ResourcesCurated list of the best Python learning resources — official documentation, books, online courses, YouTube channels, practice platforms, communities, cheat sheets, and a structured 6-month learning roadmap.Advanced PythonPython Asynchronous Programming with asyncioDeep dive into Python async/await, asyncio event loop, coroutines, tasks, async generators, aiohttp, and real-world asynchronous patterns with Hindi explanations.Advanced PythonPython Context ManagersComprehensive guide to Python context managers using with statement, __enter__ and __exit__ methods, contextlib module, nested context managers and custom resource management.Advanced PythonAdvanced Python DecoratorsMaster advanced Python decorators including decorator factories, class decorators, stacking decorators, functools.wraps, parameterized decorators, and real-world patterns.

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Python Notes

Python Debugging Techniques

Debugging is the process of finding and fixing bugs in your code. Professional developers spend significant time debugging — mastering these techniques will make you dramatically more productive.

The Debugging Mindset

Before diving into tools, adopt this mindset:

Reproduce the bug — can you consistently trigger it?
Isolate the problem — narrow down which code causes it
Understand what's happening — don't guess; verify your assumptions
Fix one thing at a time — avoid multiple simultaneous changes
Verify the fix — confirm it works and didn't break anything else

Print Debugging (Quick and Easy)

Example

Using breakpoint() and pdb

Python's built-in debugger pdb lets you step through code interactively:

python example

def process_orders(orders):
    total = 0
    for i, order in enumerate(orders):
        breakpoint()  # Pause execution here (Python 3.7+)
        # Old way: import pdb; pdb.set_trace()
        
        amount = order.get('amount', 0)
        total += amount
    return total

PDB Commands Reference

Code example

pdb Commands:
  n (next)        — Execute current line, go to next
  s (step)        — Step INTO function calls
  c (continue)    — Continue until next breakpoint
  q (quit)        — Quit debugger
  p expr          — Print the value of expression
  pp expr         — Pretty print the value
  l (list)        — Show current code context (11 lines)
  ll (longlist)   — Show full function source
  w (where)       — Show call stack
  u (up)          — Move up the call stack
  d (down)        — Move down the call stack
  b 25            — Set breakpoint at line 25
  b func_name     — Set breakpoint at function
  cl              — Clear all breakpoints
  h (help)        — Show help
  h command       — Help for specific command
  
  ! expr          — Execute any Python expression
  !x = 5          — Change variable value during debugging

Example

Conditional Breakpoints

python example

# Set a breakpoint only when a condition is true
def process_items(items):
    for i, item in enumerate(items):
        if item < 0:
            breakpoint()  # Only stops when negative item found
        process(item)

# In pdb — set conditional breakpoint
# (Pdb) b 45, x > 100   ← Break at line 45 only when x > 100
# (Pdb) b my_func, n > 50  ← Break in my_func when n > 50

The icecream Library (Better print debugging)

bash example

pip install icecream

python example

from icecream import ic

def calculate(a, b):
    ic(a, b)                       # ic| a: 5, b: 3
    result = a + b
    ic(result)                     # ic| result: 8
    ic(a + b * 2, a ** 2)         # ic| a + b * 2: 11, a ** 2: 25
    return result

# Auto-includes file, line, and variable names
# ic| my_script.py:6 in calculate()- result: 8

# Disable for production
ic.disable()
# ic.enable()  # Re-enable

# Configure prefix
ic.configureOutput(prefix='🐛 Debug: ')

Logging-Based Debugging

Example

Assertions for Debugging

python example

def calculate_percentage(value, total):
    """Calculate what percentage value is of total."""
    assert total != 0, f"Total cannot be zero! Got total={total}"
    assert isinstance(value, (int, float)), f"value must be numeric, got {type(value)}"
    assert isinstance(total, (int, float)), f"total must be numeric, got {type(total)}"
    assert 0 <= value <= total, f"value ({value}) must be between 0 and total ({total})"
    
    return (value / total) * 100

# Assertions can be disabled in production with:
# python -O my_script.py  (optimization flag disables assert)

# Use assert for:
# - Checking function preconditions
# - Verifying invariants
# - Documenting assumptions

# Don't use assert for:
# - Input validation from users (use if/raise instead)
# - Security checks (can be disabled!)

VS Code Debugging

Configure launch.json in .vscode/:

Example

VS Code debugging features:

Breakpoints — Click the gutter (line numbers) to set
Conditional breakpoints — Right-click breakpoint → Edit condition
Watch variables — Add expressions to watch panel
Call stack — See how you got to current line
Debug console — Evaluate expressions while paused

Debugging with pytest

Example

Performance Debugging (Profiling)

python example

# Method 1: Simple time measurement
import time

start = time.perf_counter()
# ... your code ...
end = time.perf_counter()
print(f"Elapsed: {end - start:.4f}s")

# Method 2: timeit for microbenchmarks
import timeit

# Benchmark a single expression
t = timeit.timeit('sum(range(1000))', number=10000)
print(f"Average: {t/10000*1000:.4f}ms")

# Method 3: cProfile — function-level profiling
import cProfile
import pstats

# Profile a function
cProfile.run('my_function()', 'profile_stats')

# Analyze results
stats = pstats.Stats('profile_stats')
stats.sort_stats('cumulative')  # Sort by cumulative time
stats.print_stats(20)           # Show top 20 functions

# Or profile from command line:
# python -m cProfile -s cumulative my_script.py

# Method 4: line_profiler — line-by-line
# pip install line_profiler
# @profile decorator (from line_profiler)
# kernprof -l -v my_script.py

# Method 5: memory_profiler
# pip install memory_profiler
# @profile decorator
# python -m memory_profiler my_script.py

Debugging Patterns

Bisect Debugging

python example

# When you don't know where the bug is, use divide and conquer
# Add checkpoints to narrow down the problem

def process_large_dataset(data):
    # Step 1: Check input
    print(f"CHECKPOINT 1: Input has {len(data)} rows")
    assert all('id' in item for item in data), "Some items missing 'id'"
    
    # Step 2: After first transformation
    transformed = transform_data(data)
    print(f"CHECKPOINT 2: After transform: {len(transformed)} rows")
    assert len(transformed) == len(data), "Transform changed row count!"
    
    # Step 3: After filter
    filtered = filter_data(transformed)
    print(f"CHECKPOINT 3: After filter: {len(filtered)} rows")
    
    # Step 4: Final result
    result = aggregate(filtered)
    print(f"CHECKPOINT 4: Final result: {result}")
    return result

Rubber Duck Debugging

Simply explain your code out loud (or to a rubber duck):

Describe what the code is supposed to do
Describe what it actually does
Often you'll find the bug while explaining!

Minimal Reproducible Example

Example

Debugging Flask Applications

Example

Summary

Debugging techniques covered:

✅ Print debugging with debug_print helper
✅ pdb and breakpoint() for interactive debugging
✅ PDB command reference (n, s, c, p, w, b)
✅ icecream for enhanced print debugging
✅ Logging strategies for production debugging
✅ Assertions for verifying assumptions
✅ VS Code debugger configuration
✅ pytest debugging flags (--pdb, -s, -v)
✅ Performance profiling (cProfile, timeit)
✅ Systematic debugging patterns (bisect, minimal repro)

*Next: Learning Resources →*

Exam Focus

Revise definitions, diagrams, examples, and short-answer points for Python Debugging Techniques.

Interview Use

Prepare one clear explanation, one practical example, and one common mistake for this Python Master Course topic.

Search Terms

python-master-course, python master course, python, master, course, resources, debugging, techniques

Continue learning this concept

PreviousCommon Python Errors and Solutions NextPython Learning Resources