Python Debugger

Debugging Process

1. Understand the Error -> 2. Reproduce -> 3. Isolate -> 4. Identify Root Cause -> 5. Fix -> 6. Verify

Step 1: Understand the Error

Reading Tracebacks

Traceback (most recent call last):        <- Read bottom to top
  File "app.py", line 45, in main         <- Entry point
    result = process_data(data)           <- Call chain
  File "processor.py", line 23, in process_data
    return transform(item)                <- Getting closer
  File "transformer.py", line 12, in transform
    return item["value"] / item["count"]  <- Error location
ZeroDivisionError: division by zero       <- The actual error

Common error types: see references/python-error-types.md

Step 2: Reproduce the Issue

Create a minimal test case that triggers the error. Answer these questions:

• What input triggered this?
• Is it consistent or intermittent?
• When did it start happening?
• What changed recently?

Step 3: Isolate the Problem

Print Debugging

def process_data(data):
    print(f"DEBUG: data type = {type(data)}")
    print(f"DEBUG: data = {data}")

    for i, item in enumerate(data):
        print(f"DEBUG: processing item {i}: {item}")
        result = transform(item)
        print(f"DEBUG: result = {result}")

    return results

Using pdb

import pdb

def problematic_function(x):
    pdb.set_trace()  # Execution stops here
    # Or use: breakpoint()  # Python 3.7+
    result = x * 2
    return result

pdb commands: see references/pdb-commands.md

Using icecream

from icecream import ic

def calculate(x, y):
    ic(x, y)  # Prints: ic| x: 5, y: 0
    result = x / y
    ic(result)
    return result

Step 4: Common Root Causes

• None values: Check return values before accessing attributes. Guard with if x is None: raise ValueError(...)
• Type mismatches: Add type hints, cast inputs explicitly. int(a) + int(b) not a + b
• Mutable default arguments: Use def f(items=None): then items = items or [] inside
• Circular imports: Use lazy imports inside functions: from .module import Class
• Async/await: Missing await returns coroutine instead of result
• Key/Index errors: Use .get(key, default) for dicts, check len() for lists
• Scope issues: global/nonlocal declarations, closure variable capture in loops
• Encoding: Specify encoding="utf-8" in open() calls
• Float precision: Use decimal.Decimal or math.isclose() for comparisons
• Resource leaks: Use with statements for files, connections, locks

Step 5: Fix Patterns

Defensive Programming

def safe_divide(a, b):
    if b == 0:
        raise ValueError("Cannot divide by zero")
    return a / b

def safe_get(data: dict, key: str, default=None):
    return data.get(key, default)

Input Validation

def process_user(user_id: int, data: dict) -> dict:
    if not isinstance(user_id, int) or user_id <= 0:
        raise ValueError(f"Invalid user_id: {user_id}")

    required_fields = ["name", "email"]
    missing = [f for f in required_fields if f not in data]
    if missing:
        raise ValueError(f"Missing required fields: {missing}")

Exception Handling

import logging

logger = logging.getLogger(__name__)

def fetch_user_data(user_id: int) -> dict:
    try:
        response = api_client.get(f"/users/{user_id}")
        response.raise_for_status()
        return response.json()
    except requests.HTTPError as e:
        logger.error(f"HTTP error fetching user {user_id}: {e}")
        raise
    except requests.ConnectionError:
        logger.error(f"Connection failed for user {user_id}")
        raise ServiceUnavailableError("API unavailable")

Step 6: Verify the Fix

import pytest

def test_transform_handles_zero_count():
    """Verify fix for ZeroDivisionError."""
    data = {"value": 10, "count": 0}

    with pytest.raises(ValueError, match="count cannot be zero"):
        transform(data)

def test_transform_normal_case():
    """Verify normal operation still works."""
    data = {"value": 10, "count": 2}
    result = transform(data)
    assert result == 5

Debugging Tools

Logging Setup

import logging

logging.basicConfig(
    level=logging.DEBUG,
    format="%(asctime)s %(name)s %(levelname)s: %(message)s",
    handlers=[
        logging.FileHandler("debug.log"),
        logging.StreamHandler(),
    ],
)

Profiling

# Time profiling
import cProfile
cProfile.run("main()", "output.prof")

# Memory profiling
from memory_profiler import profile

@profile
def memory_heavy_function():
    # ...

Using rich for better output

from rich.traceback import install
install(show_locals=True)  # Enhanced tracebacks

References

• Debug Checklist
• Common Error Types
• pdb Commands

When to Use WebSearch

• Cryptic error messages
• Library-specific errors
• Version compatibility issues
• Undocumented behavior