Python Debugger Debugging Process 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