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Python Intermediate

What is Python Profiling?

Measuring where Python code spends time and memory to identify performance bottlenecks and optimization opportunities.

Python profilers help identify slow code paths. cProfile (built-in) records function call counts and cumulative times — run with python -m cProfile script.py. line_profiler provides line-by-line timing with @profile decorator. memory_profiler tracks memory usage per line. py-spy samples running processes without code modification. For web applications, Django Debug Toolbar and Flask-Profiler show per-request metrics. Key metrics include total time, cumulative time per function, call count, and memory allocation. Common bottlenecks include unnecessary database queries (N+1), inefficient loops (use vectorized operations), excessive object creation, and blocking I/O in async code.

Related Terms

Logging Module
Python's built-in module for recording diagnostic information with configurable handlers, formatters, and log levels.
Python Property
A built-in decorator that lets you define methods that behave like attributes, enabling controlled access to instance data.
Python Iterator Protocol
The interface requiring __iter__() and __next__() methods, enabling objects to be used in for loops and other iteration contexts.
FastAPI
A modern, high-performance Python web framework for building APIs with automatic OpenAPI documentation and type validation.
Python Context Variable
A variable that maintains separate values for each execution context, enabling implicit state passing in async code without global variables.
GIL (Global Interpreter Lock)
A mutex in CPython that allows only one thread to execute Python bytecode at a time, limiting true parallelism.
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