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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

Python Interpreter
The program that reads and executes Python code, translating it into machine instructions at runtime.
Slots
A class mechanism that restricts attribute creation and reduces memory usage by using a fixed set of instance variables.
Python Descriptors
Objects that define __get__, __set__, or __delete__ methods, controlling how attribute access works on other objects.
Exception Handling
A mechanism for managing runtime errors using try, except, else, and finally blocks to prevent program crashes.
NumPy
A fundamental library for numerical computing in Python, providing efficient multi-dimensional arrays and mathematical operations.
Pip Requirements File
A text file listing Python package dependencies with version specifications for reproducible project installations.
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