Python Advance | Chapter 9: Memory Management and Performance Optimization in Python by Prakhar Chauhan | Learn Smarter
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Chapter 9: Memory Management and Performance Optimization in Python

Python's memory management is primarily characterized by automatic memory allocation and deallocation through techniques such as reference counting and garbage collection. This chapter explores various methods to monitor memory usage, profile performance, and optimize code for efficiency. By implementing strategies like using generators, built-in functions, and leveraging libraries such as NumPy and Cython, developers can significantly enhance performance and resource utilization in Python applications.

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Sections

  • 1

    Python Memory Model Overview

    This section introduces the fundamental aspects of Python's memory model, focusing on automatic memory management, object handling, and efficiency strategies.

    Learning Practice

  • 1.1

    Key Concepts

    This section provides an overview of Python's memory management, highlighting its automatic memory management system, reference counting, garbage collection mechanisms, and profiling methods for optimization.

    Learning Practice

  • 2

    Reference Counting And Garbage Collection

    This section discusses how Python manages memory through reference counting and cyclic garbage collection.

    Learning Practice

  • 2.1

    Reference Counting

    This section focuses on reference counting in Python, explaining its role in memory management and the limitations associated with circular references.

    Learning Practice

  • 2.2

    Cyclic Garbage Collection

    Cyclic garbage collection in Python addresses the issue of memory leaks resulting from circular references that evade reference counting.

    Learning Practice

  • 3

    Monitoring Memory: Sys And Gc Modules

    This section discusses the built-in `sys` and `gc` modules in Python, which are essential for monitoring memory usage and garbage collection.

    Learning Practice

  • 3.1

    Sys Module

    The sys module in Python provides functions and variables to manipulate the Python runtime environment, especially for memory management.

    Learning Practice

  • 3.2

    Gc Module

    The gc module in Python manages garbage collection and helps prevent memory leaks by identifying and deallocating unreachable objects.

    Learning Practice

  • 4

    Profiling Python Code With Cprofile And Timeit

    This section discusses profiling in Python using cProfile and timeit to identify performance bottlenecks.

    Learning Practice

  • 4.1

    Using Cprofile

    This section discusses how to use the cProfile module in Python for profiling code to enhance performance optimization.

    Learning Practice

  • 4.2

    Using Timeit

    This section discusses the 'timeit' module in Python, which is designed to measure the execution time of small code snippets efficiently.

    Learning Practice

  • 5

    Optimizing Python Code For Speed And Memory Efficiency

    This section explores various strategies for optimizing Python code, focusing on enhancing speed and memory efficiency.

    Learning Practice

  • 5.1

    General Tips

    This section provides practical tips for optimizing memory usage and performance in Python programs.

    Learning Practice

  • 5.2

    Use Generators Instead Of Lists

    This section emphasizes the advantages of using generators over lists in Python to improve memory efficiency.

    Learning Practice

  • 5.3

    Use Built-In Functions And Libraries

    Built-in functions and libraries in Python enhance performance and memory efficiency.

    Learning Practice

  • 6

    Using Built-In Tools And Third-Party Libraries

    This section focuses on leveraging built-in tools and third-party libraries in Python to improve performance and memory efficiency.

    Learning Practice

  • 6.1

    Numpy

    This section explores how NumPy enhances Python's performance capabilities through efficient memory management and optimized operations, particularly in numerical computing.

    Learning Practice

  • 6.2

    Cython

    Cython is a programming language that facilitates writing Python C extensions to enhance performance significantly.

    Learning Practice

  • 6.3

    Memory_profiler

    The memory_profiler is a third-party tool for analyzing memory usage in Python code on a line-by-line basis.

    Learning Practice

  • 6.4

    Other Libraries

    This section explores various libraries and tools that enhance Python's performance and memory management, focusing on NumPy, Cython, and specialized third-party libraries.

    Learning Practice

  • 7

    Summary And Best Practices

    This section provides essential tips for enhancing memory management and performance optimization in Python, ensuring efficient coding practices.

    Learning Practice

  • 8

    In Summary

    This section summarizes key memory management and performance optimization techniques in Python.

    Learning Practice

References

Untitled document (20).pdf

Class Notes

Memorization

What we have learnt

  • Python uses reference count...
  • Tools such as sys, gc, and ...
  • Profiling code with cProfil...

Final Test

Revision Tests