CodeStips

Code Performance

Avoiding Unnecessary Data Copies

Best Practices & Optimization

Learn why avoiding unnecessary data copies in Python boosts performance and memory efficiency, with tips to recognize and prevent them in your code.

Memory Management with tracemalloc

Libraries & Frameworks

Learn to detect and fix memory leaks in Python code using the built-in tracemalloc module to track allocations and resolve issues efficiently.

Python bisect Module Explained

Python Standard Libraries

Discover the power of Python's bisect module for efficient searches and insertions in sorted lists. Perfect for maintaining order without repeated sorting. A must-know for handling sorted data!

Python Function Performance Tips

Functions & Modules in Python

Speed up Python functions by choosing efficient data structures like sets over lists for faster code execution and better user experience.

Python Garbage Collection in OOP

Object-Oriented Programming in Python

Python uses reference counting and cyclic garbage collection to automatically manage memory in OOP, ensuring efficient and memory-safe applications.

Python slots Explained

Object-Oriented Programming in Python

Optimize memory and performance in Python classes using slots, which replaces the default attribute dictionary, reducing overhead and speeding up access.

Python Time Module Basics

Basics of Python

Learn how Python's time module handles time-related tasks like measuring performance, pausing execution, and converting time formats.

Using Built-in Functions for Speed

Best Practices & Optimization

Boost Python performance by using built-in, C-optimized functions for faster execution with large datasets and time-sensitive tasks.

Using unittest.mock Module

Testing & Debugging

The unittest.mock module in Python helps isolate code for testing by replacing dependencies with mock objects, simplifying testing of complex interactions.