


Python performance optimization practice: from basics to advanced
Basic optimization
- Use the correct Python version: Newer versions of python are generally more performant, offer better memory management and built-in optimizations .
- Choose the right library: Save time and improve performance by using purpose-built libraries instead of writing code from scratch.
- Reduce the number of loops: If possible, avoid using nested loops. Using list comprehensions and generator expressions are more efficient alternatives.
Data structure optimization
- Choose the right container: Lists are good for random access, dictionaries are good for fast key-value lookups, and tuples are good for immutable data.
- Use pre-allocated memory: By pre-allocating the size of an array or list, you can reduce the overhead of memory allocation and defragmentation.
- Using Numpy and Pandas: For scientific computing and data analysis, Numpy and pandas provide efficient array and data frame implementations.
Algorithm optimization
- Use binary search: For sorted arrays, binary search provides a faster search algorithm than linear search.
- Consider divide and conquer: Decomposing a large problem into smaller sub-problems can improve algorithm efficiency.
- Use recursion or iteration: Depending on the circumstances, a recursive or iterative implementation may be more efficient.
Code optimization
- Avoid unnecessary copies: Use pass by reference instead of pass by value to avoid creating unnecessary copies.
- Use @property and @staticmethod: These decorators can optimize property access and static method calls.
- Utilize multi-threading and parallel processing: For computationally intensive tasks, Multi-threading and parallel processing can increase the execution speed.
Advanced optimization
- Using Cython and Numba: These tools can compile Python code into faster C extensions, improving the performance of numerical calculations and data processing.
- Leveraging the CPython JIT: CPython’s JIT (just-in-time compiler) can identify and optimize certain blocks of code, thereby increasing execution speed.
- Use a memory analyzer: Using tools such as Valgrind or Pympler, you can analyze memory usage and identify memory leaks or other problems.
Demo code
- Fast matrix multiplication using Numpy arrays:
import numpy as np a = np.array([[1, 2], [3, 4]]) b = np.array([[5, 6], [7, 8]]) c = np.dot(a, b)# 高效矩阵乘法
- Use binary search for quick search:
def binary_search(arr, target): low = 0 high = len(arr) - 1 while low <= high: mid = (low + high) // 2 if arr[mid] == target: return mid elif arr[mid] < target: low = mid + 1 else: high = mid - 1 return -1# 未找到
- Use Cython to optimize numerical calculations:
import cython @cython.cclass class MyClass: def __init__(self): self.x = 0 def calculate(self, n): for i in range(n): self.x += i
in conclusion
By implementing these optimization techniques, developers can significantly improve the performance of their Python code, thereby improving application response times and the overall user experience. Remember, Performance optimization is an ongoing process that requires careful consideration of code structure, data structures, and algorithms. Through continuous optimization, developers can create Python applications that are efficient, reliable, and meet user needs.
The above is the detailed content of Python performance optimization practice: from basics to advanced. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics



PHP is mainly procedural programming, but also supports object-oriented programming (OOP); Python supports a variety of paradigms, including OOP, functional and procedural programming. PHP is suitable for web development, and Python is suitable for a variety of applications such as data analysis and machine learning.

PHP is suitable for web development and rapid prototyping, and Python is suitable for data science and machine learning. 1.PHP is used for dynamic web development, with simple syntax and suitable for rapid development. 2. Python has concise syntax, is suitable for multiple fields, and has a strong library ecosystem.

VS Code can run on Windows 8, but the experience may not be great. First make sure the system has been updated to the latest patch, then download the VS Code installation package that matches the system architecture and install it as prompted. After installation, be aware that some extensions may be incompatible with Windows 8 and need to look for alternative extensions or use newer Windows systems in a virtual machine. Install the necessary extensions to check whether they work properly. Although VS Code is feasible on Windows 8, it is recommended to upgrade to a newer Windows system for a better development experience and security.

VS Code extensions pose malicious risks, such as hiding malicious code, exploiting vulnerabilities, and masturbating as legitimate extensions. Methods to identify malicious extensions include: checking publishers, reading comments, checking code, and installing with caution. Security measures also include: security awareness, good habits, regular updates and antivirus software.

VS Code can be used to write Python and provides many features that make it an ideal tool for developing Python applications. It allows users to: install Python extensions to get functions such as code completion, syntax highlighting, and debugging. Use the debugger to track code step by step, find and fix errors. Integrate Git for version control. Use code formatting tools to maintain code consistency. Use the Linting tool to spot potential problems ahead of time.

In VS Code, you can run the program in the terminal through the following steps: Prepare the code and open the integrated terminal to ensure that the code directory is consistent with the terminal working directory. Select the run command according to the programming language (such as Python's python your_file_name.py) to check whether it runs successfully and resolve errors. Use the debugger to improve debugging efficiency.

Python is more suitable for beginners, with a smooth learning curve and concise syntax; JavaScript is suitable for front-end development, with a steep learning curve and flexible syntax. 1. Python syntax is intuitive and suitable for data science and back-end development. 2. JavaScript is flexible and widely used in front-end and server-side programming.

VS Code is available on Mac. It has powerful extensions, Git integration, terminal and debugger, and also offers a wealth of setup options. However, for particularly large projects or highly professional development, VS Code may have performance or functional limitations.
