This article explains the built‑in __name__ variable in Python, how its value differs when a script is run directly versus imported as a module, and demonstrates both scenarios with clear code examples and visual diagrams.
Learn how to use Python to batch‑process exam results from an Excel file, generate personalized admission letters with a Word template, and automatically send them via QQ email, covering environment setup, required libraries, code snippets, and step‑by‑step instructions.
Jupytext lets you convert Jupyter Notebooks to plain‑text formats, enabling seamless version control and IDE editing while preserving the visual advantages of notebooks, making data‑science projects more manageable and collaborative.
This article explains the historical reason Windows uses backslashes for paths, why hard‑coding separators causes bugs, and demonstrates how Python's pathlib module provides a clean, cross‑platform solution compared to manual string concatenation or the older os.path utilities.
This article explains the concept of Python coroutines, compares them with multithreading, outlines suitable and unsuitable scenarios, introduces the httpx library for asynchronous HTTP calls, and demonstrates performance gains through synchronous and asynchronous code examples.
This article provides a concise tutorial on Flask, covering installation, basic routing, custom converters, redirects, error handling, JSON responses, cookie and session management, and configuration techniques, all illustrated with clear code examples and screenshots for rapid web development.
This article introduces twelve powerful NumPy and Pandas functions—six for each library—explaining their purpose, usage, and providing code snippets, enabling readers to perform efficient array manipulation, data filtering, aggregation, and I/O operations, with a link to the full Jupyter Notebook on GitHub.
This guide walks you through building a Python web‑scraper that extracts NBA player information from the Hupu website, cleans and visualizes the data, and automatically creates Excel files with embedded line charts, covering URL navigation, data parsing with requests and BeautifulSoup, and chart generation with xlsxwriter.
The article examines why Python has remained popular due to its readability, extensive libraries, and ease of use, while also highlighting its performance limitations, GIL, memory usage, weak mobile support, and competition from emerging languages, concluding that Python remains a valuable but not universally optimal tool.
This article presents a thorough collection of Python best‑practice recommendations, covering coding style, language features, standard library usage, design patterns, testing, performance profiling, and tooling to help developers write clean, efficient, and maintainable Python code.
This article explains Python's property decorator and descriptor protocol, showing how to turn methods into managed attributes, perform validation, compute derived values, and illustrates their implementation with clear code examples including a Student class, a custom descriptor, and a classmethod reimplementation.
This article introduces the Python cufflinks library, showing how its simple DataFrame‑based API and built‑in themes let you create a variety of attractive charts—bar, line, scatter, bubble, subplots, box, histogram, and 3D—using just a few lines of code.
Although many developers criticize Tkinter and PyQt5 for producing unattractive interfaces, this article explains that functionality is prioritized over aesthetics, draws parallels between PyQt5 widgets and web HTML/CSS/JS components, and offers practical tips—design prototypes, leveraging QSS, and using widget properties—to dramatically improve Python desktop GUI appearance.
This tutorial walks you through configuring PyCharm live templates to automatically expand short keywords into reusable Python code snippets, showing step‑by‑step instructions, example screenshots, and tips for speeding up Django development with class‑based view boilerplate.
Despite Python’s explosive growth since 2010 and its dominance across data science, AI, and general programming, this article examines the language’s strengths—maturity, readability, extensive libraries—and its drawbacks such as speed, dynamic scope, and limited mobile support, while exploring whether emerging languages like Rust, Go, or Julia might eventually replace it.
This tutorial walks through creating a Flask‑based search engine powered by Elasticsearch, covering configuration files, logging setup, blueprint routing, pagination, detail views, and deployment options using Flask‑Script and Gunicorn, with complete code examples and a GitHub reference.
This article introduces five advanced Python features—lambda functions, map, filter, the itertools module, and generator functions—explaining their concepts, typical use cases, and providing clear code examples to demonstrate how they simplify data processing and improve memory efficiency.
This article provides a concise overview of key Python data‑analysis libraries—including NumPy, pandas, matplotlib, IPython/Jupyter, SciPy, scikit‑learn, and statsmodels—explaining their core features, typical use cases, and how they interoperate to form a powerful scientific computing ecosystem.
This guide explains how to install and use Tkinter Designer, a tool that combines Python's Tkinter library with the Figma API, allowing developers to design graphical interfaces in Figma and automatically generate runnable Python GUI code.
This guide walks through installing the Python Elasticsearch client, building a reusable class with CRUD methods, importing data from MongoDB, writing a simple Baidu Baike crawler, and scaling the workflow with Celery and Flask for a complete search‑engine pipeline.
This article introduces several powerful Python data‑visualization libraries—including Plotly, Cufflinks, Folium, Altair, and the JavaScript‑based D3.js—explains their strengths, provides installation commands, and offers practical code examples for creating interactive charts, maps, and 3D visualizations within Jupyter notebooks.
This article introduces Python exception handling fundamentals, demonstrating how to catch, manage, and retrieve error information with practical code examples—including try/except, multiple exceptions, else, finally blocks—and explains their behavior in real-world development scenarios.
This article provides a step‑by‑step guide to using Matplotlib for creating and customizing plots in Python, covering how to add titles, text, annotations, axis labels, legends, colors, markers, grids, axis limits, dual axes, filled areas, patches, and style themes, complete with runnable code examples and visual results.
This tutorial walks you through installing the jieba Python library, explains its three segmentation modes—precise, full, and search—demonstrates how to add or delete words, manage custom dictionaries, handle stop words, perform weight analysis, adjust word frequencies, and retrieve token positions, all with clear code examples and visual output.
This guide explains how to build a Python desktop GUI application with Tkinter, PyQt5, or wxPython, package it using tools like PyInstaller, and implement a robust update mechanism that detects new versions on a remote server, downloads the update, and seamlessly replaces the old files.
This article explains the main captcha varieties—input, sliding, grid, and click‑based—and provides step‑by‑step Python solutions using OCR libraries, image preprocessing, and Selenium automation to bypass them effectively.
This article introduces twelve high‑performance Numpy and Pandas functions—six for each library—explaining their purpose, typical use cases, and providing ready‑to‑run code examples to help data analysts work faster and more efficiently.
This hands‑on guide explains how to set up the cross‑platform Airtest framework, connect Android or iOS devices, record and edit Python‑based UI scripts, use image‑and value‑based assertions, generate HTML reports, and leverage Poco for element‑level automation in agile mobile development.
Learn how to install the Python wordcloud module, generate custom word clouds from text, customize fonts, colors, backgrounds, and stopwords, and integrate with Pillow and matplotlib, all illustrated with code snippets and images, to visualize text data effectively.
The June 2021 TIOBE index shows Python narrowing the gap to C for the top spot, outlines shifts among the top‑20 languages, highlights logo updates, and provides detailed rankings and trends for the top 50 and the remaining languages, offering developers insight into language popularity.
This article demonstrates how to download QQ friend avatars, compute their average RGB values, and programmatically combine them into a large collage or a template‑based composite image using Python's Pillow and OpenCV libraries, with complete source code and explanations.
This article presents a collection of practical Python techniques—including string cleaning with translation tables, iterator slicing with itertools, skipping file headers, keyword‑only arguments, custom context‑manager objects, memory‑saving __slots__, CPU and memory resource limits, export control via __all__, and simplified total ordering—illustrated with concise code examples.
This article demonstrates how websites can detect browsers driven by Selenium or Puppeteer, walks through experiments using bot.sannysoft.com, shows screenshots of detected features in both headed and headless modes, and explains why such tools often fail to evade detection.
This article introduces the Python Faker library, explains how to install and configure it with different locales, lists its extensive data‑generation methods, and demonstrates a practical example of creating a MySQL table and inserting realistic fake user records for testing purposes.
This article demonstrates how to read a Word document containing exam tables, parse question types, titles, and options using Python, and then transform the data into a structured Excel file with pandas, providing complete code and handling special whitespace characters.
This article introduces two low‑frequency Python trading strategies—a grid‑based price‑difference approach and an intraday T‑strategy—explains their implementation on the RiceQuant platform, provides sample code, and presents back‑testing results that demonstrate their performance and practical considerations.
This article explains the most frequent Python traps—such as mutable default arguments, subtle differences between x+=y and x=y+y, tuple syntax, list mutability, iteration pitfalls, closure binding, import quirks, version incompatibilities, and GIL—providing clear examples and practical solutions.
This article explains the background of migrating from MySQL to TiDB, introduces the Snowflake algorithm’s 64‑bit ID structure, discusses its advantages and disadvantages, provides a Python implementation, and highlights its impact on achieving globally unique, time‑ordered identifiers in distributed systems.
This article introduces four popular Python progress‑bar libraries—Progress, tqdm, alive‑progress, and PySimpleGUI—explaining their installation, basic usage with concise code examples, and visual output, helping developers add lightweight command‑line or graphical progress indicators to their scripts.
This article outlines six practical guidelines for writing clean, maintainable Python functions—including meaningful naming, single responsibility, comprehensive docstrings, returning useful values, limiting length, and ensuring idempotence or purity—illustrated with code examples and explanations to help developers improve function quality.
This tutorial walks you through creating an interactive Excel weather dashboard using Python's xlwings and requests libraries, covering API selection, data retrieval, code integration, Excel macro setup, UI design, and dynamic updates for multiple Chinese cities.
This tutorial walks through the complete process of building a linear regression model in Python from loading a housing price dataset, normalizing features, defining hypothesis, cost and gradient‑descent functions, visualising data and cost convergence, and testing predictions, with full source code provided.
This article demonstrates how to use Python to scrape historical lottery data, apply a custom pattern‑matching algorithm to identify winning opportunities, aggregate the results over multiple days, and export the analysis to an Excel file for further review.
This article provides a comprehensive guide to Python's built‑in Tkinter GUI library, covering installation, window creation, core widgets such as labels, buttons, text boxes, menus, canvases, and advanced features like event binding and layout management, enabling readers to build functional desktop applications.
This article demonstrates how to use Python's OpenCV and Pyzbar libraries to capture video from a webcam, decode barcodes in real time, display the results, and save captured frames, providing a practical guide for implementing barcode recognition in a retail checkout scenario.
This article explains the concept of writing Pythonic code—leveraging Python’s language features for clear, concise, and maintainable programs—by contrasting non‑Pythonic examples with idiomatic solutions, illustrating the Zen of Python, and providing practical tips and references for developers.
Guido van Rossum explains why a Python 4 release is unlikely, detailing the language’s post‑Python‑2 evolution, the focus on incremental 3.x improvements, performance goals, and how future changes like C‑compatibility or removing the GIL could finally trigger a major version jump.
This guide explains how to install the Pillow and pyBarcode Python packages and demonstrates two methods for generating various barcode formats—such as EAN13, Code39, and UPC—using the pyBarcode library, including code examples, configuration options, and saving the output as image files.
This article shows how to use Python on Windows to obtain the system drive, enumerate and delete files and directories with os.walk, copy the script to the startup folder, generate a BAT wrapper, and silently shut down the computer, illustrating a basic malicious automation technique.
This tutorial explains what Django widgets are, shows how to customize form fields with CSS classes and attributes, and demonstrates both simple Form and ModelForm examples that integrate Bootstrap styling for a polished, functional user interface.
This article introduces Python’s fundamental data structures—lists, tuples, sets, and dictionaries—detailing how to create them, use common methods, perform operations like concatenation, and understand their mutability with clear code examples.
Guido van Rossum, the creator of Python, reveals in a recent interview that a Python 4.0 is unlikely, explaining the team’s focus on incremental improvements through versions 3.9 to 3.13, performance boosts, type‑hint evolution, and the challenges of maintaining C compatibility, while also sharing his views on other languages like Rust, Go, and TypeScript.
This guide shows three practical ways—using the queryset query attribute, Django's connection object, and the Django Debug Toolbar—to inspect the raw SQL generated by Django ORM, helping developers optimize database performance and troubleshoot queries effectively.
This article introduces the Python‑based Locust load‑testing framework, explains its event‑driven architecture, weight‑based task distribution, command‑line and web UI operation, distributed execution, and practical usage patterns, then evaluates its advantages, drawbacks, and suitability for performance testing projects.
This article introduces Python lists, explains how they differ from arrays, shows two creation methods using brackets and the list() function, and demonstrates element access via indexing and slicing with clear code examples and output screenshots.
This article introduces NumPy, the essential Python library for array and matrix operations, covering creation, arithmetic, slicing, aggregation, reshaping, and real‑world examples such as machine‑learning data preparation, image handling, audio processing, and text tokenization.
This article explains Python's file handling basics, covering what files are, how to open them with the open() function, various access modes, common file object methods such as read, readline, readlines, write, tell, seek, and the use of the with statement for safe resource management, plus code examples.
In a one‑hour interview, Python’s creator Guido van Rossum shares candid opinions on Rust, Go, Julia, and TypeScript, explaining why he finds Rust powerful, Go the most Pythonic, Julia a niche but interesting language, and how Python is learning from TypeScript.
This tutorial walks beginners through the complete process of building a Python web scraper to collect internship listings from Shixi.com, covering page analysis, static vs dynamic detection, data location with XPath, pagination handling, and storing results in a CSV file using pandas.
This guide explains how to use Scapy for host discovery (TCP SYN/ACK, UDP, ARP, ICMP pings), various port‑scanning techniques (SYN, FIN, NULL, Xmas, UDP) and traceroute methods (ICMP, TCP, DNS), providing complete code examples and practical insights.
This article compares popular Python web frameworks, highlights the speed advantages of async frameworks—especially Sanic—through benchmark data, explains why asynchronous I/O matters, and discusses Sanic's ecosystem, production readiness, documentation, and community support.
A wave of malicious PyPI packages named after popular movies and TV shows, many containing spam keywords and stolen code, exposed supply‑chain risks and prompted urgent cleanup by maintainers, highlighting the need for developers to verify packages before use.
This tutorial walks through the complete process of turning a local Python project into an open‑source package, covering licensing, project structure, setup.py creation, testing, coverage, documentation, continuous integration, and finally publishing to PyPI or Conda, with practical code examples and configuration files.
This article explains how Python persistence works by serializing objects with pickle and cPickle, compares file‑based and database storage, demonstrates basic and advanced usage—including handling circular references, custom classes, and versioning—and offers practical tips for maintaining pickled data across code changes.
This tutorial walks through installing Plotly Express in a Python 3.7 Jupyter environment and demonstrates how to create a wide range of visualizations—including line, scatter, area, geographic, violin, and polar charts—using concise one‑line commands and built‑in datasets.
This guide introduces parallel-ssh, a Python-based asynchronous SSH library for small‑scale automation, covering its history, Python‑3 support, installation methods across platforms, command‑line utilities (pssh, pscp, prsync, pslurp, pnuke), usage examples, and resource considerations.
This article explains why processes are preferred over threads for stability and multi‑machine distribution, introduces Python's multiprocessing.managers for networked task queues, and provides step‑by‑step code examples to create a distributed crawler that fetches image URLs and downloads them across several machines.
This article explains why Python’s Django framework is a top choice for backend development, outlining its popularity, benefits, essential system‑planning and web‑script skills, rapid prototyping, scientific computing, and the specific Python and Django expertise needed to become a proficient developer.
This article explains Python inheritance and polymorphism, covering single, multi‑level and multiple inheritance, method overriding, and duck‑type polymorphism with clear code examples and visual illustrations to help beginners grasp OOP fundamentals effectively.
Guido van Rossum revealed that the new “Shannon Plan” backed by Microsoft will target a 2× speed boost for CPython 3.11 through a specialized bytecode interpreter, stack optimizations, and other enhancements, while preserving ABI compatibility and open‑source principles.
An enthusiastic overview walks through the fundamentals of linear and multivariate regression, explains loss functions and least‑squares optimization, shows Python implementations of fit and predict, and applies the model to the classic Boston housing dataset to illustrate feature impact and prediction.
This article introduces and explains the most useful Visual Studio Code extensions for Python developers in 2021, covering essential tools such as the Python language support, Python Snippets, Docstring Generator, Test Explorer, Preview, Type Hint, and Jupyter integration to boost productivity and code quality.
In this interview, Python’s creator discusses how the language was born, its unexpected rise in AI, its role in web and mobile development, thoughts on newer languages like Rust and TypeScript, version transitions, package management, quantum computing prospects, and his personal life and hobbies.
Guido van Rossum’s recent “Making CPython Faster” talk reveals the Shannon Plan—a four‑year effort, backed by Microsoft, to boost Python’s performance up to five times, with the first milestone targeting a 2× speedup in the upcoming Python 3.11 release through adaptive bytecode interpreters and other optimizations.
This article explains the differences between local and global variables in Python, how to modify globals with the global keyword, the behavior of mutable and immutable objects, demonstrates variable scope with code examples, and also covers references, lambda anonymous functions, and recursive implementations such as factorial calculations.
This article compares Python's built‑in urllib library with the third‑party requests library, demonstrating their usage through code examples, highlighting differences in request construction, response handling, and practical considerations for web scraping, and concludes with recommendations for choosing the more convenient tool.
This article explains Python object persistence, covering the concepts of serialization with pickle and cPickle, various storage mechanisms, handling of complex objects, reference cycles, class instance pickling, versioning strategies, and advanced techniques such as custom state methods and Pickler/Unpickler usage.
An in‑depth Python tutorial walks through the fundamentals of classes, covering creation, instance and class methods, protected and private members, core OOP features such as encapsulation, inheritance and polymorphism, as well as dynamic attribute handling with __slots__ and property decorators, illustrated with clear examples.
This guide introduces Scapy, a powerful interactive Python packet manipulation tool, covering installation, basic usage, packet creation, sending and receiving functions, layer inspection, packet export formats, sniffing, and advanced features such as sprintf and custom packet handlers, enabling network testing, analysis, and security tasks.
This article explains how to define Android UI pages as view trees, convert them to list structures, generate unique page signatures, and filter actionable events such as scrollable and clickable views using Python, enabling more effective model‑based testing of mobile applications.
This article explores the differences between Python interpreters, explains why PyPy often runs faster than CPython through JIT compilation, compares performance across several languages, and offers practical optimization techniques for Python code, all backed by code examples and benchmark results.
This tutorial walks you through creating a variety of Python visualizations—line plots, bar charts, histograms, density plots, scatter matrices, and faceted grids—using pandas, matplotlib, and seaborn, with complete code examples and tips for customizing appearance.
Guido van Rossum, now a Microsoft Distinguished Engineer, promises to boost CPython performance by up to five times without breaking existing code, detailing the team, funding, and security efforts behind the ambitious speed upgrades slated for Python 3.11 and beyond.
This article explains Python's memory management, covering reference counting, its limitations with cyclic references, and how the interpreter supplements it with mark‑sweep and generational garbage‑collection algorithms, complete with code examples and detailed diagrams of the underlying mechanisms.
This article explains Python's multiprocessing module, covering process concepts, creation of single and multiple processes, process pools, locks, inter‑process communication methods such as Event, Pipe, Queue, semaphores, and data sharing techniques, with code examples and visual illustrations.
This guide explains why manual regression testing of a WeChat mini‑program becomes a bottleneck, compares Jest‑based SDK and Minium frameworks, shows how to set up the environment, write page‑object scripts, configure and run tests, generate reports, and troubleshoot common issues.
This article explains the limitations of traditional RESTful APIs, introduces GraphQL as a flexible query language, details its core concepts such as operation types, object and scalar types, and schema definition, and provides a complete Python Flask example with code analysis, execution steps, and a discussion of its advantages and drawbacks.
This article explains the limitations of traditional RESTful APIs, introduces GraphQL's core concepts and operation types, provides a step‑by‑step Flask and Graphene implementation for querying and mutating book data, and discusses the practical pros and cons of adopting GraphQL in real‑world projects.
This article provides a comprehensive guide to Python threading, covering core concepts such as thread creation, synchronization primitives like locks, RLocks, conditions, semaphores, events, local storage, and timers, complete with practical code examples and explanations of their usage and pitfalls.
This article explains what NoSQL means, contrasts it with SQL and relational databases, walks through schema design, primary keys, indexing, and then demonstrates how to implement a lightweight key‑value NoSQL store in pure Python with full command parsing and handling.
This article provides a comprehensive guide to Python threading, covering core concepts such as threads, locks, RLock, conditions, semaphores, events, local storage, and timers, along with practical code examples for creating, managing, and synchronizing threads to improve concurrent program performance.
This article introduces the fundamentals of automatic speech recognition (ASR) for food‑sound classification, explains key audio representations and modeling approaches, and provides a fully runnable Python implementation using librosa, TensorFlow/Keras, and classic machine‑learning tools to train and predict on the Tianchi competition dataset.
This article shares a curated collection of ready‑to‑run Python and Shell scripts—including Enterprise WeChat alerts, FTP and SSH clients, SaltStack and vCenter utilities, SSL certificate checks, weather notifications, SVN backups, Zabbix password monitoring, local YUM mirroring, and high‑load detection—complete with full source code and usage notes to help engineers automate routine tasks and boost operational efficiency.
This article documents a Python web service performance optimization case, detailing the initial bottlenecks, architectural redesign with caching and message queues, load‑testing methodology, Linux TCP time‑wait tuning, and the final results achieving 50k QPS with sub‑70 ms latency.
This tutorial walks you through using Airtest to automate mobile apps—showing how to launch apps via image recognition or package name, perform likes and comments, capture screenshots, enable real‑time coordinates, and create endless swipe loops with Python code.
This article explains what NoSQL means, contrasts it with relational databases, and guides readers through building a simple Python key‑value NoSQL database, covering schema basics, querying, indexing, command parsing, and the trade‑offs between flexibility and query performance.
This article explains how the PyPy interpreter dramatically speeds up Python code through just‑in‑time (JIT) compilation, compares its performance to CPython and C, and provides a practical example demonstrating the runtime gains.
This article introduces Python functions, demonstrates how to define and call them, and explores essential time-related modules such as time, datetime, calendar, and random, providing clear code examples and output screenshots to help beginners grasp practical usage.
This article explains how to use Python's NumPy library to translate common mathematical formulas—such as powers, roots, absolute values, vector and matrix operations—into concise, executable code, covering setup, basic operations, and practical examples for data analysis and machine learning.
This tutorial shares three handy tools—a Sublime Text build system for running Python scripts, an iTerm2 imgcat setup for displaying images directly in the terminal, and a concise guide to Python's typing module for better type safety and code readability.
