This article guides functional testers on transitioning to automated testing with Python, covering core concepts, a step‑by‑step learning plan, and ten practical code snippets ranging from unittest and pytest to Selenium web and API automation.
This tutorial demonstrates how to use Python's Pandas library to read, clean, filter, sort, deduplicate, group, pivot, and export Excel data, providing step‑by‑step code examples that turn large spreadsheets into concise, analyzable summaries.
This tutorial explains how to explore the COCO keypoint detection annotation files, describes their JSON structure and fields, and provides step‑by‑step Python code using json, Pillow, and matplotlib to load images, extract keypoints, and draw both points and skeletal connections for visual analysis.
This article presents 17 versatile Python scripts that automate common tasks such as file management, web scraping, email handling, Excel processing, database interaction, system administration, and more, offering code examples and explanations to help developers and analysts improve productivity.
This tutorial explains how to convert ANSI escape sequences used for colored logging into HTML so that log output can be securely viewed with proper colors directly in a web browser, including server setup, code examples, and library usage.
This article compares FastAPI and Flask, highlighting FastAPI's speed, developer experience, and standards support, while providing practical migration guidance, code examples, configuration tips, async handling, dependency injection, testing, deployment, and documentation differences for Python web developers.
The article explains how Python 3.13 introduces a copy‑and‑patch Just‑In‑Time (JIT) compiler that compiles bytecode to machine code on first execution, reuses it to boost performance, and leverages LLVM tools for efficient runtime code generation.
This article introduces Argo Workflows, explains the challenges of YAML‑based definitions, and demonstrates how the Hera Python SDK can streamline workflow creation, testing, and submission on Alibaba Cloud ACK Serverless clusters with practical DAG‑diamond and Map‑Reduce examples.
Positron, the new beta IDE from Posit built on Visual Studio Code, offers a ready‑to‑use, cross‑platform environment for R and Python with integrated data exploration tools, seamless language switching, and easy extension management via OpenVSX, while still being an early‑stage project.
This tutorial demonstrates how to create a desktop GUI using Python's Tkinter library to fetch per‑capita GDP data from the World Bank via pandas_datareader, process it with pandas, and display interactive line charts with Matplotlib, providing a user‑friendly interface for non‑technical users.
This article explains how using a shared class attribute to store open ports in a Python security‑testing framework can cause data contamination across threads, demonstrates the problem with example code, and presents three solutions—reinitializing the attribute, using contextvars, and employing threading.local—to ensure thread‑local isolation and accurate port scanning.
This guide demonstrates how to install PyMySQL or mysql‑connector‑python, connect to a MySQL database from Python, execute LIKE‑based fuzzy queries, handle results, and safely close resources, with detailed code examples and practical tips.
This article surveys the most suitable programming languages for artificial intelligence, examining Python, R, Java, LISP, Prolog, C++, Haskell, JavaScript, and Julia, and outlines their strengths, ecosystem support, and use cases to help developers choose the right tool for AI projects.
This article introduces how to automate Excel chart generation using Python libraries like pandas and openpyxl, enabling efficient data visualization and analysis.
This article explains a Python web‑scraping issue where Selenium sometimes returns only previously scraped rows after page navigation, and provides a step‑by‑step solution using requests, pagination parameters, regex extraction of IDs, and PDF download handling to reliably collect data.
This article walks through creating a LangGraph‑based multi‑agent application that automatically generates a movie scene, selects characters, simulates dialogue, and writes the script to a Word document using local Ollama‑powered LLMs and a custom workflow.
This article explains how to craft system prompts that give an AI agent a distinct personality and emotional behavior, shows how to implement an emotion‑detection chain, compares ChatPromptTemplate.from_messages with from_template, and integrates the agent into a FastAPI service with full code examples.
This article introduces the most common Python file extensions—including .py, .ipynb, .pyi, .pyc, .pyd, .pyw, and .pyx—explains their purposes, provides example code, and demonstrates how Cython (.pyx) can significantly improve performance for compute‑intensive tasks.
This article introduces a collection of essential Python built-in functions—such as print, len, type, range, sum, sorted, abs, round, input, any/all, zip, enumerate, filter, map, reversed, and setdefault—explaining their typical use cases and providing concise code examples for each.
This tutorial walks through installing FastAPI and related packages, creating a basic FastAPI app, adding chat, PDF, and text endpoints, integrating LangChain tools for AI responses, implementing a WebSocket echo service, and running the server with uvicorn, all illustrated with code snippets and screenshots.
This article introduces eight Python libraries—Optuna, ITMO_FS, Shap-hypetune, PyCaret, floWeaver, Gradio, Terality, and Torch-Handle—that streamline data science tasks such as hyperparameter optimization, feature selection, model building, visualization, and rapid prototyping, helping users save coding time and improve productivity.
This article introduces the GitHub "Homemade Machine Learning" project, which provides pure‑Python implementations of common supervised and unsupervised machine‑learning algorithms, complete with mathematical explanations, Jupyter‑Notebook demos, installation instructions, and links to datasets for hands‑on learning.
This tutorial provides a thorough overview of Python dictionaries, covering their creation, key‑value access, addition, update, deletion, iteration, comprehensions, safe retrieval with get(), merging, sorting, built‑in functions, and unpacking for function arguments, all illustrated with clear code examples.
This article introduces several useful classes from Python's collections module—including Counter, defaultdict, deque, namedtuple, ChainMap, UserList/UserDict/UserString, as well as heapq and an OrderedDict‑based LRU cache—explaining their purposes and providing ready‑to‑run code examples for each.
This article explains how the underscore character in Python can be leveraged for quick console result reuse, selective tuple unpacking, protected and private attribute naming, and numeric readability, providing practical code examples that illustrate each technique for more efficient and readable code.
This article demonstrates how to create a functional financial calculator in Python using the Tkinter GUI library, covering installation, UI design, implementation of compound interest, present value, annuity, and loan payment calculations, along with sample code and usage examples.
This article examines how the big‑data AI development paradigm has shifted from model‑centric to data‑centric workflows, outlines the challenges of integrating data and AI teams, and details Alibaba Cloud’s end‑to‑end, serverless big‑data platform—including MaxCompute, Hologres, MaxFrame, Object Table, and vector search—designed to accelerate large‑scale AI applications.
This article explains the fundamentals and mathematics of Hidden Markov Models, illustrates their core components and basic problems, and walks through a complete Python implementation for credit‑card fraud detection, including data preparation, model training, and evaluation.
This article provides practical code examples for using key Python standard library modules—including os, sys, json, datetime, re, math, collections, urllib, random, and logging—to perform common tasks such as file operations, system info, data parsing, date handling, pattern matching, calculations, data structures, web requests, random generation, and logging.
This guide walks through adding ConversationBufferMemory to a LangChain agent, covering tool creation, memory setup, agent initialization with OpenAI function calling, prompt inspection, configuration tweaks using agent_kwargs, and best‑practice considerations for maintaining context in multi‑turn AI conversations.
This article demonstrates how Python’s expressive syntax—especially list comprehensions, dictionary and tuple comprehensions, and conditional expressions—can be used to implement fundamental linear‑algebra operations such as vector scaling, dot products, matrix transposition, projection, distance calculation, and even a one‑line linear solver, all illustrated with clear code examples.
This article explains the ticket‑purchasing workflow, identifies critical timing points for successful ticket grabbing, and presents two common technical approaches—browser automation with Selenium and reverse‑engineering ticket‑booking APIs—to automate the process, while warning about limitations and legal considerations.
This article introduces nine powerful Python standard library modules that enhance code efficiency and readability, covering context management, iterators, concurrency, file operations, functional programming, AST parsing, type hints, data classes, and async programming.
This tutorial walks through installing LangChain, creating an LLM (via own GPU resources or third‑party APIs), designing parameterized prompt templates, implementing a custom output parser for structured results, and running a complete Python example that generates culturally specific names.
This article introduces LangChain, an open‑source framework for building LLM‑powered applications, outlines its key components such as prompts, chains, agents, and retrieval‑augmented generation, compares its advantages and drawbacks, and provides step‑by‑step instructions for setting up a Python development environment.
This article introduces the fundamentals of multi‑objective optimization, explains the NSGA‑II algorithm’s non‑dominated sorting, crowding distance, and selection mechanisms, and demonstrates its application to a production‑line case study with a complete Python implementation and visualized Pareto front.
This article compares Flask and FastAPI, explains their core differences in speed, developer experience, and standards, and provides step‑by‑step migration instructions, code examples, configuration tips, and deployment strategies for building modern Python web APIs.
This article explains the mathematical basis and step‑by‑step implementation of an automatic missile tracking algorithm for shooting games, using Python’s pygame library, covering differential‑equation‑derived motion, angle calculations, image rotation handling, and complete source code.
Python’s core team proposes a calendar‑based versioning scheme (PEP 2026) that replaces semantic versioning, assigning versions as 3.YY.micro to make release dates and end‑of‑life clearer, with each release supported for five years and future releases mapped out through 2030.
This article demonstrates how to perform GET and POST requests, maintain sessions, handle retries, pass query parameters, validate responses with JSON Schema, upload files, run automated tests, and improve performance with multithreading and streaming downloads using Python's Requests library.
This article walks through a Python community member's problem of turning raw HTTP header strings and cookies into clean dictionary objects, explains why newline characters cause issues, and provides step‑by‑step code solutions using string replacement, JSON conversion, and built‑in libraries.
Discover ten useful Python automation scripts—including batch file renaming, web scraping, scheduled email alerts, data backup, social media monitoring, PDF merging, spreadsheet processing, image compression, network checks, and system resource monitoring—that simplify everyday tasks and enhance efficiency.
This article demonstrates how to use Python to scrape Weibo comments about the TV series 'The Legend of the Year', generate word clouds, compute character frequencies and co‑occurrences, and visualize the resulting character relationship network with Gephi.
Explore the versatile sorted() built‑in in Python through over ten practical examples, covering basic list and string sorting, custom key functions, reverse ordering, composite criteria, and real‑world use cases like sorting dictionaries and word‑frequency analysis, empowering you to write cleaner, more efficient code.
This article explains Python's list, dictionary, and set comprehensions, demonstrating their syntax, performance benefits, and advanced techniques such as nesting and chaining through clear examples and code snippets to help developers write more concise and efficient code.
Learn how to create native Python extension modules using Rust, set up the required tools, write and build the Rust code, and run Python scripts from a Rust binary, enabling you to combine Rust's performance and safety with Python's flexibility.
This step‑by‑step guide shows how to structure a Python project, write a setup.py and README, add code and tests, build a source distribution, create a zip package, upload it to PyPI with Twine, and finally install the library using pip.
This tutorial demonstrates how to install Selenium, log into QQ Space, and programmatically retrieve both status updates and album photos using Python's Selenium and BeautifulSoup libraries, including detailed code examples for login, scrolling, and image downloading.
This article explains how to securely convert external string data into Python literals using the ast.literal_eval function, covering its principles, advantages, basic and advanced usage examples, special scenarios, error handling, and security best practices.
This article explains the purpose and mechanics of Python's functools.partial, highlighting its ability to preset arguments, adapt function signatures, reduce repetitive code, and enhance functional programming through clear, real‑world examples and code snippets.
This article presents a concise yet thorough guide to essential Python built‑in functions such as map, filter, reduce, sorted, enumerate, zip, any/all, as well as advanced features including lambda expressions, comprehensions, itertools, context managers, partial functions, garbage collection, asyncio, decorators, generators, metaclasses, pathlib, type annotations, and enumerations, each illustrated with clear code examples.
This article introduces the pywinauto library, explains how to install it, create Application objects, locate windows and controls, and perform mouse and keyboard automation on Windows applications using Python, with detailed code examples and usage tips.
This guide explains what AI agents are, how they combine large language models with planning, memory, and tool use, and provides a step‑by‑step LangChain implementation—including environment setup, tool integration, and a runnable example that solves math and performs web searches.
This article explains Python's eval() function, demonstrates basic and variable usage, highlights security risks, and presents safer alternatives such as ast.literal_eval and restricted eval implementations, providing code examples and practical recommendations for secure code execution.
This guide walks you through what Protocol Buffers are, how to install the protoc compiler on macOS, Windows, and Python, explains the proto3 syntax—including messages, fields, enums—and demonstrates compiling .proto files and using the generated code in Python, while highlighting Protobuf's efficiency, extensibility, and multi‑language support.
This article provides a comprehensive overview of Python fundamentals for beginners, covering the language’s purpose, basic syntax, data types, control structures, functions, modules, exception handling, input/output, common built‑in functions, advanced concepts like list comprehensions and generators, and typical runtime errors with troubleshooting tips.
This article explores Python's callable() function, explaining its purpose, principles, and practical applications through detailed code examples, helping readers master this essential tool for understanding Python's object-oriented and functional programming features.
This article demonstrates practical Python command‑line techniques—including the -m option for running modules, testing network ports, launching a quick HTTP server, formatting JSON, creating a minimal text editor, building executable packages, encoding/decoding data, retrieving system configuration, and compressing files—all without writing additional code.
This article presents six practical Python techniques—including exception handling, list comprehensions, regular expressions, web scraping with BeautifulSoup, lambda functions, and simple data visualizations using matplotlib—to help developers write cleaner, more efficient code and enhance their programming workflow.
This article introduces the concepts and core principles of RESTful APIs and provides a step‑by‑step Python Flask tutorial, including environment setup, code examples for CRUD operations, and guidance on testing, authentication, error handling, and versioning.
This article walks through a real‑world Python automation question about merging two large tables, explains why a naïve for‑loop is inefficient, and demonstrates a faster pandas‑based approach with clear code and step‑by‑step reasoning.
SlashData’s 26th Developer Nation survey, covering over 10,000 developers from 135 countries, shows JavaScript remains the most popular language, Python overtakes Java for second place, and emerging languages like Rust and Go experience rapid growth, while new developers face distinct adoption challenges.
An extensive guide lists essential PyCharm shortcut keys—including editing, navigation, search, refactoring, and running commands—providing English translations of each key combination and its function to help Python developers work more efficiently in their daily coding tasks.
This article presents six practical techniques—hard‑coding, using loops, reading from Excel/CSV, loading YAML/JSON files, leveraging pytest parameterization, and querying databases—to manage and reuse API test parameters in Python, each illustrated with clear code examples.
This guide explains how to create an Enterprise WeChat application, obtain the required corp ID and secret, and use a Python script to send notification messages via the WeChat API, complete with code examples and expected results.
This article walks through a Python solution for validating FIFO inventory rules in Excel files, using pandas and Tkinter to create a GUI tool that reads multiple sheets, checks operation and stock dates, highlights violations, and saves the results, while also addressing common import errors and offering troubleshooting tips.
This guide introduces Apache Spark's Python API, showing how to install PySpark, launch an interactive shell, create a SparkSession, read and write data from various sources, perform transformations, and apply key performance‑tuning practices for efficient big‑data processing.
This guide explains Python's multithreading and multiprocessing concepts, compares thread pools and process pools, provides practical code examples for task execution and file downloading, and offers best‑practice advice for efficient concurrent programming.
This article provides a detailed tutorial on the Python MoviePy library, covering its core classes, video and audio manipulation methods, common visual effects, and practical code examples for creating, editing, and exporting video clips.
The article explains how the __init__.py file marks a directory as a Python package, enables package initialization, controls namespace exposure, defines package‑level variables and metadata, supports dynamic loading, performance optimizations, and can serve as an entry point, while noting changes introduced in Python 3.3 and PEP 420.
This guide shows why a Python Matplotlib 3D plot of a logistic‑regression loss surface may fail, provides a complete code example, explains version‑specific issues, and demonstrates a working solution that renders both 2D and 3D visualizations correctly.
This article explains the special built‑in identifiers __main__ and __name__ in Python, describing how they indicate a module's execution context, how to use them to separate script and library code, and provides multiple practical code examples.
This article introduces the open‑source Python library TRACC, explains how to install it, prepare destination and travel‑cost data, and walk through a step‑by‑step workflow—including code snippets—to compute potential, passive, and minimum travel‑cost accessibility metrics for urban areas.
This guide explains how to fine‑tune OpenAI’s pre‑trained models, covering data preparation, environment setup, API usage, code examples, hyper‑parameter tuning, monitoring, and best practices to achieve better performance with less data and compute resources.
This guide walks you through the fundamentals of API automation testing, from understanding HTTP basics and selecting tools, to building a Pytest framework, writing test cases and scripts, integrating tests into CI/CD pipelines, and continuously improving test results.
This article explains how to use Python's os module and python-docx library to recursively traverse a folder hierarchy, read .docx files, search for a specific keyword such as "nan", and output the names of matching documents, with optional performance tips.
This article reviews the ten most promising programming languages for 2025 and beyond, explaining their strengths, typical use cases, and why they are valuable choices for developers seeking to stay ahead in web, mobile, game, and system development.
This guide explains how to encapsulate a powerful, singleton-based logging system in Python using the MyLog class, covering OOP principles, code structure, and practical usage examples for flexible and maintainable log handling.
argparse is a standard Python library for creating user-friendly command-line interfaces, allowing you to define parameters and options, generate help messages, and parse input with error handling.
This article presents a practical approach to automatically generate software test cases by leveraging LangChain, PDF parsing, vector‑database retrieval, and large language models, comparing it with existing tools, detailing implementation steps, code examples, experimental results, and future improvement directions.
This article demonstrates how Python metaclasses can be leveraged to automatically add authentication headers, manage configuration, log requests, handle exceptions, generate test cases, switch environments, validate parameters, control API versioning, register response validators, and inject dependencies, streamlining backend development.
This article introduces Numba, a Python just‑in‑time compiler, explains why it is advantageous over alternatives, demonstrates how to apply its @jit, @njit, @vectorize and other decorators, and shows how to run accelerated code on CPUs and GPUs using CUDA.
This article introduces Treelite, an open‑source library that compiles XGBoost, LightGBM, and scikit‑learn tree models into optimized shared libraries, explains its branch‑prediction and comparison‑simplification techniques, and provides step‑by‑step Python examples showing significant inference speed gains across different batch sizes.
This tutorial walks through installing LangChain, creating an LLM with either self‑hosted or third‑party models, designing custom prompt templates, configuring output parsers for structured results, and running a complete Python example that generates culturally specific names using OpenAI's API.
This tutorial demonstrates how to accelerate processing of a multi‑million‑row CSV dataset by dividing the work into parallel tasks using Python's multiprocessing, joblib, and tqdm libraries, comparing serial, multi‑process, batch, and process‑map approaches with detailed timing results.
The article explains the purpose and functionality of Python’s unittest.TestLoader class, detailing its default behavior, discovery method, various loading functions, sorting options, customizable attributes, and how subclassing can tailor test loading for more flexible and efficient test organization.
This article walks through the complete process of creating an AI‑driven electronic‑pet bot on the Coze platform, covering concept brainstorming, database design, image‑generation pipelines, Python code snippets, multi‑agent workflow orchestration, and final deployment to the bot store.
This article explores advanced Python programming concepts including decorators, metaclasses, class methods, and dynamic attributes, providing practical code examples and explanations for each technique to enhance understanding of object-oriented programming patterns.
This article walks through the practical use of JD’s self‑developed Vearch vector database—covering cluster creation, space setup, data insertion, and both text and vector search—illustrating how it integrates with LangChain and OpenAI embeddings to enable retrieval‑augmented generation for large language models.
This article presents a collection of Python scripts that automate key HR tasks—including resume parsing, interview invitation emailing, pay‑slip creation, hiring‑channel analysis, turnover calculation, satisfaction survey reporting, salary competitiveness comparison, employee data updates, Word document generation, and leave‑request handling—complete with ready‑to‑run code examples.
A graduate student and his lab used Baidu Comate, an AI‑powered coding assistant, to automate repetitive Python scripts for converting 150 GB of DICOM images to PNG, reducing a week‑long, three‑person effort to two days for a single developer and boosting overall team efficiency.
This article demonstrates how Python can automate common operations tasks such as remote command execution, log file parsing, system monitoring with alerts, batch software deployment, and file backup and recovery, providing code examples using libraries like paramiko, regex, psutil, fabric, and shutil.
This article explains Python's argument unpacking feature and demonstrates ten practical examples—including batch API calls, request handling, test data validation, configuration management, user creation, parameterized tests, logging, database updates, dynamic query building, and environment switching—to streamline automation testing.
This article explores various Python function parameter techniques including positional, keyword, default, variable arguments, and parameter unpacking to enhance flexibility and reusability in API automation testing.
This article walks through a Python web‑scraping solution for downloading individual files from a site, presenting the problem, multiple approaches, and a complete, ready‑to‑run script, while also offering tips for handling large data and avoiding IP blocks.
This article presents an extensive, categorized catalog of Python libraries covering environments, package management, web frameworks, databases, data processing, machine learning, testing, DevOps, and many other domains, offering developers a convenient reference to discover and adopt the most popular tools for their projects.
This article demonstrates eight practical examples of leveraging Python's built‑in command‑line modules—such as -m, telnetlib, http.server, json.tool, idlelib, zipapp, encodings.rot_13, base64, sysconfig, and zipfile—to perform useful tasks without writing any Python source code.
An experienced Python user shares a practical solution to a pandas data‑processing issue, demonstrating both a direct pow(x, 0.5) approach and a list‑comprehension alternative, complete with code snippets and tips for handling large or sensitive datasets.
This article explains the fundamentals and engineering practices of large language models (LLM), retrieval‑augmented generation (RAG) and AI agents, compares small and large embedding models, provides Python code for vector‑database RAG with Chroma, and discusses integration, use cases, and future challenges in AI development.
This article explains how to use a large language model to automatically convert long‑form texts into Alpaca‑style question‑answer pairs, build a LangChain processing chain, and then fine‑tune a model such as Phi‑3‑mini‑4k‑instruct with LoRA, providing full Python code examples.
