Microsoft announced a dedicated Faster CPython team, led by Mark Shannon and Guido van Rossum, to boost Python’s execution speed through a multi‑stage plan that has already delivered 10‑60% performance gains in Python 3.11 while emphasizing community collaboration and open‑source contributions.
This tutorial walks through creating a simple test device management platform using Python, Flask, and PostgreSQL, covering framework selection, database operations, API definition, and front‑end rendering with HTML and jQuery for a complete end‑to‑end web solution.
This article explains how Python can automate common operations tasks—remote command execution, log parsing, system monitoring with alerts, batch software deployment, and backup/recovery—providing code examples and practical tips to improve efficiency and reduce manual errors.
The article examines ChatGPT’s underlying software architecture, detailing its cloud deployment on AWS and Azure, database choices like PostgreSQL and Redis, front‑end technologies such as TypeScript and React, core AI frameworks including PyTorch and Triton, as well as its container orchestration, monitoring, and programming language ecosystem.
This article introduces beginners to fundamental computer concepts, explains what a program is, and provides a detailed guide on Python variables, their naming rules, common data types, type‑conversion functions, and basic arithmetic operations with illustrative code examples.
This article explains Python's Boolean type, how True and False are represented, demonstrates the bool() function, shows truthy and falsy values, and illustrates logical operators and arithmetic with booleans, while also covering the special None value and common pitfalls.
This article evaluates the five leading Python test automation frameworks—Robot Framework, Pytest, unittest (PyUnit), Behave, and Lettuce—detailing their installation requirements, key features, advantages, disadvantages, and suitability for functional, unit, or behavior‑driven testing to help developers choose the right tool.
This article presents ten practical Python automation scripts covering image and video processing, PDF conversion, API interaction, system notifications, grammar and spell correction, downloading, news fetching, and GUI creation, each accompanied by concise code examples for quick implementation.
The article introduces OpenAI's newly released ChatGPT and Whisper APIs, explains their reduced costs, outlines usage details and model versions, and provides Python code examples for integrating these powerful conversational and speech‑to‑text models into third‑party applications.
The gRPC 1.53.0 release introduces a series of core improvements, language‑specific enhancements for C++, C#, Python and Ruby, as well as bug fixes and new APIs, providing higher stability and performance for modern high‑performance RPC applications.
This article demonstrates how to install and configure the highway‑env reinforcement‑learning environment, set up a DQN agent in Python, and train it on various traffic scenarios, providing code examples and performance visualizations.
This article explains the most frequently used regular expression symbols in Python, covering ordinary metacharacters, the OR operator, escape sequences, anchors, quantifiers, grouping, and shorthand character classes, each illustrated with clear code examples and explanations.
This article introduces the lightweight Python search library Whoosh, outlines its key features, demonstrates how to define a schema, create an index from a CSV dataset, and perform full‑text queries with example code, making it suitable for small‑scale search projects.
This article shares practical Python tips for using PyYAML safely, keeping key order, customizing list indentation, outputting readable Unicode, and introduces alternative YAML libraries such as oyaml, StrictYAML, and ruamel.yaml with code examples.
This tutorial provides a comprehensive introduction to core Python concepts, covering numeric operations, string handling, built‑in functions, common data structures, class and object mechanics, as well as useful utilities such as iteration tools, file I/O and JSON serialization.
DBSCAN is a density‑based clustering algorithm that automatically discovers arbitrarily shaped clusters and isolates noise, with detailed explanations of core, border, and noise points, step‑by‑step examples, Python implementations using scikit‑learn, and guidance on key parameters such as eps and min_samples.
This article demonstrates how to compare the distribution of a variable across control and treatment groups using Python, covering data generation, visual techniques such as boxplots, histograms, KDE, CDF, QQ and ridgeline plots, and a suite of statistical tests including t‑test, SMD, Mann‑Whitney, permutation, chi‑square, Kolmogorov‑Smirnov and ANOVA for both two‑group and multi‑group scenarios.
This article explains Linear Discriminant Analysis (LDA) as a pattern‑recognition technique that projects data onto a low‑dimensional space to maximize class separation, details its mathematical formulation with between‑class and within‑class scatter matrices, and provides a complete Python implementation using scikit‑learn on the Iris dataset, including visualization of the results.
This article introduces Explainable AI (XAI), outlines its importance, describes a step-by-step workflow, and reviews ten Python libraries—including SHAP, LIME, ELI5, Shapash, Anchors, BreakDown, Interpret‑Text, AI Explainability 360, OmniXAI, and XAI—providing usage examples and code snippets.
Robusta is a Python‑based open‑source platform that layers on top of monitoring stacks like Prometheus to automatically detect, diagnose, and remediate Kubernetes alerts through built‑in automations, optional web UI, and Helm‑based installation for cloud‑native environments.
SQLAlchemy 2.0.7 has been released, addressing a critical caching bug in custom operator expressions, fixing typing issues with composite(), adding the PostgreSQL CITEXT type, and improving dialect compatibility, with all changes back‑ported to earlier stable releases.
Feature Recursive Elimination (RFE) is a powerful feature‑selection technique that iteratively trains a model, discards the weakest features, and repeats until a desired number of features remains, helping prevent overfitting and improve model performance, illustrated with a complete Python example using scikit‑learn.
The article shows a straightforward Python + Selenium script, generated by ChatGPT, that logs into a website and verifies pagination, illustrated with code screenshots and brief commentary on the author’s experience finding the problem on Zhihu.
A detailed post‑mortem of Digg's disastrous v4 launch reveals how a Python function with a mutable default argument caused massive cache overloads, forced emergency fixes, and ultimately contributed to the company's downfall, offering crucial lessons for backend engineers on safe defaults and release planning.
This article introduces parallel programming concepts, memory architectures, execution models, Python threading versus multiprocessing performance, and reviews several Python parallel libraries such as Ray, Dask, Dispy, ipyparallel, and Joblib for building scalable concurrent applications.
This article introduces the ELECTRE multi-criteria decision analysis method, outlines its standard and improved procedures, and provides a complete Python implementation with sample data, demonstrating how to normalize criteria, compute concordance and discordance matrices, and derive preference rankings.
This article presents five practical Python idioms—range(), zip(), reversed(), enumerate(), and sorted()—explaining how to replace less efficient loops with built‑in functions to improve execution speed, readability, and memory usage, accompanied by clear code examples.
This article introduces a modular Selenium UI automation framework built with the Page Object Model, layered architecture, Pytest execution, and YML‑driven test data, explaining its components, configuration benefits, implementation steps, and how it enhances maintainability, scalability, and CI integration.
This article explains the CRITIC (Criteria Importance Through Intercriteria Correlation) method, its step‑by‑step procedure, mathematical formulation, and a complete Python implementation that computes criterion weights for multi‑criteria decision making.
This article introduces thirteen lesser‑known Python features—including list stepping, the find method, iterators, doctest, yield statements, dictionary get, for/else and while/else loops, named string formatting, recursion limits, conditional expressions, argument unpacking, the special __hello__ import, and multiline strings—each illustrated with clear code examples.
This article presents five practical Python methods—including for‑loops, unpacking, functools.reduce, map, and ChainMap—to combine multiple dictionaries, each illustrated with code snippets and images, helping developers choose the most suitable approach for their data merging needs.
This tutorial demonstrates how to build a classic "code rain" screen using Python's pygame library, guiding readers through imports, window setup, character rendering, and a concise main loop that produces the animated effect in roughly thirty lines of code.
This guide explains two basic ways to execute Python programs—using the interactive command-line interpreter for quick, unsaved tests and writing a script in a text editor to run via the terminal—illustrated with a simple "hello world" example and practical tips.
This article demonstrates how to store monthly sunspot numbers in the CnosDB time‑series database and use TensorFlow to build a 1DConv‑LSTM neural network for forecasting sunspot activity, covering data import, database insertion, train‑test splitting, model definition, training, and result visualization.
Data Envelopment Analysis (DEA) is a non‑parametric technique for evaluating relative efficiency of decision‑making units, with CRS and VRS models explained, followed by a Python implementation using PuLP to assess airline route efficiency and interpret overall, technical, and scale efficiencies.
An extensive collection of Python programming tips and tricks, ranging from built‑in functions like all/any and collections to advanced features such as type hints, virtual environments, and third‑party libraries, presented alphabetically to help developers quickly enhance their code efficiency and readability.
This article explains the TOPSIS multi‑criteria decision‑making technique, outlines its mathematical formulation, walks through the seven procedural steps, and demonstrates a complete Python example that normalizes data, computes distances, scores alternatives, and ranks the best solution.
Learn how to automate the construction of detailed 3D scenes in Cinema 4D using Python, by importing CAD layers, generating walls, doors, windows, furniture, lighting, and cameras, enabling efficient VR visualizations for interior design projects.
This article explains how to configure and implement request throttling in Django Rest Framework, walks through the relevant source code in throttling.py and views.py, demonstrates testing the limit, and shows how to trigger custom DingTalk alerts when the limit is exceeded.
This article introduces the Analytic Hierarchy Process (AHP), explains its hierarchical modeling, mathematical foundations, step‑by‑step implementation, and provides complete Python code for constructing comparison matrices, calculating weights, and performing consistency checks to ensure reliable results.
This article demonstrates how to use Python and the pywifi library to perform Wi‑Fi password brute‑force attacks, provides both command‑line and Tkinter‑based graphical tools, explains scanning nearby networks, handling password dictionaries, and discusses limitations such as the lack of multithreading.
This article introduces Vizard, a C/C++‑based virtual‑reality development platform with Python support, and demonstrates how to load avatars and objects, create random‑walk behavior, implement dialogue actions, control character movement via keyboard, capture mouse input, and combine these techniques into a complete interactive 3D scene.
This tutorial explains how to set up the Kivy framework on macOS and Linux, write a simple Python hello‑world app, test the installation, install and configure Buildozer, and use it to package the app into an Android APK while addressing common build issues.
This article explores the “push‑step aggregation” technique featured in the drama “Microscope under the Ming,” revealing that it is essentially the Shoelace Theorem for quickly computing polygon areas, complete with mathematical derivation, visual illustrations, and a Python implementation.
This article walks through a real‑world Python web‑scraping problem, demonstrating how to extract clean table data using pandas.read_html and an alternative XPath approach, complete with code snippets, output screenshots, and practical tips for robust data collection.
This tutorial presents a thorough overview of essential Python concepts, demonstrating numeric operations, string conversions, built‑in functions, diverse data structures, object‑oriented programming techniques, and useful utilities such as file handling, iteration tools, and JSON serialization, all illustrated with clear code examples.
This article demonstrates how to use Python and pandas to automatically read a text‑based question bank, parse questions and options, and export the data to an Excel file, providing complete code examples and a step‑by‑step explanation of the process.
This article introduces Numba, a Just‑in‑Time compiler for Python that transforms functions into fast machine code using LLVM, explains why it lets you stay in pure Python, demonstrates basic @jit/@njit usage, advanced decorators, GPU execution with CUDA, and interoperability with C/C++ libraries.
The article introduces d3blocks, a Python library that extends D3.js's interactive visualization capabilities to Python, demonstrating how to create interactive scatter, network, Sankey, and image slider charts with just a few lines of code and offering examples with code snippets and GIF illustrations.
Linear programming optimizes a linear objective under linear constraints, and this article explains its theory, common solution methods such as Simplex, Interior‑Point, and Branch‑and‑Bound, illustrates a production‑planning case, and provides a complete Python implementation using SciPy’s linprog function.
This article introduces the Shapley value concept, its fairness axioms, demonstrates its use in a profit‑allocation problem and in interpreting machine‑learning models with SHAP, and provides complete Python implementations for both cases.
This article introduces Plotly, a powerful Python library for creating interactive line, scatter, and bar charts, explains how to install and use it in offline mode within Jupyter notebooks, and provides complete code examples for each chart type along with visual results.
Factor analysis, a statistical technique for uncovering underlying common factors among variables, is explained alongside its distinction from PCA, detailed procedural steps, adequacy tests, and a hands‑on Python implementation using the factor_analyzer library with visualizations and factor rotation methods.
This article walks through a real‑world Python regex question, explains the purpose of the re.S and re.I flags, and provides a concise solution that helped the community solve the problem efficiently.
This article explains the fundamentals of Recurrent Neural Networks and Long Short‑Term Memory units, their gating mechanisms, and demonstrates a practical Python Keras example that predicts future PM2.5 concentrations using an LSTM model.
This article explains the Expectation‑Maximization (EM) algorithm, detailing its iterative E‑step and M‑step processes, mathematical formulation, and practical Python implementation for estimating parameters of mixed linear regression models, while highlighting convergence considerations and common pitfalls.
This article presents 30 concise Python code snippets that can be understood in under 30 seconds, covering common tasks such as checking duplicates, computing byte size, merging dictionaries, measuring execution time, and more, providing quick, practical solutions for everyday programming needs.
Streamlit is a free, open‑source Python framework that lets machine‑learning engineers quickly turn scripts into interactive apps, offering features such as top‑down script execution, widget‑as‑variable handling, caching, GPU support, and seamless integration with version‑control tools, all without requiring separate frontend development.
This article introduces a curated collection of Python algorithm resources, covering sorting and searching techniques, cryptographic methods, and neural‑network implementations, while linking to a GitHub repository and offering supplemental visualizations and a free Python learning QR code.
This guide explains how to use Python's httpx library and the curl command‑line tool to perform comprehensive HTTP/HTTPS testing—including basic requests, chunked transfers, HTTP/2, SSL/TLS configuration, and dynamic DNS resolution—complete with code examples and setup instructions.
This article walks through a Python web‑crawler problem raised in a community, showing step‑by‑step how to start the project, troubleshoot terminal errors in PyCharm, and verify the directory structure using the tree command, providing a clear solution for beginners.
Learn how the Difference-in-Differences (DiD) method estimates policy impacts by comparing treatment and control groups over time, explore its mathematical model, see a concrete traffic‑restriction example, and follow a step‑by‑step Python implementation with data analysis and visualization.
This article explains how to determine whether a time‑series dataset exhibits a monotonic trend using the non‑parametric Mann‑Kendall test, walks through its statistical foundations, shows the calculation steps with sample sales data, and provides a complete Python implementation for practical analysis.
This article explains the concept of Granger causality, outlines the four possible test outcomes, and provides a step‑by‑step Python implementation using statsmodels to determine whether one time‑series statistically causes another.
This article presents a series of Python mini-game tutorials, including code for coin-collecting, ping-pong, skiing, space shooter, whack-a-mole, dinosaur runner, match-3, Tetris, snake, 24-point puzzle, alien invasion, tic-tac-toe, and more, offering complete source files and gameplay explanations.
This article explains how InstructGPT's reward model is trained by collecting human‑annotated ranking sequences instead of absolute scores, describes the rank‑loss formulation, provides Python code for the model and loss computation, and presents experimental results demonstrating the approach.
This article demonstrates how to compare the distribution of a variable across treatment and control groups using Python, covering data simulation, visual techniques such as boxplots, histograms, KDE, CDF, QQ and ridgeline plots, and statistical tests including t‑test, SMD, Mann‑Whitney, permutation, chi‑square, KS and ANOVA.
The article reviews essential Python data‑preprocessing and statistical‑analysis tools—including missing‑value imputation, outlier trimming, scaling, binning, knee‑point detection, correlation, chi‑square testing, linear regression, Wilson scoring, PCA weighting, text tokenization and sentiment analysis, plus visualization with matplotlib/seaborn and big‑data access via pyodps.
This article explains how RESTful API design reduces testing complexity by using unique URIs, standard HTTP methods, and JSON data exchange, and shows how test engineers can extend their frameworks with serialization support and added PUT/DELETE operations.
This tutorial demonstrates how to use Python's requests library to fetch video data from the YY Live API, parse the JSON response, extract video URLs, and programmatically download and save the videos, including code snippets for single and batch downloads.
This article introduces the most useful additions and modern replacements in recent Python releases—including pathlib, the secrets module, zoneinfo, dataclasses, proper logging, f‑strings, the built‑in tomllib, and the transition from distutils to setuptools—showing why and how to adopt them in everyday code.
Learn how to use Python's pandas library to automatically scan multiple folders, extract specified columns from hundreds of Excel files, and merge them into a single workbook, complete with step-by-step code examples and visual results.
This article presents thirteen practical advanced Python scripts covering tasks such as internet speed testing, Google searching, web automation, lyric retrieval, EXIF extraction, OCR, image cartoonization, recycle bin cleaning, Windows version detection, PDF conversion, color conversion, and website status checking, each with ready-to-use code examples.
This article explains the principles of probabilistic programming languages and walks through constructing a basic PPL in Python, covering model definition with latent and observed variables, distribution handling, DAG traversal for log‑density computation, and demonstrates evaluation with example code and visualizations.
This article walks through a Python‑based solution for cleaning and extracting data using Pandas, presenting multiple approaches—including regex, zero‑width spaces, and slicing—to help readers resolve common data‑processing challenges efficiently.
This tutorial explains how to call C and C++ code from Python by compiling source files into shared libraries, using the ctypes module to load and invoke functions, and demonstrates both simple C functions and C++ class methods with complete code examples and compilation instructions.
Recent Python ecosystem trends highlight the rise of Rust bindings via PyO3, the emergence of pure‑Python web UI frameworks like Streamlit and Pynecone, and growing emphasis on static type safety with tools such as mypy and pydantic, signaling a shift toward higher performance and reliability.
This article introduces four practical techniques for measuring Python code performance, including the built‑in time module, Jupyter’s %%time magic, line_profiler for per‑line timing, and memory_profiler for detailed memory consumption, complete with example code and interpretation of results.
A comprehensive collection of 2023 Python practice questions—including fill‑in‑the‑blank, true/false, and short‑answer items—covers topics from multimedia and database programming to threading, networking, GUI, exception handling, file I/O, OOP, and core language features, with sample code snippets for deeper understanding.
The article analyzes 2023 programming language trends, highlighting Python's continued dominance, the ongoing decline of PHP and Java, and Rust's rising popularity due to its safety and performance, while noting the broader industry perception and adoption patterns.
This article combines a light‑hearted Valentine’s Day introduction for programmers with a detailed presentation of the classic Two Sum problem, including problem description, constraints, example inputs and outputs, multiple language implementations, and a complexity analysis.
This article explains Python coroutines, compares them with multithreading, outlines their suitable scenarios, and demonstrates how using the httpx library for asynchronous HTTP requests can dramatically improve request performance in backend testing, backed by concrete code examples and benchmark results.
This article walks through a Python web‑scraping solution that extracts GDP data from a website using requests, lxml, and pandas, then stores the results in a CSV file, providing complete code and step‑by‑step explanation for readers.
This tutorial walks through building a Python program that detects and reads vehicle license plates by preprocessing images with OpenCV, extracting contours, isolating the plate region, and applying Tesseract OCR to convert the characters into text, complete with environment setup and code examples.
This article explains what Python lambda (anonymous) functions are, shows their syntax, demonstrates typical use cases with built‑in functions and pandas, and outlines four common pitfalls—returning values, assigning to variables, ignoring better alternatives, and overusing them—while providing code examples and best‑practice recommendations.
This article introduces XNote, a lightweight personal note system with rich data management, cross‑platform support, and extensible plugins, detailing its key features, system architecture, directory layout, required dependencies, and step‑by‑step installation and configuration instructions for Python environments.
This article explains Scrapy's comprehensive crawling framework and Twisted's event‑driven networking engine, detailing their core concepts, workflow, code execution process, and how to debug Scrapy spiders using breakpoint tracing, providing a deep technical overview for backend developers.
This article explains why loops are needed in programming, introduces Python's for‑in and while loops, demonstrates their syntax with practical examples—including range variations, break/continue statements, and nested loops such as the multiplication table—and provides exercises on prime checking and GCD/LCM calculation.
This article explains what ULID (Universally Unique Lexicographically Sortable Identifier) is, compares it with UUID, details its features, binary layout, and encoding, and provides practical Python examples for generating and manipulating ULIDs in distributed systems.
This article explains why feature selection is crucial for machine learning, outlines the general workflow, compares filter, wrapper, embedded, and synthesis approaches, and provides practical Python examples—including Pearson correlation, chi‑square tests, mutual information, variance selection, recursive elimination, L1 regularization, and PCA—complete with code snippets and visualizations.
This article introduces four practical methods for monitoring Python code performance—using the built‑in time module, the %%time IPython magic, line_profiler for per‑line timing, and memory_profiler for detailed memory usage—complete with code examples and interpretation of results.
This article demonstrates how to download the Google 1‑gram dataset, load the ~1.4 billion rows with Python and NumPy (using the PyTubes library), compute yearly word frequencies, visualize the rise of "Python" and compare it with Pascal and Perl, while discussing performance challenges and future improvements.
This article demonstrates a Python-based hand‑gesture recognition pipeline using OpenCV, covering video capture, skin detection via YCrCb conversion, contour extraction, and visualization, with complete source code and step‑by‑step explanations suitable for beginners.
This article introduces the Python library plottable, which builds highly customizable tables on top of Matplotlib, and provides a complete example showing data loading, column styling, and rendering of a styled table along with additional recommended reading links.
This article walks through a Python visualization issue raised in a community chat, shows the problematic code and error screenshots, explains the root cause identified by an expert, and provides the corrected implementation so readers can resolve similar problems themselves.
This guide explains why actively experimenting with code beats passive reading, outlines a step‑by‑step method for mastering source code, and presents a curated list of twenty free Python3 projects—complete with executables and source—that are ideal for beginners and advanced learners alike.
This article demonstrates how a machine‑learning enthusiast used ChatGPT to generate, modify, and refine Keras code for training, evaluating, visualizing, and deploying a neural‑network model that classifies handwritten digits from the classic MNIST dataset, showcasing the full development workflow.
CPython’s garbage collector combines reference counting with a cyclic collector that uses generational strategies, fat pointers, and optimized object structures to efficiently identify and reclaim unreachable objects, handling cycles, weak references, and memory layout details while minimizing overhead.
Based on analysis of over 12 million developer job postings from October 2021 to November 2022, DevJobsScanner identified the eight programming languages with the highest demand in 2022, highlighting JavaScript/TypeScript’s dominance, the rise of Python and Java, and emerging trends for Go, C#, PHP, C/C++, and Ruby.
This tutorial explains how to prepare advertising click data, train a decision‑tree classifier, and generate clear visualizations using scikit‑learn and dtreeviz, while also showing how to inspect individual prediction paths and feature importance.
