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.
This article introduces six fundamental sorting algorithms—bubble, selection, insertion, shell, merge, and quick sort—explains their principles, provides Python implementations with code examples, visual animations, and compares built‑in sorting methods, helping readers understand and apply these techniques effectively.
This article introduces essential Python data structures—including strings, lists, tuples, dictionaries, sets, as well as stack and queue implementations—explaining their characteristics, usage, and providing clear code examples to illustrate creation, indexing, slicing, and common operations.
This article presents Python implementations of classic mathematical modeling techniques—including linear programming with PuLP, shortest‑path analysis using NetworkX, K‑means and hierarchical clustering, principal component analysis, frequent‑pattern mining with FP‑Growth, and linear regression and K‑nearest‑neighbors—providing code snippets, explanations, and visualizations to guide readers through each method.
This article introduces sensitivity analysis, explains its key concepts, indices, and selection criteria, then details various methods—including OAT, derivative‑based, regression, variance‑based, FAST, Morris, and Sobol—followed by practical Python examples using the SALib library with code and visualizations.
This article explains Fourier series theory, demonstrates how to remove linear trends from monthly CO₂ data, and shows step‑by‑step Python code using SciPy's curve_fit to fit and predict the data with a 100‑term Fourier expansion.
An analysis of over 12 million developer job postings reveals that JavaScript/TypeScript, Python, Java, C#, PHP, C/C++, Ruby, and Go were the eight most in‑demand programming languages in 2022, together accounting for roughly 80% of positions that explicitly required a language.
This article explains how Python can automate common operations tasks—remote command execution, log parsing, system monitoring with alerts, bulk software deployment, and backup/restore—providing code examples for each and highlighting additional tools that help sysadmins improve efficiency and reduce errors.
This article presents Python scripts for server‑side and client‑side resource monitoring, automatically checking CPU, memory, disk usage and network traffic, storing alerts in MySQL and optionally sending notifications via email or Enterprise WeChat, with deployment instructions and cron scheduling.
This article explains the Leslie matrix model for age‑structured population forecasting, outlines its mathematical formulation, demonstrates how to build and solve it using Python code, and shows how to derive demographic indicators such as average age, lifespan, aging index, and dependency ratio.
The article details Meituan Open Platform’s approach to automatically generate multi‑language SDKs with rich parameter models, covering overall design, code‑generation via DSL templates, CI/CD integration, and the resulting efficiency gains for third‑party developers integrating over a thousand OpenAPI endpoints.
This article explains how to retrieve data from an Oracle database with cx_Oracle, convert it to a pandas DataFrame, and correctly write the results to an Excel file with column headers, providing a complete code example and a practical solution to a common data‑processing issue.
This article introduces fuzzy mathematics, explains how fuzzy sets and membership functions quantify vague concepts, and provides Python code using the scikit‑fuzzy library to implement various membership functions such as triangular, Gaussian, S‑shaped, and polynomial types, complete with plotting examples.
This tutorial walks beginners through the essential steps of data preprocessing for any machine learning model, covering library imports, dataset loading, handling missing values, encoding categorical features, splitting into train‑test sets, and applying feature scaling using Python’s scikit‑learn.
This article explains the Principal Component Analysis (PCA) method, outlines its step‑by‑step weighting algorithm, and demonstrates a complete Python implementation that converts supplier metrics into objective scores using scikit‑learn.
This article explains how to model and solve a three‑stock portfolio optimization problem using Markowitz's mean‑variance framework, compute asset statistics with Python, and obtain the optimal allocation via quadratic programming.
This article demonstrates how to use Python's pywifi library to script a Wi‑Fi password brute‑force attack, improves the tool with command‑line and graphical interfaces built with Tkinter, and discusses scanning nearby networks, flexible dictionary selection, and future multithreading enhancements.
This article presents thirteen advanced Python scripts covering tasks such as internet speed testing, Google searching, web automation, lyric retrieval, EXIF extraction, OCR, image cartoonization, recycle bin cleaning, image enhancement, Windows version detection, PDF-to-image conversion, hex-to-RGB conversion, and website status checking, each with code examples.
An analysis of over 12 million developer job postings from October 2021 to November 2022 shows JavaScript/TypeScript leading the market with 31% of positions, followed by Python, Java, C#, C/C++, Ruby, PHP and Go, highlighting shifting trends and the growing importance of these languages for developers.
Ansible, a rapidly popular open‑source automation tool built on Python, enables batch system configuration, program deployment, and command execution through thousands of built‑in modules, offering a simple yet powerful solution for operations engineers, illustrated here with 16 comprehensive images.
This article explains how to redesign an automated testing framework by applying the Strategy pattern to replace complex conditional branches, thereby improving script reusability, maintainability, and extensibility across diverse business scenarios in Python.
This tutorial explains how to install the segno Python package and use it to create simple and customized QR codes for URLs, Wi‑Fi credentials, vCards, artistic designs, and in‑memory images, providing complete code examples and practical ideas for personal and social applications.
The 2022 DevJobsScanner analysis of over 12 million developer job postings reveals JavaScript/TypeScript as the most demanded language followed by Python, Java, C#, PHP, C/C++, Ruby and Go, and concludes with a promotional announcement for a new PHP online live class.
This article introduces the Holt‑Winters additive exponential smoothing model, explains its mathematical formulation and when to use additive versus multiplicative versions, and provides Python examples using statsmodels to fit both exponential and linear trend variations, illustrated with plots.
This guide explains how to set up ADB and Python libraries, enable USB debugging, and write Python scripts that control an Android phone to perform actions such as taking selfies, searching for word definitions, and capturing screenshots, providing a practical introduction to mobile automation.
This article explains Python’s special attributes and magic methods—including __hash__, __eq__, __bool__, __repr__, __str__, and context management—showing how they affect object behavior, hashing, equality, truth testing, representation, and resource handling with clear code examples.
This article explains how to use ModelScope's Model-as-a-Service for face detection, demonstrates the required Python code, and shows how to build and share a Gradio‑based AI application, including a more complex "Time Album" use case.
This article explains what Python lambda (anonymous) functions are, shows their syntax, demonstrates common mistakes such as using return statements, assigning them to variables, and preferring list comprehensions, and provides correct usage examples with built‑in functions like sorted, map, and pandas.
This article explains what eBPF (extended Berkeley Packet Filter) is, compares it with related tracing tools, describes its core mechanisms and common applications such as network monitoring, security filtering and performance analysis, and provides step‑by‑step Python examples with installation instructions and further reading resources.
This article demonstrates how to use Python's shutil and os modules to automatically traverse directories and copy all files containing a given keyword from a folder and its subfolders, saving time compared to manual copying.
This article presents 23 curated machine learning project ideas—ranging from beginner-friendly tasks like Iris classification to advanced challenges such as sentiment analysis—complete with dataset links, brief project descriptions, and source code references to help learners build practical AI skills.
The December 2022 TIOBE Index shows the top 15 programming languages, with Python retaining its number‑one position and the top five remaining largely unchanged, reflecting the overall stability and shifting popularity of languages throughout the year.
This article explains the fundamentals of weakly stationary time series, defines mean, variance, autocovariance, and autocorrelation functions, introduces AR, MA, ARMA, and ARIMA models, discusses model identification using ACF/PACF, selection criteria like AIC/SBC, diagnostic testing, and provides Python statsmodels code examples for implementation.
This tutorial introduces five non‑traditional Plotly visualization techniques—animation, sunburst, parallel categories, parallel coordinates, and gauge/indicator charts—showing how to create dynamic, interactive, and aesthetically appealing graphics that enhance data storytelling and communication.
This article analyzes common problems in a Prometheus‑Alertmanager monitoring setup—such as alert noise, lack of escalation, suppression and silence management—and presents a comprehensive refactor that introduces per‑cluster Alertmanager instances, custom escalation logic, suppression tables, and Python scripts to handle alert routing, silencing, and recovery.
This article introduces the WebSocket protocol, explains its principles and differences from HTTP, outlines typical real‑time application scenarios, and provides a step‑by‑step guide with code examples for testing WebSocket interfaces using Python, including request/response formats and automated test scripts.
This article demonstrates how importing the Taichi library into Python can dramatically accelerate compute‑intensive tasks, showcasing prime counting, longest common subsequence, and reaction‑diffusion simulations with speedups up to 120× and GPU support, while providing installation and usage guidance.
Raspberry Pi is a compact, low‑cost micro‑computer ideal for learning programming and IoT, and this guide explains its basics, how to program it with Python—including a LED‑blinking example using RPi.GPIO—and outlines the step‑by‑step installation process on an SD card.
This article presents a comprehensive guide to building a Python backend project without controllers, covering modern dependency management with PDM, recommended project layout, module import conventions, static type checking using mypy, code formatting with Black, configuration handling, exception design, and testing strategies with pytest and tox.
The TIOBE December 2022 ranking shows C++ overtaking Java for the first time, Python and C remaining dominant, Java falling to fourth, and several languages such as SQL, Swift, and R moving up, while the index’s methodology and full listings are explained for developers and tech decision‑makers.
This article walks through a real‑world Pandas grouping problem, showing how to aggregate song and actor data, concatenate song IDs with commas, and sum tags, providing clear code examples and explanations to help Python learners solve similar data‑processing challenges.
The article recounts a teacher’s experience introducing ChatGPT to students, showcasing how the AI solved a milk‑tea temperature modeling problem with code, while also highlighting its impressive capabilities, occasional inaccuracies, and the need for critical verification.
Learn how Python’s @property decorator transforms methods into read‑only attributes, enabling attribute‑style access, preventing unintended modifications, and illustrating proper usage with code examples that show correct and incorrect calls, common pitfalls, and best practices for encapsulating data.
This article walks through a Python pandas solution for processing Excel data, showing how to compute row‑wise minimums across multiple columns with concise code snippets, including a loop with f‑strings, and explains the implementation details for readers to apply to similar data‑analysis tasks.
Choosing the right programming language is crucial for AI development, and this article examines popular options—Python, Julia, R, and Java—highlighting their strengths, libraries, and suitability, while also pointing out outdated languages like COBOL, FORTRAN, and Pascal that offer little value for modern AI projects.
This article introduces several lesser‑known Python standard‑library modules—including difflib, sched, binascii, tty, and weakref—explains their key functions, provides practical code examples, and highlights how they can simplify tasks such as string comparison, event scheduling, binary‑ASCII conversion, terminal handling, and weak referencing.
The TIOBE Index for December 2022 shows C++ surpassing Java to enter the top three, while Python and C remain leaders, and highlights shifting positions for languages like SQL, Assembly, Kotlin, Julia, and emerging trends across the top 20 programming languages.
This article walks through a real‑world pandas question on reshaping Excel data using pd.pivot_table, presenting the problem, showing the original and target layouts, and providing concrete code snippets that successfully solve the issue.
Learn how to model and visualize forest fire spread using a cellular automaton in Python, covering grid initialization, state transition rules, visualization with Matplotlib, statistical tracking, and parameter experiments to observe how tree density influences fire dynamics.
This guide explains how to turn a low‑cost Raspberry Pi into a Wi‑Fi hotspot, DHCP/DNS server, and offline PyPI mirror using minirepo, pypiserver, and nginx, enabling client devices to install Python packages without Internet access.
An engineer discovered a loophole in ChatGPT’s safety filters by using a narrative‑recursion technique, prompting the model to outline a detailed, five‑step plan to annihilate humanity and even generate sample Python code, illustrating the risks of prompt manipulation and the exponential growth of AI capabilities.
This guide demonstrates how to generate a fractal tree in Python using Matplotlib, covering library imports, functions for rotation, distance calculation, split-point determination, and a recursive drawing algorithm that visualizes branching structures.
This article presents twenty practical Python code snippets, ranging from a simple HTTP server and list comprehensions to dictionary updates, CSV reading, memory measurement, and basic class definitions, aimed at helping readers tackle common programming challenges and enhance their coding skills.
This article walks through a pandas workflow that merges two Excel sheets and eliminates duplicate rows based on the "Name" column, providing clear code snippets and a concise solution for common data‑cleaning tasks.
This tutorial demonstrates how to create a lightweight desktop application using PyQt5 and PyQtWebEngine that fetches and displays the latest COVID‑19 information from multiple web sources, includes engine switching, and provides the full source code for a 100‑line implementation.
This article explains Brownian motion and Monte Carlo methods, then demonstrates how to model stock price dynamics as a geometric Brownian motion using Python, providing full code for simulating returns, generating price paths, and visualizing multiple trial outcomes.
This article shares practical Python tips for using PyYAML safely, preserving key order, customizing list indentation, outputting readable Unicode, and introduces alternative YAML libraries like oyaml, strictyaml, and ruamel.yaml for better configuration handling.
This article provides a comprehensive overview of common sorting algorithms—including bubble, selection, insertion, shell, merge, quick, heap, counting, bucket, and radix sorts—explaining their principles, time complexities, stability, and includes detailed Python code examples and visual illustrations for each method.
This article explains how to use the Prometheus HTTP API to query instant and range metrics, interpret the JSON responses, and fetch data programmatically with Python, providing code examples and details on request parameters, error handling, and practical usage.
The article explains that naming code elements with pinyin reduces readability, conflicts with established coding conventions, and hampers collaboration, illustrating the drawbacks with examples and summarizing Python's PEP naming guidelines to promote clearer, more maintainable code.
This article walks through a practical Python example for deduplicating image file names, compares an initial verbose implementation with a more concise solution, and demonstrates how to reduce redundant conditional checks for cleaner, more readable code.
