This article explains how to use Python's requests and BeautifulSoup libraries to crawl Divnil.com, extract image detail page URLs, parse each page for high‑resolution image links, and download the images into a local folder with a complete, runnable script.
According to the latest August TIOBE ranking, Python's market share rose to a record 15.42%, while other languages such as PHP, Go, Ruby, Rust, and Kotlin experienced notable position changes, reflecting shifting popularity trends across the programming ecosystem.
Numba is a Python just‑in‑time compiler that transforms functions into fast native machine code, enabling near C‑level performance without rewriting code; by adding simple decorators like @jit or @njit, you can accelerate loops, NumPy operations, and even leverage parallel or GPU execution.
This article introduces thirty unique and practical Python packages that simplify various aspects of data workflows, including model training notifications, progress tracking, data validation, statistical calculations, date handling, and more, providing installation commands and code examples for each tool.
This article introduces ten Python data‑visualization libraries—ranging from the classic matplotlib to modern interactive tools like Bokeh and Plotly—explaining their main features, typical use cases, and where to find more information.
This article explains how to use Python to copy a specific area of an Excel worksheet together with its formatting, walks through the provided code solution, highlights common pitfalls, and offers a concise, refined implementation for seamless Excel automation.
This guide explains how to build a custom Label Studio ML backend by extending LabelStudioMLBase to wrap a YOLOv5 or MMDetection model, modify its prediction logic, launch the service, and configure the frontend for automated object‑detection annotation, including deployment details and a recruitment notice.
This article examines the historical origin of the ++ and -- operators, explains why they were introduced, demonstrates that modern compilers no longer gain performance from them, and argues that functional‑style design, iterator‑based loops, and operator‑overloading concerns have led many contemporary languages to remove or deprecate these operators.
This article introduces Python's DB‑API, lists supported databases, demonstrates how to install and use PyMySQL for basic CRUD operations, shows techniques for preventing SQL injection, and explains two connection‑pooling models with DBUtils, including code examples and a brief promotional note at the end.
This article explains why discretizing continuous variables improves model stability, introduces three common binning techniques—equal-width, equal-frequency, and clustering—and demonstrates how to implement each using scikit‑learn's KBinsDiscretizer with Python code examples on a synthetic score dataset.
This tutorial demonstrates how to accelerate processing of a multi‑million‑row CSV dataset by splitting the work into sub‑tasks and applying Python's multiprocessing, joblib, and tqdm libraries for serial, parallel, and batch processing, showing significant speed‑ups and best‑practice code snippets.
This article demonstrates how to replace tedious manual Excel reporting by using Python libraries pandas and xlwings to read multiple sheets, merge data, write the combined DataFrame back to Excel, and apply conditional formatting such as font colors, borders, and cell shading based on statistical thresholds.
This guide explains the theory behind Vector Autoregression (VAR) models, outlines the complete modeling workflow—including data preparation, stationarity and cointegration testing, lag order selection, parameter estimation, stability diagnostics, and impulse‑response and variance‑decomposition analysis—and provides a full Python implementation with code examples.
This guide explains how to combine the open‑source labeling platform Label Studio with the YOLOv5 object‑detection model to achieve semi‑automatic annotation, covering installation of both tools, project creation, dataset configuration, and training a custom YOLOv5 model on your own data.
This article introduces the fundamentals of convolutional neural networks (CNN), explains key layers such as convolution, pooling, and fully connected layers, and provides a step‑by‑step Python implementation using Keras to load data, construct, compile, train, and evaluate a CNN model on the digits dataset.
This article explains the purpose of the Singleton design pattern, demonstrates multiple Python implementations—including module‑level singletons, decorators, class‑based approaches, __new__ method, and metaclass techniques—and shows how to add thread‑safety with locks to ensure only one instance exists across concurrent executions.
This article presents a collection of practical Python and Jupyter Notebook tips—including pandas‑profiling for quick data exploration, interactive plotting with Cufflinks/Plotly, useful magic commands, debugging shortcuts, and styled alert boxes—to boost productivity and streamline data analysis workflows.
Learn how to load the California housing dataset, preprocess features, construct a Keras sequential regression MLP, train it with SGD, evaluate performance, and make predictions, all illustrated with concise Python code snippets.
This article explains why Python is an ideal language for data analysis, covering its simplicity, extensive libraries, compatibility with big‑data platforms, and a step‑by‑step workflow that includes data acquisition, storage, preprocessing, modeling, and visualization, while also highlighting useful tools and resources.
An in‑depth guide to sklearn.svm explains SVM classification and regression, details key parameters such as C and kernel types, demonstrates how to use GridSearchCV for hyperparameter tuning, and provides complete Python code examples for iris classification and California housing price prediction.
This tutorial walks through using Keras to create, compile, train, and evaluate a multi‑layer perceptron for image classification on the Fashion MNIST dataset, covering data loading, model construction with the Sequential API, hyperparameter choices, and prediction of new samples.
Django 4.1 has been officially released, introducing asynchronous ORM interfaces, class‑based view async handlers, database‑level model validation, and enhanced form rendering accessibility, while also marking the end of mainstream support for Django 4.0, which will receive only security fixes until April 2023; the article also provides pip and Git commands for installing the latest stable or development versions and a brief overview of Django’s core principles.
Alibaba Cloud reports slowing internet demand but rising industrial digitalization, while Python hits a historic market‑share high and GitLab bans Windows and prunes idle projects, together highlighting evolving trends in cloud strategy, programming language popularity, and open‑source platform security.
This tutorial explains how to create a WeChat intelligent chatbot using Python, itchat, and the Turing API, covering module installation, API interaction, message handling with decorators, QR‑code login, and continuous execution, all demonstrated with concise code examples.
This article introduces essential Python string methods—including upper(), lower(), count(), strip(), lstrip(), rstrip(), split(), join(), replace(), isdigit(), isspace(), startswith(), and endswith()—explaining their use cases with clear examples and code snippets for effective text processing.
This article explains the PageObject design pattern for Selenium-based test automation in Python, detailing its principles, structure, and step-by-step implementation with example code for pages, base classes, test cases, and execution scripts to improve maintainability and reduce duplication.
Fury is a JIT‑based native multi‑language serialization framework that automatically handles shared and cyclic references, offers zero‑copy support, and delivers 20‑200× speed improvements over existing solutions, making it a high‑performance drop‑in replacement for Java, Python, Go, and C++ serialization needs.
This article explains how to parse intricate datetime strings in Python using the datetime module, presents multiple code solutions with full examples, and provides a reusable function to convert UTC timestamps to Beijing time, helping readers handle common time‑formatting challenges.
Using a real‑world housing example, this article explains supervised and unsupervised learning, walks through building a price‑prediction function, introduces gradient descent for optimizing weights, and highlights pitfalls like overfitting, offering a practical introduction to core machine‑learning concepts.
This article explains the concept of differencing for extracting deterministic information, outlines when to use first, second, or seasonal differencing, introduces the ARIMA(p,d,q) model and its special cases, and provides a complete Python example with code and visualizations.
This article introduces an AI-powered makeup transfer technique that copies makeup from any reference image onto a user's photo, explains its advantages over traditional sticker-based apps, outlines a three‑step workflow, and provides sample Python code runnable on Huawei Cloud ModelArts.
This article explains why native pointers do not exist in Python, describes the underlying object model, immutable versus mutable objects, the distinction between variables and names, and demonstrates several practical ways to emulate pointer behavior using mutable containers, custom classes, and the ctypes module.
This guide explains how to leverage Seaborn's FacetGrid to create multiple coordinated plots, covering basic usage, parameter options, and customization techniques such as adding legends, titles, axis labels, figure size, and DPI settings, with complete Python code examples.
This article explains Python's conditional statements, covering the syntax and usage of single‑branch if, two‑branch if‑else, multi‑branch if‑elif‑else structures, the pass keyword, ternary expressions, nesting rules, and best‑practice tips for writing clear and efficient control‑flow code.
Learn to load the sunspots.csv dataset, fit an ARMA(4,2) model with statsmodels, evaluate it using AIC/BIC, visualize original and predicted values, and forecast the 1989 sunspot count, all with step‑by‑step Python code and plots.
This tutorial walks you through creating a fully functional Snake game in Python with Pygame, covering required environment setup, visual assets, and a complete source code example that demonstrates grid handling, movement logic, collision detection, and scoring.
This tutorial explains how to build a Windows keylogger in Python by importing Win32 DLLs with ctypes, registering a low‑level keyboard hook, defining the hook procedure and data structures, and handling installation, event processing, and cleanup, while providing full source code and usage notes.
This article explains the various meanings of single and double underscores in Python, outlines the five common underscore naming patterns, demonstrates how name mangling works for double‑leading underscores, and shows practical uses of the underscore as a throwaway variable and REPL placeholder.
This tutorial explains how to create a Windows key‑logging spy program by registering low‑level hooks through the user32 and kernel32 DLLs, using Python's ctypes library to call the required WinAPI functions, define callback prototypes, and manage hook installation and removal.
This tutorial explains how to create, initialize, retrieve, update, and delete entries in Python dictionaries using both literal syntax and built‑in functions, demonstrates the use of the get, update, pop, and fromkeys methods, and shows common pitfalls and best‑practice code examples.
This tutorial walks through building a simple Python web crawler for the QDaily website, covering target analysis, environment setup, SQLite database creation, data extraction with requests and BeautifulSoup, storing articles and comments, performing basic analysis, and visualizing results with a word cloud.
This tutorial introduces clustering, explains why no single algorithm fits all data, and provides step‑by‑step Python examples using scikit‑learn for ten popular unsupervised learning methods, complete with code snippets and visualizations to illustrate results.
This article explains how to manage Python project dependencies using pip and conda, covering basic pip commands, dependency trees with pipdeptree, clean removal with pip‑autoremove, and creating, activating, and exporting isolated environments with Conda, including practical examples and best‑practice recommendations.
Learn a step-by-step approach to predict seasonal time series—such as product sales or climate data—using the seasonal coefficient method, illustrated with a quarterly refrigerator sales case study and a complete Python implementation that computes next year's quarterly forecasts.
Meta has officially added Rust, Hack, Python, and C++ as its preferred backend languages, outlining strict criteria such as core library support, security, operational risk, expertise, and developer experience, while also noting community‑supported languages and its long‑term commitment to Rust amid broader VR ambitions.
This article presents a detailed survey of over thirty Python web frameworks, describing their official sites, main features, advantages, and drawbacks, and offers guidance on selecting popular options such as Django, Flask, FastAPI, and many others for various development needs.
This article explains how Python variables reference objects in memory, distinguishes between shallow and deep copying, demonstrates three common copying techniques (slicing, factory functions, and the copy method) with lists and tuples, and shows how to use the copy module for deep copies.
This article explains the modular programming philosophy in Python, describing the differences between modules and packages, the advantages of modular design such as improved readability, reusability, and collaborative development, and includes illustrative diagrams and promotional resources for further learning.
This article demonstrates how to perform multiple linear regression using scikit‑learn's LinearRegression and the statsmodels library, covering data preparation, model fitting, code examples, and interpretation of statistical results for a cement heat‑release case study.
This article explains Python's file handling fundamentals, covering the open() function syntax, file object methods, various read/write modes, encoding options, pointer manipulation with seek() and tell(), and best practices to avoid data loss when reading or writing large files.
This article introduces ten essential JupyterLab extensions—ranging from a debugger and table of contents to spreadsheet integration, system monitoring, AI‑powered code completion, variable inspection, and interactive plotting—that together transform the JupyterLab environment into a more powerful, IDE‑like workspace for Python data‑science developers.
This article explains Python's four main composite data types—tuple, list, set, and dictionary—detailing their properties, mutability, typical operations, performance considerations, and appropriate use cases, accompanied by example code illustrating immutability of tuples.
This article explains the concepts of odds and odds ratios, shows how logistic regression estimates them, and demonstrates practical model fitting and prediction using Python's statsmodels and scikit‑learn libraries with real‑world examples.
This article explains how to use ANSI escape sequences and the Colorama library in Python to display colored, styled, and background‑colored text in terminal windows, covering 16‑color and 256‑color schemes, escape character formats, style codes, and reusable functions with code examples.
This article explains Python's branching statements—including single‑branch if, two‑branch if‑else, multi‑branch if‑elif‑else, and nested structures—provides syntax, example code for number comparison, ID gender detection, BMI calculation, and discusses best practices in Python programming.
This article explains the construction of logistic regression models, the use of the sigmoid function, maximum likelihood estimation, and least‑squares estimation for grouped data, illustrated with a housing‑purchase case study and complete Python code for fitting and predicting probabilities.
This article introduces the mathematical foundations of ordinary least squares, ridge, and LASSO regression, explains why LASSO requires coordinate descent, presents two real-world case studies with data, and provides complete Python code for fitting, visualizing, and interpreting LASSO models.
This article explains ridge regression by defining the estimator, outlining its key properties, discussing methods for choosing the ridge parameter, and demonstrating its application to economic data with Python code and visualizations.
This guide provides a comprehensive overview of pip, covering installation, upgrading, package installation with version control, batch installs via requirements files, uninstallation, environment freezing, package inspection, outdated checks, compatibility verification, and downloading packages for offline use, all illustrated with practical command‑line examples.
This article explains several methods to break out of nested loops in Python, including using identical break conditions, flag variables, raising exceptions, combining else clauses, and leveraging function returns, illustrated with a prime-number game example and complete code snippets.
This article explains Python's compilation process, introduces the AST module, and demonstrates how to parse, pretty‑print, traverse, and transform abstract syntax trees to automatically rename ambiguous variables and inject logging statements, ending with a discussion of broader AST applications.
This article explains how multicollinearity undermines ordinary least squares estimates, introduces ridge and LASSO regularization as remedies, and demonstrates their application on a 1966 French economic dataset using Python’s statsmodels, complete with code and interpretation of results.
This article presents 30 Python programming exercises covering string manipulation, loops, conditionals, and data structures, each accompanied by clear explanations and complete code examples.
This tutorial explains how to generate synthetic digit images, train a CNN model to recognize handwritten numbers and operators, segment scanned math worksheets using projection techniques, evaluate each expression with Python's eval, and overlay the results on the original image to provide automatic grading feedback.
This article lists ten frequent Python pitfalls—including mutable default arguments, class variable misuse, incorrect exception handling, scope errors, list mutation during iteration, closure binding issues, circular imports, module name conflicts, Python 2/3 incompatibilities, and improper __del__ usage—explaining each problem with examples and providing clear solutions.
This article demonstrates how to use Python’s built‑in functions and os.walk to automatically locate and delete specific subsystem files and folders, addresses Windows’ 256‑character path limit by prefixing paths with "\\?\", and provides complete code examples for safe bulk cleanup.
This article explains Python's four main composite data types—tuple, list, set, and dictionary—detailing their characteristics, typical operations, performance considerations, and appropriate usage scenarios, while providing code examples and practical guidance for selecting the right structure in different programming contexts.
This article explores several Python techniques—including the time, datetime, and dateutil modules, as well as pure arithmetic and NumPy—to reliably transform a "HH:MM:SS" string into the total number of seconds, while addressing timezone and year‑offset nuances.
This article explains how to define and use Python functions, covering required, default, variable, keyword, and named keyword parameters, with detailed code examples for function definition, parameter passing, and invocation, as well as notes on return values and best practices.
This article explains the concepts of time and space complexity, demonstrates their practical measurement with Python's list and set structures, and compares their lookup speed and memory consumption using concrete code examples and empirical results.
Explore a thorough NumPy tutorial covering array iteration with nditer, reshaping functions like reshape, flat, and flatten, dimension modifications, transposition, axis swapping, broadcasting, stacking, concatenation, splitting, element manipulation, string utilities, arithmetic, statistical, sorting, searching, and file I/O, all illustrated with clear Python code examples.
This tutorial introduces the lightweight Python Bottle framework, covering installation via pip or source, creating a simple HelloWorld server, using static and dynamic routes, serving files, handling POST requests and file uploads, and tips for local and network deployment.
This article explains five practical ways to use Python's -m option, including launching a simple HTTP server, generating documentation with pydoc, debugging with pdb, timing code snippets via timeit, and reliably installing packages with python -m pip, especially in multi‑version environments.
This article presents a step‑by‑step mathematical modeling of yeast biomass growth using a discrete logistic model, fits the model parameters via least‑squares, validates it with Python code, and discusses its prediction error and broader applicability to S‑shaped population dynamics.
This article walks through a common Pandas time‑handling issue, showing how to convert a timestamp column to a YYYY‑MM‑DD date format with concise code, complete with example data, output screenshots, and a brief conclusion for Python learners.
This article explains the theory behind two‑factor ANOVA, distinguishes cases with and without interaction, presents the mathematical model, and demonstrates a complete Python implementation using statsmodels, including data setup, model fitting, and interpretation of the ANOVA table.
The article explains how DevOps relies on automation tools like Docker and Jenkins, argues that Python’s ease of use, versatility, and automation capabilities make it the optimal programming language for DevOps professionals, and advises continuous learning for operations staff transitioning into DevOps roles.
This article compiles and explains various common anomaly detection techniques—including distribution‑based, distance‑based, density‑based, clustering, tree‑based, dimensionality‑reduction, classification, and prediction methods—providing theoretical descriptions, algorithmic steps, advantages, limitations, and Python code examples for each approach.
This article introduces the Python APScheduler framework, explains its four core components, shows how to install it, and provides a step‑by‑step guide for integrating background task scheduling into a Flask application with appropriate scheduler, job store, executor, and trigger selections.
This article traces the evolution of the software testing profession, shares the author’s personal career path from functional testing to test development, and provides a step‑by‑step guide—including architecture, code samples, and deployment—for creating a Python‑based API test data service and a Vue.js web UI to showcase it.
This article presents a step‑by‑step mathematical model for reducing a 100 kg, BMI‑34.6 individual to a healthy weight, using weekly calorie intake, metabolic rates, exercise data, a two‑stage plan, and Python code to simulate the process.
This article walks through building a Python3 program that uses the Pillow library to identify skin regions in images, applies color‑space heuristics to classify pixels, merges connected skin areas, and decides whether an image is pornographic based on configurable rules, complete with code samples and testing results.
This tutorial walks through loading Google stock data, preprocessing it with scaling, constructing past‑window features, building and tuning an LSTM model using GridSearchCV, evaluating predictions, and finally forecasting future values, all illustrated with complete Python code.
This tutorial demonstrates multiple pandas techniques—including datetime mapping, MonthBegin/MonthEnd offsets, floor operations, period conversion, and asfreq—to accurately retrieve the first and last day of a month for any given date, while highlighting common pitfalls and performance considerations.
This article presents eight practical Python data‑analysis techniques—including one‑line list definitions, lambda expressions, map and filter functions, NumPy arange/linspace, Pandas axis handling, DataFrame concatenation/merging/joining, the apply method, and pivot tables—each illustrated with clear code examples and explanations.
This article introduces Faust, an open‑source Python library that brings Kafka Streams‑style stream processing to Python, covering its features, installation, a step‑by‑step example, typed data models, and how to run real‑time data pipelines with async support.
This article demonstrates how to install the psutil Python library and use it to gather detailed CPU, memory, disk, network, and process information on a system, providing code examples and explanations for each type of metric.
This guide introduces Bpytop, the Python‑based, visually appealing terminal resource monitor, outlines its key features, explains the required prerequisites, and provides step‑by‑step instructions—including code snippets—for installing and operating the tool on Linux systems.
This article explains the concept of one‑way ANOVA, walks through a real‑world example comparing four manufacturing processes, and demonstrates how to conduct the analysis in Python using statsmodels, interpreting the resulting F‑statistic and p‑value to assess significance.
This article presents a curated collection of 20 commonly used Python libraries for general development, followed by a comprehensive list of Python tools for system administration, standard library modules, and useful utilities, offering readers a practical reference for enhancing their Python projects.
Learn how to turn a Raspberry Pi 4B into a continuously running WeChat robot using Python and the itchat library, covering automatic replies, AI‑powered chat via an online API, scheduled weather broadcasts, camera photo capture, and video calls with linphone, complete with full source code.
This guide shows how to calculate common statistical measures such as mean, median, range, variance, standard deviation, covariance, and correlation using NumPy functions, and demonstrates the equivalent operations with Pandas DataFrames, including a table of useful DataFrame methods for statistical analysis.
This guide explains how to set up Celery, a distributed asynchronous task framework, to use Redis both as the message broker and result backend, covering project structure, configuration code, task definition, execution, state monitoring, and common control operations.
This article walks through using the Kivy framework and Buildozer tool to create a simple Python mobile app, covering environment setup on macOS and CentOS, writing a hello‑world program, testing it, packaging it into an APK, and troubleshooting common build issues.
This article introduces Vizard, a C/C++‑based virtual‑reality development platform with Python bindings, and demonstrates how to load avatars and objects, implement a random‑walk pigeon, create talking characters, handle character movement via keyboard, capture mouse motion, and combine these techniques into a functional VR scene.
This article explains Python return functions and anonymous (lambda) functions, demonstrating how to create higher‑order functions, use closures to defer execution, and simplify code with lambda expressions, complete with practical code examples and detailed explanations.
This tutorial explains how data analysts can automate the creation, processing, and distribution of statistical reports using Python by reading various data sources, performing DataFrame calculations, generating visualizations, and sending the results via email, thereby saving time and reducing errors.
This article demonstrates how adding two gevent monkey‑patch lines to a feapder spider reduces CPU usage from 121% to 99% while changing the effective thread count from 36 to 12, and discusses the underlying principle, performance trade‑offs, and future directions for coroutine support.
This article demonstrates four Pandas techniques—between with loc, cut, qcut, and value_counts—to discretize numeric variables into bins, assigning grades A, B, C to exam scores, and shows how to generate synthetic data, define bin boundaries, and count records per bin.
This article walks through the development of a simple advertising recommendation system using Python, covering data collection, preprocessing with label encoding, text embedding via Torch, constructing an MLP model, and initiating training, while reflecting on the challenges faced by Python developers in the big‑data era.
