This article introduces the cutecharts Python library that generates hand‑drawn style visualizations, explains how to install it, describes common chart parameters, and provides detailed API references and code demos for Bar, Line, Pie, Radar, and Scatter charts, complete with example images.
This tutorial walks through using Selenium to handle pop‑ups and simple numeric captchas on a web portal, captures the captcha image, applies binary thresholding, recognizes the text with Tesseract OCR, and then submits the login credentials, including retry logic for failed recognitions.
This guide explains five key PyTorch tensor functions—expand, permute, tolist, narrow, and where—detailing their purpose, usage, and code examples so you can manipulate tensors efficiently on CPU or GPU.
This tutorial walks you through using Python's requests, lxml, and JSON parsing to automatically crawl the King of Glory website, extract hero skin URLs, and download the images into organized folders while displaying progress in the console.
This article demonstrates how to configure Python's logging module for console and file output, extend it with HTTPHandler for remote logging, and improve performance by using custom handlers, threading, thread pools, and asynchronous aiohttp calls to avoid blocking the main program.
This article explains the five underscore naming patterns in Python—single leading, single trailing, double leading, double leading and trailing, and a solitary underscore—detailing their conventions, the name‑mangling mechanism, and how they affect class attributes and imports.
This article introduces Python's DB‑API for interacting with various databases, explains how to use PyMySQL and SQLAlchemy for MySQL, demonstrates basic CRUD operations, safeguards against SQL injection, and shows how to implement thread‑safe connection pools with DBUtils for scalable backend applications.
This tutorial walks through creating a Python program that scrapes product links, names, and images from a Taobao store, logs the process, exports the data to Excel, and provides a user‑friendly Tkinter GUI for searching and controlling the scraper.
This article walks through creating a Python web scraper that extracts financial data for over three thousand listed companies, starting from a simple pandas script and progressively adding error handling, MySQL storage, and multiprocessing to build a robust, production‑ready tool.
This article provides a comprehensive guide to Python’s zip() function, covering basic, advanced, and creative usages, explaining its underlying implementation, highlighting limitations such as the “shortest‑iterator” behavior, and presenting solutions like zip_longest and the upcoming PEP‑618 changes.
This article presents a practical solution for eliminating POI label overlap on maps by applying 2D collision‑detection techniques and the A‑Star path‑finding algorithm to intelligently position labels and draw connector lines, complete with Python and OpenCV code examples.
This article introduces the official Matplotlib cheatsheet on GitHub, highlights its comprehensive coverage of common and advanced plotting techniques, and provides links to high‑resolution images and code examples, helping Python users master data visualization efficiently.
This tutorial introduces Seaborn—a Python statistical visualization library built on matplotlib—covers its advantages, installation methods, a step‑by‑step workflow for importing data, setting up the canvas, creating various plot types (histogram, scatter, bar, line, box, violin, heatmap, etc.), and demonstrates a practical example with full code snippets and visual outputs.
This article explores Python’s core concept that everything is an object, explaining how variables reference objects, the role of the id() and type() functions, and the differences between mutable and immutable objects with practical code examples.
This article introduces time series fundamentals, classification, and challenges for internet businesses, then provides a step‑by‑step Python tutorial on ARIMA modeling—including data loading, stationarity testing, differencing, ACF/PACF analysis, AIC‑based order selection, model training, prediction, error evaluation, exogenous variable integration, and diagnostic checks.
This article analyzes a Scrapy‑Redis crawl of Lianjia data from July 2020, cleans and enriches the dataset, and uses pandas profiling and visualizations to uncover Shanghai’s second‑hand housing price distribution, regional hotspots, housing types, and market dynamics during the first half of 2020.
This tutorial walks you through checking the pre‑installed Python version, locating its binaries, downloading Python 3.6.5 source, installing required dependencies, compiling and installing the interpreter, creating symlinks, and configuring environment variables on a Linux system.
PaddleOCR, the 2020 breakthrough in open‑source OCR, offers ultra‑light 3.5 MB multilingual models, high F1‑score performance across diverse scenarios, easy installation via pip, comprehensive documentation, custom training support, and deployment options for both server and mobile platforms, all backed by detailed benchmarks and code examples.
This tutorial walks you through installing openpyxl, understanding Excel's workbook‑sheet‑cell hierarchy, and provides step‑by‑step code examples for reading, writing, styling, and manipulating Excel files using Python, serving as a handy reference for developers.
This article explains how to implement a user‑based collaborative filtering recommendation system in Python, covering data loading, preprocessing, cosine‑similarity computation, neighbor selection, rating prediction, and generating top‑5 movie recommendations with detailed code examples.
This article explains how to use Python's pandas and numpy libraries to batch‑process dozens of CSV or Excel files, extracting the maximum and minimum values from a specific column efficiently without manual effort.
Discover five essential, free Python tools—including Python Tutor, IPython, Jupyter Notebook, Anaconda, and Skulpt—that boost productivity, simplify learning, and streamline development for anyone seeking powerful, community‑driven solutions.
This tutorial explains how to construct a recommendation system by loading transaction data, creating sparse user‑item and item‑item matrices, applying Log‑Likelihood Ratio for item similarity, and indexing the results into ElasticSearch for real‑time serving, using Python and open‑source big‑data tools.
This article explains Python's mutable and immutable types, shows why a += b and a = a + b behave differently for lists and dictionaries, and clarifies the underlying __add__ versus __iadd__ methods with illustrative diagrams.
This guide explains why a common Bokeh runtime error occurs due to an outdated package, walks you through upgrading Bokeh in PyCharm with step‑by‑step screenshots, and shows the resulting error‑free plot rendering.
This tutorial introduces Python's requests‑html library, covering installation, basic page fetching, link extraction, element selection with CSS and XPath, rendering JavaScript, pagination, direct HTML usage, custom request options, form login, and practical crawling examples.
To enhance object detection robustness, the article discusses various data augmentation methods—including rotation, flipping, random cropping, scaling, color jitter, blurring, transparency adjustment, and image partitioning—providing code examples and illustrating their impact on model performance with before‑and‑after results.
This article provides an extensive overview of feature engineering, covering feature understanding, cleaning, construction, selection, transformation, and dimensionality reduction techniques, illustrated with Python code using the Titanic dataset, and offers practical guidelines for improving data quality and model performance in machine learning projects.
This article explains the principles of leaky‑bucket and token‑bucket rate‑limiting algorithms, compares their behavior in high‑concurrency e‑commerce scenarios, and provides a complete Python implementation to illustrate how token‑bucket can handle burst traffic while maintaining system stability.
Using Python Selenium, the author collected 4,403 Taobao listings for men's shorts, cleaned the data, visualized regional sales, price distribution, top shops, and product characteristics, ultimately identifying the best-selling items and revealing market trends.
Discover five open‑source stock‑trading utilities—funds, ZVT, QUANTAXIS, StockAnalysisSystem, and match‑trade—each offering real‑time data, backtesting, multi‑asset support, and high‑performance matching to help programmers build powerful quantitative finance applications.
This article describes a Python‑based approach that identifies and eliminates WeChat group members who repeatedly post advertisements by defining heuristic rules, capturing screen coordinates with the pynput library, simulating mouse‑clicks and keyboard shortcuts, and processing the extracted data to flag suspicious accounts.
This article surveys the rise of microservices and presents a comprehensive list of widely used Java, .NET, Node.js, Go, and Python frameworks, highlighting their features, adoption statistics, and suitability for building scalable, loosely‑coupled backend systems.
This article explains how Linux command‑line pipelines work, highlights the limitations of traditional shell scripts, and demonstrates how a concise Python script can replace them for counting unique users in log files while remaining easy to read, reusable, and powerful.
This article walks through a comprehensive analysis of a 3.43 GB sales dataset from a Brazilian automotive service chain, covering data loading, cleaning, exploratory visualizations, time‑series forecasting with ARIMA, RFM customer segmentation, product clustering, and key business insights using Python and Tableau.
This article introduces Python as an easy-to-learn language and reviews ten essential IDEs—including Vim, Eclipse with PyDev, Sublime Text, Emacs, Komodo Edit, PyCharm, Wing, PyScripter, Eric, and an interactive editor—helping developers choose the right tool to boost productivity.
This tutorial explains how to use Python and ADB to create a group‑control script that connects multiple Android devices, extracts package information, launches apps, performs UI actions via YAML, and cleans up, providing a practical foundation for mobile automation testing.
This article introduces a curated collection of lesser‑known Python libraries for data‑science tasks—including wget, pendulum, imbalanced‑learn, flashtext, fuzzywuzzy, pyflux, ipyvolume, dash, and gym—detailing their purpose, installation commands, and concise code examples to help practitioners expand their toolkit.
This article explains Python's eval function, its syntax and parameters, demonstrates how it parses and evaluates expressions, shows practical examples with globals and locals, and discusses important security considerations when executing dynamic code.
The team built a log‑based replay framework that pulls and cleans old gateway logs, samples requests, concurrently replays them against both old and new gateways, automatically compares JSON responses with configurable ignore rules, retries failures, and uses matching response distributions to safely verify and migrate thousands of APIs.
This article demonstrates a step‑by‑step Python Selenium solution for completely automating Taobao login, covering dependency setup, class design, handling the sliding captcha, and alternative login via Sina Weibo, complete with full source code and usage instructions.
This tutorial walks you through creating a fully functional Tetris clone using Python's PyQt5 library, covering game logic, rendering, user controls, shape management, line clearing, and includes complete source code for all core classes and methods.
This tutorial shows how to use Python's urllib and json libraries to call the Kuaidi100 API, retrieve real‑time logistics data for various Chinese courier companies, and display the tracking timeline, while explaining how to discover the correct request URL via browser dev tools.
This article walks through loading the Speed Dating dataset, examining its features and missing values, visualizing match rates by gender and age, performing correlation analysis, and building a logistic regression model with SMOTE oversampling to predict whether a pair will successfully match.
This comprehensive tutorial introduces machine learning fundamentals, its history, differences from traditional programming, key characteristics, and why Python is the preferred language, then explores supervised, unsupervised, and reinforcement learning, popular algorithms, detailed K‑Nearest Neighbors examples for classification and regression, and the essential steps to build and evaluate models.
This article explores the classic Speed Dating dataset, performing data cleaning, exploratory analysis of match rates, gender and age effects, correlation studies, and finally building a logistic regression model with SVMSMOTE oversampling to predict matchmaking success, achieving around 83% accuracy.
This article explains how pandas automatically aligns DataFrames during arithmetic, how to use the fill_value parameter to avoid NaNs, and demonstrates essential APIs such as isna, dropna, and fillna for detecting and handling missing data in Python.
This article provides a step‑by‑step guide on using the Python Arrow library to obtain UTC and local times, format dates, shift time values, parse arbitrary timestamps, and convert Arrow objects to native datetime, date, and time types, complete with code examples and result screenshots.
This article compares eight popular Python visualization libraries, outlining their strengths, weaknesses, and ideal use‑cases while providing clear code examples to help developers pick the right tool for their data‑storytelling needs.
This article provides a comprehensive guide to Python's regular expression (re) module, covering fundamental concepts, key functions such as match, sub and compile, flag modifiers, pattern syntax, practical code examples, and real‑world scraping use cases.
This article describes a complete solution for generating customized Robot Framework test reports by parsing the output.xml file, extracting test case statistics and details with Python, rendering them into an HTML template using Jinja2, and finally sending the report via email.
This article outlines the release timeline for Python 3.8‑3.10 and showcases four major Python 3.10 enhancements—bit_count() for counting set bits, a strict mode for zip(), read‑only mapping proxies for dictionary views, and the removal of deprecated collection ABC aliases—complete with illustrative code snippets.
Learn how to use Python's openpyxl and glob modules to automatically extract rows with purchase quantities over 50 from single or thousands of Excel files, create new workbooks, and merge results, with complete code examples for both basic and advanced scenarios.
This article introduces Python as a beginner‑friendly, portable language and presents eleven of the best Python compilers and interpreters—detailing their features, platforms, and typical use cases—to help developers choose the right tool for web, data science, or cross‑language projects.
This article demonstrates how to use Python to crawl 3,032 Chinese recipe entries from Douguo.com, clean the data with Pandas, and create insightful visualizations—including rating distributions, cuisine comparisons, and ingredient word clouds—using pyecharts, providing complete code snippets and analysis of the results.
This guide explains how to configure Celery to use Redis as both broker and result backend, covering project layout, code setup, task definition, execution flow, task states, and common control operations for asynchronous Python workloads.
This article introduces the KMP string‑matching algorithm, explains its practical scenarios, details the construction and purpose of the Next array, provides a step‑by‑step Python implementation, and analyzes its linear‑time complexity for efficient pattern searching.
This tutorial explains the concept of password‑brute‑force, introduces the open‑source sshfucker library and a custom multithreaded Python script that uses Paramiko to enumerate SSH credentials from a dictionary, and provides step‑by‑step code examples and usage instructions while warning against illegal use.
This article presents twenty classic NumPy problems covering array lookup, modification, conversion, sampling, slicing, string operations, rounding, reshaping, linear algebra, and more, each accompanied by concise Python code examples and visual illustrations to help you master advanced data manipulation techniques.
Learn how to replace repetitive Excel payroll creation with a Python script that reads, copies, styles, and inserts rows, updates formulas, and outputs a ready-to-use payroll sheet, complete with full code examples and detailed explanations.
This article describes how a team released resources after shutting down a business line, focusing on the difficulties of deleting Redis keys from a shared cluster, the step‑by‑step approach using code search, Python scripts, SCAN and DEBUG OBJECT commands, and the implementation of a custom key‑prefix serializer to prevent future issues.
This guide presents seven practical techniques—from using Pandas Profiling and Cufflinks‑Plotly visualizations to mastering IPython magic commands, Jupyter formatting, keyboard shortcuts, multiple outputs, and live slide conversion with RISE—to accelerate and enrich everyday data analysis workflows.
This article walks through constructing a multiple linear regression model for house price prediction using Python, covering data exploration, dummy variable creation, model fitting with statsmodels, diagnosing multicollinearity via VIF, and applying optimizations to improve predictive accuracy.
This article explains the fundamentals of object‑oriented programming in Python, covering concepts such as encapsulation, inheritance, polymorphism, class definition, instance creation, special methods, garbage collection, and the differences between equality and identity, all illustrated with clear code examples and diagrams.
The article introduces the TcloudServer cloud testing platform, outlines its key features such as cloud devices, workflow management, and dashboards, and provides detailed configuration instructions including Python settings, Jenkins pipeline setup, and parameter definitions for automated mobile monkey testing.
This guide walks Jupyter Notebook beginners through essential configuration steps—including altering the default working directory, setting a preferred browser, securing the notebook with a password, installing useful extensions, and customizing the notebook’s appearance—empowering a smoother, more personalized Python workflow.
This article introduces Alibaba Cloud’s free PAI‑DSW cloud IDE for AI development, explains the evolution of machine learning, guides users through creating notebooks, running Python code, and demonstrates a complete speed‑dating dataset analysis and predictive modeling pipeline using logistic regression and data‑balancing techniques.
This guide explains how to generate S3 presigned URLs for PUT, GET, and POST operations using Python's boto3 library, providing code examples, usage tips, and recommendations for secure, unauthenticated file transfers in cloud environments.
This article compares Anaconda, direct Python installation, virtualenv, and Miniconda, explaining their respective advantages, how they manage Python versions and packages, and which tool is best suited for beginners setting up a Python development environment.
This tutorial shows how to craft playful romantic messages and visual heart art using Python and JavaScript code snippets, including ASCII‑based text, a one‑line heart generator, QR code creation, and a canvas‑drawn rose, all illustrated with example outputs.
Coroutines, also known as micro‑threads, allow a single thread to pause and resume functions, offering higher efficiency than traditional multithreading by eliminating context‑switch overhead and lock contention, with Python’s generator‑based implementation enabling lock‑free producer‑consumer patterns and seamless multi‑core utilization via processes.
This article shares playful programming examples—including Python string‑manipulation, ASCII‑code mapping, and a one‑line heart‑shape generator—as well as JavaScript and canvas snippets that let you create romantic text and graphics for the Qixi festival.
This guide demonstrates how to manage Kubernetes Jobs using the Python client library, covering installation, initialization, creating, updating, monitoring, listing, and deleting jobs with both YAML and dictionary approaches, plus code snippets for watching events and retrieving associated pods.
This guide shows two methods to confirm that Anaconda is installed correctly on Windows 10 and provides step‑by‑step instructions for adding Anaconda and its Scripts folder to the system PATH to resolve the “conda is not recognized” issue.
This comprehensive tutorial introduces Python, explains its uses in web development, IoT, and machine learning, guides you through installation, IDE selection, Jupyter Notebook setup, and covers core concepts such as variables, data types, operators, control flow, functions, modules, file handling, and more, complete with code examples and visual illustrations.
Learn how to set up Oracle Database for Python, install and configure the 64‑bit Instant Client, resolve common issues like Chinese character encoding, install the cx_Oracle driver, and perform data queries using fetchone, fetchall, and pandas DataFrames.
This article introduces a curated selection of lesser‑known Python packages—such as wget, pendulum, imbalanced‑learn, FlashText, fuzzywuzzy, PyFlux, ipyvolume, Dash, and Gym—detailing their installation commands, core functionalities, and code examples to help data scientists expand their toolkit beyond the usual pandas, scikit‑learn, and matplotlib.
Analyzing recent Stack Overflow and GitHub data, this article predicts that Python, JavaScript (Node.js), and Go will be the three most popular programming languages in 2021, explaining their broad applications, ease of use, and strong community support that drive their rising demand.
This tutorial shows how to use Python's openpyxl and python-docx libraries to read company data from an Excel sheet, replace placeholder tags in a Word contract template, and automatically generate individualized contracts for each company in a batch process.
Learn how to enforce Python coding standards by using Flake8 for local static analysis and pre-commit hooks for Git integration, covering installation, configuration, and practical usage to automatically detect style violations, syntax errors, and complexity issues before committing code.
This tutorial shows Python developers how to configure the interpreter so that a short "peace mantra" is automatically printed before any script or interactive session runs, using usercustomize.py, PYTHONSTARTUP, and simple shell setup to help keep bugs at bay.
This article explains the node2vec algorithm, its mathematical foundations, biased random‑walk sampling strategy with parameters p and q, implementation details using the Alias method, and demonstrates its superior performance on node classification and visualization tasks compared with DeepWalk and LINE.
This article experimentally validates consistent hashing by adding and removing nodes in a simulated cluster, examines key redistribution, reveals load‑imbalance and avalanche vulnerabilities, and proposes solutions such as virtual nodes and adaptive replica factors, providing Python code examples and detailed observations.
Learn how to download and install Anaconda on a Windows 10 PC with this detailed, illustrated tutorial, covering software download, version selection, installation steps, environment variable configuration, and final setup, ensuring beginners can get the Python distribution up and running quickly.
The article examines Python's rapid rise since its 1991 debut, highlighting its free, cross‑platform nature, strong community support, growing popularity on StackOverflow, and expanding job market, while questioning whether it will dominate future software testing, AI, and machine‑learning development.
This article explains the principles of consistent hashing, compares it with simple modulo and virtual bucket methods, demonstrates how to build a hash ring, map nodes and keys, and shows through Python code how node addition or removal only affects a small portion of the data.
This article demonstrates how to use Python to crawl over 200,000 Bilibili comments, analyze popular memes across different content categories, and presents a step‑by‑step guide to extracting danmu via network requests with code examples for practical data mining.
This article presents thirty concise Python tasks and code snippets that demonstrate practical techniques—from checking duplicates and measuring memory usage to manipulating strings, lists, dictionaries, and control flow—offering beginners and seasoned developers quick, hands‑on examples to deepen their understanding of core language features.
This tutorial introduces Python's Requests library, covering installation, basic GET and POST requests, adding headers and parameters, handling proxies, cookies, sessions, and disabling SSL verification, with clear code examples for each feature.
This article explains the core concepts of TensorFlow Serving, shows how to prepare the environment with Docker, convert TensorFlow 2.x models to the SavedModel format, configure version policies, warm‑up the service, and invoke predictions via gRPC or HTTP interfaces.
This guide walks you through installing Django on Windows, verifying the installation, generating a new project with startproject, understanding its file structure, applying migrations, and launching the development server to see a working web page, all with clear command‑line steps.
When a business platform’s settings page took 36 seconds to load, we used Chrome’s Network timing, Python profiling, and MySQL query analysis to identify backend bottlenecks, then applied redesign, thread‑pooling, batch queries, and ORM optimizations, reducing response time from 37.6 s to 1.47 s.
Facebook’s open‑source Pysa tool statically scans Python code to detect data‑flow vulnerabilities, XSS and SQL‑injection risks, leveraging Pyre and Zoncolan techniques, achieving rapid analysis of millions of lines and uncovering 44% of Instagram’s security flaws in early 2020.
Python’s versatility makes it essential across fields—from AI and cloud computing to web development, web scraping, game creation, and data analysis—offering high demand, lucrative careers for IT professionals and productivity boosts for non‑IT workers, as illustrated by real‑world examples and industry trends.
This article explains why Python developers often need to run JavaScript—especially obfuscated code—during web scraping, demonstrates how to use the ExecJS library to execute both simple and heavily obfuscated JavaScript from Python, and shows that the approach works seamlessly without de‑obfuscation.
This tutorial explains what Python .whl (wheel) files are, how they differ from source distributions, and why using wheels dramatically speeds up package installation, illustrated with real pip install examples and compatibility considerations.
This article explains Python iterators and generators, covering their core methods, custom iterator classes with __iter__ and __next__, handling StopIteration, and practical generator examples such as a Fibonacci sequence, all illustrated with clear code snippets.
This article examines the most common Selenium automation hurdles—including false‑positive results, waiting for JavaScript loading, scalability, dynamic content, pop‑up alerts, window switching, mobile testing limitations, incomplete automation, and reporting—while offering practical Python‑based solutions and code examples to improve test stability and efficiency.
This article explains how intelligent recommendation differs from generic marketing, introduces association‑rule concepts such as support, confidence, and lift, and provides a step‑by‑step Python implementation using the Apriori algorithm to generate and interpret market‑basket recommendations.
This article outlines nine practical Python skills—ranging from problem‑solving mindset and avoiding XY problems to mastering strings, lists, loops, functions, OOP, and PEP‑8 conventions—that help beginners transition into confident intermediate developers.
