This article explains several Python techniques—using identical break conditions, flag variables, exceptions, loop‑else clauses, and function returns—to exit nested loops, demonstrated through a prime‑number game where the program stops when a non‑prime input is entered.
This article benchmarks Python list versus set lookups on large datasets, explains why sets are dramatically faster, and delves into the internal C‑level implementations of list as a dynamic array and set/dict as hash tables, including resizing rules and collision handling.
This article explains the fundamental differences between threads and processes, the role of Python's Global Interpreter Lock, and provides a comprehensive guide to using the multiprocessing and concurrent.futures modules—including their main classes, synchronization primitives, and practical code examples—for effective concurrent programming in Python.
This comprehensive Pandas tutorial walks you through loading CSV and Excel files, creating Series and DataFrames, performing basic operations, cleaning data, handling missing values, working with hierarchical indexes, grouping, merging, concatenating, and applying time‑series techniques, all illustrated with clear code examples and screenshots.
This tutorial introduces Streamlit—a Python library for building interactive web apps—covering installation, layout, widgets, data caching, charting, and example projects such as dynamic data exploration and a face‑detection demo, all with complete code snippets.
This tutorial explains how to use Python's Pygame library to generate random-colored, expanding ripple circles that originate from mouse clicks, complete with code examples and visual screenshots of the resulting effect.
This article introduces five advanced Plotly visualization techniques—including animated bar charts, scatter animations, sunburst diagrams, parallel categories, parallel coordinates, and gauge indicators—showcasing how to create dynamic, interactive graphics in Python to make data stories more compelling and insightful.
This article explains how running a Python file directly with python3 script.py differs from using python3 -m module, focusing on the resulting changes to sys.path, import behavior, and the search order of modules.
This tutorial demonstrates how to collect the top 2000 entries of the 2021 China Hurun Rich List using Python's requests library, process the data with pandas, and create insightful visualizations in Tableau, covering data extraction, cleaning, and analysis steps.
Playwright, Microsoft’s open‑source end‑to‑end UI testing framework, supports all major browsers, multiple programming languages, isolated contexts, auto‑waiting, and powerful selectors, making it a fast‑growing alternative to Selenium for developers seeking reliable, cross‑browser automation.
FastAPI is a high‑performance, easy‑to‑learn Python web framework that lets you build robust APIs quickly, offering features like speed, concise code, automatic validation, and OpenAPI compatibility, with simple installation via pip and interactive documentation out of the box.
Learn how to delete elements from Python lists using three primary methods—remove for value-based removal, pop for index-based removal that returns the element, and del for deleting by index, range, or entire objects—complete with clear code examples and key usage notes.
This tutorial demonstrates how to build a simple aiohttp web server in Python, install required dependencies, retrieve random Mars photos from the NASA API, validate image size and color mode using Pillow, and serve the selected image directly to the browser.
This tutorial demonstrates how to load the Fashion MNIST dataset, explore and preprocess the images, build and compile a neural network with TensorFlow 2.x, train the model, evaluate its accuracy, and use the trained model to make predictions on clothing images, providing complete Python code examples throughout.
This tutorial introduces pyecharts, the Python wrapper for Baidu's Echarts library, explains how it generates a render.html file, and provides complete code examples for drawing various map visualizations—including basic maps, maps without labels, visual‑mapped maps, world maps, and regional maps—while also showing how to replace the dummy data with your own dataset.
This article compares six Python alternatives to traditional classes—plain classes, tuples, dictionaries, named tuples, attrs, dataclasses, and Pydantic—examining their syntax, validation capabilities, mutability, string representation, JSON (de)serialization, memory usage, and performance to help developers choose the most suitable data‑modeling approach.
This article compares PyInstaller and Nuitka for turning Python projects into standalone executables, highlighting Nuitka's dramatically smaller file size, faster build times, and better performance while explaining installation steps, command‑line options, and how to handle third‑party dependencies.
This article introduces a Python 2.7 script that automates the creation of iOS and Android app icons and screenshots, detailing required environments, usage commands, code explanation, and platform-specific considerations such as icon sizes, rounding for Android, and handling of portrait and landscape images.
The article demonstrates, with Python code, how applying class_weight—first using the default logistic regression, then the balanced option, and finally manually tuned weights via grid search—can raise the F1 score from 0 to about 0.16 on imbalanced data, and discusses further techniques such as feature engineering and threshold adjustment.
This article explains Python's conditional (ternary) operators, the use of if‑else expressions, logical and/or operators, recursion fundamentals including stack frames and recursion limits, and demonstrates list, set, and dictionary comprehensions with clear code examples.
This tutorial explains how to work with time‑series data in Python, covering dataset preparation, resampling techniques, Prophet forecasting, LSTM‑based prediction, and hierarchical clustering, while illustrating each step with visual examples and code snippets.
This article examines PEP 659, the Specializing Adaptive Interpreter proposed for CPython, explains its non‑JIT approach, details the warm‑up, adaptive, specializing and de‑optimisation phases, and walks through the actual source‑code implementation using the LOAD_GLOBAL instruction as a concrete example.
This guide introduces BertViz, an interactive visualization tool for transformer models such as BERT, GPT‑2 and T5, explains how to install it via pip along with required dependencies, and demonstrates head, model, and neuron view visualizations with code examples in Jupyter.
This tutorial shows how to fetch real‑time COVID‑19 data from Baidu, then use Python's pyecharts library to build province‑level maps and a dynamic timeline carousel, complete with code snippets and visual results.
This guide introduces Uber's open‑source Kepler.gl library for geospatial visualization in Jupyter notebooks, covering installation via pip, a simple map example, adding CSV or DataFrame data, customizing through the UI, retrieving configurations, and exporting the map to an HTML file.
This tutorial explains how to build a high‑performance Minesweeper bot in Python by capturing the game window, segmenting the board into 16×16 pixel cells, recognizing each cell’s state via OpenCV color analysis, and applying a simple logical algorithm to flag mines and click safe squares.
This article demonstrates how to use MediaPipe's Python API to extract 468 3D facial landmark points, configure detection parameters, and visualize the results with OpenCV, providing code examples and references to related research papers.
Flybirds is a BDD‑based, open‑source UI automation framework for mobile apps that provides a plug‑in architecture, CLI scaffolding, multilingual support, rich reporting, and seamless integration with CI pipelines, enabling developers to write natural‑language feature files and run them on Android and iOS devices.
This article explains a Python fan's question about extracting list items that contain specific keywords, presents two practical solutions—regular list indexing and using the operator module—and shows the resulting outputs with code snippets and screenshots.
This article walks through the complete implementation of an Alien Invasion game using Python's Pygame library, explaining the modular structure, key classes, functions, and game mechanics while providing full source code for each component.
This tutorial demonstrates how to create a lightweight Streamlit web app in Python that lets users select and run common NLP services—sentiment analysis, named‑entity recognition, and text summarization—by integrating libraries such as TextBlob, spaCy, and Gensim, with clear code examples and visual output.
This guide explains how to set up a Flask backend on Windows or CentOS, generate the required signatures for WeChat's unified order API, obtain a prepay_id, and call the mini‑program wx.requestPayment interface with the correct parameters for successful payment processing.
This article demonstrates how to use a Python script to perform a brute‑force attack against the DVWA login page by sending HTTP GET requests with custom headers and payloads, iterating over username and password dictionaries, recording response status, content length, and saving results to CSV or TXT files.
This tutorial explains how to create a Siri‑style conversational robot in Python by configuring the environment, describing the speech‑recognition and chatbot principles, and showing the implementation that uses Baidu speech recognition and the Turing chatbot API.
This tutorial walks you through collecting movie information from Maoyan using Python web‑scraping, storing the results in CSV, and then applying pandas, matplotlib, and WordCloud to analyze and visualize trends such as release years, genres, regions, durations, and ratings across China and the world.
The IEEE Spectrum 2021 programming language ranking shows Python retaining the top spot across overall and multiple sub‑rankings while Microsoft’s C# jumps from 23rd in 2020 to 6th in 2021, reflecting shifting industry demand and emerging trends.
This article explains Python's automatic garbage collection, covering reference counting, the mark-and-sweep algorithm, generational collection, default thresholds, and when to disable or adjust the collector for performance‑critical code.
This article presents a series of Python fundamentals tutorials, covering number reversal, adding class docstrings, setting file encoding, rotating strings, implementing a console progress bar, redirecting print output to files, and merging two lists with sorting, each accompanied by concise code examples.
This tutorial shows how to build a Python script that scrapes daily recipe articles from a website, formats the content, and automatically sends it via email using SMTP, providing a practical example of web scraping and email automation.
This article explains how Ctrip tackled the long, costly ML model development‑to‑deployment pipeline by adopting and extending MLflow for full lifecycle management, covering model persistence, tracking, serving, custom pyfunc models, Dockerized deployment, scaling, and performance monitoring.
Python decorators are a powerful syntactic feature that allow functions, methods, or classes to be wrapped and extended, enabling reusable code for logging, caching, authentication, and more, with detailed explanations of their underlying principles, manual implementations, syntax sugar, and usage with classes and built‑in decorators.
This guide explains how to use Selenium to automate testing of Electron desktop applications on macOS, covering ChromeDriver selection, Electron project creation, packaging into a DMG, writing Python test scripts, and controlling already‑running apps via remote debugging.
This article compares Python's while and for loops, shows benchmark results revealing that for loops run faster due to fewer Python‑level operations, and demonstrates that using built‑in functions like sum or applying a mathematical formula can make looping dramatically faster, often eliminating the loop entirely.
This guide explains how to set up an Enterprise WeChat alert application, obtain the required corpid and secret, and use a Python script with WeChat Work APIs to send real‑time notification messages to all users on mobile devices.
This tutorial explains how to generate donut (ring) charts using Python's Matplotlib library, covering basic pie‑to‑donut conversion, custom colors, label positioning, background styling, annotation markers, and multi‑level donut visualizations with complete code examples.
This article traces the historical origins of coroutines from early COBOL compiler challenges, explains their cooperative execution model, compares them with thread scheduling, and reviews how modern languages like Python, C++ and Go implement coroutine support to improve IO‑bound performance.
This guide introduces Kepler.gl, Uber’s open‑source geospatial visualization library, shows how to install it in a Jupyter notebook, demonstrates creating maps from CSV, GeoJSON or DataFrame sources, customizing charts, extracting configurations, and exporting the result to an HTML file for easy sharing.
This article presents a complete Python implementation of a guess‑the‑word (Hangman) game, explaining its mechanics, providing detailed source code, and offering a simplified version for readers to learn basic programming concepts and game logic.
This article explains why third‑party payment platforms like Alipay are used, describes Alipay's payment flow, outlines the configuration steps to obtain APPID and keys, and provides a complete Python implementation for integrating Alipay into backend services.
This tutorial walks you through building a Python scraper that extracts video titles and URLs from Pear Video, then downloads the videos to your computer using single‑threaded requests, a thread‑pool, and asynchronous asyncio techniques, complete with full code examples.
This article introduces Python process concepts, including process creation with the multiprocessing module, process states, comparison with threads, and practical examples of creating, managing, and communicating with processes, followed by a comprehensive overview of Python list types, their structure, operations, and common usage patterns.
This article walks through creating a Python arithmetic interpreter with the PLY library, covering token definitions, lexer rules, BNF grammar, parser implementation, operator precedence, and a runnable REPL, providing complete code and explanations for each step.
This article presents a collection of Python pitfalls such as random sampling, lambda binding, copying, equality vs identity, and type checks, followed by a concise guide for C/C++ programmers transitioning to Python, and showcases useful tools like CSV handling, itertools, collections, and performance‑debugging techniques.
This tutorial walks through using Selenium to log into Alibaba International, scrape supplier phone numbers and related details across multiple pages, save the data to CSV, download product images, and finally embed those images into an Excel workbook for easy reference.
This article explains the differences between synchronous and asynchronous execution, explores various I/O programming models—including blocking, non‑blocking, multiplexed, signal‑driven, and asynchronous I/O—and describes how event‑driven architectures apply these concepts in server‑side development.
This article demonstrates how to build a reproducible fraud‑detection workflow using scikit‑learn's Pipeline class, comparing a manual script with a pipeline‑based approach on the IEEE‑CIS Kaggle dataset and showing the benefits of modular, repeatable ML code.
This article provides a Python script that uses the itchat and schedule libraries to automatically log in to WeChat, select friends with remarks, and send randomly chosen New Year greeting messages to each contact at a specified date and time.
This article explains a streamlined method to extract and download watermark‑free Douyin short videos by inspecting network requests, locating hidden video URLs, and using a concise Python script with the jsonpath library, highlighting its advantages and remaining limitations.
This tutorial explains how to use the pyecharts library—a Python wrapper for Baidu's ECharts—to generate a wide range of interactive visualizations, including bar, line, pie, map, heatmap, scatter, 3D, and combined charts, with complete code examples and rendering instructions for Jupyter notebooks.
Discover the essential Python tools, IDEs, and libraries for 2022—including PyCharm, Jupyter Notebook, Keras, pip, Scikit‑Learn, Sphinx, Selenium, and Beautiful Soup—to enhance productivity, streamline coding, and empower developers across web, data science, and AI projects.
This tutorial shows how to use Python, ADB, and OpenCV to automate the mobile game Princess Connect, covering environment setup, device communication, screen capture, image matching, OCR, and script snippets for clicking, typing, and switching accounts.
This article surveys the ten most prominent Python libraries for machine learning released in 2021, highlighting both internationally popular and high‑performing Chinese open‑source projects, and explains their main features, performance advantages, and where to find them on GitHub.
This article dissects Optuna’s internal design by building three miniature versions (Minituna v1‑v3) that illustrate its main components, storage layer, sampling APIs, pruning mechanisms, and joint‑sampling concepts, while comparing them with Optuna’s full implementation.
This article explains how to use Python's socket module together with I/O multiplexing techniques such as select, poll, epoll, and the high‑level selectors module to build non‑blocking, scalable network servers that can handle multiple client connections concurrently.
This comprehensive tutorial walks you through installing Selenium and ChromeDriver, initializing browsers (including headless mode), navigating pages, locating elements using various strategies, interacting with forms, handling multiple windows, performing mouse and keyboard actions, implementing explicit and implicit waits, executing JavaScript, and managing cookies for robust web automation and scraping.
This comprehensive guide explains QR code fundamentals—including their structure, versions, error‑correction levels, and data capacity—details the full encoding pipeline from requirement analysis to matrix construction, outlines the decoding steps, explores commercial use cases, and provides practical Python examples using python‑qrcode, Amazing‑QR, and Zxing.
This article reviews the ten most notable Python libraries for machine learning in 2021, covering tools for handling nested data, notebook versioning, lightweight model demos, data management, multimodal augmentation, model monitoring, object detection, fast inference, time‑series forecasting, and neural search.
This tutorial explains pandas' four primary data merging functions—concat, merge, append, and join—detailing their usage, key parameters, and practical code examples to help you efficiently combine DataFrames in various scenarios.
This article provides an in‑depth overview of QR codes, covering their definition, types, structural features, versioning, error‑correction levels, encoding and decoding processes, commercial uses, design considerations, and practical Python examples for generating and reading QR codes.
This guide explains how to collect images from Zhihu’s “美女” topic using Python’s Requests and lxml, filter them with Baidu’s AipFace SDK based on face presence, gender, authenticity, and beauty score, and store the qualified pictures locally.
This article walks through building an image‑captioning system using a TensorFlow 2.0 encoder‑decoder with Bahdanau attention, covering dataset preparation, feature extraction with InceptionV3, model architecture, training with teacher forcing, and inference on the Flickr8K dataset.
This tutorial demonstrates how NumPy can be used to generate and display grayscale, color, gradient, and colormap‑enhanced images in Python with just a few lines of code, covering module import, array creation, image conversion, and advanced color mapping techniques.
This article presents a thorough collection of frequently used pandas commands for importing data, preprocessing, cleaning, transforming, analyzing, and visualizing datasets, complete with code examples and explanations to help Python developers perform efficient data analysis tasks.
This article introduces Python's collections module, explaining the purpose and usage of its specialized container types—including Counter, defaultdict, OrderedDict, namedtuple, deque, and ChainMap—along with their most common methods and practical code examples.
This guide demonstrates how to use a second‑hand housing dataset to create various reports—frequency tables, cross‑tables, and summary statistics—and visualise them with bar and stacked bar charts using Python's pandas library.
This article presents practical Python best‑practice guidelines, including avoiding hard‑coded numeric literals by using enums, limiting raw string manipulation in favor of object‑oriented query building, keeping literal expressions readable, and applying useful tips such as treating booleans as numbers, handling long strings, using infinity constants, and understanding thread‑safety and string‑concatenation performance.
This article demonstrates how to use Python and pandas to automatically add a column containing each workbook or sheet name to every sheet in multiple Excel files and then merge all the data into a single file, dramatically reducing manual effort.
This article introduces CatBoost, outlines its key advantages such as automatic handling of categorical features, symmetric trees, and feature combination, and provides a step‑by‑step Python tutorial—including data preparation, model training, visualization, and feature importance analysis—using a CTR prediction dataset.
This tutorial explains how to solve the LeetCode "Lucky Numbers in a List" problem in Python by extracting unique elements, counting their occurrences with map and count, pairing them with zip, filtering with lambda, sorting the result, and finally compressing the whole logic into a single expressive line of code.
This article explains the theory behind variational autoencoders, details their KL‑divergence loss, provides a complete TensorFlow implementation, and demonstrates reconstruction, latent‑space visualization, and novel image generation through sampling and interpolation.
This article explains how to use TensorFlow’s tf.data API to construct high‑performance, flexible data pipelines—from loading images or tensors, applying transformations and data augmentation, to batching, shuffling, caching, prefetching, and feeding the pipeline directly into model.fit for training.
This article introduces the Python libraries memory_profiler and Pytorch‑Memory‑Utils, demonstrates how to measure line‑by‑line CPU memory consumption and GPU memory usage in notebooks and scripts, and explains the additional overhead introduced by PyTorch during model loading.
This tutorial walks through building a TensorFlow 2.0 autoencoder from scratch, preparing the FashionDB dataset, visualizing raw images, projecting them into PCA and t‑SNE spaces, constructing encoder and decoder layers, training the model, and visualizing the resulting latent space to reveal image clusters.
This guide walks you through analyzing a target e‑commerce site, using Selenium to automate searches, extracting product details across multiple pages, handling pagination, parsing HTML with PyQuery, and persisting the collected data into a MySQL database with robust error handling.
This article explains the fundamentals of random forest regression, describes why it outperforms single decision trees for nonlinear or noisy data, defines bootstrapping and bagging, and provides a step‑by‑step Python example using NumPy, Pandas, and Scikit‑Learn’s RandomForestRegressor with data loading, preprocessing, model training, prediction, and evaluation via MSE and R².
The article explains how to keep external API contracts unchanged while replacing a data source by importing all microservice endpoints into Postman, writing pre‑request and test scripts, and optionally using a Python Flask service with deepdiff to automatically compare old and new JSON responses.
This guide provides a comprehensive Vim setup for Python development, covering .vimrc basics, window splitting, code folding, one‑key execution, plugin management with Vundle, auto‑completion tools, syntax checking, color schemes, file navigation, and useful shortcuts.
This article explains how to perform Chinese part-of-speech tagging using the jieba.posseg library in Python, including loading stop words, extracting article content via Newspaper3k, applying precise mode segmentation, filtering, and presenting results in a pandas DataFrame.
The article explains how concurrent updates can cause data loss in high‑traffic scenarios and demonstrates using optimistic and pessimistic locking in PostgreSQL with Python, including EFCore‑style token checks and a retry mechanism via the tenacity library.
This tutorial walks you through creating a fully interactive sales‑data dashboard using Python's Pandas for data handling, Plotly for visualizations, and Streamlit to assemble a sleek web interface, covering data import, sidebar filters, key metrics, charts, and deployment steps.
The article explains why monitoring production ML models is essential to detect data and target drift, describes the open‑source Evidently AI library and its statistical tests, and demonstrates its use on a weather‑forecast example and a plant‑seedling image classification case, including dashboards, code snippets, and visual analysis of drift impact.
This tutorial introduces the Python pyecharts library for creating various map visualizations with Echarts, explains how the code generates HTML files, and provides complete example functions for basic, label‑less, continuous and piecewise visual maps, world and regional maps, plus guidance for using real data.
This tutorial introduces Plotly for Python, showing how to install it, configure offline mode in Jupyter, and create line, scatter, and bar charts with complete code examples and explanations of each visualization type.
This tutorial shows how to crawl the China Meteorological Administration website to obtain today's weather and a six‑day forecast, handle Chinese encoding, extract data with XPath, and save the results into a CSV file using Python's requests and lxml libraries.
This tutorial demonstrates two Python Turtle methods for drawing a festive Christmas tree, providing step‑by‑step code examples, explanations of the drawing logic, and visual results to help beginners learn basic graphics programming in Python.
This article explains practical techniques for improving NLP model accuracy on massive corpora, covering challenges of multi‑field text, word‑embedding choices, a fasttext‑based regression demo with book‑review data, feature engineering tricks, and a comparison with tf‑idf + LASSO.
This article reviews the lack of a standard Python project management tool, compares four popular solutions—CookieCutter, PyScaffold, PyBuilder, and Poetry—detailing their installation, generated directory structures, and build commands, while showing how to use Make, tox, and other utilities for testing and packaging.
This article introduces the concept of signals, explains the features of the Python Blinker library, demonstrates various usage patterns—including named, anonymous, multicast, and topic‑based signals—as well as how to integrate Blinker with Flask for custom and built‑in Flask signals, highlighting advantages and limitations.
This article explains the fundamental differences between threads and processes, the role of Python's Global Interpreter Lock (GIL), and how to use the multiprocessing package—including Process, Pool, Queue, Pipe, and synchronization primitives—as well as an overview of concurrent.futures for high‑level concurrent programming in Python.
This tutorial shows how to extract Kindle eBooks, convert them to HTML, clean the markup with BeautifulSoup, and batch‑translate the content line‑by‑line using DeepL API Pro, producing a bilingual eBook with minimal manual effort.
