This article demonstrates how to use Python's pdfminer library and regular expressions to automatically extract unified social credit codes from multiple PDF files, providing step‑by‑step code for single‑file extraction and scalable batch processing.
This article explains how to determine the smallest positive integer absent from an unsorted array using an in‑place linear‑time algorithm, covering problem analysis, core insight, step‑by‑step implementation details, Python code, and complexity evaluation.
Discover five obscure yet powerful Python standard‑library modules—difflib, sched, binaascii, tty, and weakref—along with their key functions, usage examples, and practical tips, enabling developers of any skill level to expand their toolkit and streamline tasks such as string comparison, scheduling, encoding, terminal handling, and memory management.
This article introduces the powerful open‑source Python visualization library Plotly, showing how a single line of code can generate interactive, publication‑ready charts—from basic bar and box plots to advanced 3‑D and heatmap visualizations—while integrating seamlessly with pandas and Jupyter notebooks.
This tutorial demonstrates how to use Python's pandas library to read an Excel file, identify the first and last non‑zero entries in each row, and output their values and column positions, complete with code examples and performance tips.
This article introduces the classic Traveling Salesman Problem, explains its mathematical formulation, discusses its NP‑hard nature, and demonstrates a practical solution in Python by generating random city coordinates, building a distance graph with NetworkX, applying an approximation algorithm, and visualizing the resulting tour.
This article introduces the tsfresh Python package, explains why traditional machine‑learning models struggle with time‑series data, and demonstrates how tsfresh can automatically generate and select hundreds of useful features—including statistical, nonlinear, and signal‑processing metrics—while supporting big‑data frameworks such as Dask and Spark.
This tutorial explains how to convert single‑variable and multivariate time‑series data into supervised learning formats using pandas shift() and a custom series_to_supervised() function, covering one‑step, multi‑step, and sequence forecasting examples with complete Python code.
This article walks through a real‑world Python encoding problem where Unicode \u sequences cause garbled Chinese text, demonstrates the faulty code, and provides a corrected solution using json.dumps with ensure_ascii=False and GBK encoding to produce proper Base64 output.
This article presents 20 practical Python one‑liner code snippets, covering loops, conditionals, data structures, functions, recursion, file handling, classes, and more, each with concise explanations and ready‑to‑run examples to help developers write cleaner, more efficient code.
This article explores effective strategies for visualizing one‑ to six‑dimensional data using Python libraries such as pandas, matplotlib, and seaborn, demonstrating each technique with the UCI Wine Quality dataset and providing code snippets for histograms, heatmaps, pair plots, 3‑D scatter plots, bubble charts, and more.
This article walks through a real‑world pandas example that filters an Excel sheet by department and ID length, shows the complete Python code, displays the resulting output, and explains how the solution resolved a reader's data‑processing question.
This article introduces Numba, a Python Just-in-time compiler, explains why it is advantageous over alternatives, demonstrates how to apply its decorators such as @jit, @njit, @vectorize, and @cuda for CPU and GPU acceleration, and provides practical code examples and tips for optimal performance.
This article demonstrates how to convert a Python list into a dictionary, sort the resulting mapping by numeric weight values (handling both 't' and 'kg' units), and extends the technique to nested lists and back‑to‑list conversions, providing multiple code solutions and explanations.
This guide reviews seven popular Python IDEs—Eric, Geany, PyCharm, Spyder, Thonny, Visual Studio Code, and Bluefish—highlighting their key features, platform support, and suitability for beginners, scientific work, or professional development, helping readers pick the right tool for their projects.
Microsoft assembled a six‑engineer team, including Guido van Rossum, to accelerate CPython according to a multi‑phase plan, resulting in Python 3.11 delivering 10‑60% speed improvements and showcasing how coordinated open‑source effort can overcome Python’s historic performance limitations.
This tutorial walks you through installing Matplotlib, importing it alongside NumPy, and creating a variety of common charts—including line, scatter, bar, histogram, pie, and box plots—detailing the required code, parameters, and customization options for each visualization.
Learn how to perform one-dimensional interpolation in Python using SciPy's interp1d function, explore various interpolation methods such as linear, nearest, quadratic, and cubic, and visualize the results with Matplotlib to compare their fitting behaviors on sample data.
This guide explains why Python is vital for mathematical modeling, outlines its key features, and provides a step‑by‑step tutorial for installing Anaconda and launching Jupyter Notebook, enabling beginners to write and run their first Python code quickly.
Python outshines Matlab in cost, openness, community support, cross‑platform flexibility, extensive libraries, especially for machine learning, and offers a richer ecosystem of IDEs and flexible coding styles, making it a superior, free alternative for modern scientific and engineering modeling tasks.
This article demonstrates how to move all zeros to the end of a Python list using two concise code snippets, explains the reasoning behind each approach, and provides a clear example for readers to apply the technique in their own projects.
This article explains the concept of one‑factor (one‑way) ANOVA, walks through a lamp‑life example with four manufacturing processes, derives the within‑ and between‑group sum‑of‑squares formulas, and shows how to execute the test in Python using statsmodels.
The November TIOBE index reveals that Python, C, Java and C++ continue strong growth while Rust gains momentum, and provides detailed rankings from the top 10 to the top 100, offering insights for developers choosing languages and tracking industry popularity trends.
auto-py-to-exe is a GUI front‑end for PyInstaller that simplifies packaging Python scripts into standalone executables; this guide explains installation, key configuration options such as script location, one‑file mode, console window, icon, additional files, and demonstrates a complete example by packaging a simple calculator program.
The November TIOBE ranking reveals Python, C, Java, and C++ maintaining top positions with steady growth, while Rust holding 20th place, and languages like Ruby, Swift, and Visual Basic declining, and Fortran falling out of the top 20, highlighting shifting trends in programming language popularity.
The article examines how viral “hacker” videos misuse simple batch commands, then explains three practical Python methods—using os.system, Selenium WebDriver, and the built‑in webbrowser module—to reliably open a URL, comparing their capabilities and providing sample code.
This article walks through a pandas data‑processing problem, showing how to concatenate province, city, and district fields using vectorized string operations with clear code examples and resulting output to help readers apply the technique.
An overview of Python’s primary interpreter implementations—including CPython, Jython, IronPython, PyPy, and Cython—highlights their origins, execution models, compatibility, performance characteristics, and unique advantages, helping developers choose the right runtime for their specific needs.
This article introduces ten useful pip techniques—including installation, upgrading, version-specific installs, uninstalling, inspecting package details, checking for conflicts, using domestic mirrors, downloading without installing, and batch installing from requirements files—to help Python developers manage packages more effectively.
This article explains why Python developers should avoid explicit for loops, demonstrating how list comprehensions, generator expressions, map/reduce, itertools, and function extraction can produce more concise, readable, and maintainable code while reducing indentation and improving structure.
This curated list compiles recent articles on evaluation and optimization models, covering Data Envelopment Analysis, ANP, VIKOR, various grey analysis methods, competition ranking techniques, case studies, linear and integer programming, and a range of optimization algorithms with Python examples.
This article walks through a real‑world Pandas question, showing how to apply the map function, replace method, and str.contains() to transform column values, with step‑by‑step code examples and explanations.
This article explains how to identify, order‑select, estimate parameters, validate, and forecast ARMA time‑series models, covering the Akaike Information Criterion, various estimation techniques, and diagnostic tests such as the Ljung‑Box test, with practical Python implementation guidance.
Grey system theory provides a framework for analyzing, modeling, and predicting systems with incomplete information, using minimal data and accumulation techniques to improve forecast accuracy, as demonstrated through a sales data case study with Python implementation.
This article explains how a Python web‑scraping request failed due to extra nested parameters, shows the step‑by‑step debugging process, and demonstrates that deleting trivial 0/1 values can restore the request, offering a practical tip for similar crawling issues.
This article presents a curated collection of over 100 frequently used Python functions across twelve categories, providing concise explanations and ready-to-run code snippets to help beginners quickly memorize and apply essential built‑in operations, from basic I/O to decorators and regular expressions.
This article explains ULID (Universally Unique Lexicographically Sortable Identifier), compares it with UUID, outlines its technical specifications, lists its advantages and typical application scenarios, and provides detailed Python examples for generating and manipulating ULIDs.
This article presents a comprehensive collection of Python coding principles, idioms, library usage guidelines, design patterns, internal mechanisms, tooling recommendations, and performance profiling techniques to help developers write clean, efficient, and maintainable code.
This article presents a step‑by‑step tutorial for building a console‑based student information management system in Python, covering system design, functional requirements, detailed function implementations for adding, deleting, modifying, searching, and displaying records, and provides the full source code with execution screenshots.
Microsoft’s Faster CPython team, formed with Guido van Rossum and six engineers, aims to accelerate CPython by up to five‑fold through staged improvements, already delivering 10‑60% speed gains in Python 3.11 while emphasizing collaborative development and community value.
This article explains the method of moments for estimating population parameters, outlines its step‑by‑step derivation, and demonstrates a Python implementation that estimates a basketball player's shooting odds from binary outcome data using.
The article examines the open‑source Perspective library—originating from J.P. Morgan’s FinOS—and its multi‑language architecture that combines C++/WASM, Rust, JavaScript, and Python to deliver high‑performance, framework‑free data visualizations for real‑time financial analytics, highlighting its use of Apache Arrow, Web Components, and Jupyter integration.
This article introduces the Scrapeasy Python library, showing how a single line of code can scrape websites, retrieve links, images, PDFs, and videos, and download them to local storage, making web data extraction fast and straightforward.
This article outlines frequent Python pitfalls in data‑science workflows—such as neglecting virtual environments, overusing notebooks, hard‑coding absolute paths, ignoring warnings, avoiding list comprehensions, skipping type hints, writing unreadable pandas chains, not following PEP guidelines, and not using coding assistance tools—and provides practical solutions to improve code quality and productivity.
This article explains how to use pandas' sort_values and drop_duplicates functions to eliminate duplicate entries in a DataFrame, preserving the row with the maximum age (or other criteria), and provides multiple code examples and parameter details for effective data cleaning.
This article categorizes machine learning algorithms into supervised, unsupervised, and reinforcement learning, then details ten common algorithms—including linear regression, logistic regression, decision trees, SVM, Naive Bayes, K‑NN, K‑means, random forest, and dimensionality reduction—accompanied by clear Python code examples and illustrative diagrams.
The paper analyzes why Alibaba Mama’s intelligent creative video service suffers low GPU utilization—due to Python GIL blocking, lack of kernel fusion, and serialized CUDA streams—and details service‑level changes (separate CPU/GPU processes, shared‑memory queues, priority scheduling) and operator‑level kernel‑fusion techniques (channels‑last layouts, custom pooling, TensorRT conversion) that raise utilization from ~30 % to near 100 % and boost throughput by 75 %.
Python 3.11 has been officially released, introducing a range of new language features such as fine‑grained error locations, exception groups, built‑in TOML parsing, asyncio task groups, enhanced regular expressions, significant performance gains, and numerous typing improvements like Self types and variadic generics.
This article introduces genetic algorithms, explains their biological inspiration, details each step of the algorithm, demonstrates solving the knapsack problem, and provides a complete Python implementation using the TPOT library for feature selection and regression on the Big Mart Sales dataset.
This article explains ULID—a lexicographically sortable, 128‑bit identifier combining timestamp and randomness—detailing its advantages over UUID, specifications, binary layout, common use cases, and provides Python code examples for generating and manipulating ULIDs.
This article introduces key Python libraries—including NumPy, Pandas, Matplotlib, Statsmodels, Scikit‑Learn, and Keras—detailing their core functionalities for data handling, statistical modeling, and machine‑learning tasks, and provides concise usage insights for each package.
This article introduces major economic and financial data analysis tools—including Python with its scientific libraries, R, Stata, Matlab, EViews, SAS, and SPSS—detailing their origins, key features, typical applications, and where to find more information.
This article explains the Butterfly Optimization Algorithm (BOA), covering its biological inspiration, core concepts such as scent-based global and local search, key parameters, iteration process, and provides a complete Python implementation with example usage and visualisation of convergence.
This comprehensive guide walks you through Python's file handling—from opening and closing files, using absolute and relative paths, and various read/write modes, to copying files, working with CSV, in‑memory streams, and serializing data with JSON and pickle—complete with clear code examples.
This tutorial walks through creating a simple “Hello World” Apache Airflow DAG by setting up the Python file, importing modules, defining the DAG object, adding a PythonOperator task, writing the callable function, and running the DAG with Airflow’s webserver and scheduler.
This article evaluates popular Docker base images for Python applications—Ubuntu, CentOS, Debian, Amazon Linux 2, official Python images, and Alpine—by outlining stability, security updates, dependency freshness, library availability, Python version, image size, and build time, ultimately recommending Amazon Linux 2 and Debian/Ubuntu over Alpine.
This article provides a comprehensive overview of PyMuPDF, the Python binding for MuPDF, covering its core features, supported document formats, installation methods, and detailed code examples for opening, rendering, extracting, and manipulating PDF and other documents.
This article shows how to handle irregular JSON strings exported from a database by using a concise Pandas lambda expression that replaces empty actor lists with a default ID, providing a quick data‑cleaning solution for Python developers.
Matplotlib does not render Chinese text by default, showing squares instead, but you can fix this by setting a Chinese font directly in code, adjusting rcParams, or editing the config file; the example demonstrates loading a local font file and applying it to axis labels and titles.
This article provides a concise introduction to a wide range of popular Python libraries for artificial intelligence and data science, such as NumPy, OpenCV, scikit-image, Pillow, Scikit-learn, TensorFlow, PyTorch, and many others, accompanied by practical code snippets and performance comparisons.
ULID (Universally Unique Lexicographically Sortable Identifier) combines a millisecond-precision timestamp with high-entropy randomness to produce 128‑bit, URL‑safe, lexicographically sortable IDs that avoid UUID’s collision risks, offering advantages such as monotonic ordering, compact Base32 encoding, and suitability for distributed database primary keys.
This article demonstrates how to read a CSV file with Pandas, use regular expressions to extract tag names from a string column, and transform the results into separate rows using the explode function, while also presenting alternative community‑sourced approaches.
This tutorial walks through building a Python script that scans nearby Wi‑Fi networks and performs brute‑force password cracking, first using a command‑line approach and then enhancing it with a Tkinter graphical interface, while also discussing limitations and possible multithreading improvements.
The article explains the Artificial Bee Colony (ABC) algorithm, its biological inspiration, core components, and detailed steps—including employed, onlooker, and scout bee phases—followed by a complete Python implementation applied to a pressure‑vessel design optimization problem.
This article introduces fifteen of the most useful Python packages—ranging from data visualization and game development to HTTP requests and home automation—explaining their core features, typical use cases, and why they are valuable tools for Python programmers.
gRPC is a high‑performance, open‑source RPC framework built on HTTP/2 and Protocol Buffers, and this guide explains its core concepts, communication patterns, protobuf usage, and provides step‑by‑step Python examples for creating, compiling, and running client‑server services, plus deployment tips.
A simple benchmark using Bubble Sort on a 10,000‑element random array compares several popular programming languages, revealing that Python performs the worst while Go (Golang) delivers a striking performance advantage even over Node.js.
Discover ten practical Python automation scripts—from HTML parsing and QR code scanning to screenshot capture, PDF manipulation, StackOverflow querying, mobile device control, hardware monitoring, Instagram uploading, and video watermarking—that streamline repetitive tasks and boost productivity across diverse workflows.
This article explains ULID (Universally Unique Lexicographically Sortable Identifier) as a time‑based, collision‑resistant alternative to UUID, outlines its specifications, highlights its advantages, and provides practical Python examples for generating and manipulating ULIDs.
This article introduces the Seagull Optimization Algorithm (SOA), explains how it mimics seagull migration and attack behaviors to solve optimization problems, details its migration and prey‑attack phases, outlines the step‑by‑step workflow, and provides a full Python implementation with code examples.
This article explains what ULID is, why it can be a better alternative to UUID, outlines its key characteristics and specification, and provides practical Python examples for generating and handling ULIDs in distributed backend systems.
This article introduces the GoPUP Python library, explains how to install it, demonstrates retrieving Weibo index data with code examples, shows how to visualize the results using Pandas, Jupyter and Matplotlib, and lists the wide range of public data APIs the library supports.
This guide demonstrates how to use the python‑jenkins library to interact with Jenkins via its API, covering installation, configuration, job creation, copying, deletion, node management, plugin handling, and scripting examples for automating CI/CD pipelines.
This article introduces the powerful open‑source Plotly library for Python, showing how a single line of code can create interactive charts such as bar, box, scatter, time‑series, and advanced visualizations, while also covering installation, theme customization, and integration with Jupyter Notebook and Plotly Chart Studio.
This article walks through using Python's re module to remove specific patterns—including multiline sections—from a text file, explains the role of the re.S flag, and provides a complete, ready‑to‑run code example for batch text cleaning.
This article introduces gRPC, explains its HTTP/2 and Protocol Buffers foundations, walks through writing and compiling .proto files, provides complete Python server and client examples, and shows how to quickly set up the environment and integrate gRPC into microservice and Kubernetes deployments.
This tutorial explains how to build, train, and evaluate a multivariate LSTM model using Keras for hourly air‑pollution forecasting, covering data preparation, model design, prediction, and inverse scaling back to original units.
This article introduces the ten most important programming languages for cybersecurity, explains why each language matters, and shows how mastering them can boost your ability to protect systems, detect threats, and build secure applications in an increasingly digital world.
This tutorial explains the fundamentals of Serverless architecture, outlines its BaaS and FaaS components, presents three typical use cases, and provides a step‑by‑step guide—including Python code and deployment instructions—to create an automated daily email service on Alibaba Cloud Serverless.
This tutorial walks through OCR fundamentals using pytesseract and chineseocr_lite, demonstrates how to push notifications via Server酱, provides reusable Python image‑processing utilities, and shows how to dump and parse Android UI XML for automated interactions.
This article shares the author’s personal transition from manual functional testing to automation testing, outlines a detailed learning roadmap covering Python, Selenium, Robot Framework, API testing, CI/CD, and provides curated resources and QR‑code links for aspiring automation engineers.
This article presents a concise, organized collection of over 100 commonly used Python functions across twelve categories—including basic I/O, control flow, data structures, modules, and file operations—providing code examples and explanations to help beginners quickly memorize and apply them.
This article demonstrates how to use Python libraries such as itchat, jieba, matplotlib, snownlp, and TencentYoutuyun to retrieve WeChat friend information and perform data analysis on gender distribution, avatar usage, signature sentiment, and geographic location, presenting results with charts and word clouds.
This article walks through several Python techniques—including list splitting, combined list‑processing methods, and regular‑expression extraction—to efficiently parse mixed text and URL strings, illustrating each approach with code snippets and results.
This article explains the structure and operation of recurrent neural networks (RNNs), their limitations, how long short‑term memory (LSTM) networks overcome these issues with gated mechanisms, and provides a complete Python implementation for time‑series airline passenger forecasting.
This article provides a comprehensive Python tutorial covering numeric operations, string manipulation, function definitions, data structures, class creation, object introspection, and useful built‑in tools, complete with clear code examples for each concept.
This article explains the detailed‑balance condition for Markov chains, shows why finding a transition matrix for a given stationary distribution is hard, and demonstrates how Metropolis‑Hastings modifies MCMC to achieve higher acceptance rates with a concrete Python example.
This article explains several Python/Pandas techniques for dropping columns that contain only missing or zero values, presenting three code snippets and a brief discussion of their usage for data cleaning.
This article explains the basic definition of Markov chains, illustrates a stock‑market example with transition matrices, demonstrates convergence through Python simulations, and shows how the steady‑state distribution enables sampling for Monte Carlo methods.
Pyston 2.3.5, a fork of CPython 3.8 maintained by Anaconda, delivers double‑digit performance gains for network‑service workloads, offers two‑fold speedups on micro‑benchmarks like chaos.py and nbody.py, and remains highly compatible with the full CPython feature set and C API.
This article explains Python decorators by first reviewing functions as first‑class objects, then showing how to create simple timing decorators, extend them with *args/**kwargs, and build higher‑order decorators like a repeat‑n‑times wrapper, complete with practical code examples.
This article introduces LightGBM, a high‑performance gradient boosting framework, explains its advantages over XGBoost, and provides step‑by‑step Python code for building classification and regression models on the Iris dataset, including model training, evaluation, and visualizing feature importance and tree structures.
This guide walks through a comprehensive set of feature‑selection strategies—from removing unused or missing columns to handling multicollinearity, low‑variance features, and using PCA—complete with Python code examples and visualizations to help you build leaner, more interpretable machine‑learning models.
This article walks through a real‑world Pandas problem where a column of tag strings is parsed, extracted, and transformed into dummy variables with get_dummies, providing clear code snippets and explanations to help Python developers solve similar data‑processing challenges.
This article explains the teaching-learning based optimization (TLBO) algorithm, its teaching and learning phases, provides a complete Python implementation, and demonstrates its performance on minimizing a simple quadratic function with visualized convergence.
This article presents practical Python performance optimization techniques—including using local variables, minimizing function calls, avoiding repeated loop calculations, preferring direct imports, leveraging dictionaries for lookups, iterating over items, employing generator expressions, and pre‑compiling code—to improve speed and reduce resource consumption.
This article provides a comprehensive overview of PyMuPDF, the Python binding for the lightweight MuPDF library, covering its purpose, supported document formats, key features such as rendering, text extraction and PDF manipulation, installation methods, and detailed code examples for common operations.
This article explains the core concepts of XGBoost as a boosting tree algorithm, describes how it builds ensembles of decision trees to predict outcomes, and provides complete Python implementations for classification and regression using the Scikit-learn interface, along with visualizations of trees and feature importance.
This article provides a detailed introduction to Android Debug Bridge (ADB), covering its concepts, architecture, environment setup, common issues, command syntax, and practical automation examples using both shell scripts and Python, enabling developers to control devices, perform UI actions, and schedule tasks efficiently.
This article provides a detailed tutorial on Celery, covering its architecture, installation, task structures, basic and advanced usage, integration with Django, and code examples for creating workers, scheduling tasks, and retrieving results, enabling developers to implement robust asynchronous processing in Python applications.
