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5000 articles · Page 36 of 50
Model Perspective
Model Perspective
Aug 29, 2022 · Artificial Intelligence

Solving the Rastrigin Function with a Genetic Algorithm in Python

This article introduces the multimodal Rastrigin function, explains its challenges for optimization, and demonstrates a complete Python implementation of a genetic algorithm—including encoding, selection, crossover, mutation, and decoding—to locate the function’s global minimum, with visual results and performance analysis.

OptimizationPythonRastrigin function
0 likes · 6 min read
Solving the Rastrigin Function with a Genetic Algorithm in Python
Python Programming Learning Circle
Python Programming Learning Circle
Aug 29, 2022 · Fundamentals

Introduction to PyMuPDF: Features, Installation, and Usage Guide

This article provides a comprehensive overview of PyMuPDF, the Python binding for the lightweight MuPDF library, detailing its core features, installation steps, and practical code examples for opening documents, extracting metadata, rendering pages, manipulating PDFs, and performing advanced operations such as merging, splitting, and saving files.

DocumentProcessingPDFManipulationPyMuPDF
0 likes · 12 min read
Introduction to PyMuPDF: Features, Installation, and Usage Guide
Model Perspective
Model Perspective
Aug 28, 2022 · Artificial Intelligence

Visualizing and Implementing the Rastrigin Function in Python

The Rastrigin function, a challenging multimodal benchmark used to test optimization algorithms, is explained with its mathematical form, visual plot, and a complete Python implementation for generating and visualizing its landscape.

PythonRastrigin functionalgorithm testing
0 likes · 2 min read
Visualizing and Implementing the Rastrigin Function in Python
21CTO
21CTO
Aug 28, 2022 · Backend Development

Ruby vs Python for Web Development: Which Framework Wins?

This article compares Ruby and Python as high‑level, object‑oriented languages for web development, outlining their histories, core features, popular frameworks, performance statistics, and suitability for different project needs to help developers choose the right tool.

BackendPythonRuby
0 likes · 8 min read
Ruby vs Python for Web Development: Which Framework Wins?
Python Programming Learning Circle
Python Programming Learning Circle
Aug 26, 2022 · Fundamentals

Commonly Used Python Functions and Examples for Beginners

This article presents a concise collection of over one hundred frequently used Python functions across twelve categories—basic I/O, control flow, data structures, string formatting, dictionaries, custom functions, threading, modules, file handling, decorators, and regular expressions—each illustrated with clear code examples to help beginners quickly memorize and apply them.

Code ExamplesFunctionsPython
0 likes · 8 min read
Commonly Used Python Functions and Examples for Beginners
360 Quality & Efficiency
360 Quality & Efficiency
Aug 26, 2022 · Backend Development

Using ReportLab Table to Create Custom PDF Greeting Cards in Python

This article demonstrates how to use Python's ReportLab library, specifically the Table and TableStyle classes, to programmatically generate PDF documents such as custom greeting cards, covering basic Table usage, styling options, and step-by-step code examples for creating and customizing PDF layouts.

Greeting CardPDFPDF generation
0 likes · 8 min read
Using ReportLab Table to Create Custom PDF Greeting Cards in Python
Python Programming Learning Circle
Python Programming Learning Circle
Aug 25, 2022 · Fundamentals

Plotly Basics: Offline Plotting, Traces, Layout, and Customization in Jupyter Notebook

This article introduces Plotly's offline plotting in Jupyter Notebook, explains the key parameters of plotly.offline.iplot, demonstrates how to create and combine traces, and shows how to customize layout elements such as fonts, titles, axes, legends, grids, and other figure properties using Python code.

Data VisualizationJupyter NotebookOffline Plotting
0 likes · 15 min read
Plotly Basics: Offline Plotting, Traces, Layout, and Customization in Jupyter Notebook
Model Perspective
Model Perspective
Aug 23, 2022 · Fundamentals

How Prophet Implements Time Series Decomposition and Trend Modeling

This article explains Prophet’s algorithmic approach to time‑series forecasting, covering decomposition into trend, seasonality, holidays and error components, logistic and piecewise linear trend models, automatic change‑point detection, Fourier‑based seasonality, holiday handling, model fitting with PyStan, and practical Python code examples.

ProphetPythonTime Series Forecasting
0 likes · 12 min read
How Prophet Implements Time Series Decomposition and Trend Modeling
Model Perspective
Model Perspective
Aug 22, 2022 · Operations

How to Evaluate Airline Route Efficiency with DEA and Python

This article presents a full case study that uses Data Envelopment Analysis (DEA) with Python to assess the efficiency of 13 airlines based on fleet size, fuel consumption, employee count, passenger‑miles and freight‑ton‑miles, detailing data preparation, model construction, solution steps, results and practical conclusions.

AirlineData Envelopment AnalysisPython
0 likes · 11 min read
How to Evaluate Airline Route Efficiency with DEA and Python
Python Programming Learning Circle
Python Programming Learning Circle
Aug 22, 2022 · Big Data

20 Data Visualization Tools: From Entry‑Level to Expert Solutions

This article surveys twenty data‑visualization tools—covering entry‑level options like Excel, online JavaScript libraries such as D3 and Google Chart API, interactive GUI utilities, map frameworks, advanced desktop environments, and expert‑grade platforms like R, Weka and Gephi—highlighting their key features, formats supported and typical use cases.

JavaScriptMappingPython
0 likes · 11 min read
20 Data Visualization Tools: From Entry‑Level to Expert Solutions
Aikesheng Open Source Community
Aikesheng Open Source Community
Aug 22, 2022 · Databases

Using mysqlreplicate to Set Up MySQL Replication: Introduction, Command Usage, and Source‑Code Analysis

This article introduces the mysqlreplicate utility, explains how to run it with common command‑line options, and provides a step‑by‑step source‑code analysis that checks server‑id and UUID uniqueness, InnoDB compatibility, storage‑engine consistency, binary‑log settings, and finally creates and starts the replication process.

DevOpsPythonSQL
0 likes · 19 min read
Using mysqlreplicate to Set Up MySQL Replication: Introduction, Command Usage, and Source‑Code Analysis
Model Perspective
Model Perspective
Aug 20, 2022 · Fundamentals

Unlock SciPy’s Sparse Graph Algorithms: Shortest Paths, MSTs & More

This article lists the key SciPy sparse‑graph functions—such as connected components, Laplacian, various shortest‑path algorithms, traversals, minimum spanning tree, flow and matching utilities—and provides Python code examples demonstrating their use.

Graph AlgorithmsPythonminimum spanning tree
0 likes · 4 min read
Unlock SciPy’s Sparse Graph Algorithms: Shortest Paths, MSTs & More
Model Perspective
Model Perspective
Aug 19, 2022 · Fundamentals

Mastering SciPy Optimize: From Root Finding to Global Optimization

This guide introduces SciPy's optimize module, covering scalar and multivariate minimization, global optimization algorithms, root finding, linear programming, and assignment problems, complete with clear Python code examples and explanations of each method's usage and output.

PythonRoot Findingnumerical methods
0 likes · 7 min read
Mastering SciPy Optimize: From Root Finding to Global Optimization
Python Programming Learning Circle
Python Programming Learning Circle
Aug 17, 2022 · Big Data

Game Industry User Data Analysis: Registration Distribution, Payment Metrics, and Consumption Patterns

This article presents a comprehensive Python-based analysis of a large game dataset (2.29 million records, 109 fields), covering user registration trends, payment rates, ARPU/ARPPU calculations, level‑based spending behavior, and consumption patterns of resources and acceleration items, with visualizations and actionable conclusions.

Game AnalyticsPythonbig data
0 likes · 11 min read
Game Industry User Data Analysis: Registration Distribution, Payment Metrics, and Consumption Patterns
Model Perspective
Model Perspective
Aug 16, 2022 · Fundamentals

Boost Python Speed Instantly with Numba: A Practical Guide

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.

JIT compilationPerformance OptimizationPython
0 likes · 7 min read
Boost Python Speed Instantly with Numba: A Practical Guide
Python Programming Learning Circle
Python Programming Learning Circle
Aug 16, 2022 · Fundamentals

30 Useful Python Packages for Data Workflows

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.

Data WorkflowPythonTutorial
0 likes · 15 min read
30 Useful Python Packages for Data Workflows
政采云技术
政采云技术
Aug 16, 2022 · Artificial Intelligence

Integrating YOLOv5 and MMDetection with Label Studio via a Custom ML Backend

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.

LabelStudioMLBackendObjectDetection
0 likes · 14 min read
Integrating YOLOv5 and MMDetection with Label Studio via a Custom ML Backend
Python Programming Learning Circle
Python Programming Learning Circle
Aug 15, 2022 · Fundamentals

Why Modern Programming Languages Have Dropped ++/-- Operators: A Design‑Philosophy Perspective

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.

C++Functional ProgrammingProgramming Language Design
0 likes · 25 min read
Why Modern Programming Languages Have Dropped ++/-- Operators: A Design‑Philosophy Perspective
Python Programming Learning Circle
Python Programming Learning Circle
Aug 15, 2022 · Databases

Using Python DB‑API to Operate MySQL: PyMySQL Basics, Connection Pooling, and Safe SQL Practices

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.

DB-APIDatabase Connection PoolMySQL
0 likes · 17 min read
Using Python DB‑API to Operate MySQL: PyMySQL Basics, Connection Pooling, and Safe SQL Practices
Model Perspective
Model Perspective
Aug 14, 2022 · Artificial Intelligence

Mastering Feature Binning with sklearn: Uniform, Quantile, and K‑Means Methods

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.

KBinsDiscretizerPythondata preprocessing
0 likes · 5 min read
Mastering Feature Binning with sklearn: Uniform, Quantile, and K‑Means Methods
Python Programming Learning Circle
Python Programming Learning Circle
Aug 13, 2022 · Big Data

Parallel Processing of Large CSV Files in Python Using multiprocessing, joblib, and tqdm

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.

Pythonbig datadata cleaning
0 likes · 10 min read
Parallel Processing of Large CSV Files in Python Using multiprocessing, joblib, and tqdm
Python Programming Learning Circle
Python Programming Learning Circle
Aug 12, 2022 · Fundamentals

Automating Excel Reports with Python xlwings and pandas

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.

Data ProcessingExcel AutomationPython
0 likes · 10 min read
Automating Excel Reports with Python xlwings and pandas
Model Perspective
Model Perspective
Aug 11, 2022 · Fundamentals

Master VAR Modeling: Theory, Workflow, and Full Python Implementation

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.

PythonTime Serieseconometrics
0 likes · 9 min read
Master VAR Modeling: Theory, Workflow, and Full Python Implementation
政采云技术
政采云技术
Aug 11, 2022 · Artificial Intelligence

Semi‑Automatic Annotation with Label Studio and YOLOv5: Installation, Project Setup, and Model Training

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.

Label StudioObject DetectionPython
0 likes · 11 min read
Semi‑Automatic Annotation with Label Studio and YOLOv5: Installation, Project Setup, and Model Training
Model Perspective
Model Perspective
Aug 10, 2022 · Artificial Intelligence

Master CNN Basics: Build, Train, and Evaluate a Convolutional Neural Network

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.

CNNKerasPython
0 likes · 5 min read
Master CNN Basics: Build, Train, and Evaluate a Convolutional Neural Network
Python Programming Learning Circle
Python Programming Learning Circle
Aug 10, 2022 · Fundamentals

Implementing Thread‑Safe Singleton Pattern in Python

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.

DecoratorDesign PatternLock
0 likes · 10 min read
Implementing Thread‑Safe Singleton Pattern in Python
Python Programming Learning Circle
Python Programming Learning Circle
Aug 10, 2022 · Fundamentals

Python and Jupyter Notebook Tips: Data Profiling, Interactive Plotting, and Handy Magic Commands

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.

Jupyter NotebookMagic CommandsPython
0 likes · 9 min read
Python and Jupyter Notebook Tips: Data Profiling, Interactive Plotting, and Handy Magic Commands
Python Programming Learning Circle
Python Programming Learning Circle
Aug 9, 2022 · Big Data

Why Choose Python for Data Analysis? A Comprehensive Guide

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.

Data AnalysisPythonworkflow
0 likes · 6 min read
Why Choose Python for Data Analysis? A Comprehensive Guide
Model Perspective
Model Perspective
Aug 8, 2022 · Artificial Intelligence

Mastering sklearn.svm: Parameters, Grid Search, and Real-World Examples

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.

GridSearchCVPythonSVM
0 likes · 6 min read
Mastering sklearn.svm: Parameters, Grid Search, and Real-World Examples
Model Perspective
Model Perspective
Aug 8, 2022 · Artificial Intelligence

Build a Multi‑Layer Perceptron with Keras: Step‑by‑Step Guide

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.

Fashion-MNISTKerasMLP
0 likes · 16 min read
Build a Multi‑Layer Perceptron with Keras: Step‑by‑Step Guide
21CTO
21CTO
Aug 8, 2022 · Backend Development

What's New in Django 4.1? Explore Async ORM, Improved Forms, and Upgrade Guides

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.

Async ORMBackendDjango
0 likes · 3 min read
What's New in Django 4.1? Explore Async ORM, Improved Forms, and Upgrade Guides
21CTO
21CTO
Aug 8, 2022 · Cloud Computing

How Alibaba Cloud, Python’s Surge, and GitLab’s Policies Reveal Tech Shifts

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.

Alibaba CloudCloud ComputingGitLab
0 likes · 5 min read
How Alibaba Cloud, Python’s Surge, and GitLab’s Policies Reveal Tech Shifts
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 5, 2022 · Fundamentals

Why Fury Serialization Beats Kryo: 20‑200× Faster Cross‑Language Performance

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.

PerformancePythoncross-language
0 likes · 38 min read
Why Fury Serialization Beats Kryo: 20‑200× Faster Cross‑Language Performance
Model Perspective
Model Perspective
Aug 4, 2022 · Artificial Intelligence

How Supervised Learning Predicts House Prices – A Hands‑On Guide

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.

Linear RegressionPythongradient descent
0 likes · 13 min read
How Supervised Learning Predicts House Prices – A Hands‑On Guide
Python Programming Learning Circle
Python Programming Learning Circle
Aug 3, 2022 · Fundamentals

Why Python Has No Pointers and How to Simulate Them

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.

Mutable TypesObject modelPython
0 likes · 18 min read
Why Python Has No Pointers and How to Simulate Them
Python Programming Learning Circle
Python Programming Learning Circle
Aug 3, 2022 · Fundamentals

Python Conditional Statements: if, if‑else, if‑elif‑else, and Nested if

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.

Programming FundamentalsPythonconditional statements
0 likes · 7 min read
Python Conditional Statements: if, if‑else, if‑elif‑else, and Nested if
Python Programming Learning Circle
Python Programming Learning Circle
Aug 1, 2022 · Information Security

Implementing Windows Hooks in Python Using ctypes

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.

KeyloggerPythonctypes
0 likes · 8 min read
Implementing Windows Hooks in Python Using ctypes
Python Programming Learning Circle
Python Programming Learning Circle
Jul 30, 2022 · Backend Development

Python Web Scraping Tutorial: Crawling QDaily, Storing in SQLite, Analyzing Data and Generating a Word Cloud

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.

BeautifulSoupData AnalysisPython
0 likes · 6 min read
Python Web Scraping Tutorial: Crawling QDaily, Storing in SQLite, Analyzing Data and Generating a Word Cloud
MaGe Linux Operations
MaGe Linux Operations
Jul 29, 2022 · Artificial Intelligence

Master 10 Popular Clustering Algorithms in Python with Scikit‑Learn

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.

ClusteringPythonUnsupervised Learning
0 likes · 24 min read
Master 10 Popular Clustering Algorithms in Python with Scikit‑Learn
Python Programming Learning Circle
Python Programming Learning Circle
Jul 29, 2022 · Fundamentals

Managing Python Project Dependencies with pip and Conda

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.

PythonVirtual Environmentsconda
0 likes · 13 min read
Managing Python Project Dependencies with pip and Conda
Model Perspective
Model Perspective
Jul 28, 2022 · Fundamentals

How to Forecast Seasonal Time Series with the Seasonal Coefficient Method

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.

PythonTime Seriesseasonal forecasting
0 likes · 4 min read
How to Forecast Seasonal Time Series with the Seasonal Coefficient Method
21CTO
21CTO
Jul 28, 2022 · Backend Development

Why Meta Chose Rust, Hack, Python, and C++ as Its Core Backend Languages

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.

Backend DevelopmentC++Language Selection
0 likes · 6 min read
Why Meta Chose Rust, Hack, Python, and C++ as Its Core Backend Languages
Python Programming Learning Circle
Python Programming Learning Circle
Jul 27, 2022 · Backend Development

Comprehensive Overview of Python Web Frameworks

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.

Backend DevelopmentDjangoFastAPI
0 likes · 20 min read
Comprehensive Overview of Python Web Frameworks
Python Programming Learning Circle
Python Programming Learning Circle
Jul 27, 2022 · Fundamentals

Understanding Shallow and Deep Copy in Python

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.

Deep CopyListPython
0 likes · 6 min read
Understanding Shallow and Deep Copy in Python
Python Programming Learning Circle
Python Programming Learning Circle
Jul 27, 2022 · Fundamentals

Modular Programming Concepts and Benefits in Python

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.

Modular ProgrammingModulesPython
0 likes · 6 min read
Modular Programming Concepts and Benefits in Python
Python Programming Learning Circle
Python Programming Learning Circle
Jul 26, 2022 · Fundamentals

Top 10 JupyterLab Extensions to Boost Data‑Science Productivity

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.

Data ScienceJupyterLabPython
0 likes · 7 min read
Top 10 JupyterLab Extensions to Boost Data‑Science Productivity
Python Programming Learning Circle
Python Programming Learning Circle
Jul 25, 2022 · Fundamentals

Python Branching Statements: if, if‑else, if‑elif‑else, and Nested Structures

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.

Programming FundamentalsPythonbranching
0 likes · 7 min read
Python Branching Statements: if, if‑else, if‑elif‑else, and Nested Structures
Model Perspective
Model Perspective
Jul 24, 2022 · Artificial Intelligence

How to Build and Estimate a Logistic Regression Model for Grouped Data

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.

Logistic RegressionMaximum LikelihoodPython
0 likes · 7 min read
How to Build and Estimate a Logistic Regression Model for Grouped Data
Model Perspective
Model Perspective
Jul 23, 2022 · Artificial Intelligence

LASSO Regression Explained: Theory, Case Studies, and Python Code

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.

LASSOPythonmachine learning
0 likes · 8 min read
LASSO Regression Explained: Theory, Case Studies, and Python Code
Python Programming Learning Circle
Python Programming Learning Circle
Jul 22, 2022 · Fundamentals

Essential pip Tips and Commands for Python Package Management

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.

InstallationPythoncommand line
0 likes · 7 min read
Essential pip Tips and Commands for Python Package Management
Python Programming Learning Circle
Python Programming Learning Circle
Jul 22, 2022 · Fundamentals

Techniques for Exiting Nested Loops in Python

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.

LoopsPythonbreak
0 likes · 6 min read
Techniques for Exiting Nested Loops in Python
Model Perspective
Model Perspective
Jul 21, 2022 · Artificial Intelligence

Tackling Multicollinearity: Ridge and LASSO Regression Explained with Python

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.

LASSOPythonRegularization
0 likes · 7 min read
Tackling Multicollinearity: Ridge and LASSO Regression Explained with Python
Python Programming Learning Circle
Python Programming Learning Circle
Jul 21, 2022 · Artificial Intelligence

Building an Automatic Math Problem Grading System with Python and Convolutional Neural Networks

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.

CNNOCRPython
0 likes · 26 min read
Building an Automatic Math Problem Grading System with Python and Convolutional Neural Networks
Python Programming Learning Circle
Python Programming Learning Circle
Jul 21, 2022 · Fundamentals

10 Common Python Mistakes and How to Avoid Them

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.

Pythonbest practicescommon mistakes
0 likes · 12 min read
10 Common Python Mistakes and How to Avoid Them
Python Programming Learning Circle
Python Programming Learning Circle
Jul 20, 2022 · Fundamentals

Understanding Python's Composite Data Types: Tuple, List, Set, and Dictionary

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.

Data StructuresProgramming FundamentalsPython
0 likes · 7 min read
Understanding Python's Composite Data Types: Tuple, List, Set, and Dictionary
MaGe Linux Operations
MaGe Linux Operations
Jul 18, 2022 · Fundamentals

Unlock NumPy: Comprehensive Guide to Array Iteration, Reshaping, and Advanced Operations

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.

Data ScienceNumPyPython
0 likes · 26 min read
Unlock NumPy: Comprehensive Guide to Array Iteration, Reshaping, and Advanced Operations
Python Programming Learning Circle
Python Programming Learning Circle
Jul 18, 2022 · Fundamentals

Five Practical Uses of Python’s -m Option

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.

HTTP serverPythoncommand-line
0 likes · 4 min read
Five Practical Uses of Python’s -m Option
Model Perspective
Model Perspective
Jul 17, 2022 · Fundamentals

Modeling Yeast Growth with a Discrete Logistic Equation

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.

Pythonlogistic modelmathematical modeling
0 likes · 5 min read
Modeling Yeast Growth with a Discrete Logistic Equation