Awesome GitHub RepositoriesRandomized Data Retrieval

This project is a comprehensive collection of common computer science algorithms and data structures implemented in Swift. It serves as an educational reference and library for studying computational complexity, algorithmic logic, and data structure engineering through practical code examples. The repository provides a wide suite of data structure implementations, including various types of linked lists, heaps, hash tables, and an extensive range of hierarchical trees such as Red-Black, B-Tree, and Splay trees. It also covers diverse sorting and searching techniques, from basic bubble sort to

This project is a feature-rich Go client library designed for interacting with Redis. It serves as a comprehensive interface for managing remote data stores, enabling developers to execute standard database commands, handle complex data structures, and perform asynchronous operations within Go applications. The library distinguishes itself through its support for advanced Redis capabilities, including connection pooling, pipelining, and transactional integrity. It provides specialized primitives for managing distributed clusters, including automated topology updates and request routing to sha

StatsD is a metrics aggregator and UDP collection server that collects system counters and timers. It functions as a time-series data forwarder, receiving high-frequency metric updates via a lightweight line protocol and summarizing them before flushing the data to a backend. The project features a pluggable metrics backend framework, allowing aggregated statistics to be routed to various third-party monitoring services or time-series databases such as Graphite. It supports horizontal scaling and high availability through a proxy ring distribution system that forwards incoming packets across

Redis is a high-performance in-memory key-value store that functions as a distributed cache, message broker, and NoSQL database. It provides sub-millisecond read and write access to data stored in RAM and can operate as a vector database for indexing high-dimensional embeddings. The system supports a wide range of data storage and synchronization primitives, including the management of strings, hashes, lists, sets, and JSON documents. It enables real-time data operations through atomic transactions, hybrid persistence using snapshots and append-only logs, and high-availability configurations

PathPlanning is a library of animated path planning algorithms that includes implementations of A-star, Dijkstra, RRT, and spline-based trajectory generation for both 2D and 3D environments. The project provides a collection of motion planning algorithms that demonstrate how robots can find collision-free paths through continuous spaces, with each algorithm rendered as a step-by-step visual animation to show how the search or tree grows over time. The library covers three main categories of path planning: sampling-based methods like RRT, RRT-star, and BIT-star that grow trees by randomly samp

This project is an educational resource and a collection of instructional materials for performing data manipulation and statistical analysis using Python. It provides a comprehensive set of guides and code examples for using the Pandas, NumPy, and Matplotlib libraries to analyze structured data. The resource includes a dedicated guide for reshaping, cleaning, and aggregating tabular data and time series via Pandas, alongside a reference for high-performance vectorized operations and linear algebra using NumPy. It also features tutorials for creating publication-quality charts, distribution p

VisiData is a terminal-based interactive data analysis tool and browser designed for exploring, filtering, and sorting large tabular datasets. It functions as a structured data inspector that loads and flattens complex formats like JSON, XML, and PCAP into interactive sheets, as well as a terminal file manager for navigating directories and performing staged filesystem operations. The project distinguishes itself by rendering data visualizations, such as scatter plots and histograms, directly in the terminal using Unicode Braille characters. It provides a Python-based data wrangling environme

itsy-bitsy-data-structures is a collection of fundamental computer science data structures implemented in JavaScript. It serves as an educational resource and algorithm study guide, providing simplified code implementations of classic data organization patterns to demonstrate internal logic and usage. The project provides clear and concise JavaScript implementations of stacks, queues, and linked lists. These examples are designed for learning, technical interview preparation, and studying the mechanical behavior of core data structures through code. The implementations utilize various comput

LogicStack-LeetCode is a curated repository of solved algorithm problems and data structure implementations, primarily drawn from the LeetCode platform. Its core identity is a structured collection of solutions designed to support technical interview preparation and competitive programming practice, with each solution accompanied by complexity analyses to help engineers understand performance trade-offs. The repository distinguishes itself through its breadth of coverage across fundamental algorithmic patterns and data structures. It includes implementations for array manipulation, string pro

Autoscraper es una biblioteca de web scraping automático y extractor de datos basado en patrones que aprende reglas de extracción a partir de datos de muestra. Identifica y recupera texto, URLs y elementos HTML de páginas web analizando valores de muestra para replicar patrones de datos a través de diferentes URLs. El sistema funciona como un gestor de modelos de web scraping, permitiendo a los usuarios guardar y recargar reglas aprendidas para mantener una extracción de datos consistente. Admite la exportación e importación de reglas de scraping a un sistema de archivos local para evitar repetir el proceso de entrenamiento para el mismo sitio web. La biblioteca cubre la extracción automatizada de datos web y la recolección de contenido web mediante el aprendizaje de patrones basado en muestras y la recuperación de elementos posicionales. Puede recuperar tanto puntos de datos específicos como todos los elementos en una página que coincidan con los patrones identificados a partir de los datos de muestra iniciales.

imbalanced-learn is a dataset balancing framework and Python machine learning extension designed to resample training data and reduce the impact of class imbalance. It provides a toolkit of algorithms for adjusting class distributions to improve model performance on minority class prediction. As a scikit-learn resampling library, it extends the ecosystem with specialized tools for balancing datasets through over-sampling and under-sampling techniques. This allows for the correction of skewed class proportions to reduce model bias toward the majority class. The library implements the scikit-l

This is an interactive Python tutorial delivered as a collection of Jupyter notebooks. It is designed as a structured learning path for beginners, teaching fundamental language concepts through a sequence of lessons that combine explanatory text with runnable code cells and embedded practice exercises. Each notebook is a self-contained unit that introduces a topic, demonstrates it with a minimal code example, and then asks the learner to write code themselves, receiving immediate feedback from the browser-based execution environment. The curriculum is built on a progressive concept-stacking mo

collect.js is a dependency-free JavaScript library that provides a fluent, chainable interface for manipulating arrays and objects. It mirrors the Laravel Collection API, offering a consistent set of methods for data transformation across JavaScript and Laravel backend environments. The library stores collection data as plain arrays internally and supports fluent method chaining, where each method returns a new collection instance. The library distinguishes itself by closely replicating the Laravel Collection API in JavaScript, mapping each PHP method to an equivalent JavaScript implementatio

Chance is a JavaScript library for generating random data, designed to produce realistic test data for automated tests and prototypes. It uses a Mersenne Twister pseudo-random number generator that accepts an optional seed value, enabling reproducible sequences of random values across multiple runs. The library provides a wide range of generators for common data types, including random integers, floats, booleans, characters, strings, and dates, all with configurable ranges and character pools. It can generate realistic geographic data like addresses, as well as financial data such as credit c

Swift Algorithms is a library of sequence and collection algorithms for Swift, providing operations for splitting, cycling, combining, and sampling collections with lazy evaluation. It extends Swift's standard library with algorithms that work across all types conforming to Sequence and Collection protocols, enabling efficient data processing without intermediate storage. The library specializes in combinatorial enumeration, generating all possible permutations, combinations, and product sequences from collections for testing and exploration. It also includes utilities for splitting collectio

Carp is a statically typed Lisp compiler that compiles Lisp-like syntax directly to C source code, enabling seamless integration with existing C libraries and low-level system programming. It manages memory deterministically at compile time using ownership tracking and linear types, eliminating garbage collection pauses and runtime overhead while ensuring type safety through an inferred static type system. The language distinguishes itself through compile-time macro expansion and metaprogramming capabilities, allowing code generation and transformation before final binary output. It enforces

Orange3 is a visual data mining platform that provides an interactive canvas for building data analysis workflows without writing code. At its core, it offers a widget-based visual programming environment where users connect configurable components to perform data preprocessing, machine learning model training, statistical evaluation, and interactive visualization. The platform is built on NumPy-backed data tables with domain descriptors that define variable names, types, and roles, and includes a lazy SQL query proxy for working with database tables without loading all data into memory. The

Vega-Lite is a high-level declarative language for specifying interactive, multi-view visualizations. It compiles a concise JSON specification into a full Vega visualization, automatically inferring scales, axes, and legends from encoding declarations. The grammar-of-graphics encoding maps data fields to visual channels such as position, color, size, and shape, while a multi-view composition grammar enables layered, faceted, concatenated, and repeated layouts. Reactive parameter binding links named parameters to input widgets, selections, and expressions for dynamic updates. The project suppo

Danfo.js es una biblioteca de análisis y preprocesamiento de datos para JavaScript que proporciona estructuras de datos etiquetadas de alto rendimiento. Implementa dataframes y series para permitir análisis de datos complejos, computación estadística y la manipulación de datos tabulares estructurados. El proyecto sirve como una biblioteca de preprocesamiento para machine learning, ofreciendo utilidades para codificación de etiquetas categóricas, one-hot encoding y escalado y estandarización de características numéricas. Facilita específicamente la conversión de estructuras de datos etiquetadas en tensores para el entrenamiento y evaluación de modelos. La biblioteca cubre un amplio conjunto de capacidades, incluyendo estadísticas descriptivas, operaciones relacionales como merge y join, y procesamiento de series temporales. Incluye herramientas para limpieza, filtrado y agrupación de datos, así como una interfaz de visualización para generar gráficos interactivos directamente desde los dataframes. El sistema soporta la importación y exportación de datos mediante formatos CSV, JSON y Excel.

Pokemon-Terminal es un rotador de fondos de pantalla de línea de comandos y gestor de temas de fondo de terminal. Funciona como un orquestador de imágenes basado en shell que modifica la configuración del sistema y los archivos de configuración para renderizar gráficos personalizados a través de varios emuladores de terminal y sistemas operativos. La utilidad permite la selección y visualización automatizada de imágenes como fondos de terminal o fondos de pantalla del sistema. Cuenta con rotación dinámica de temas y la capacidad de recorrer bibliotecas de imágenes utilizando aleatorización y filtros regionales. El sistema cubre la gestión de la estética de la CLI a través de una base de datos de activos, permitiendo a los usuarios localizar entradas específicas, añadir fondos personalizados y actualizar fondos de pantalla del escritorio. Proporciona un estilo de terminal multiplataforma para garantizar una aplicación de temas consistente en diferentes entornos.