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12 dépôts

Awesome GitHub RepositoriesData Type Inspection

Examination and modification of schema metadata during query planning.

Distinguishing note: Focuses on inspecting and managing types rather than converting data values.

Explore 12 awesome GitHub repositories matching data & databases · Data Type Inspection. Refine with filters or upvote what's useful.

Awesome Data Type Inspection GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • pola-rs/polarsAvatar de pola-rs

    pola-rs/polars

    38,855Voir sur GitHub↗

    Polars is a high-performance columnar data processing library designed for efficient analytical workflows. It functions as a structured data library that organizes information into typed columns, utilizing the Apache Arrow memory format to enable zero-copy data sharing and cache-friendly, vectorized operations. The engine is built to handle large-scale tabular datasets, providing both local and distributed analytical runtimes that scale from single-machine environments to multi-node clusters. The project distinguishes itself through a sophisticated lazy query engine that constructs abstract e

    Examines and modifies column datatypes during query planning to handle heterogeneous data sources.

    Rustarrowdataframedataframe-library
    Voir sur GitHub↗38,855
  • ml-explore/mlxAvatar de ml-explore

    ml-explore/mlx

    27,047Voir sur GitHub↗

    This project is a machine learning array framework and tensor computation library designed for high-performance numerical computing. It provides a comprehensive suite of tools for constructing and training neural networks, featuring an automatic differentiation engine that facilitates gradient-based optimization and complex mathematical modeling. The library distinguishes itself through a unified memory architecture that allows data to be shared across CPU and GPU devices without explicit copies, significantly reducing data movement overhead. Its execution model relies on a lazy evaluation en

    Exposes array metadata to assist in numerical precision and range calculations.

    C++mlx
    Voir sur GitHub↗27,047
  • prestodb/prestoAvatar de prestodb

    prestodb/presto

    16,711Voir sur GitHub↗

    Presto is a distributed SQL query engine designed for high-performance analytical processing across heterogeneous data sources. It functions as a data federation platform and massively parallel processing engine, allowing users to execute interactive queries against diverse storage systems without requiring data migration. By mapping remote metadata and structures to a unified relational namespace, it enables seamless cross-platform analysis through a standard SQL interface. The engine distinguishes itself through a pluggable connector architecture and a shared-nothing distributed processing

    Returns the underlying data type name of a given expression to assist with debugging and schema validation.

    Javabig-datadatahadoop
    Voir sur GitHub↗16,711
  • rougier/numpy-100Avatar de rougier

    rougier/numpy-100

    13,812Voir sur GitHub↗

    This project is a curated collection of programming exercises designed to build proficiency in numerical computing and data manipulation. It provides a structured learning path for mastering multidimensional array operations, vectorized arithmetic, and statistical analysis. The repository focuses on developing practical expertise in array-based workflows, emphasizing techniques such as memory management, efficient data processing, and the replacement of explicit loops with vectorized operations. Users engage with hands-on challenges that cover the full lifecycle of numerical data, from initia

    Retrieves metadata including data types, dimensions, and floating-point limits for array inspection.

    Pythonbinderexercisesnotebook
    Voir sur GitHub↗13,812
  • jbeder/yaml-cppAvatar de jbeder

    jbeder/yaml-cpp

    5,931Voir sur GitHub↗

    yaml-cpp is a C++ library for parsing and emitting YAML 1.2 documents. It provides a complete YAML processing pipeline, from reading YAML content into a traversable node tree to writing in-memory data structures back as YAML text. The library represents parsed YAML as a mutable tree of typed nodes, supporting scalars, sequences, maps, and aliases. It uses a recursive-descent parser to build this node tree, and a stream-based emitter to generate YAML output incrementally. Template-based type conversion enables compile-time serialization between YAML nodes and C++ types, including support for c

    Queries whether a node is a scalar, sequence, map, null, or undefined to handle heterogeneous data safely.

    C++
    Voir sur GitHub↗5,931
  • hexfiend/hexfiendAvatar de HexFiend

    HexFiend/HexFiend

    5,859Voir sur GitHub↗

    HexFiend is a native macOS hex editor designed for opening and editing binary files of any size without loading them entirely into memory. It uses a 64-bit addressable data model with memory-mapped file access, enabling navigation of multi-gigabyte files while maintaining byte-level undo for insert and delete operations. The editor distinguishes itself through a diff algorithm for binary file comparison that accounts for insertions and deletions, and a template-based structure parsing system that uses user-defined scripts to visualize binary file layouts. It also includes a typed data interpr

    Interprets raw bytes as integers, floats, or other types with configurable endianness and signedness.

    Objective-C
    Voir sur GitHub↗5,859
  • mrdbourke/zero-to-mastery-mlAvatar de mrdbourke

    mrdbourke/zero-to-mastery-ml

    5,839Voir sur GitHub↗

    Ce projet est un cursus éducatif en machine learning et une plateforme d'apprentissage délivrée via des Jupyter Notebooks interactifs. Il sert de guide complet pour maîtriser le toolkit de science des données Python, fournissant des tutoriels structurés pour le calcul numérique, la manipulation de données tabulaires et la visualisation statistique. Le cursus inclut des guides d'implémentation spécifiques pour Scikit-Learn et un cours pratique sur TensorFlow pour construire, entraîner et déployer des réseaux de neurones et des modèles de vision par ordinateur. Il couvre le processus de bout en bout de la construction de modèles prédictifs, de la formulation initiale du problème et de la catégorisation des tâches au déploiement des modèles via des interfaces web interactives. Le projet couvre une large surface de capacités incluant le calcul numérique avec des tableaux multidimensionnels, l'analyse exploratoire des données et les routines de prétraitement des données. Il fournit des flux de travail détaillés pour l'apprentissage supervisé et non supervisé, les pipelines de machine learning automatisés, l'optimisation des hyperparamètres et l'évaluation des modèles utilisant des métriques de classification et la validation croisée. Le contenu éducatif est organisé sous forme d'une série de notebooks qui entremêlent code Python et explications narratives pour documenter les flux de travail en science des données.

    Provides capabilities for retrieving metadata including data types and dimensions from numerical arrays.

    Jupyter Notebookdata-sciencedeep-learningmachine-learning
    Voir sur GitHub↗5,839
  • paradigmxyz/rethAvatar de paradigmxyz

    paradigmxyz/reth

    5,652Voir sur GitHub↗

    Reth is a modular, high-performance Ethereum execution layer client written in Rust. It serves as a full Ethereum node that syncs, validates, and serves blockchain data, functioning as an archive node implementation, a high-throughput RPC node server, and a snapshot sync tool. The project is built around a modular component architecture that allows assembling custom node behavior by swapping independent Rust crates for consensus, execution, mempool, and networking. The client distinguishes itself through a staged sync pipeline that downloads headers and bodies online before processing the res

    Shrinks storage footprint by 30-50% depending on node type using efficient data layouts.

    Rust
    Voir sur GitHub↗5,652
  • biolab/orange3Avatar de biolab

    biolab/orange3

    5,635Voir sur GitHub↗

    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

    Provides a method to inspect whether data arrays are stored as dense or sparse representations.

    Python
    Voir sur GitHub↗5,635
  • racket/racketAvatar de racket

    racket/racket

    5,157Voir sur GitHub↗

    Racket est un langage de programmation généraliste multi-paradigme de la famille Lisp, conçu pour la création de langages. Il fonctionne comme un atelier de langage, fournissant une plateforme pour concevoir et implémenter des langages de programmation personnalisés via un système flexible de macros et de modules. Le système se distingue en offrant une suite complète pour l'ingénierie sémantique, permettant la construction de sous-ensembles de langages spécialisés et de couches éducatives. Il inclut des outils pour la conception de langages personnalisés, tels que la génération de lexer et de parser, ainsi que la capacité de définir des règles d'expansion de module et une sélection de langage dynamique au moment de la lecture. Le projet fournit un environnement de développement intégré avec un éditeur intégré, un débogueur visuel et un gestionnaire de paquets logiciels. Sa surface de capacités s'étend à une bibliothèque standard généraliste couvrant le rendu graphique 2D, le traitement de données binaires, l'intégration SQL et de bases de données déductives, et la construction d'interfaces utilisateur graphiques. L'environnement prend en charge la compilation du code source en fichiers exécutables autonomes pour la distribution.

    Provides predefined types for interpreting raw bytes as big-endian or little-endian integers, floats, and byte strings.

    Racketracket
    Voir sur GitHub↗5,157
  • kuzudb/kuzuAvatar de kuzudb

    kuzudb/kuzu

    3,965Voir sur GitHub↗

    Kùzu is an embedded property graph database engine designed for high-performance analytical queries and local data management. It operates as a library within the host application process, utilizing a columnar-based storage architecture and just-in-time query compilation to execute complex graph traversals and pattern matching efficiently. By mapping database files directly into system memory, it ensures data durability and high-speed access while maintaining ACID-compliant transactional integrity. The engine distinguishes itself by integrating vector similarity search and full-text search di

    Returns internal data type names to assist with schema validation and debugging.

    C++cypherdatabaseembeddable
    Voir sur GitHub↗3,965
  • xtensor-stack/xtensorAvatar de xtensor-stack

    xtensor-stack/xtensor

    3,748Voir sur GitHub↗

    xtensor is a C++ multidimensional array library for numerical computing that provides N-dimensional containers with an interface mirroring the NumPy API. It utilizes a lazy evaluation expression engine to defer numerical computations until assignment, which minimizes memory allocations and intermediate copies. The library features a foreign memory array adaptor that allows it to wrap external buffers, such as NumPy arrays, to perform numerical operations in-place without duplicating data. It further optimizes performance through lazy broadcasting and a system that manages the lifetime of temp

    Retrieves metadata including total size, dimension count, and axis lengths for array expressions.

    C++c-plus-plus-14multidimensional-arraysnumpy
    Voir sur GitHub↗3,748
  1. Home
  2. Data & Databases
  3. Data Type Inspection

Explorer les sous-tags

  • Array Inspection1 sous-tagRetrieves metadata including data types, dimensions, and floating-point limits. **Distinct from Data Type Inspection:** Focuses on metadata inspection, distinct from schema-level type inspection.
  • Binary Type InspectorsInspectors that interpret raw bytes as integers, floats, and other types with configurable endianness. **Distinct from Data Type Inspection:** Distinct from Data Type Inspection: focuses on interpreting raw binary bytes as typed values, not schema metadata.
  • Numeric Byte InterpretersInterpreters that display selected bytes as integers or floats with configurable endianness. **Distinct from Data Type Inspection:** Distinct from Data Type Inspection: focuses on numeric interpretation of raw bytes, not schema metadata.
  • Typed Byte InspectorsInspectors that interpret raw bytes as typed values with configurable endianness and signedness. **Distinct from Data Type Inspection:** Distinct from Data Type Inspection: focuses on interpreting raw binary bytes as typed values, not schema metadata.
  • YAML Node Types1 sous-tagQueries whether a node is a scalar, sequence, map, null, or undefined to handle heterogeneous data safely. **Distinct from Data Type Inspection:** Distinct from Data Type Inspection: focuses on YAML-specific node types, not general schema metadata.