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33 dépôts

Awesome GitHub RepositoriesData Analysis

Libraries for processing, profiling, and analyzing datasets.

Explore 33 awesome GitHub repositories matching part of an awesome list · Data Analysis. Refine with filters or upvote what's useful.

Awesome Data Analysis GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • pathwaycom/pathwayAvatar de pathwaycom

    pathwaycom/pathway

    62,959Voir sur GitHub↗

    Pathway is a high-performance data processing framework designed for building unified batch and streaming pipelines. It functions as an orchestrator for complex data transformations, utilizing a differential dataflow engine to process updates incrementally. By treating static datasets and continuous event streams with identical logic, the platform ensures exactly-once processing semantics and consistent results across diverse data sources. The framework distinguishes itself through its specialized support for real-time artificial intelligence and retrieval-augmented generation. It features in

    Real-time data processing framework.

    Pythonbatch-processingdata-analyticsdata-pipelines
    Voir sur GitHub↗62,959
  • pandas-dev/pandasAvatar de pandas-dev

    pandas-dev/pandas

    49,039Voir sur GitHub↗

    Pandas is a high-performance data analysis library that provides a comprehensive framework for manipulating, cleaning, and transforming structured datasets. It centers on labeled one-dimensional and two-dimensional data structures, allowing users to construct, filter, and reshape tabular information while performing complex arithmetic and logical operations. The library distinguishes itself through a sophisticated indexing engine that enables automatic data alignment during calculations and relational merges. By utilizing a block-based memory layout, it optimizes cache locality for vectorized

    High-performance data structures and analysis tools.

    Pythonalignmentdata-analysisdata-science
    Voir sur GitHub↗49,039
  • 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

    Fast DataFrame library implemented in Rust.

    Rustarrowdataframedataframe-library
    Voir sur GitHub↗38,855
  • data-centric-ai-community/fg-data-profilingAvatar de Data-Centric-AI-Community

    Data-Centric-AI-Community/fg-data-profiling

    13,609Voir sur GitHub↗

    This project is a data profiling and exploratory data analysis tool designed to generate automated quality reports for Pandas and Spark dataframes. It serves as a system for computing descriptive statistics, identifying correlations, and analyzing univariate and multivariate data patterns. The tool provides specialized capabilities for comparing different versions of datasets to identify changes in data quality and distributions. It includes a dedicated profiler for time-dependent data to extract statistical information such as seasonality and auto-correlation. The software covers a broad an

    Generates data profiling reports for DataFrames.

    Python
    Voir sur GitHub↗13,609
  • ydataai/ydata-profilingAvatar de ydataai

    ydataai/ydata-profiling

    13,388Voir sur GitHub↗

    Ydata-profiling is an automated exploratory data analysis framework designed to generate comprehensive statistical reports and visual summaries from dataframes. It functions as a diagnostic tool for assessing data quality, identifying missing values, duplicates, and outliers, while providing a scalable engine for profiling massive datasets across distributed enterprise environments. The project distinguishes itself through its ability to handle large-scale data through distributed task orchestration and lazy stream processing, which minimizes memory overhead during complex computations. It in

    Listed in the “Data Analysis” section of the Awesome Python awesome list.

    Pythonbig-data-analyticsdata-analysisdata-exploration
    Voir sur GitHub↗13,388
  • simonw/datasetteAvatar de simonw

    simonw/datasette

    11,198Voir sur GitHub↗

    Datasette is a tool for publishing and sharing SQLite databases as public websites. It functions as a data publishing system that provides searchable interfaces and JSON APIs to expose the contents of SQLite files. The project enables both server-side and client-side execution. It can operate as an API server or as a database browser that runs entirely within a web browser using WebAssembly, allowing for serverless database access. The system supports a variety of deployment strategies, including containerized images for cloud hosting and a local development server for testing. It includes c

    Tool for exploring and publishing data.

    Pythonasgiautomatic-apicsv
    Voir sur GitHub↗11,198
  • modin-project/modinAvatar de modin-project

    modin-project/modin

    10,389Voir sur GitHub↗

    Modin is a distributed dataframe library and parallel data processing engine designed to handle large datasets that exceed system memory. It functions as a distributed computing framework that parallelizes data manipulation tasks across multiple CPU cores or clusters to increase throughput and avoid memory errors. The project mirrors the Pandas API, allowing for the distribution of data workflows without changing core code logic. It utilizes a pluggable backend interface, which enables users to switch between different distributed execution engines to optimize performance based on available h

    Scalable drop-in replacement for pandas.

    Pythonanalyticsdata-sciencedataframe
    Voir sur GitHub↗10,389
  • gonum/gonumAvatar de gonum

    gonum/gonum

    8,316Voir sur GitHub↗

    Gonum is a numerical computing library for the Go programming language, providing a collection of packages for scientific computing, linear algebra, statistics, and optimization. It functions as a framework for performing complex numerical computations and solving systems of linear equations. The project includes a dedicated graph analysis framework for modeling network graphs and solving connectivity and pathfinding problems. It also provides a statistical analysis toolkit for computing descriptive and inferential statistics and estimating mixture entropy. The library's capability surface c

    Scientific computing library.

    Godata-analysisgogolang
    Voir sur GitHub↗8,316
  • usefathom/fathomAvatar de usefathom

    usefathom/fathom

    8,005Voir sur GitHub↗

    Fathom is a privacy-focused website analytics server written in Go. It monitors website traffic and page views without collecting personal data or using intrusive cookies, providing a self-hosted alternative for traffic monitoring. The system utilizes a Preact-based dashboard interface for visualizing traffic patterns and reports. Data is persisted in a SQL database analytics store, with support for MySQL, PostgreSQL, and SQLite. The project covers the collection of visitor data via lightweight tracking snippets and the management of that data through a pluggable storage layer. It includes m

    Website analytics tool.

    Goanalyticsfathomfathom-analytics
    Voir sur GitHub↗8,005
  • ibis-project/ibisAvatar de ibis-project

    ibis-project/ibis

    6,574Voir sur GitHub↗

    Ibis is a portable Python dataframe library and multi-backend query engine that provides a unified interface for executing data transformations across diverse compute engines. It functions as a Python SQL expression compiler and dialect transpiler, allowing users to define data logic once and execute it across cloud warehouses, embedded databases, and distributed clusters without rewriting code. The project distinguishes itself through a database backend abstraction that decouples transformation logic from the underlying execution engine. It enables polyglot data workflows by mixing raw SQL s

    Portable dataframe library for multiple backends.

    Pythonbigqueryclickhousedatabase
    Voir sur GitHub↗6,574
  • aws/aws-sdk-pandasAvatar de aws

    aws/aws-sdk-pandas

    4,107Voir sur GitHub↗

    aws-sdk-pandas est une bibliothèque Python qui intègre les dataframes pandas avec les services AWS, agissant comme un outil ETL de données cloud et un connecteur de lac de données. Elle fournit une interface unifiée pour déplacer et transformer les données entre des dataframes en mémoire et le stockage cloud, les bases de données et les entrepôts de données. Le projet se distingue comme un orchestrateur de calcul distribué capable de soumettre des charges de travail basées sur pandas à des clusters EMR et des environnements de traitement sans serveur. Il se spécialise davantage dans la coordination du traitement de données distribué via l'initialisation de clusters Ray pour gérer des jeux de données qui dépassent la mémoire d'une seule machine. La bibliothèque couvre un large éventail de capacités, incluant la gestion du stockage d'objets pour S3, l'exécution de requêtes SQL pour Athena et Redshift, et l'intégration avec des bases de données NoSQL, graphes et séries temporelles. Elle inclut également des utilitaires pour la gestion des métadonnées via le catalogue Glue, l'indexation de données OpenSearch et la gestion des actifs de business intelligence dans QuickSight. La fonctionnalité supplémentaire inclut la récupération de secrets, l'analyse des journaux CloudWatch et la gestion des ensembles de règles de qualité des données.

    Pandas integration for AWS.

    Pythonamazon-athenaamazon-sagemaker-notebookapache-arrow
    Voir sur GitHub↗4,107
  • go-gota/gotaAvatar de go-gota

    go-gota/gota

    3,271Voir sur GitHub↗

    Gota: DataFrames and data wrangling in Go (Golang)

    Dataframe manipulation library.

    Go
    Voir sur GitHub↗3,271
  • starpig1129/ai-data-analysis-multiagentAvatar de starpig1129

    starpig1129/AI-Data-Analysis-MultiAgent

    1,762Voir sur GitHub↗

    DATAGEN is a powerful brand name that represents our vision of leveraging artificial intelligence technology for data generation and analysis. The name combines "DATA" and "GEN"(generation), perfectly embodying the core functionality of this project - automated data analysis and research through…

    Multi-agent system for data analysis and report generation.

    Python
    Voir sur GitHub↗1,762
  • stripe/veneurAvatar de stripe

    stripe/veneur

    1,746Voir sur GitHub↗

    A distributed, fault-tolerant pipeline for observability data

    Distributed data processing pipeline.

    Go
    Voir sur GitHub↗1,746
  • rocketlaunchr/dataframe-goAvatar de rocketlaunchr

    rocketlaunchr/dataframe-go

    1,287Voir sur GitHub↗

    DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration

    Dataframe implementation for statistics and machine learning.

    Godata-sciencedataframedataframes
    Voir sur GitHub↗1,287
  • techascent/tech.ml.datasetAvatar de techascent

    techascent/tech.ml.dataset

    749Voir sur GitHub↗

    A Clojure high performance data processing system

    Dataframe library for processing and machine learning.

    Clojure
    Voir sur GitHub↗749
  • netflix/pigpenAvatar de Netflix

    Netflix/PigPen

    565Voir sur GitHub↗

    Map-Reduce for Clojure

    Map-reduce framework for data processing.

    Clojure
    Voir sur GitHub↗565
  • desbordante/desbordante-coreAvatar de Desbordante

    Desbordante/desbordante-core

    484Voir sur GitHub↗

    Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.

    Data profiler for pattern discovery.

    C++anomaly-detectioncorrelationsdata-analytics
    Voir sur GitHub↗484
  • project-ryoma/ryomaAvatar de project-ryoma

    project-ryoma/ryoma

    406Voir sur GitHub↗

    AI Powered Data Agent framework, a comprehensive solution for data analysis, engineering, and visualization.

    Data agent framework for analysis and visualization.

    Python
    Voir sur GitHub↗406
  • mastodonc/kixi.statsAvatar de MastodonC

    MastodonC/kixi.stats

    368Voir sur GitHub↗

    A library of statistical distribution sampling and transducing functions

    Statistical distribution and sampling functions.

    Clojure
    Voir sur GitHub↗368
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