Projects sharing features with Dataframe

This project is a comprehensive pandas data analysis tutorial and instructional guide designed for learning data manipulation and analysis. It serves as a tabular data processing guide and a manual for time series analysis, providing a structured approach to cleaning, merging, and transforming datasets. The repository functions as a data feature engineering course, providing tutorials on constructing and selecting dataset features to improve machine learning model performance. It also includes a vectorized data operations guide for performing element-wise mathematical computations and matrix

dplyr is an R data manipulation library that provides a grammar for transforming tabular data frames. It functions as an in-memory data frame processor and a relational data algebra tool, using a consistent set of verbs to filter, select, and summarize data. The project includes a SQL translation engine that converts high-level data manipulation expressions into optimized queries. This allows users to perform transformations directly on remote relational databases and cloud storage without pulling data locally. The library covers a broad range of tabular operations, including column mutation

Tortoise ORM is an asynchronous object-relational mapper for Python that mirrors Django's model and queryset API while running on asyncio. It defines database tables as Python classes with typed fields and supports foreign key, many-to-many, and one-to-one relations, providing a chainable query API for filtering, annotating, grouping, and prefetching related objects without blocking the event loop. The ORM includes a built-in migration engine that detects model changes, generates migration files, and applies or reverts schema changes through a command-line tool. It connects to PostgreSQL, MyS

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

mimic-code is a clinical data analysis framework and toolset for processing deidentified electronic health records and intensive care unit data. It provides a healthcare SQL query library and a processing tool to transform raw health records into formats suitable for longitudinal analysis and machine learning. The project features a medical research notebook environment that integrates with cloud-hosted datasets, allowing for remote querying and analysis. It includes a DICOM imaging pipeline to retrieve chest radiographs and link medical imaging with structured clinical metadata. The framewo

Pinot is a distributed, columnar analytical database designed for high-concurrency, low-latency query processing. It functions as a real-time OLAP datastore, enabling interactive, user-facing analytics by ingesting and querying massive datasets from both streaming and batch sources. The system architecture relies on a centralized controller for cluster coordination and a distributed segment-based storage model to ensure horizontal scalability. The platform distinguishes itself through a hybrid ingestion pipeline that unifies real-time event streams and historical batch data into a single quer

This repository serves as an educational collection of Jupyter notebooks designed to demonstrate distributed data processing and machine learning workflows. It provides a structured resource for learning how to perform large-scale statistical analysis, execute relational queries, and develop predictive models using Python and Apache Spark. The project distinguishes itself by offering practical, interactive guides that bridge the gap between theoretical distributed computing concepts and applied data science. By utilizing notebook environments, it enables users to document and execute code for

Velociraptor is a digital forensics and incident response platform, endpoint detection and response system, and visibility tool. It provides a query engine and remote forensic collector used to hunt for indicators of compromise and perform triage across a fleet of hosts. The system is distinguished by its specialized query language for interrogating host state and parsing binary files. It features a notebook environment that combines markdown documentation with executable query cells to standardize investigative workflows and enable collaborative reporting. The platform covers a wide range o

Papermill is a Jupyter notebook execution engine and parameterization framework designed to run notebooks programmatically. It allows users to inject custom input values into notebooks to execute the same logic across different datasets, transforming interactive notebooks into reproducible data science pipelines. The project functions as a language-agnostic notebook runner and orchestrator, supporting kernels for Python, R, Julia, and Scala. It is distinguished by its cloud-integrated runner capabilities, featuring built-in handlers to read and write notebooks directly to storage providers su

This project is a community-curated directory of open-source software designed for deployment in private server environments and home labs. It serves as a comprehensive resource for discovering independent, self-hosted alternatives to mainstream cloud services, enabling users to maintain full data ownership and control over their digital infrastructure. The directory is structured through a hierarchical taxonomy that organizes a vast collection of applications into logical categories, ranging from media management and data analytics to private communication and team productivity tools. It dis

Positron is a data science integrated development environment and AI-powered code editor designed for polyglot development, specifically supporting Python and R. It functions as a remote compute workspace that separates the user interface from the execution kernel via SSH or container integration. The environment features a deep integration of large language models that provide context-aware suggestions and automated data analysis by accessing real-time interpreter state, in-memory objects, and plot outputs. It distinguishes itself through a polyglot runtime bridge that enables cross-language

smartTable is a data grid component and table view framework for Android. It functions as an annotation-driven UI library that uses class and field markers to automatically map data models to visual columns and define table properties. The project distinguishes itself through deep spreadsheet integration, providing tools to import and export data to and from Excel files while preserving cell formatting, styles, colors, and alignment. It also features a canvas-based rendering system that supports complex grid layouts with merged cells, frozen headers, and column locking. The library covers a

This project is a comprehensive collection of high-performance UI components and SDKs designed for building data-rich, cross-platform applications in Flutter. It provides a unified library of modular widgets and processing tools that enable developers to implement complex interface requirements, including advanced scheduling, tabular data management, and interactive data visualization, using a consistent declarative architecture. The library distinguishes itself through specialized, enterprise-grade capabilities that extend beyond standard UI elements. It includes a robust document processing

This project is a JavaScript spreadsheet component and web-based data table that provides an interactive grid for managing tabular data. It functions as a cross-framework UI component compatible with React, Vue, and jQuery, and can also be embedded as a standalone web component. The grid is specifically designed for XLSX interoperability, allowing users to import and export XLSX files while preserving formulas and styles. It distinguishes itself through real-time collaboration for simultaneous multi-user editing and a flexible system for creating custom cell editors and specialized column typ

Kotlinx.html is a domain-specific language for Kotlin that provides a type-safe builder pattern for constructing HTML markup. By leveraging nested function calls and lambda expressions, the library enforces schema constraints at compile time to ensure the generation of valid document structures and the prevention of common syntax errors. The library distinguishes itself through an extension-based schema that allows developers to define custom tags and attributes without modifying the core source code. It supports both server-side rendering and direct browser document object model manipulation

Hazelcast is a distributed data platform that combines an in-memory data grid with a stream processing engine to support real-time analytics and event-driven applications. It functions as a partitioned, distributed key-value store that replicates data across cluster nodes to provide low-latency access and high availability. The platform also serves as a distributed SQL query engine, allowing users to execute standard SQL statements against both in-memory datasets and external data sources. What distinguishes Hazelcast is its use of a distributed consensus subsystem to maintain strongly consis

Countly is a self-hosted product analytics and engagement platform that tracks user behavior across mobile, web, and desktop applications. It collects and analyzes device properties, user actions, and session lifecycle data to understand engagement patterns, while also providing crash reporting, push notification delivery, and A/B testing capabilities. The platform is designed for privacy-first deployment, with built-in consent management and the ability to run entirely on private infrastructure. The platform distinguishes itself through its comprehensive feature set that combines analytics w

Vaex is a high-performance Apache Arrow DataFrame library and out-of-core data processing engine designed to handle billion-row tabular datasets in Python. It functions as a lazy evaluation framework that defers computations and transformations until results are required, enabling the processing of datasets that exceed available system RAM by mapping files directly from disk. The project distinguishes itself as a tool for big data visualization and exploration, specifically integrated for use within interactive notebooks. It provides specialized capabilities for machine learning feature engin

Tabulator is an interactive data table library and virtual DOM data grid used to create high-performance tables from JSON or arrays. It functions as a hierarchical data viewer and a spreadsheet interface component, capable of rendering thousands of records efficiently through viewport-based virtualization and progressive loading. The library distinguishes itself by providing a full spreadsheet interface mode with multi-sheet management, cell range selection, and bulk copy-paste capabilities. It supports complex data architectures, including nested data field mapping, expandable tree structure

GreptimeDB is a distributed, open-source time-series database built for unified observability. It stores and queries metrics, logs, and traces together in a single columnar engine, supporting both SQL and PromQL for analysis. The database is designed as a Kubernetes-native operator with a decoupled compute and storage architecture, enabling horizontal scaling and multi-region deployment. What distinguishes GreptimeDB is its role as a multi-protocol ingestion gateway, accepting data through OpenTelemetry, Prometheus Remote Write, InfluxDB, Loki, Elasticsearch, Kafka, and MQTT protocols without

This project is a Python-based framework that functions as a generative AI agent for programmatic data analysis. It enables users to interact with structured data sources through natural language prompts, translating these requests into executable code to perform analysis, data cleaning, and visualization. By maintaining conversational context across multi-turn interactions, the system allows for iterative exploration and the building of complex data narratives. The framework distinguishes itself through a robust semantic layer and secure execution model. It maps raw datasets to descriptive m

This is a grammar of graphics visualization library used to build charts by mapping tabular data to visual marks. It functions as an SVG data visualization tool and an exploratory data analysis API, allowing users to render complex visualizations and geographic maps. The library features a GeoJSON map renderer that projects spherical coordinates into two-dimensional pixel space and an Apache Arrow visualization interface for high-efficiency data processing. Its capability surface covers data transformation through binning and grouping, visual encoding via automatic scale inference and color

Typia is a compile-time code generator that transforms TypeScript type annotations into runtime validation, serialization, and schema functions without requiring decorators or separate schema files. It generates optimized validation and serialization code during TypeScript compilation, producing dedicated functions for each type that eliminate runtime schema objects for faster execution. The project extends this core capability into several integrated areas. It generates fully typed client SDKs from NestJS controller source code, keeping server and client types synchronized automatically. It

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

Data engineering practice repository providing tutorials, distributed processing engines, and Python data pipeline automation scripts. The system encompasses automated data validation, distributed compute aggregation, embedded columnar querying, lazy evaluation planning, partitioned storage export, and cloud storage retrieval. The capability surface covers cloud integration and storage, data engineering and pipelines, data processing and analytics, data quality and testing, database and storage, file management, and monitoring and observability.

This library provides a framework for defining typed, immutable data structures in PHP. It enables developers to construct structured objects from raw input arrays, ensuring consistent data shapes and schema integrity across different layers of an application. By enforcing strict property types and validation rules during instantiation, the library prevents invalid data states and simplifies the handling of complex information. The project distinguishes itself through its use of attribute-driven metadata, which allows for property renaming, custom validation, and serialization logic directly

This project is a comprehensive library of practical Python code examples and patterns. It provides a collection of scripts and snippets designed to demonstrate a wide range of programming tasks, from basic syntax to advanced implementation patterns. The repository focuses on several core domains, including the implementation of concurrency and multithreading examples, data analysis snippets for cleaning and manipulating tabular data, and various data visualization examples. It also covers automation scripts for file system management and a variety of general programming patterns. Additional