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Projects sharing features with Interactive
13 open-source projects similar to dotnet/interactive, ranked by shared indexed features. Tags may describe platforms or build tools rather than the same primary purpose. Check each project’s use case, license, and deployment requirements before treating it as a replacement.
- velocidex/velociraptor
Velocidex/velociraptor
3,769View on GitHubVelociraptor 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
Godigital-forensicsendpoint-discoveryendpoint-protection - vaexio/vaex
vaexio/vaex
8,506View on GitHubVaex 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
Python - kotlin/dataframe
Kotlin/dataframe
1,049View on GitHubThis library is a data processing framework for the JVM that provides a type-safe environment for manipulating structured tabular data. It functions as a comprehensive toolset for performing complex data transformations, aggregations, and statistical analysis, while leveraging compile-time schema validation to ensure structural integrity across data pipelines. The project distinguishes itself through its deep integration with interactive notebook environments and its use of compile-time code generation. By automatically deriving and enforcing schemas from raw inputs, it generates type-safe ac
Kotlindata-analysisdata-sciencedataframe
