{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis"}],"enrichment":{"capability":"Visions defines and detects semantic data types across pandas, numpy, spark, and Python sequences, automatically inferring the most appropriate type even when data has been transformed or corrupted.","skillfed_tags":["data-type-inference","tabular-data","multi-backend"],"use_cases":["Automatically detect and correct data types in CSV or database imports where type information is lost or ambiguous.","Build domain-specific type systems for specialized data processing pipelines (e.g., financial, scientific, or categorical data).","Clean and standardize tabular data before analysis by inferring the intended type and casting accordingly.","Integrate type detection into data profiling or validation workflows to flag unexpected type patterns.","Extend type detection to custom backends (e.g., Dask) by registering new type implementations via annotations."],"what_it_does":"Visions is a semantic data type library that goes beyond simple type detection by building a graph of type relationships and using that structure to both identify what type your data actually is and infer what it should be. It handles common real-world messiness\u2014integers stored as floats with trailing zeros, missing values, strings that represent numbers\u2014and can automatically clean and cast data to the inferred type.\n\nThe package works across multiple data backends (pandas, numpy, spark, and plain Python sequences) through a dispatch-based architecture, letting you define custom semantic types for domain-specific purposes. It ships with a complete default typeset covering common use cases, and you can extend it by registering new type implementations for frameworks like Dask.","worth_installing":"Yes. Visions is actively maintained, has no known vulnerabilities, installs with low friction, and solves a real problem in data processing\u2014inferring semantic types from messy real-world data. It is particularly valuable if you work with pandas or need customizable type detection across multiple backends. The permissive BSD-4-Clause license poses no barrier to adoption."},"id":"visions","links":{"html":"https://skillfed.io/packages/visions","md":"https://skillfed.io/packages/visions.md","pypi":"https://pypi.org/project/visions/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-26","license_spdx":"BSD-4-Clause","license_treatment":"permissive","name":"visions","python_support":"supports_current","summary":"Visions"},"popularity":{"monthly_downloads":1374454,"position":3988,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.8.2"}