Normalnormal

Normalnormal is an open-source software library and methodological framework for data normalization and distribution management in machine learning and statistics. The name signals the library’s focus on normalizing data toward approximate Gaussian distributions to improve comparability across features and datasets.

Origin and development: The project emerged in 2019 from a community of data scientists who sought a unified toolkit for normalization that could be integrated with existing data pipelines. The name combines "normal distribution" with "normalization." The library has been maintained on a public repository with contributions from researchers and practitioners worldwide, and it emphasizes interoperability with common data science workflows.

Features: Normalnormal provides several normalization strategies, including z-score standardization, min–max scaling, robust scaling for outlier-prone data, and transformations toward normality such as Box-Cox and Yeo-Johnson. It includes utilities to assess normality, detect outliers, and compare normalization schemes through cross-validation and visualization. The library prioritizes compatibility with major machine learning frameworks and can be used in Python with bindings for additional languages.

Impact and reception: It has been adopted in educational settings and in small to medium-scale data projects for consistent preprocessing. Critics note that routine normalization choices may introduce biases if not carefully matched to downstream models, and that abstraction can obscure underlying data characteristics when used without domain knowledge. Development continues through community contributions and ongoing documentation improvements.

See also: data normalization, standardization, Box-Cox transformation, Yeo-Johnson transformation, Min–Max scaling, z-score.