Software design principles apply to data science and machine learning codebases just as to general applications: enumerated/finite value sets should be modeled with an enumeration type rather than ad hoc constants, abstractions (abstract base classes or structural-typing protocols) should expose only the operations actually depended upon by client code so that the abstraction provides genuine implementation independence, and sequential data-processing steps should avoid reusing a single mutable variable to hold successively different intermediate values, since that variable then has different meaning at different points in the program. Function composition — combining a sequence of unary functions into a single function via reduction, so a value flows through each function in turn — is presented as a general mechanism for expressing multi-step data pipelines without relying on repeated variable reassignment or deeply nested function calls. This belongs to the domain of software design principles as applied to data science pipelines, specifically abstraction design, type consistency, and functional composition.
Software design principles apply to data science and machine learning codebases just as to general applications: enumerated/finite value sets should be modeled with an enumeration type rather than ad…