This chapter motivates the choice of Python as a mathematical workbench, contrasting its open-source ecosystem and rich numerical libraries with legacy tools such as MATLAB, R, and Mathematica. After establishing a reproducible development environment, it builds a rigorous foundation in the language itself—covering core syntax and semantics, primitive and composite data types, control-flow constructs, functional abstraction, input/output, object-oriented design patterns, and iterator/generator paradigms—all while emphasising mathematically literate coding style, numerical precision, and unit-tested best practices that will underpin every subsequent chapter.