We present a framework in which program analysis – type checking, bug finding, and equivalence verification – is organized as computing the Čech cohomology of a semantic presheaf over a program's site category. The presheaf assigns refinement-type information to observation sites and restricts it along data-flow morphisms. The cohomology group $H^{0}$ is the space of globally consistent typings. The first cohomology group $H^{1}$ classifies gluing obstructions – bugs, type errors, and equivalence failures – each localized to a specific pair of disagreeing sites. This formulation yields three concrete results unavailable in prior work: (1) the rank of $H^1$ over $F_{2}$ counts the minimum independent fixes; (2) $H_{1}(U, Iso) = 0$ is sound and complete for behavioral equivalence; (3) Mayer-Vietoris enables compositional, incremental obstruction counting. We implement the framework in Deppy, a Python analysis tool, and evaluate it on a suite of 375 benchmarks: 133 bug-detection programs, 134 equivalence pairs, and 108 specification-satisfaction checks. Deppy achieves {100% bug-detection recall} (69% precision, F1 = 81%), 99% equivalence accuracy with zero false equivalences, and 98% spec accuracy with zero false satisfactions – outperforming mypy and pyright, which report zero findings on unannotated code. The analysis models Python semantics as algebraic geometry: variables live on the generic fiber (non-None) unless on the closed nullable subscheme, integers form Spec($\mathbb{Z}$) with no bounded section (no overflow), and short-circuit evaluation defines an open-set topology on the presheaf.