  Array-like collection data structures are widely established in Python's
scientific computing-ecosystem for high-performance computations. The structure
maps well to regular, gridded lattice structures that are common to
computational problems in physics and geosciences. High performance is,
however, only guaranteed for static computations with a fixed computational
domain. We show that for dynamic computations within an actively changing
computational domain, the array-like collections provided by NumPy and its
derivatives are a bottleneck for large computations. In response, we describe
the integration of naturally-dynamic collection data structures (e.g.
double-linked lists) into NumPy simulations and \textit{ctypes}-based
C-bindings. Our benchmarks verify and quantify the performance increase
attributed to the change of the collection data structure. Our application
scenario, a Lagrangian (oceanic) fluid-flow particle simulation within the
\textit{Parcels} framework, demonstrates the speed-up yield in a realistic
setting and demonstrates the novel capabilities that are facilitated by
optimised collection data structures.
