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Array-like collection data structures are widely established in Python'sscientific computing-ecosystem for high-performance computations. The structuremaps well to regular, gridded lattice structures that are common tocomputational problems in physics and geosciences. High performance is,however, only guaranteed for static computations with a fixed computationaldomain. We show that for dynamic computations within an actively changingcomputational domain, the array-like collections provided by NumPy and itsderivatives are a bottleneck for large computations. In response, we describethe integration of naturally-dynamic collection data structures (e.g.double-linked lists) into NumPy simulations and \textit{ctypes}-basedC-bindings. Our benchmarks verify and quantify the performance increaseattributed to the change of the collection data structure. Our applicationscenario, a Lagrangian (oceanic) fluid-flow particle simulation within the\textit{Parcels} framework, demonstrates the speed-up yield in a realisticsetting and demonstrates the novel capabilities that are facilitated byoptimised collection data structures.

Speeding up Python-based Lagrangian Fluid-Flow Particle Simulations via Dynamic Collection Data Structures