Sound static analyses for large subsets of static programming languages such as C are now widespread. For example the Astrée static analyzer soundly overapproximates the behavior of C programs that do not contain any dynamic code loading, longjmp statements nor recursive functions. The sound and precise analysis of widely used dynamic programming languages like JavaScript and Python remains a challenge. This paper examines the variation of static analyses of Python-in precision, time and memory usage-by adapting three parameters: (i) the value sensitivity, (ii) the allocation sensitivity and (iii) the activation of an abstract garbage collector. It is not clear yet which level of sensitivity constitutes a sweet spot in terms of precision versus efficiency to achieve a meaningful Python analysis. We thus perform an experimental evaluation using a prototype static analyzer on benchmarks a few thousand lines long. Key findings are: the value analysis does not improve the precision over type-related alarms; the value analysis is three times costlier than the type analysis; the allocation sensitivity depends on the value sensitivity; using an abstract garbage collector lowers memory usage and running times, but does not affect precision.