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Accurately performing date and time calculations in software is non-trivial due to the inherent complexity and variability of temporal concepts such as time zones, daylight saving time (DST) adjustments, leap years and leap seconds, clock drifts, and different calendar systems. Although the challenges are frequently discussed in the grey literature, there has not been any systematic study of date/time issues that have manifested in real software systems. To bridge this gap, we qualitatively study 151 bugs and their associated fixes from open-source Python projects on GitHub to understand: (a) the conceptual categories of date/time computations in which bugs occur, (b) the programmatic operations involved in the buggy computations, and (c) the underlying root causes of these errors. We also analyze metrics such as bug severity and detectability as well as fix size and complexity. Our study produces several interesting findings and actionable insights, such as (1) time-zone-related mistakes are the largest contributing factor to date/time bugs; (2) a majority of the studied bugs involved incorrect construction of date/time values; (3) the root causes of date/time bugs often involve misconceptions about library API behavior, such as default conventions or nuances about edge-case behavior; (4) most bugs occur within a single function and can be patched easily, requiring only a few lines of simple code changes. Our findings indicate that static analysis tools can potentially find common classes of high-impact bugs and that such bugs can potentially be fixed automatically. Based on our insights, we also make concrete recommendations to software developers to harden their software against date/time bugs via automated testing strategies.