Update dependencies - #44
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pandas 3.0 changed several defaults: all-string object columns are now auto-inferred to a new string dtype (breaking object-dtype detection in dt_cols_to_str), missing values in such columns collapse pd.NA to nan (breaking summarize_freq's display formatting), and pd.Timestamp now defaults to microsecond resolution instead of nanosecond (breaking StatusManager's schema validation). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01YYA95TygUoyxhmKnvT83bN
…ization dt_cols_to_str's purpose is to make columns JSON-serializable, which pandas 3's auto-inferred string dtype (na_value=nan) already satisfies for the na="drop"/"null" paths actually used by save_geo_output. Normalizing to na_value=<NA> only mattered for strict dtype-identity checks in the tests, not for the function's actual behavior, so relax those checks instead of changing dt_cols_to_str. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01YYA95TygUoyxhmKnvT83bN
Use pandas 3's "us" resolution default throughout StatusManager instead of pinning to "ns". Nanosecond resolution added no real precision (Python's datetime only carries microseconds) while needlessly narrowing the representable date range to ~1677-2262; "us" avoids that and matches the new pandas default, so there's no more special-casing needed in add(). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01YYA95TygUoyxhmKnvT83bN
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Changes
us, matching the python datetime precision) instead of nanoseconds.