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Reviewed commit: aa675e3a64
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| return None | ||
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| with csv_path.open('r', encoding='utf-8-sig', newline='') as f: | ||
| rows = list(csv.DictReader(f)) |
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Stream recent-change rows instead of materializing the CSV
This line loads the entire file into memory before computing recent-change signals, so every dataset with both a date and numeric column incurs an O(n) in-memory copy in addition to profiling; on large CSVs this can significantly slow analysis or trigger memory failures. Since analyze_multiple_csv is otherwise stream-oriented, this regression can break large-file workflows and should be replaced with incremental windowed processing.
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| raw = str(row.get(ncol, '')).strip().replace(',', '') | ||
| if not raw: | ||
| continue | ||
| try: | ||
| values[ncol] = float(raw) |
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Parse recent-change numerics with the shared normalizer
Recent-change detection parses numeric cells with float(raw) after only removing commas, which drops values like currency/percent/parenthesized numbers that profiling already treats as numeric via _to_float; in those common formats, dtype is float but prev_vals/recent_vals stay empty and the 최근 급변 reason is silently missed. Reusing the same numeric normalization here would keep reason generation consistent with the profile stage.
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