egutierrez
63a9cb5273
Datascience: aggregate_by_group, deduplicate_entities/relations, detect_drift, diff_entities/relations, extract_entities/relations_llm, hotness_score, melt, merge_graphs, pivot, build_entity/relation_schema_prompt. Finance: avellaneda_stoikov_quotes, generate_gbm_prices, generate_taker_order, hawkes_intensity + módulo finance.py. Cybersecurity: envelope_encrypt/decrypt + módulo cybersecurity.py. Pipelines: extraction_pipeline, monte_carlo_market, run_market_sim. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
72 lines
2.3 KiB
Python
72 lines
2.3 KiB
Python
"""GROUP BY + agregaciones sobre datos tabulares list[dict]."""
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from collections import defaultdict
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def aggregate_by_group(
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rows: list[dict],
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group_by: list[str],
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aggs: dict[str, str],
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) -> list[dict]:
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"""Agrupa filas por una o varias columnas y aplica agregaciones.
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Equivalente a SQL GROUP BY con funciones de agregacion.
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La funcion 'collect' acumula todos los valores en una lista.
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Los valores None se ignoran en agregaciones numericas (sum, mean, min, max).
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Args:
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rows: Lista de dicts con los datos.
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group_by: Lista de columnas por las que agrupar.
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aggs: Dict de {columna: funcion}. Funciones: sum, mean, count,
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min, max, first, last, collect.
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Returns:
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Lista de dicts con las columnas de group_by mas los campos agregados.
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El orden de las filas sigue el orden de primera aparicion del grupo.
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"""
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# Mantener orden de grupos con lista de claves
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group_keys: list[tuple] = []
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seen_groups: set[tuple] = set()
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buckets: dict[tuple, dict[str, list]] = defaultdict(lambda: defaultdict(list))
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for row in rows:
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gk = tuple(row.get(col) for col in group_by)
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if gk not in seen_groups:
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seen_groups.add(gk)
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group_keys.append(gk)
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for col in aggs:
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val = row.get(col)
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buckets[gk][col].append(val)
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def _aggregate(vals: list, func: str):
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if func == "collect":
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return vals
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if func == "count":
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return len(vals)
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if func == "first":
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return vals[0] if vals else None
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if func == "last":
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return vals[-1] if vals else None
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# Para sum, mean, min, max: ignorar None
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numeric = [v for v in vals if v is not None]
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if not numeric:
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return None
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if func == "sum":
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return sum(numeric)
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if func == "mean":
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return sum(numeric) / len(numeric)
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if func == "min":
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return min(numeric)
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if func == "max":
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return max(numeric)
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raise ValueError(f"Funcion de agregacion no soportada: {func}")
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result = []
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for gk in group_keys:
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record: dict = dict(zip(group_by, gk))
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for col, func in aggs.items():
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record[col] = _aggregate(buckets[gk][col], func)
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result.append(record)
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return result
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