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>
90 lines
2.9 KiB
Python
90 lines
2.9 KiB
Python
"""Pivot table sin pandas para datos tabulares list[dict]."""
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from collections import defaultdict
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def pivot(
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rows: list[dict],
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index: str,
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columns: str,
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values: str,
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agg: str = "sum",
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) -> list[dict]:
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"""Transforma datos del formato largo al formato ancho (pivot table).
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Agrupa por `index`, expande los valores unicos de `columns` como nuevas
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columnas y agrega la columna `values` con la funcion indicada.
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Args:
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rows: Lista de dicts con los datos en formato largo.
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index: Nombre de la columna que actua como indice de filas.
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columns: Nombre de la columna cuyos valores unicos se convierten en columnas.
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values: Nombre de la columna cuyos valores se agregan.
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agg: Funcion de agregacion: sum, count, mean, min, max, first, last.
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Returns:
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Lista de dicts con una fila por valor unico de index y una columna
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por cada valor unico de columns. Valores numericos faltantes rellenados
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con 0, valores no numericos con None.
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"""
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# Recopilar valores unicos de columns (orden de aparicion)
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col_values: list = []
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seen_cols: set = set()
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index_order: list = []
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seen_index: set = set()
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for row in rows:
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idx = row.get(index)
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col = row.get(columns)
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if idx not in seen_index:
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seen_index.add(idx)
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index_order.append(idx)
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if col not in seen_cols:
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seen_cols.add(col)
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col_values.append(col)
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# Acumular: groups[index_val][col_val] = lista de values
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groups: dict[any, dict[any, list]] = defaultdict(lambda: defaultdict(list))
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for row in rows:
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idx = row.get(index)
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col = row.get(columns)
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val = row.get(values)
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if val is not None:
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groups[idx][col].append(val)
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# Determinar si los valores son numericos (para relleno de 0)
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sample_vals = [v for g in groups.values() for vs in g.values() for v in vs]
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is_numeric = all(isinstance(v, (int, float)) for v in sample_vals) if sample_vals else True
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def _aggregate(vals: list, func: str):
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if not vals:
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return 0 if is_numeric else None
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if func == "sum":
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return sum(vals)
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if func == "count":
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return len(vals)
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if func == "mean":
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return sum(vals) / len(vals)
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if func == "min":
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return min(vals)
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if func == "max":
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return max(vals)
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if func == "first":
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return vals[0]
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if func == "last":
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return vals[-1]
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raise ValueError(f"Funcion de agregacion no soportada: {func}")
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result = []
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for idx in index_order:
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record: dict = {index: idx}
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for col in col_values:
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vals = groups[idx][col]
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if vals:
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record[col] = _aggregate(vals, agg)
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else:
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record[col] = 0 if is_numeric else None
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result.append(record)
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return result
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