feat: funciones Python datascience, finance, cybersecurity y pipelines

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>

python/functions/datascience/pivot.md

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+---
+name: pivot
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: pure
+signature: "def pivot(rows: list[dict], index: str, columns: str, values: str, agg: str = 'sum') -> list[dict]"
+description: "Pivot table sin pandas. Agrupa por index, expande valores unicos de columns como nuevas columnas y agrega values con la funcion indicada (sum, count, mean, min, max, first, last)."
+tags: [datascience, tabular, pivot, transform, aggregation, python]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: ["collections"]
+tested: true
+tests:
+  - "Pivot basico con sum"
+  - "Pivot con count y mean"
+  - "Valores faltantes rellenados con 0"
+  - "Una sola fila"
+  - "Multiples valores por celda requieren agregacion"
+test_file_path: "python/functions/datascience/pivot_test.py"
+file_path: "python/functions/datascience/pivot.py"
+---
+
+## Ejemplo
+
+```python
+rows = [
+    {"region": "US", "product": "A", "sales": 10},
+    {"region": "US", "product": "B", "sales": 20},
+    {"region": "EU", "product": "A", "sales": 15},
+]
+pivot(rows, index="region", columns="product", values="sales")
+# [{"region": "US", "A": 10, "B": 20}, {"region": "EU", "A": 15, "B": 0}]
+```
+
+## Notas
+
+Funcion pura sin dependencias externas (solo collections.defaultdict de stdlib).
+Preserva el orden de aparicion de los valores de index y columns.
+Valores numericos faltantes se rellenan con 0; no numericos con None.