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- .claude/CLAUDE.md - .claude/commands/subagentes.md - .claude/rules/INDEX.md - .mcp.json - bash/functions/cybersecurity/analyze_dns.md - bash/functions/cybersecurity/audit_http_headers.md - bash/functions/cybersecurity/audit_ssh_config.md - bash/functions/cybersecurity/check_firewall.md - bash/functions/cybersecurity/detect_suspicious_users.md - bash/functions/cybersecurity/encrypt_file.md - ... Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
57 lines
2.1 KiB
Markdown
57 lines
2.1 KiB
Markdown
---
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name: pivot
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kind: function
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lang: py
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domain: datascience
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version: "1.0.0"
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purity: pure
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signature: "def pivot(rows: list[dict], index: str, columns: str, values: str, agg: str = 'sum') -> list[dict]"
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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)."
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tags: [datascience, tabular, pivot, transform, aggregation, python, pendiente-usar]
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uses_functions: []
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uses_types: []
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returns: []
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returns_optional: false
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error_type: ""
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imports: ["collections"]
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params:
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- name: rows
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desc: "lista de dicts en formato largo (ej: [{'region': 'US', 'product': 'A', 'sales': 10}, ...])"
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- name: index
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desc: "columna que sera el indice de filas en la tabla pivote (ej: 'region'). Valores unicos -> filas."
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- name: columns
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desc: "columna cuyos valores unicos se expanden como nuevas columnas (ej: 'product'). Valores unicos -> columnas."
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- name: values
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desc: "columna con los valores a agregar en las celdas (ej: 'sales')"
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- name: agg
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desc: "funcion de agregacion si hay multiples valores por celda (defecto: 'sum'). Otras: count, mean, min, max, first, last."
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output: "lista de dicts en formato ancho, donde el indice y las columnas expandidas forman el schema"
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tested: true
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tests:
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- "Pivot basico con sum"
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- "Pivot con count y mean"
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- "Valores faltantes rellenados con 0"
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- "Una sola fila"
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- "Multiples valores por celda requieren agregacion"
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test_file_path: "python/functions/datascience/pivot_test.py"
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file_path: "python/functions/datascience/pivot.py"
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---
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## Ejemplo
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```python
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rows = [
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{"region": "US", "product": "A", "sales": 10},
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{"region": "US", "product": "B", "sales": 20},
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{"region": "EU", "product": "A", "sales": 15},
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]
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pivot(rows, index="region", columns="product", values="sales")
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# [{"region": "US", "A": 10, "B": 20}, {"region": "EU", "A": 15, "B": 0}]
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```
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## Notas
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Funcion pura sin dependencias externas (solo collections.defaultdict de stdlib).
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Preserva el orden de aparicion de los valores de index y columns.
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Valores numericos faltantes se rellenan con 0; no numericos con None.
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