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>
41 lines
1.3 KiB
Markdown
41 lines
1.3 KiB
Markdown
---
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name: melt
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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 melt(rows: list[dict], id_vars: list[str], value_vars: list[str] | None = None, var_name: str = 'variable', value_name: str = 'value') -> list[dict]"
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description: "Inversa de pivot. Convierte columnas en filas (formato largo). Cada combinacion de id_vars + value_var genera una fila. Si value_vars es None, derrite todas las columnas no-id."
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tags: [datascience, tabular, melt, unpivot, transform, python]
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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: []
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tested: true
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tests:
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- "Melt basico"
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- "Multiples id_vars"
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- "value_vars None derrite todas las columnas no-id"
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- "Fila con campo faltante en value_vars"
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test_file_path: "python/functions/datascience/melt_test.py"
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file_path: "python/functions/datascience/melt.py"
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---
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## Ejemplo
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```python
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rows = [{"region": "US", "q1": 10, "q2": 20}]
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melt(rows, id_vars=["region"], value_vars=["q1", "q2"])
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# [{"region": "US", "variable": "q1", "value": 10},
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# {"region": "US", "variable": "q2", "value": 20}]
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```
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## Notas
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Funcion pura sin dependencias externas.
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Si un campo de value_vars no existe en la fila, su valor sera None.
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El parametro value_vars=None es util cuando se desconoce el schema exacto.
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