feat(browser): auto-commit con 178 cambios

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-06-20 18:22:23 +02:00
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+---
+id: summarize_categorical_py_datascience
+name: summarize_categorical
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: pure
+signature: "def summarize_categorical(values: list, top_k: int = 10) -> dict"
+description: "Profiles a categorical/text column for EDA: top-k frequencies, mode, distinct count, Shannon entropy (bits), imbalance ratio and string-length stats. None is dropped; empty string counts as a value. Produces the `categorical_sub` block of an eda ColumnProfile."
+tags: [eda, categorical, frequency, entropy, profiling, datascience, pure]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: [math, collections]
+example: |
+  from summarize_categorical import summarize_categorical
+  result = summarize_categorical(["a", "a", "b", "c", "a", None, ""])
+tested: true
+tests:
+  - "test_summarize_categorical_repeated"
+  - "test_summarize_categorical_empty"
+  - "test_summarize_categorical_all_none"
+  - "test_summarize_categorical_single_value"
+  - "test_summarize_categorical_top_k"
+  - "test_summarize_categorical_keys"
+test_file_path: "python/functions/datascience/summarize_categorical_test.py"
+file_path: "python/functions/datascience/summarize_categorical.py"
+params:
+  - name: values
+    desc: "List of categorical/text values. None entries are discarded from every computation; an empty string \"\" is kept as the empty-string category (counts and has length 0)."
+  - name: top_k
+    desc: "Maximum number of most-frequent values to include in the `top` list. Default 10. Does not affect n_distinct/entropy/imbalance."
+output: "Dict with the exact keys top, mode, mode_pct, n_distinct, entropy, imbalance, len_mean, len_min, len_max. `top` is a list of {value, count, pct} sorted by count descending (pct over the non-null total). When there are no non-null values, top=[] and every other key is None. With a single distinct value, entropy=0.0 and imbalance=1.0."
+---
+
+## Ejemplo
+
+```python
+from summarize_categorical import summarize_categorical
+
+summarize_categorical(["a", "a", "b", "c", "a", None, ""])
+# {
+#   "top": [
+#     {"value": "a", "count": 3, "pct": 0.5},
+#     {"value": "b", "count": 1, "pct": 0.1666...},
+#     {"value": "c", "count": 1, "pct": 0.1666...},
+#     {"value": "",  "count": 1, "pct": 0.1666...},
+#   ],
+#   "mode": "a", "mode_pct": 0.5,
+#   "n_distinct": 4,
+#   "entropy": 1.79...,        # Shannon entropy in bits
+#   "imbalance": 3.0,          # max_count(3) / min_count(1)
+#   "len_mean": 0.833..., "len_min": 0, "len_max": 1,
+# }
+```
+
+## Cuando usarla
+
+Úsala al perfilar una columna categórica o de texto en un EDA: cuando necesites
+el bloque `categorical` de un ColumnProfile del grupo `eda` (top valores, moda,
+cardinalidad, entropía/desbalanceo de la distribución y estadísticas de longitud
+de los strings). Pásale la lista de valores crudos de la columna; `None` se
+ignora automáticamente.
+
+## Notas
+
+Función pura, solo stdlib (`collections.Counter` + `math.log2`). No usa numpy ni
+pandas. La entropía es de Shannon en base 2 (bits): 0.0 con un único valor
+distinto, máxima cuando todos los valores son distintos. `imbalance` es
+`max_count / min_count` sobre los valores distintos (1.0 si solo hay uno).