feat(browser): auto-commit con 178 cambios

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

python/functions/pipelines/profile_table_test.py

@@ -0,0 +1,83 @@
+"""Tests para profile_table — pipeline EDA one-shot del grupo `eda`.
+
+Crea una DuckDB temporal con tres columnas representativas:
+  - id_str: enteros guardados como VARCHAR ('10','20',...) -> debe promocionarse
+    a inferred_type "numeric" y recibir un bloque col["numeric"].
+  - precio: numerica nativa (DOUBLE).
+  - categoria: categorica textual.
+Luego corre profile_table(write_report=False) y verifica el contrato.
+"""
+
+import os
+import tempfile
+
+import duckdb
+
+from pipelines.profile_table import profile_table
+
+
+def _make_db() -> str:
+    """Crea una DuckDB temporal con la tabla de prueba y devuelve su path."""
+    tmp_dir = tempfile.mkdtemp(prefix="profile_table_test_")
+    db_path = os.path.join(tmp_dir, "t.duckdb")
+    con = duckdb.connect(db_path)
+    con.execute(
+        "CREATE TABLE items ("
+        "  id_str VARCHAR,"   # enteros guardados como texto
+        "  precio DOUBLE,"    # numerica nativa
+        "  categoria VARCHAR" # categorica
+        ")"
+    )
+    rows = [
+        ("10", 9.5, "alfa"),
+        ("20", 12.0, "beta"),
+        ("30", 7.25, "alfa"),
+        ("40", 15.75, "gamma"),
+        ("50", 3.0, "beta"),
+        ("60", 22.4, "alfa"),
+    ]
+    con.executemany("INSERT INTO items VALUES (?, ?, ?)", rows)
+    con.close()
+    return db_path
+
+
+def _col(profile: dict, name: str) -> dict:
+    return next(c for c in profile["columns"] if c["name"] == name)
+
+
+def test_varchar_integer_promotes_to_numeric():
+    db_path = _make_db()
+    r = profile_table(db_path, "items", sample=5000, write_report=False)
+
+    # status ok y sin tocar disco.
+    assert r["status"] == "ok", r
+    assert r["report_md_path"] is None
+    assert r["report_json_path"] is None
+
+    prof = r["profile"]
+
+    # La columna VARCHAR-entera se promociono a numeric con bloque numeric.
+    id_col = _col(prof, "id_str")
+    assert id_col["inferred_type"] == "numeric", id_col["inferred_type"]
+    assert id_col["numeric"] is not None
+    assert id_col["numeric"]["min"] == 10.0
+    assert id_col["numeric"]["max"] == 60.0
+
+    # La numerica nativa sigue siendo numeric con su bloque.
+    precio_col = _col(prof, "precio")
+    assert precio_col["inferred_type"] == "numeric"
+    assert precio_col["numeric"] is not None
+
+    # La categorica recibe su bloque categorical.
+    cat_col = _col(prof, "categoria")
+    assert cat_col["inferred_type"] in ("categorical", "text")
+    assert cat_col["categorical"] is not None
+    assert cat_col["categorical"]["mode"] == "alfa"
+
+    # key_candidates es una lista; quality_score existe (tabla y columnas).
+    assert isinstance(prof["key_candidates"], list)
+    assert prof["quality_score"] is not None
+    assert id_col["quality_score"] is not None
+
+    # type_breakdown recalculado refleja la promocion (>=2 numeric).
+    assert prof["type_breakdown"]["numeric"] >= 2