feat(browser): auto-commit con 178 cambios

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

python/functions/datascience/describe_numeric_test.py

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+"""Tests para describe_numeric."""
+
+import os
+import sys
+
+sys.path.insert(0, os.path.dirname(__file__))
+
+from describe_numeric import describe_numeric
+
+# Keys that every result dict must always contain (the eda numeric_sub contract).
+_EXPECTED_KEYS = {
+    "min", "max", "mean", "median", "mode", "std", "variance", "cv",
+    "p1", "p5", "p25", "p50", "p75", "p95", "p99", "iqr",
+    "skew", "kurtosis", "n_outliers", "outlier_pct",
+    "zero_pct", "negative_pct", "distribution_type", "histogram",
+}
+
+
+def test_lista_con_outlier_y_none():
+    """Lista con outlier claro y None descartado."""
+    # Tight cluster around 2-4 plus a None to drop and a clear extreme outlier.
+    # A wide cluster (n=40) keeps std small so the extreme value's z-score
+    # exceeds the 3.0 threshold used by detect_outliers.
+    cluster = [1, 2, 2, 3, 4] * 8  # 40 numeric values, mode == 2
+    values = cluster + [None, 1000]
+    result = describe_numeric(values)
+
+    # Contract: all keys present.
+    assert set(result.keys()) == _EXPECTED_KEYS
+
+    # Non-numeric / missing dropped: 41 numeric values remain.
+    assert result["min"] == 1.0
+    assert result["max"] == 1000.0
+
+    # mean/median reasonable: median sits in the cluster, mean pulled up by 1000.
+    assert result["median"] < result["mean"]
+    assert 0.0 < result["median"] <= 5.0
+    assert result["mean"] > result["median"]
+
+    # mode = most frequent (2 appears twice per block).
+    assert result["mode"] == 2.0
+
+    # At least one z-score outlier detected (the 1000).
+    assert result["n_outliers"] >= 1
+    assert result["outlier_pct"] > 0.0
+
+    # Histogram non-empty and counts cover every numeric value.
+    assert len(result["histogram"]) > 0
+    total = sum(bucket["count"] for bucket in result["histogram"])
+    assert total == 41
+    for bucket in result["histogram"]:
+        assert "lo" in bucket and "hi" in bucket and "count" in bucket
+
+    # No zeros, no negatives in this sample.
+    assert result["zero_pct"] == 0.0
+    assert result["negative_pct"] == 0.0
+
+
+def test_lista_vacia_todo_none():
+    """Lista vacia (o sin numericos) devuelve todas las claves en None."""
+    result = describe_numeric([None, "abc", float("nan")])
+
+    assert set(result.keys()) == _EXPECTED_KEYS
+    for key in _EXPECTED_KEYS - {"histogram"}:
+        assert result[key] is None, f"{key} debe ser None"
+    assert result["histogram"] == []
+
+
+def test_cv_none_cuando_mean_cero():
+    """cv es None cuando la media es 0."""
+    # Symmetric around zero so mean == 0.
+    result = describe_numeric([-2, -1, 0, 1, 2])
+    assert result["mean"] == 0.0
+    assert result["cv"] is None
+    assert result["zero_pct"] == 20.0
+    assert result["negative_pct"] == 40.0
+
+
+def test_iqr_y_percentiles():
+    """iqr = p75 - p25 y percentiles coherentes."""
+    result = describe_numeric(list(range(1, 101)))  # 1..100
+    assert result["iqr"] == result["p75"] - result["p25"]
+    assert result["p1"] <= result["p25"] <= result["p50"] <= result["p75"] <= result["p99"]
+    assert result["min"] == 1.0
+    assert result["max"] == 100.0