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
parent 7d100e7f3e
commit 763e06c127
178 changed files with 19917 additions and 317 deletions
@@ -0,0 +1,166 @@
+"""Tests para render_eda_markdown."""
+
+import sys
+import os
+
+sys.path.insert(0, os.path.dirname(__file__))
+
+from render_eda_markdown import render_eda_markdown
+
+
+def _sample_profile(correlations=None, llm=None):
+    return {
+        "table": "sales",
+        "source": "data/sales.csv",
+        "profiled_at": "2026-06-20T10:00:00Z",
+        "n_rows": 1000,
+        "n_cols": 2,
+        "size_bytes": 40960,
+        "duplicate_rows": 3,
+        "duplicate_pct": 0.003,
+        "constant_cols": [],
+        "all_null_cols": [],
+        "null_cell_pct": 0.015,
+        "type_breakdown": {"numeric": 1, "categorical": 1},
+        "quality_score": 0.92,
+        "key_candidates": ["order_id"],
+        "correlations": correlations,
+        "llm": llm,
+        "models": None,
+        "columns": [
+            {
+                "name": "price",
+                "physical_type": "DOUBLE",
+                "inferred_type": "float",
+                "semantic_type": "currency",
+                "count": 1000,
+                "n_rows": 1000,
+                "null_count": 0,
+                "null_pct": 0.0,
+                "distinct_count": 857,
+                "unique_pct": 0.857,
+                "flags": [],
+                "quality_score": 0.95,
+                "numeric": {
+                    "min": 1.0,
+                    "max": 99.0,
+                    "mean": 42.5,
+                    "median": 40.0,
+                    "std": 12.3,
+                    "p25": 30.0,
+                    "p75": 55.0,
+                    "p95": 80.0,
+                    "p99": 95.0,
+                    "skew": 0.4,
+                    "kurtosis": 2.1,
+                    "outlier_pct": 0.012,
+                    "distribution_type": "right-skewed",
+                    "histogram": [
+                        {"lo": 0, "hi": 25, "count": 100},
+                        {"lo": 25, "hi": 50, "count": 500},
+                        {"lo": 50, "hi": 75, "count": 300},
+                        {"lo": 75, "hi": 100, "count": 50},
+                    ],
+                },
+                "categorical": None,
+                "datetime": None,
+            },
+            {
+                "name": "region",
+                "physical_type": "VARCHAR",
+                "inferred_type": "string",
+                "semantic_type": "category",
+                "count": 1000,
+                "n_rows": 1000,
+                "null_count": 10,
+                "null_pct": 0.01,
+                "distinct_count": 3,
+                "unique_pct": 0.003,
+                "flags": ["low_cardinality"],
+                "quality_score": 0.80,
+                "numeric": None,
+                "categorical": {
+                    "top": [
+                        {"value": "north", "count": 500, "pct": 0.5},
+                        {"value": "south", "count": 300, "pct": 0.3},
+                        {"value": "east", "count": 200, "pct": 0.2},
+                    ],
+                    "mode": "north",
+                    "mode_pct": 0.5,
+                    "n_distinct": 3,
+                    "entropy": 1.48,
+                },
+                "datetime": None,
+            },
+        ],
+    }
+
+
+def test_contains_title_and_sections():
+    md = render_eda_markdown(_sample_profile())
+    assert "# EDA — sales" in md
+    assert "## Overview" in md
+    assert "## Columnas" in md
+    assert "## Numéricas" in md
+    assert "## Categóricas" in md
+
+
+def test_contains_column_names():
+    md = render_eda_markdown(_sample_profile())
+    assert "price" in md
+    assert "region" in md
+
+
+def test_contains_sparkline():
+    md = render_eda_markdown(_sample_profile())
+    # Histogram sparkline must render with block characters.
+    assert "histogram: `" in md
+    assert any(block in md for block in "▁▂▃▄▅▆▇█")
+
+
+def test_pct_fields_scaled_by_100():
+    # *_pct fields are fractions 0-1; the render must show them ×100.
+    md = render_eda_markdown(_sample_profile())
+    # unique_pct=0.857 -> "85.70%" (must NOT show the raw "0.86%").
+    assert "85.7" in md
+    assert "0.86%" not in md
+    # categorical top pct=0.5 -> "50.0%".
+    assert "50.0" in md
+    # outlier_pct=0.012 -> "1.20%".
+    assert "1.20%" in md
+
+
+def test_pct_handles_none_as_blank():
+    profile = {
+        "table": "t",
+        "columns": [
+            {
+                "name": "c",
+                "inferred_type": "float",
+                "null_pct": None,
+                "unique_pct": None,
+                "quality_score": 0.5,
+            }
+        ],
+    }
+    # None pct renders as empty cell, never "None%" or a crash.
+    md = render_eda_markdown(profile)
+    assert "None%" not in md
+
+
+def test_tolerates_none_correlations_and_llm():
+    md = render_eda_markdown(_sample_profile(correlations=None, llm=None))
+    assert "## Correlaciones" not in md
+    assert "## Análisis LLM" not in md
+    # Still produced the main body.
+    assert "# EDA — sales" in md
+
+
+def test_tolerates_empty_profile():
+    md = render_eda_markdown({})
+    assert "# EDA — (unnamed)" in md
+
+
+def test_tolerates_none_profile():
+    md = render_eda_markdown(None)
+    assert "# EDA — (unnamed)" in md