fix(explain): non-string column labels and duplicate-label errors in explain_clean and infer_roles

src/freshdata/api.py

@@ -1123,6 +1123,13 @@ def _engine_mode(cfg: CleanConfig) -> EngineMode:
     return "balanced" if mode == "balanced" else "aggressive"
 
 
+def _require_unique_labels(frame: pd.DataFrame, func: str) -> None:
+    """Reject duplicate column labels, which make ``frame[col]`` a DataFrame."""
+    if not frame.columns.is_unique:
+        duplicated = sorted({str(c) for c in frame.columns[frame.columns.duplicated()]})
+        raise ValueError(f"{func} requires unique column labels; duplicated: {duplicated}")
+
+
 def infer_roles(
     df: pd.DataFrame,
     *,
@@ -1142,21 +1149,24 @@ def infer_roles(
 
     cfg = merge_options(config, strategy=strategy, **options)
     frame = to_pandas(df)
+    _require_unique_labels(frame, "infer_roles")
     contexts = build_contexts(frame, cfg)
     mode = _engine_mode(cfg)
     hints = cfg.semantic_context if isinstance(cfg.semantic_context, dict) else {}
     column_hints = hints.get("columns", {}) if isinstance(hints.get("columns"), dict) else {}
     rows = []
-    for col, ctx in sorted(contexts.items()):
+    # Rows keep the original label (so ``frame[row["column"]]`` round-trips);
+    # ordering uses its string form so mixed int/str labels are comparable.
+    for col, ctx in sorted(contexts.items(), key=lambda kv: str(kv[0])):
         primary = None
         if ctx.missing_ratio > 0:
             primary = rank_missing_models(frame, col, ctx, cfg, mode=mode).primary
         hint = None
-        col_hint = column_hints.get(col)
+        col_hint = column_hints[col] if col in column_hints else column_hints.get(str(col))
         if isinstance(col_hint, dict):
             hint = col_hint.get("semantic_type")
         inferred = infer_semantic_type(
-            col,
+            str(col),
             frame[col],
             role=ctx.role,
             hint=str(hint) if hint else None,

src/freshdata/explain.py

@@ -10,14 +10,35 @@
 from pandas.api.types import is_numeric_dtype
 
 from .adapters.polars import to_pandas
-from .api import infer_roles
+from .api import _require_unique_labels, infer_roles
 from .cleaner import run_pipeline
 from .config import CleanConfig, merge_options
 from .engine.context import build_contexts
 from .render.mixins import HtmlReprMixin
 from .report import Action, CleanReport
 
 
+def _str_keys(mapping: dict[Any, Any]) -> dict[str, Any]:
+    return {str(k): v for k, v in mapping.items()}
+
+
+def _require_distinct_label_names(df: pd.DataFrame, func: str) -> None:
+    """Reject distinct labels sharing a string form (e.g. ``1`` and ``"1"``).
+
+    The report keys every per-column mapping by ``str(label)``, so such labels
+    would silently merge into one entry.
+    """
+    by_name: dict[str, list[object]] = defaultdict(list)
+    for col in df.columns:
+        by_name[str(col)].append(col)
+    colliding = [labels for labels in by_name.values() if len(labels) > 1]
+    if colliding:
+        raise ValueError(
+            f"{func} requires column labels with distinct string forms; "
+            f"colliding: {colliding}"
+        )
+
+
 def _column_stats(df: pd.DataFrame) -> dict[str, dict[str, Any]]:
     stats: dict[str, dict[str, Any]] = {}
     for col in df.columns:
@@ -47,6 +68,8 @@ def _cell_changes(before: pd.DataFrame, after: pd.DataFrame) -> dict[str, int]:
     mark untouched values as changed.  If either index carries duplicate
     labels, alignment is ambiguous and the conservative whole-column count is
     kept.  Columns that exist only in ``after`` count all their cells.
+
+    Keys are ``str(label)``, matching :func:`_column_stats`.
     """
     changes: dict[str, int] = {}
     shared = [c for c in after.columns if c in before.columns]
@@ -58,7 +81,7 @@ def _cell_changes(before: pd.DataFrame, after: pd.DataFrame) -> dict[str, int]:
             aligned_after = after.loc[common]
         else:
             for col in after.columns:
-                changes[col] = len(after)
+                changes[str(col)] = len(after)
             return changes
     for col in shared:
         left = aligned_before[col]
@@ -77,10 +100,10 @@ def _cell_changes(before: pd.DataFrame, after: pd.DataFrame) -> dict[str, int]:
                 if pd.isna(a) != pd.isna(b)
                 or (not pd.isna(a) and not pd.isna(b) and a != b)
             )
-        changes[col] = changed
+        changes[str(col)] = changed
     for col in after.columns:
         if col not in before.columns:
-            changes[col] = len(after)
+            changes[str(col)] = len(after)
     return changes
 
 
@@ -97,7 +120,9 @@ def _narratives(
         if action.column:
             by_col[action.column].append(action)
 
-    for col, ctx in sorted(contexts.items()):
+    # Contexts are keyed by the original label; actions record ``str(label)``.
+    for label, ctx in sorted(contexts.items(), key=lambda kv: str(kv[0])):
+        col = str(label)
         col_actions = by_col.get(col, [])
         engine_actions = [a for a in col_actions if a.rationale]
         if engine_actions:
@@ -173,14 +198,17 @@ def summary(self) -> str:
     def to_frame(self) -> pd.DataFrame:
         """One row per column: before/after dtype and changed-cell count."""
         rows = []
-        for col in sorted(set(self.before_stats) | set(self.after_stats)):
-            before = self.before_stats.get(col, {})
-            after = self.after_stats.get(col, {})
+        before_stats = _str_keys(self.before_stats)
+        after_stats = _str_keys(self.after_stats)
+        cell_changes = _str_keys(self.cell_changes)
+        for col in sorted(set(before_stats) | set(after_stats)):
+            before = before_stats.get(col, {})
+            after = after_stats.get(col, {})
             rows.append({
                 "column": col,
                 "before_dtype": before.get("dtype"),
                 "after_dtype": after.get("dtype"),
-                "changed_cells": self.cell_changes.get(col, 0),
+                "changed_cells": cell_changes.get(col, 0),
             })
         return pd.DataFrame(
             rows, columns=["column", "before_dtype", "after_dtype", "changed_cells"]
@@ -193,9 +221,10 @@ def to_dict(self) -> dict[str, Any]:
             "rows_after": self.rows_after,
             "cols_before": self.cols_before,
             "cols_after": self.cols_after,
-            "before_stats": self.before_stats,
-            "after_stats": self.after_stats,
-            "cell_changes": self.cell_changes,
+            # JSON object keys must be strings (tuple MultiIndex labels are not).
+            "before_stats": _str_keys(self.before_stats),
+            "after_stats": _str_keys(self.after_stats),
+            "cell_changes": _str_keys(self.cell_changes),
             "actions_by_step": self.actions_by_step,
             "narratives": self.narratives,
             "warnings": list(self.report.warnings),
@@ -213,6 +242,8 @@ def explain_clean(
     """Run clean() and return a structured before/after explanation."""
     cfg = merge_options(config, strategy=strategy, **options)
     df = to_pandas(df)  # accept polars frames like the other public entry points
+    _require_unique_labels(df, "explain_clean")
+    _require_distinct_label_names(df, "explain_clean")
     before_stats = _column_stats(df)
     cleaned, report = run_pipeline(df, cfg)
 

src/freshdata/render/renderers.py

@@ -233,8 +233,9 @@ def render_explain(rep: Any) -> str:
     safe_rows: list[list[str]] = []
     risky_rows: list[list[str]] = []
     for col, changes in sorted(rep.cell_changes.items(), key=lambda kv: kv[1], reverse=True):
-        before = rep.before_stats.get(col, {})
-        after = rep.after_stats.get(col, {})
+        # Per-column dicts are keyed by the string form of the label.
+        before = rep.before_stats.get(str(col), {})
+        after = rep.after_stats.get(str(col), {})
         risky = bool(after.get("dtype") and before.get("dtype")
                      and after["dtype"] != before["dtype"]) or changes > 0.5 * max(
                          1, rep.rows_before)

tests/test_explain_column_labels.py

@@ -0,0 +1,162 @@
+"""explain_clean / infer_roles with non-string and duplicate column labels.
+
+Regressions for #232 (parts 3 and 6) and #265 (part 3).
+"""
+
+from __future__ import annotations
+
+import json
+import warnings
+
+import pandas as pd
+import pytest
+
+import freshdata as fd
+from freshdata.explain import ExplainReport, _cell_changes
+
+
+@pytest.fixture(autouse=True)
+def _quiet():
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        yield
+
+
+def _int_label_frame() -> pd.DataFrame:
+    # Integer labels, as produced by ``pd.read_csv(header=None)``.
+    return pd.DataFrame({0: [" a", "b ", "c", "d"], 1: [1.0, 2.0, 3.0, 4.0]})
+
+
+def _multiindex_frame() -> pd.DataFrame:
+    return pd.DataFrame(
+        [[1, " a", None], [2, "b", 3.0]],
+        columns=pd.MultiIndex.from_tuples([("a", "x"), ("a", "y"), ("b", "z")]),
+    )
+
+
+def _mixed_label_frame() -> pd.DataFrame:
+    return pd.DataFrame({0: [1.0, None, 3.0, 4.0], "name": ["a", "b", None, "d"]})
+
+
+# -- #232 part 3: integer / tuple labels -------------------------------------
+
+
+def test_explain_integer_labels_key_every_mapping_by_string():
+    rep = fd.explain_clean(_int_label_frame(), verbose=False)
+    for mapping in (rep.before_stats, rep.after_stats, rep.cell_changes):
+        assert set(mapping) == {"0", "1"}
+    assert rep.cell_changes["0"] == 2
+
+
+def test_explain_integer_labels_to_frame_reports_changed_cells():
+    frame = fd.explain_clean(_int_label_frame(), verbose=False).to_frame()
+    row = frame.set_index("column").loc["0"]
+    assert row["changed_cells"] == 2
+    assert row["before_dtype"] == "object"
+    assert row["after_dtype"] == "object"
+
+
+def test_explain_integer_labels_html_shows_dtypes():
+    html = fd.explain_clean(_int_label_frame(), verbose=False).to_html()
+    assert "<td>object</td>" in html
+    assert "<td>float64</td>" in html
+
+
+def test_cell_changes_integer_labels_use_string_keys():
+    before = pd.DataFrame({0: [1, 2], 1: ["a", "b"]})
+    after = pd.DataFrame({0: [1, 9], 1: ["a", "b"], 2: [0, 0]})
+    assert _cell_changes(before, after) == {"0": 1, "1": 0, "2": 2}
+
+
+def test_explain_integer_labels_narratives_match_actions():
+    df = pd.DataFrame({0: [1.0, None, 3.0, 4.0, None, 6.0], "b": [1, 2, 3, 4, 5, 6]})
+    rep = fd.explain_clean(df, verbose=False)
+    assert any(line.startswith("`0`:") for line in rep.narratives), rep.narratives
+
+
+def test_explain_multiindex_labels_render_and_serialize():
+    rep = fd.explain_clean(_multiindex_frame(), verbose=False)
+    assert "('a', 'y')" in rep.cell_changes
+    json.dumps(rep.to_dict())
+    html = rep._repr_html_()
+    assert html is not None
+    assert html == rep.to_html()
+
+
+def test_explain_to_dict_stringifies_non_string_keys():
+    base = fd.explain_clean(pd.DataFrame({"a": [1, 2]}), verbose=False)
+    rep = ExplainReport(
+        strategy=base.strategy,
+        rows_before=2,
+        rows_after=2,
+        cols_before=1,
+        cols_after=1,
+        before_stats={("a", "x"): {"dtype": "int64"}},
+        after_stats={("a", "x"): {"dtype": "int64"}},
+        cell_changes={("a", "x"): 1},
+        actions_by_step={},
+        narratives=[],
+        report=base.report,
+        roles=base.roles,
+    )
+    payload = json.loads(json.dumps(rep.to_dict()))
+    assert payload["cell_changes"] == {"('a', 'x')": 1}
+    assert rep.to_frame()["changed_cells"].tolist() == [1]
+
+
+# -- #232 part 6: mixed int/str labels ---------------------------------------
+
+
+def test_explain_clean_mixed_labels():
+    rep = fd.explain_clean(_mixed_label_frame(), verbose=False)
+    assert rep.to_frame()["column"].tolist() == ["0", "name"]
+    assert rep.to_html()
+
+
+def test_infer_roles_mixed_labels_keep_original_labels():
+    df = _mixed_label_frame()
+    roles = fd.infer_roles(df)
+    assert roles["column"].tolist() == [0, "name"]
+    for label in roles["column"]:
+        assert df[label] is not None  # labels round-trip into the frame
+
+
+def test_infer_roles_integer_label_semantic_hint_by_string_key():
+    df = pd.DataFrame({0: ["a@b.com", "c@d.com"], 1: [1, 2]})
+    roles = fd.infer_roles(df, semantic_context={"columns": {"0": {"semantic_type": "email"}}})
+    assert roles.set_index("column").loc[0, "semantic_type"] == "email"
+
+
+def test_infer_roles_multiindex_labels():
+    roles = fd.infer_roles(_multiindex_frame())
+    assert roles["column"].tolist() == [("a", "x"), ("a", "y"), ("b", "z")]
+
+
+# -- #265 part 3: duplicate labels -------------------------------------------
+
+
+def test_explain_clean_rejects_duplicate_labels():
+    df = pd.DataFrame([[1.0, 2.0], [None, 4.0], [3.0, 5.0]], columns=["a", "a"])
+    with pytest.raises(
+        ValueError, match=r"explain_clean requires unique column labels; duplicated: \['a'\]"
+    ):
+        fd.explain_clean(df, verbose=False)
+
+
+def test_infer_roles_rejects_duplicate_labels():
+    df = pd.DataFrame([[1.0, 2.0], [None, 4.0], [3.0, 5.0]], columns=["a", "a"])
+    with pytest.raises(
+        ValueError, match=r"infer_roles requires unique column labels; duplicated: \['a'\]"
+    ):
+        fd.infer_roles(df)
+
+
+def test_explain_clean_rejects_labels_with_same_string_form():
+    df = pd.DataFrame({1: [1.0, None], "1": ["a", None]})
+    with pytest.raises(ValueError, match=r"distinct string forms; colliding: \[\[1, '1'\]\]"):
+        fd.explain_clean(df, verbose=False)
+
+
+def test_infer_roles_accepts_labels_with_same_string_form():
+    roles = fd.infer_roles(pd.DataFrame({1: [1.0, None], "1": ["a", None]}))
+    assert sorted(map(repr, roles["column"])) == ["'1'", "1"]