@@ -24,7 +24,23 @@ def _diff_kind(raw_missing: bool, clean_missing: bool) -> str:
     return "value_change"
 
 
+def _by_value(series: pd.Series) -> pd.Series:
+    """``series`` with a categorical dtype replaced by its plain object values.
+
+    Comparing two Categorical Series raises ``TypeError`` unless both share
+    identical categories (and ordering), which a messy/clean pair almost never
+    does -- repairing the values is exactly what changes the categories. Cells
+    are compared by value, so decode categoricals to the object values an
+    object-dtype column would hold.
+    """
+    if isinstance(series.dtype, pd.CategoricalDtype):
+        return series.astype(object)
+    return series
+
+
 def _column_diffs(messy: pd.Series, clean: pd.Series, column: str) -> list[ValueDiff]:
+    messy = _by_value(messy)
+    clean = _by_value(clean)
     raw_na = messy.isna()
     clean_na = clean.isna()
     # A cell differs when values are unequal, except both-missing which is equal.
 
@@ -0,0 +1,215 @@
+"""``fd.learn`` on categorical columns.
+
+Comparing two pandas Categoricals raises ``TypeError`` unless their categories
+are identical, and a messy/clean pair almost never shares categories -- the
+repair is what changes them. Learning must compare, align and learn such
+columns by value, exactly as it does for the object-dtype equivalent. The
+fixtures avoid version-specific pandas APIs so they run unchanged on pandas
+1.5 (Python 3.9 lane) and pandas 2.x.
+"""
+
+from __future__ import annotations
+
+import json
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import freshdata as fd
+from freshdata.learning import LearningProfile, learn, load_profile, save_profile
+from freshdata.learning.align import align_pair
+from freshdata.learning.diff import compute_diff
+
+_RAW = [
+    "Deliverd",
+    "SHIPPED",
+    "shipped ",
+    "delivered",
+    "SHIPPED",
+    "Deliverd",
+    "shipped ",
+    "SHIPPED",
+]
+_CLEAN = [
+    "delivered",
+    "shipped",
+    "shipped",
+    "delivered",
+    "shipped",
+    "delivered",
+    "shipped",
+    "shipped",
+]
+_IDS = [f"A{i}" for i in range(len(_RAW))]
+
+
+def _frames(raw: list, clean: list) -> tuple[pd.DataFrame, pd.DataFrame]:
+    ids = [f"A{i}" for i in range(len(raw))]
+    return (
+        pd.DataFrame({"order_id": ids, "status": raw}),
+        pd.DataFrame({"order_id": ids, "status": clean}),
+    )
+
+
+def _as_object(df: pd.DataFrame) -> pd.DataFrame:
+    """The object-dtype equivalent of ``df`` (categoricals decoded)."""
+    return df.assign(
+        **{
+            str(c): df[c].astype(object)
+            for c in df.columns
+            if isinstance(df[c].dtype, pd.CategoricalDtype)
+        }
+    )
+
+
+def _learned(profile: LearningProfile) -> str:
+    """Everything a profile learned, as canonical JSON (timestamps excluded)."""
+    audit = profile.audit().to_dict()
+    payload = {
+        "rules": [r.to_dict() for r in profile.rules],
+        "value_maps": {c: m.to_dict() for c, m in sorted(profile.value_maps.items())},
+        "examples": profile.examples.to_dict() if profile.examples is not None else None,
+        "value_patterns": profile.memory.value_patterns if profile.memory is not None else None,
+        "notes": audit.get("notes"),
+        "demotions": audit.get("demotions"),
+        "holdout_metrics": audit.get("holdout_metrics"),
+    }
+    return json.dumps(payload, sort_keys=True, default=str)
+
+
+def _assert_parity(messy: pd.DataFrame, clean: pd.DataFrame, **kwargs) -> LearningProfile:
+    profile = learn(messy, clean, **kwargs)
+    reference = learn(_as_object(messy), _as_object(clean), **kwargs)
+    assert _learned(profile) == _learned(reference)
+    return profile
+
+
+def test_repro_categoricals_with_different_categories() -> None:
+    messy = pd.DataFrame({"c": pd.Categorical([" a", "B ", "a"])})
+    clean = pd.DataFrame({"c": pd.Categorical(["a", "b", "a"])})
+    profile = fd.learn(messy, clean)
+    assert isinstance(profile, LearningProfile)
+    assert _learned(profile) == _learned(fd.learn(_as_object(messy), _as_object(clean)))
+
+
+def test_diff_compares_categoricals_by_value() -> None:
+    messy, clean = _frames(pd.Categorical(_RAW), pd.Categorical(_CLEAN))
+    summary = compute_diff(align_pair(messy, clean, key="order_id"))
+    reference = compute_diff(align_pair(_as_object(messy), _as_object(clean), key="order_id"))
+    assert {col: [d.to_dict() for d in diffs] for col, diffs in summary.column_diffs.items()} == {
+        col: [d.to_dict() for d in diffs] for col, diffs in reference.column_diffs.items()
+    }
+    pairs = {(d.raw_value, d.clean_value, d.support) for d in summary.column_diffs["status"]}
+    assert pairs == {
+        ("SHIPPED", "shipped", 3),
+        ("Deliverd", "delivered", 2),
+        ("shipped ", "shipped", 2),
+    }
+    # Only the values changed, never the dtype.
+    assert summary.schema_diffs.dtype_changes == {}
+
+
+@pytest.mark.parametrize(
+    ("messy_status", "clean_status"),
+    [
+        pytest.param(pd.Categorical(_RAW), pd.Categorical(_CLEAN), id="both-different-categories"),
+        pytest.param(
+            pd.Categorical(_RAW, categories=sorted(set(_RAW) | set(_CLEAN))),
+            pd.Categorical(_CLEAN, categories=sorted(set(_RAW) | set(_CLEAN))),
+            id="both-same-categories",
+        ),
+        pytest.param(pd.Categorical(_RAW), list(_CLEAN), id="categorical-messy-object-clean"),
+        pytest.param(list(_RAW), pd.Categorical(_CLEAN), id="object-messy-categorical-clean"),
+        pytest.param(
+            pd.Categorical(_RAW, ordered=True),
+            pd.Categorical(_CLEAN, ordered=True),
+            id="both-ordered",
+        ),
+        pytest.param(
+            pd.Categorical(_RAW, ordered=True),
+            pd.Categorical(_CLEAN, categories=["shipped", "delivered"], ordered=True),
+            id="ordered-different-order",
+        ),
+        pytest.param(
+            pd.Categorical(_RAW, ordered=True), pd.Categorical(_CLEAN), id="ordered-vs-unordered"
+        ),
+    ],
+)
+@pytest.mark.parametrize("key", [None, "order_id"])
+def test_learning_matches_object_dtype(messy_status, clean_status, key) -> None:
+    messy, clean = _frames(messy_status, clean_status)
+    profile = _assert_parity(messy, clean, key=key, min_support=2)
+    entries = {(e.raw_value, e.clean_value) for e in profile.value_maps["status"].entries}
+    assert entries == {
+        ("SHIPPED", "shipped"),
+        ("shipped ", "shipped"),
+        ("Deliverd", "delivered"),
+    }
+
+
+def test_categorical_key_column() -> None:
+    messy, clean = _frames(pd.Categorical(_RAW), pd.Categorical(_CLEAN))
+    messy["order_id"] = pd.Categorical(messy["order_id"])
+    clean = clean.iloc[::-1].reset_index(drop=True)
+    clean["order_id"] = pd.Categorical(clean["order_id"], categories=list(reversed(_IDS)))
+    _assert_parity(messy, clean, key="order_id", min_support=2)
+
+
+def test_missing_cells_in_categoricals() -> None:
+    raw = ["x", "N/A", "y", np.nan, "x", "N/A", "y", np.nan, "x", "N/A"]
+    clean = ["x", np.nan, "y", np.nan, "x", np.nan, "y", np.nan, "x", np.nan]
+    messy_df, clean_df = _frames(pd.Categorical(raw), pd.Categorical(clean))
+    summary = compute_diff(align_pair(messy_df, clean_df, key="order_id"))
+    # Both-missing cells are equal; only the sentinel cells differ.
+    (diff,) = summary.column_diffs["status"]
+    assert (diff.raw_value, diff.clean_value, diff.support, diff.kind) == (
+        "N/A",
+        None,
+        3,
+        "value_to_missing",
+    )
+    profile = _assert_parity(messy_df, clean_df, key="order_id", min_support=2)
+    assert any(r.rule == "sentinel" for r in profile.rules)
+
+
+def test_imputed_categorical_cells() -> None:
+    raw = ["a", np.nan, "a", "b", np.nan, "a", "a", "b", np.nan, "a"]
+    clean = ["a", "a", "a", "b", "a", "a", "a", "b", "a", "a"]
+    messy_df, clean_df = _frames(pd.Categorical(raw), pd.Categorical(clean))
+    profile = _assert_parity(messy_df, clean_df, key="order_id", min_support=2)
+    assert profile.value_maps == {}  # imputation is never a literal map
+
+
+def test_sensitive_categorical_column_learns_like_object() -> None:
+    raw = ["A@X.COM ", "b@@y.com", "c@z.com"] * 3 + ["d@@w.com"]
+    clean = ["a@x.com", "b@y.com", "c@z.com"] * 3 + ["d@w.com"]
+    messy = pd.DataFrame({"email": pd.Categorical(raw)})
+    clean_df = pd.DataFrame({"email": pd.Categorical(clean)})
+    _assert_parity(messy, clean_df, privacy="none", include_sensitive=True, min_support=2)
+    masked = learn(messy, clean_df, min_support=2)
+    reference = learn(_as_object(messy), _as_object(clean_df), min_support=2)
+    assert [e.masked for e in masked.value_maps["email"].entries] == [
+        e.masked for e in reference.value_maps["email"].entries
+    ]
+
+
+def test_categorical_profile_round_trips_and_replays_like_object(tmp_path) -> None:
+    messy, clean = _frames(pd.Categorical(_RAW), pd.Categorical(_CLEAN))
+    profile = learn(messy, clean, key="order_id", min_support=2)
+    reference = learn(_as_object(messy), _as_object(clean), key="order_id", min_support=2)
+
+    path = tmp_path / "categorical.fdprofile"
+    save_profile(profile, path)
+    loaded = load_profile(path)
+    assert loaded.profile_id == profile.profile_id == reference.profile_id
+    assert _learned(loaded) == _learned(profile)
+
+    batch = pd.DataFrame(
+        {"order_id": ["B1", "B2", "B3"], "status": ["Deliverd", "SHIPPED", "shipped "]}
+    )
+    categorical_batch = batch.assign(status=pd.Categorical(batch["status"]))
+    replayed = fd.clean(categorical_batch, profile=loaded, semantic_mode="auto")
+    expected = fd.clean(batch, profile=reference, semantic_mode="auto")
+    assert replayed["status"].astype(object).tolist() == ["delivered", "shipped", "shipped"]
+    assert replayed["status"].astype(object).tolist() == expected["status"].astype(object).tolist()