BUG: pytables with non-nano timedelta64

doc/source/whatsnew/v3.0.0.rst

@@ -1192,6 +1192,7 @@ MultiIndex
 I/O
 ^^^
 - Bug in :class:`DataFrame` and :class:`Series` ``repr`` of :py:class:`collections.abc.Mapping` elements. (:issue:`57915`)
+- Bug in :meth:`DataFrame.to_hdf` and :func:`read_hdf` with ``timedelta64`` dtypes with non-nanosecond resolution failing to round-trip correctly (:issue:`63239`)
 - Fix bug in ``on_bad_lines`` callable when returning too many fields: now emits
   ``ParserWarning`` and truncates extra fields regardless of ``index_col`` (:issue:`61837`)
 - Bug in :func:`pandas.json_normalize` inconsistently handling non-dict items in ``data`` when ``max_level`` was set. The function will now raise a ``TypeError`` if ``data`` is a list containing non-dict items (:issue:`62829`)

pandas/core/computation/pytables.py

@@ -13,6 +13,7 @@
     Any,
     ClassVar,
     Self,
+    cast,
 )
 
 import numpy as np
@@ -44,7 +45,10 @@
 )
 
 if TYPE_CHECKING:
-    from pandas._typing import npt
+    from pandas._typing import (
+        TimeUnit,
+        npt,
+    )
 
 
 class PyTablesScope(_scope.Scope):
@@ -225,15 +229,19 @@ def stringify(value):
             if conv_val.tz is not None:
                 conv_val = conv_val.tz_convert("UTC")
             return TermValue(conv_val, conv_val._value, kind)
-        elif kind in ("timedelta64", "timedelta"):
+        elif kind.startswith("timedelta"):
+            unit = "ns"
+            if "[" in kind:
+                unit = cast("TimeUnit", kind.split("[")[-1][:-1])
             if isinstance(conv_val, str):
                 conv_val = Timedelta(conv_val)
             elif lib.is_integer(conv_val) or lib.is_float(conv_val):
                 conv_val = Timedelta(conv_val, unit="s")
             else:
                 conv_val = Timedelta(conv_val)
-            conv_val = conv_val.as_unit("ns")._value
+            conv_val = conv_val.as_unit(unit)._value
             return TermValue(int(conv_val), conv_val, kind)
+
         elif meta == "category":
             metadata = extract_array(self.metadata, extract_numpy=True)
             result: npt.NDArray[np.intp] | np.intp | int

pandas/io/pytables.py

@@ -2702,8 +2702,12 @@ def convert(self, values: np.ndarray, nan_rep, encoding: str, errors: str):
             # recreate with tz if indicated
             converted = _set_tz(converted, tz, dtype)
 
-        elif dtype == "timedelta64":
-            converted = np.asarray(converted, dtype="m8[ns]")
+        elif dtype.startswith("timedelta64"):
+            if dtype == "timedelta64":
+                # from before we started storing timedelta64 unit
+                converted = np.asarray(converted, dtype="m8[ns]")
+            else:
+                converted = np.asarray(converted, dtype=dtype)
         elif dtype == "date":
             try:
                 converted = np.asarray(
@@ -3086,8 +3090,13 @@ def read_array(self, key: str, start: int | None = None, stop: int | None = None
                 tz = getattr(attrs, "tz", None)
                 ret = _set_tz(ret, tz, dtype)
 
-            elif dtype == "timedelta64":
-                ret = np.asarray(ret, dtype="m8[ns]")
+            elif dtype and dtype.startswith("timedelta64"):
+                if dtype == "timedelta64":
+                    # This was written back before we started writing
+                    # timedelta64 units
+                    ret = np.asarray(ret, dtype="m8[ns]")
+                else:
+                    ret = np.asarray(ret, dtype=dtype)
 
         if transposed:
             return ret.T
@@ -3324,7 +3333,7 @@ def write_array(
             node._v_attrs.value_type = f"datetime64[{value.dtype.unit}]"
         elif lib.is_np_dtype(value.dtype, "m"):
             self._handle.create_array(self.group, key, value.view("i8"))
-            getattr(self.group, key)._v_attrs.value_type = "timedelta64"
+            getattr(self.group, key)._v_attrs.value_type = str(value.dtype)
         elif isinstance(value, BaseStringArray):
             vlarr = self._handle.create_vlarray(self.group, key, _tables().ObjectAtom())
             vlarr.append(value.to_numpy())
@@ -5175,8 +5184,12 @@ def _unconvert_index(data, kind: str, encoding: str, errors: str) -> np.ndarray
             index = DatetimeIndex(data)
         else:
             index = DatetimeIndex(data.view(kind))
-    elif kind == "timedelta64":
-        index = TimedeltaIndex(data)
+    elif kind.startswith("timedelta64"):
+        if kind == "timedelta64":
+            # created before we stored resolution information
+            index = TimedeltaIndex(data)
+        else:
+            index = TimedeltaIndex(data.view(kind))
     elif kind == "date":
         try:
             index = np.asarray([date.fromordinal(v) for v in data], dtype=object)
@@ -5413,7 +5426,7 @@ def _dtype_to_kind(dtype_str: str) -> str:
     elif dtype_str.startswith("datetime64"):
         kind = dtype_str
     elif dtype_str.startswith("timedelta"):
-        kind = "timedelta64"
+        kind = dtype_str
     elif dtype_str.startswith("bool"):
         kind = "bool"
     elif dtype_str.startswith("category"):

pandas/tests/io/pytables/test_append.py

@@ -848,7 +848,7 @@ def test_append_raise(tmp_path, using_infer_string):
             store.append("df", df)
 
 
-def test_append_with_timedelta(tmp_path):
+def test_append_with_timedelta(tmp_path, unit):
     # GH 3577
     # append timedelta
 
@@ -860,6 +860,7 @@ def test_append_with_timedelta(tmp_path):
         }
     )
     df["C"] = df["A"] - df["B"]
+    df["C"] = df["C"].astype(f"m8[{unit}]")
     df.loc[3:5, "C"] = np.nan
 
     path = tmp_path / "test_append_with_timedelta.h5"

pandas/tests/io/pytables/test_store.py

@@ -1017,11 +1017,12 @@ def test_duplicate_column_name(tmp_path, setup_path):
     assert other.equals(df)
 
 
-@pytest.mark.xfail(reason="non-nano TimedeltaIndex does not round-trip")
-def test_preserve_timedeltaindex_type(setup_path):
+def test_preserve_timedeltaindex_type(setup_path, unit):
     # GH9635
     df = DataFrame(np.random.default_rng(2).normal(size=(10, 5)))
-    df.index = timedelta_range(start="0s", periods=10, freq="1s", name="example")
+    df.index = timedelta_range(
+        start="0s", periods=10, freq="1s", name="example", unit=unit
+    )
 
     with ensure_clean_store(setup_path) as store:
         store["df"] = df