Handle schema inference in Dataset with empty list col (#319)

* Add test for schema inference with empty list

* Only use `list_val_dtype` if col is a list

merlin/core/dispatch.py

@@ -16,7 +16,7 @@
 import enum
 import functools
 import itertools
-from typing import Callable, Union
+from typing import Callable, Optional, Union
 
 import dask.dataframe as dd
 import numpy as np
@@ -311,7 +311,7 @@ def series_has_nulls(s):
         return s.has_nulls
 
 
-def list_val_dtype(ser: SeriesLike) -> np.dtype:
+def list_val_dtype(ser: SeriesLike) -> Optional[np.dtype]:
     """
     Return the dtype of the leaves from a list or nested list
 
@@ -322,16 +322,21 @@ def list_val_dtype(ser: SeriesLike) -> np.dtype:
 
     Returns
     -------
-    np.dtype
-        The dtype of the innermost elements
+    Optional[np.dtype]
+        The dtype of the innermost elements if we find one
     """
     if is_list_dtype(ser):
         if cudf is not None and isinstance(ser, cudf.Series):
             if is_list_dtype(ser):
                 ser = ser.list.leaves
             return ser.dtype
         elif isinstance(ser, pd.Series):
-            return pd.core.dtypes.cast.infer_dtype_from(next(iter(pd.core.common.flatten(ser))))[0]
+            try:
+                return pd.core.dtypes.cast.infer_dtype_from(
+                    next(iter(pd.core.common.flatten(ser)))
+                )[0]
+            except StopIteration:
+                return None
     if isinstance(ser, np.ndarray):
         return ser.dtype
     # adds detection when in merlin column

merlin/io/dataset.py

@@ -1212,13 +1212,12 @@ def sample_dtypes(self, n=1, annotate_lists=False):
 
         if annotate_lists:
             _real_meta = self._real_meta[n]
-            annotated = {
-                col: {
-                    "dtype": list_val_dtype(_real_meta[col]) or _real_meta[col].dtype,
-                    "is_list": is_list_dtype(_real_meta[col]),
-                }
-                for col in _real_meta.columns
-            }
+            annotated = {}
+            for col in _real_meta.columns:
+                is_list = is_list_dtype(_real_meta[col])
+                dtype = list_val_dtype(_real_meta[col]) if is_list else _real_meta[col].dtype
+                annotated[col] = {"dtype": dtype, "is_list": is_list}
+
             return annotated
 
         return self._real_meta[n].dtypes

tests/unit/io/test_dataset.py

@@ -49,3 +49,9 @@ def test_false_with_cudf_and_gpu(self):
     def test_false_missing_cudf_or_gpu(self):
         with pytest.raises(RuntimeError):
             Dataset(make_df({"a": [1, 2, 3]}), cpu=False)
+
+
+def test_infer_list_dtype_unknown():
+    df = pd.DataFrame({"col": [[], []]})
+    dataset = Dataset(df, cpu=True)
+    assert dataset.schema["col"].dtype.element_type.value == "unknown"