change error to warning for fill col with None or nan

ibis_ml/steps/_impute.py

@@ -10,13 +10,19 @@
 
 if TYPE_CHECKING:
     from collections.abc import Iterable
+import warnings
 
 _DOCS_PAGE_NAME = "imputation"
 
 
 def _fillna(col, val):
-    if val is None or col.type().is_floating() and math.isnan(val):
-        raise ValueError(f"Cannot fill column {col.get_name()!r} with `None` or `NaN`")
+    if val is None or (col.type().is_numeric() and math.isnan(val)):
+        warnings.warn(
+            "Imputation requires at least one non-missing value in "
+            f"column {col.get_name()!r}",
+            UserWarning,
+            stacklevel=2,
+        )
     if col.type().is_floating():
         return (col.isnull() | col.isnan()).ifelse(val, col)  # noqa: PD003
     else:

tests/test_impute.py

@@ -1,3 +1,5 @@
+import math
+
 import ibis
 import numpy as np
 import pandas as pd
@@ -50,10 +52,15 @@ def test_fillna(train_table):
     expected = pd.DataFrame({"floating_col": [0]})
     tm.assert_frame_equal(result.execute(), expected, check_dtype=False)
 
-    # test _fillna with None
-    step = ml.FillNA("floating_col", None)
+
+@pytest.mark.parametrize("val", [None, math.nan])
+def test_fillna_with_none(train_table, val):
+    step = ml.FillNA("floating_col", val)
     step.fit_table(train_table, ml.core.Metadata())
-    with pytest.raises(
-        ValueError, match="Cannot fill column 'floating_col' with `None` or `NaN`"
+    test_table = ibis.memtable({"floating_col": [1.0, None]})
+    with pytest.warns(
+        UserWarning,
+        match="Imputation requires at least one non-missing value in "
+        "column 'floating_col'",
     ):
         step.transform_table(test_table)