Raise in query if dtype is not supported

python/cudf/cudf/tests/test_query.py

@@ -209,3 +209,26 @@ def test_query_with_index_keyword(query, a_val, b_val, c_val):
     expect = pdf.query(query)
 
     assert_eq(out, expect)
+
+
+@pytest.mark.parametrize(
+    "data, query",
+    [
+        # Only need to test the dtypes that pandas
+        # supports but that we do not
+        (["a", "b", "c"], "data == 'a'"),
+    ],
+)
+def test_query_unsupported_dtypes(data, query):
+    gdf = cudf.DataFrame({"data": data})
+
+    # make sure the query works in pandas
+    pdf = gdf.to_pandas()
+    pdf_result = pdf.query(query)
+
+    expect = pd.DataFrame({"data": ["a"]})
+    assert_eq(expect, pdf_result)
+
+    # but fails in cuDF
+    with pytest.raises(TypeError):
+        gdf.query(query)

python/cudf/cudf/utils/queryutils.py

@@ -10,9 +10,20 @@
 import cudf
 from cudf.core.column import column_empty
 from cudf.utils import applyutils
+from cudf.utils.dtypes import (
+    BOOL_TYPES,
+    DATETIME_TYPES,
+    NUMERIC_TYPES,
+    TIMEDELTA_TYPES,
+)
 
 ENVREF_PREFIX = "__CUDF_ENVREF__"
 
+SUPPORTED_QUERY_TYPES = {
+    np.dtype(dt)
+    for dt in NUMERIC_TYPES | DATETIME_TYPES | TIMEDELTA_TYPES | BOOL_TYPES
+}
+
 
 class QuerySyntaxError(ValueError):
     pass
@@ -197,6 +208,20 @@ def query_execute(df, expr, callenv):
 
     # compile
     compiled = query_compile(expr)
+    columns = compiled["colnames"]
+
+    # prepare col args
+    colarrays = [cudf.core.dataframe.extract_col(df, col) for col in columns]
+
+    # wait to check the types until we know which cols are used
+    if any(col.dtype not in SUPPORTED_QUERY_TYPES for col in colarrays):
+        raise TypeError(
+            "query only supports numeric, datetime, timedelta, "
+            "or bool dtypes."
+        )
+
+    colarrays = [col.data_array_view for col in colarrays]
+
     kernel = compiled["kernel"]
     # process env args
     envargs = []
@@ -214,13 +239,6 @@ def query_execute(df, expr, callenv):
             raise NameError(msg.format(name))
         else:
             envargs.append(val)
-    columns = compiled["colnames"]
-    # prepare col args
-
-    colarrays = [
-        cudf.core.dataframe.extract_col(df, col).data_array_view
-        for col in columns
-    ]
 
     # allocate output buffer
     nrows = len(df)