Fix error in cudf.to_numeric when a bool input is passed (#10431)

Fixes: #10049 

This PR fixes an incorrect error being displayed when `cudf.to_numeric` is called by passing an input with `bool` dtype. Technically `bool` is the lowest `dtype` size possible in `numpy` so this is a no-op because `to_numeric` is aimed at downcasting inputs.

Authors:
  - GALI PREM SAGAR (https://github.com/galipremsagar)

Approvers:
  - Vyas Ramasubramani (https://github.com/vyasr)

URL: #10431

python/cudf/cudf/core/tools/numeric.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2018-2020, NVIDIA CORPORATION.
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
 
 import warnings
 
@@ -144,16 +144,16 @@ def to_numeric(arg, errors="raise", downcast=None):
         col = col.as_numerical_column("d")
 
     if downcast:
-        downcast_type_map = {
-            "integer": list(np.typecodes["Integer"]),
-            "signed": list(np.typecodes["Integer"]),
-            "unsigned": list(np.typecodes["UnsignedInteger"]),
-        }
-        float_types = list(np.typecodes["Float"])
-        idx = float_types.index(cudf.dtype(np.float32).char)
-        downcast_type_map["float"] = float_types[idx:]
-
-        type_set = downcast_type_map[downcast]
+        if downcast == "float":
+            # we support only float32 & float64
+            type_set = [
+                cudf.dtype(np.float32).char,
+                cudf.dtype(np.float64).char,
+            ]
+        elif downcast in ("integer", "signed"):
+            type_set = list(np.typecodes["Integer"])
+        elif downcast == "unsigned":
+            type_set = list(np.typecodes["UnsignedInteger"])
 
         for t in type_set:
             downcast_dtype = cudf.dtype(t)

python/cudf/cudf/tests/test_numerical.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
 
 import numpy as np
 import pandas as pd
@@ -397,3 +397,19 @@ def test_series_construction_with_nulls(dtype, input_obj):
     expect = pd.Series(np_data, dtype=np_dtypes_to_pandas_dtypes[dtype])
     got = cudf.Series(np_data, dtype=dtype).to_pandas(nullable=True)
     assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data", [[True, False, True]],
+)
+@pytest.mark.parametrize(
+    "downcast", ["signed", "integer", "unsigned", "float"]
+)
+def test_series_to_numeric_bool(data, downcast):
+    ps = pd.Series(data)
+    gs = cudf.from_pandas(ps)
+
+    expect = pd.to_numeric(ps, downcast=downcast)
+    got = cudf.to_numeric(gs, downcast=downcast)
+
+    assert_eq(expect, got)