Change default dtype for get_dummies to bool (#13174)

This PR changes the default dtype for get_dummies to bool from uint8 to match pandas-2.0: pandas-dev/pandas#48022

Author: galipremsagar

python/cudf/cudf/_lib/transform.pyx

@@ -163,7 +163,7 @@ def one_hot_encode(Column input_column, Column categories):
         move(c_result.second),
         owner=owner,
         column_names=[
-            x if x is not None else 'null' for x in pylist_categories
+            x if x is not None else '<NA>' for x in pylist_categories
         ]
     )
     return encodings

python/cudf/cudf/core/reshape.py

@@ -609,7 +609,7 @@ def get_dummies(
     cats=None,
     sparse=False,
     drop_first=False,
-    dtype="uint8",
+    dtype="bool",
 ):
     """Returns a dataframe whose columns are the one hot encodings of all
     columns in `df`
@@ -640,23 +640,23 @@ def get_dummies(
         columns. Note this is different from pandas default behavior, which
         encodes all columns with dtype object or categorical
     dtype : str, optional
-        Output dtype, default 'uint8'
+        Output dtype, default 'bool'
 
     Examples
     --------
     >>> import cudf
     >>> df = cudf.DataFrame({"a": ["value1", "value2", None], "b": [0, 0, 0]})
     >>> cudf.get_dummies(df)
        b  a_value1  a_value2
-    0  0         1         0
-    1  0         0         1
-    2  0         0         0
+    0  0      True     False
+    1  0     False      True
+    2  0     False     False
 
     >>> cudf.get_dummies(df, dummy_na=True)
-       b  a_None  a_value1  a_value2
-    0  0       0         1         0
-    1  0       0         0         1
-    2  0       1         0         0
+       b  a_<NA>  a_value1  a_value2
+    0  0   False      True     False
+    1  0   False     False      True
+    2  0    True     False     False
 
     >>> import numpy as np
     >>> df = cudf.DataFrame({"a":cudf.Series([1, 2, np.nan, None],
@@ -669,11 +669,11 @@ def get_dummies(
     3  <NA>
 
     >>> cudf.get_dummies(df, dummy_na=True, columns=["a"])
-       a_1.0  a_2.0  a_nan  a_null
-    0      1      0      0       0
-    1      0      1      0       0
-    2      0      0      1       0
-    3      0      0      0       1
+       a_<NA>  a_1.0  a_2.0  a_nan
+    0   False   True  False  False
+    1   False  False   True  False
+    2   False  False  False   True
+    3    True  False  False  False
 
     >>> series = cudf.Series([1, 2, None, 2, 4])
     >>> series
@@ -684,12 +684,12 @@ def get_dummies(
     4       4
     dtype: int64
     >>> cudf.get_dummies(series, dummy_na=True)
-       null  1  2  4
-    0     0  1  0  0
-    1     0  0  1  0
-    2     1  0  0  0
-    3     0  0  1  0
-    4     0  0  0  1
+        <NA>      1      2      4
+    0  False   True  False  False
+    1  False  False   True  False
+    2   True  False  False  False
+    3  False  False   True  False
+    4  False  False  False   True
     """
 
     if cats is None:

python/cudf/cudf/tests/test_onehot.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
 
 from string import ascii_lowercase
 
@@ -23,19 +23,13 @@
         (range(10), [1, 2, 3, 4, 5] * 2),
     ],
 )
-def test_get_dummies(data, index):
+@pytest.mark.parametrize("dtype", ["bool", "uint8"])
+def test_get_dummies(data, index, dtype):
     gdf = DataFrame({"x": data}, index=index)
     pdf = pd.DataFrame({"x": data}, index=index)
 
-    encoded_expected = pd.get_dummies(pdf, prefix="test")
-    encoded_actual = cudf.get_dummies(gdf, prefix="test")
-
-    utils.assert_eq(
-        encoded_expected,
-        encoded_actual,
-        check_dtype=len(data) != 0,
-    )
-    encoded_actual = cudf.get_dummies(gdf, prefix="test", dtype=np.uint8)
+    encoded_expected = pd.get_dummies(pdf, prefix="test", dtype=dtype)
+    encoded_actual = cudf.get_dummies(gdf, prefix="test", dtype=dtype)
 
     utils.assert_eq(
         encoded_expected,
@@ -63,16 +57,13 @@ def test_onehot_get_dummies_multicol(n_cols):
 @pytest.mark.parametrize("nan_as_null", [True, False])
 @pytest.mark.parametrize("dummy_na", [True, False])
 def test_onehost_get_dummies_dummy_na(nan_as_null, dummy_na):
-    pdf = pd.DataFrame({"a": [0, 1, np.nan]})
-    df = DataFrame.from_pandas(pdf, nan_as_null=nan_as_null)
+    df = cudf.DataFrame({"a": [0, 1, np.nan]}, nan_as_null=nan_as_null)
+    pdf = df.to_pandas(nullable=nan_as_null)
 
     expected = pd.get_dummies(pdf, dummy_na=dummy_na, columns=["a"])
     got = cudf.get_dummies(df, dummy_na=dummy_na, columns=["a"])
 
-    if dummy_na and nan_as_null:
-        got = got.rename(columns={"a_null": "a_nan"})[expected.columns]
-
-    utils.assert_eq(expected, got)
+    utils.assert_eq(expected, got, check_like=True)
 
 
 @pytest.mark.parametrize(
@@ -120,12 +111,12 @@ def test_get_dummies_with_nan():
     )
     expected = cudf.DataFrame(
         {
-            "a_null": [0, 0, 0, 1],
-            "a_1.0": [1, 0, 0, 0],
-            "a_2.0": [0, 1, 0, 0],
-            "a_nan": [0, 0, 1, 0],
+            "a_<NA>": [False, False, False, True],
+            "a_1.0": [True, False, False, False],
+            "a_2.0": [False, True, False, False],
+            "a_nan": [False, False, True, False],
         },
-        dtype="uint8",
+        dtype="bool",
     )
     actual = cudf.get_dummies(df, dummy_na=True, columns=["a"])
 
@@ -163,13 +154,13 @@ def test_get_dummies_array_like_with_nan():
     ser = cudf.Series([0.1, 2, 3, None, np.nan], nan_as_null=False)
     expected = cudf.DataFrame(
         {
-            "a_null": [0, 0, 0, 1, 0],
-            "a_0.1": [1, 0, 0, 0, 0],
-            "a_2.0": [0, 1, 0, 0, 0],
-            "a_3.0": [0, 0, 1, 0, 0],
-            "a_nan": [0, 0, 0, 0, 1],
+            "a_<NA>": [False, False, False, True, False],
+            "a_0.1": [True, False, False, False, False],
+            "a_2.0": [False, True, False, False, False],
+            "a_3.0": [False, False, True, False, False],
+            "a_nan": [False, False, False, False, True],
         },
-        dtype="uint8",
+        dtype="bool",
     )
     actual = cudf.get_dummies(ser, dummy_na=True, prefix="a", prefix_sep="_")