From 1eb5cd6d0824fa8329b41d4a0e9b39983b923772 Mon Sep 17 00:00:00 2001
From: Kian Eliasi <kian.elbo@gmail.com>
Date: Wed, 10 Aug 2022 16:14:50 +0430
Subject: [PATCH 01/11] ENH: Warn when dtype is not passed to get_dummies

---
 pandas/core/reshape/encoding.py          |  8 +++
 pandas/tests/reshape/test_get_dummies.py | 87 +++++++++++++++---------
 2 files changed, 61 insertions(+), 34 deletions(-)

diff --git a/pandas/core/reshape/encoding.py b/pandas/core/reshape/encoding.py
index da4de8cc57e65..bbea1baefcf0f 100644
--- a/pandas/core/reshape/encoding.py
+++ b/pandas/core/reshape/encoding.py
@@ -3,11 +3,13 @@
 from collections import defaultdict
 import itertools
 from typing import Hashable
+import warnings
 
 import numpy as np
 
 from pandas._libs.sparse import IntIndex
 from pandas._typing import Dtype
+from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.common import (
     is_integer_dtype,
@@ -228,6 +230,12 @@ def _get_dummies_1d(
     codes, levels = factorize_from_iterable(Series(data))
 
     if dtype is None:
+        warnings.warn(
+            "The default dtype will change from 'uint8' to 'bool', "
+            "please specify a dtype to silence this warning",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
         dtype = np.dtype(np.uint8)
     # error: Argument 1 to "dtype" has incompatible type "Union[ExtensionDtype, str,
     # dtype[Any], Type[object]]"; expected "Type[Any]"
diff --git a/pandas/tests/reshape/test_get_dummies.py b/pandas/tests/reshape/test_get_dummies.py
index 6c9a60caaa2be..063421f82a05a 100644
--- a/pandas/tests/reshape/test_get_dummies.py
+++ b/pandas/tests/reshape/test_get_dummies.py
@@ -45,6 +45,11 @@ def test_get_dummies_raises_on_dtype_object(self, df):
         with pytest.raises(ValueError, match=msg):
             get_dummies(df, dtype="object")
 
+    def test_get_dummies_warns_default_dtype(self, df):
+        msg = "The default dtype will change from 'uint8' to 'bool'"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            get_dummies(df)
+
     def test_get_dummies_basic(self, sparse, dtype):
         s_list = list("abc")
         s_series = Series(s_list)
@@ -121,9 +126,11 @@ def test_get_dummies_just_na(self, sparse):
         just_na_series = Series(just_na_list)
         just_na_series_index = Series(just_na_list, index=["A"])
 
-        res_list = get_dummies(just_na_list, sparse=sparse)
-        res_series = get_dummies(just_na_series, sparse=sparse)
-        res_series_index = get_dummies(just_na_series_index, sparse=sparse)
+        res_list = get_dummies(just_na_list, dtype=np.uint8, sparse=sparse)
+        res_series = get_dummies(just_na_series, dtype=np.uint8, sparse=sparse)
+        res_series_index = get_dummies(
+            just_na_series_index, dtype=np.uint8, sparse=sparse
+        )
 
         assert res_list.empty
         assert res_series.empty
@@ -169,7 +176,7 @@ def test_get_dummies_unicode(self, sparse):
         e = "e"
         eacute = unicodedata.lookup("LATIN SMALL LETTER E WITH ACUTE")
         s = [e, eacute, eacute]
-        res = get_dummies(s, prefix="letter", sparse=sparse)
+        res = get_dummies(s, dtype=np.uint8, prefix="letter", sparse=sparse)
         exp = DataFrame(
             {"letter_e": [1, 0, 0], f"letter_{eacute}": [0, 1, 1]}, dtype=np.uint8
         )
@@ -179,7 +186,7 @@ def test_get_dummies_unicode(self, sparse):
 
     def test_dataframe_dummies_all_obj(self, df, sparse):
         df = df[["A", "B"]]
-        result = get_dummies(df, sparse=sparse)
+        result = get_dummies(df, dtype=np.uint8, sparse=sparse)
         expected = DataFrame(
             {"A_a": [1, 0, 1], "A_b": [0, 1, 0], "B_b": [1, 1, 0], "B_c": [0, 0, 1]},
             dtype=np.uint8,
@@ -200,7 +207,7 @@ def test_dataframe_dummies_string_dtype(self, df):
         # GH44965
         df = df[["A", "B"]]
         df = df.astype({"A": "object", "B": "string"})
-        result = get_dummies(df)
+        result = get_dummies(df, dtype=np.uint8)
         expected = DataFrame(
             {
                 "A_a": [1, 0, 1],
@@ -234,7 +241,7 @@ def test_dataframe_dummies_mix_default(self, df, sparse, dtype):
 
     def test_dataframe_dummies_prefix_list(self, df, sparse):
         prefixes = ["from_A", "from_B"]
-        result = get_dummies(df, prefix=prefixes, sparse=sparse)
+        result = get_dummies(df, dtype=np.uint8, prefix=prefixes, sparse=sparse)
         expected = DataFrame(
             {
                 "C": [1, 2, 3],
@@ -255,7 +262,7 @@ def test_dataframe_dummies_prefix_list(self, df, sparse):
 
     def test_dataframe_dummies_prefix_str(self, df, sparse):
         # not that you should do this...
-        result = get_dummies(df, prefix="bad", sparse=sparse)
+        result = get_dummies(df, dtype=np.uint8, prefix="bad", sparse=sparse)
         bad_columns = ["bad_a", "bad_b", "bad_b", "bad_c"]
         expected = DataFrame(
             [[1, 1, 0, 1, 0], [2, 0, 1, 1, 0], [3, 1, 0, 0, 1]],
@@ -280,7 +287,9 @@ def test_dataframe_dummies_prefix_str(self, df, sparse):
         tm.assert_frame_equal(result, expected)
 
     def test_dataframe_dummies_subset(self, df, sparse):
-        result = get_dummies(df, prefix=["from_A"], columns=["A"], sparse=sparse)
+        result = get_dummies(
+            df, dtype=np.uint8, prefix=["from_A"], columns=["A"], sparse=sparse
+        )
         expected = DataFrame(
             {
                 "B": ["b", "b", "c"],
@@ -298,7 +307,7 @@ def test_dataframe_dummies_subset(self, df, sparse):
         tm.assert_frame_equal(result, expected)
 
     def test_dataframe_dummies_prefix_sep(self, df, sparse):
-        result = get_dummies(df, prefix_sep="..", sparse=sparse)
+        result = get_dummies(df, dtype=np.uint8, prefix_sep="..", sparse=sparse)
         expected = DataFrame(
             {
                 "C": [1, 2, 3],
@@ -317,11 +326,13 @@ def test_dataframe_dummies_prefix_sep(self, df, sparse):
 
         tm.assert_frame_equal(result, expected)
 
-        result = get_dummies(df, prefix_sep=["..", "__"], sparse=sparse)
+        result = get_dummies(df, dtype=np.uint8, prefix_sep=["..", "__"], sparse=sparse)
         expected = expected.rename(columns={"B..b": "B__b", "B..c": "B__c"})
         tm.assert_frame_equal(result, expected)
 
-        result = get_dummies(df, prefix_sep={"A": "..", "B": "__"}, sparse=sparse)
+        result = get_dummies(
+            df, dtype=np.uint8, prefix_sep={"A": "..", "B": "__"}, sparse=sparse
+        )
         tm.assert_frame_equal(result, expected)
 
     def test_dataframe_dummies_prefix_bad_length(self, df, sparse):
@@ -330,7 +341,7 @@ def test_dataframe_dummies_prefix_bad_length(self, df, sparse):
             "encoded (2)"
         )
         with pytest.raises(ValueError, match=msg):
-            get_dummies(df, prefix=["too few"], sparse=sparse)
+            get_dummies(df, dtype=np.uint8, prefix=["too few"], sparse=sparse)
 
     def test_dataframe_dummies_prefix_sep_bad_length(self, df, sparse):
         msg = re.escape(
@@ -338,12 +349,12 @@ def test_dataframe_dummies_prefix_sep_bad_length(self, df, sparse):
             "encoded (2)"
         )
         with pytest.raises(ValueError, match=msg):
-            get_dummies(df, prefix_sep=["bad"], sparse=sparse)
+            get_dummies(df, dtype=np.uint8, prefix_sep=["bad"], sparse=sparse)
 
     def test_dataframe_dummies_prefix_dict(self, sparse):
         prefixes = {"A": "from_A", "B": "from_B"}
         df = DataFrame({"C": [1, 2, 3], "A": ["a", "b", "a"], "B": ["b", "b", "c"]})
-        result = get_dummies(df, prefix=prefixes, sparse=sparse)
+        result = get_dummies(df, dtype=np.uint8, prefix=prefixes, sparse=sparse)
 
         expected = DataFrame(
             {
@@ -453,16 +464,18 @@ def test_get_dummies_basic_drop_first(self, sparse):
 
         expected = DataFrame({"b": [0, 1, 0], "c": [0, 0, 1]}, dtype=np.uint8)
 
-        result = get_dummies(s_list, drop_first=True, sparse=sparse)
+        result = get_dummies(s_list, dtype=np.uint8, drop_first=True, sparse=sparse)
         if sparse:
             expected = expected.apply(SparseArray, fill_value=0)
         tm.assert_frame_equal(result, expected)
 
-        result = get_dummies(s_series, drop_first=True, sparse=sparse)
+        result = get_dummies(s_series, dtype=np.uint8, drop_first=True, sparse=sparse)
         tm.assert_frame_equal(result, expected)
 
         expected.index = list("ABC")
-        result = get_dummies(s_series_index, drop_first=True, sparse=sparse)
+        result = get_dummies(
+            s_series_index, dtype=np.uint8, drop_first=True, sparse=sparse
+        )
         tm.assert_frame_equal(result, expected)
 
     def test_get_dummies_basic_drop_first_one_level(self, sparse):
@@ -473,27 +486,31 @@ def test_get_dummies_basic_drop_first_one_level(self, sparse):
 
         expected = DataFrame(index=np.arange(3))
 
-        result = get_dummies(s_list, drop_first=True, sparse=sparse)
+        result = get_dummies(s_list, dtype=np.uint8, drop_first=True, sparse=sparse)
         tm.assert_frame_equal(result, expected)
 
-        result = get_dummies(s_series, drop_first=True, sparse=sparse)
+        result = get_dummies(s_series, dtype=np.uint8, drop_first=True, sparse=sparse)
         tm.assert_frame_equal(result, expected)
 
         expected = DataFrame(index=list("ABC"))
-        result = get_dummies(s_series_index, drop_first=True, sparse=sparse)
+        result = get_dummies(
+            s_series_index, dtype=np.uint8, drop_first=True, sparse=sparse
+        )
         tm.assert_frame_equal(result, expected)
 
     def test_get_dummies_basic_drop_first_NA(self, sparse):
         # Test NA handling together with drop_first
         s_NA = ["a", "b", np.nan]
-        res = get_dummies(s_NA, drop_first=True, sparse=sparse)
+        res = get_dummies(s_NA, dtype=np.uint8, drop_first=True, sparse=sparse)
         exp = DataFrame({"b": [0, 1, 0]}, dtype=np.uint8)
         if sparse:
             exp = exp.apply(SparseArray, fill_value=0)
 
         tm.assert_frame_equal(res, exp)
 
-        res_na = get_dummies(s_NA, dummy_na=True, drop_first=True, sparse=sparse)
+        res_na = get_dummies(
+            s_NA, dtype=np.uint8, dummy_na=True, drop_first=True, sparse=sparse
+        )
         exp_na = DataFrame({"b": [0, 1, 0], np.nan: [0, 0, 1]}, dtype=np.uint8).reindex(
             ["b", np.nan], axis=1
         )
@@ -502,14 +519,14 @@ def test_get_dummies_basic_drop_first_NA(self, sparse):
         tm.assert_frame_equal(res_na, exp_na)
 
         res_just_na = get_dummies(
-            [np.nan], dummy_na=True, drop_first=True, sparse=sparse
+            [np.nan], dtype=np.uint8, dummy_na=True, drop_first=True, sparse=sparse
         )
         exp_just_na = DataFrame(index=np.arange(1))
         tm.assert_frame_equal(res_just_na, exp_just_na)
 
     def test_dataframe_dummies_drop_first(self, df, sparse):
         df = df[["A", "B"]]
-        result = get_dummies(df, drop_first=True, sparse=sparse)
+        result = get_dummies(df, dtype=np.uint8, drop_first=True, sparse=sparse)
         expected = DataFrame({"A_b": [0, 1, 0], "B_c": [0, 0, 1]}, dtype=np.uint8)
         if sparse:
             expected = expected.apply(SparseArray, fill_value=0)
@@ -517,7 +534,7 @@ def test_dataframe_dummies_drop_first(self, df, sparse):
 
     def test_dataframe_dummies_drop_first_with_categorical(self, df, sparse, dtype):
         df["cat"] = Categorical(["x", "y", "y"])
-        result = get_dummies(df, drop_first=True, sparse=sparse)
+        result = get_dummies(df, dtype=np.uint8, drop_first=True, sparse=sparse)
         expected = DataFrame(
             {"C": [1, 2, 3], "A_b": [0, 1, 0], "B_c": [0, 0, 1], "cat_y": [0, 1, 1]}
         )
@@ -532,7 +549,7 @@ def test_dataframe_dummies_drop_first_with_categorical(self, df, sparse, dtype):
     def test_dataframe_dummies_drop_first_with_na(self, df, sparse):
         df.loc[3, :] = [np.nan, np.nan, np.nan]
         result = get_dummies(
-            df, dummy_na=True, drop_first=True, sparse=sparse
+            df, dtype=np.uint8, dummy_na=True, drop_first=True, sparse=sparse
         ).sort_index(axis=1)
         expected = DataFrame(
             {
@@ -552,18 +569,20 @@ def test_dataframe_dummies_drop_first_with_na(self, df, sparse):
 
         tm.assert_frame_equal(result, expected)
 
-        result = get_dummies(df, dummy_na=False, drop_first=True, sparse=sparse)
+        result = get_dummies(
+            df, dtype=np.uint8, dummy_na=False, drop_first=True, sparse=sparse
+        )
         expected = expected[["C", "A_b", "B_c"]]
         tm.assert_frame_equal(result, expected)
 
     def test_get_dummies_int_int(self):
         data = Series([1, 2, 1])
-        result = get_dummies(data)
+        result = get_dummies(data, dtype=np.uint8)
         expected = DataFrame([[1, 0], [0, 1], [1, 0]], columns=[1, 2], dtype=np.uint8)
         tm.assert_frame_equal(result, expected)
 
         data = Series(Categorical(["a", "b", "a"]))
-        result = get_dummies(data)
+        result = get_dummies(data, dtype=np.uint8)
         expected = DataFrame(
             [[1, 0], [0, 1], [1, 0]], columns=Categorical(["a", "b"]), dtype=np.uint8
         )
@@ -605,7 +624,7 @@ def test_dataframe_dummies_preserve_categorical_dtype(self, dtype, ordered):
     def test_get_dummies_dont_sparsify_all_columns(self, sparse):
         # GH18914
         df = DataFrame.from_dict({"GDP": [1, 2], "Nation": ["AB", "CD"]})
-        df = get_dummies(df, columns=["Nation"], sparse=sparse)
+        df = get_dummies(df, dtype=np.uint8, columns=["Nation"], sparse=sparse)
         df2 = df.reindex(columns=["GDP"])
 
         tm.assert_frame_equal(df[["GDP"]], df2)
@@ -613,7 +632,7 @@ def test_get_dummies_dont_sparsify_all_columns(self, sparse):
     def test_get_dummies_duplicate_columns(self, df):
         # GH20839
         df.columns = ["A", "A", "A"]
-        result = get_dummies(df).sort_index(axis=1)
+        result = get_dummies(df, dtype=np.uint8).sort_index(axis=1)
 
         expected = DataFrame(
             [[1, 1, 0, 1, 0], [2, 0, 1, 1, 0], [3, 1, 0, 0, 1]],
@@ -627,7 +646,7 @@ def test_get_dummies_duplicate_columns(self, df):
 
     def test_get_dummies_all_sparse(self):
         df = DataFrame({"A": [1, 2]})
-        result = get_dummies(df, columns=["A"], sparse=True)
+        result = get_dummies(df, dtype=np.uint8, columns=["A"], sparse=True)
         dtype = SparseDtype("uint8", 0)
         expected = DataFrame(
             {
@@ -652,4 +671,4 @@ def test_get_dummies_with_string_values(self, values):
         msg = "Input must be a list-like for parameter `columns`"
 
         with pytest.raises(TypeError, match=msg):
-            get_dummies(df, columns=values)
+            get_dummies(df, dtype=np.uint8, columns=values)

From efa678b8a9b1357ff4473d9fa1996100b4b99515 Mon Sep 17 00:00:00 2001
From: Kian Eliasi <kian.elbo@gmail.com>
Date: Wed, 10 Aug 2022 18:33:40 +0430
Subject: [PATCH 02/11] Edit get_dummies' dtype warning

---
 pandas/core/reshape/encoding.py          | 4 ++--
 pandas/tests/reshape/test_get_dummies.py | 1 +
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/pandas/core/reshape/encoding.py b/pandas/core/reshape/encoding.py
index bbea1baefcf0f..aa2dc6f6e6a3d 100644
--- a/pandas/core/reshape/encoding.py
+++ b/pandas/core/reshape/encoding.py
@@ -231,8 +231,8 @@ def _get_dummies_1d(
 
     if dtype is None:
         warnings.warn(
-            "The default dtype will change from 'uint8' to 'bool', "
-            "please specify a dtype to silence this warning",
+            "In a future version of pandas the default dtype will change from "
+            "'uint8' to 'bool', please specify a dtype to silence this warning",
             FutureWarning,
             stacklevel=find_stack_level(),
         )
diff --git a/pandas/tests/reshape/test_get_dummies.py b/pandas/tests/reshape/test_get_dummies.py
index 063421f82a05a..71c8ac42bd3f2 100644
--- a/pandas/tests/reshape/test_get_dummies.py
+++ b/pandas/tests/reshape/test_get_dummies.py
@@ -46,6 +46,7 @@ def test_get_dummies_raises_on_dtype_object(self, df):
             get_dummies(df, dtype="object")
 
     def test_get_dummies_warns_default_dtype(self, df):
+        # https://github.com/pandas-dev/pandas/issues/45848
         msg = "The default dtype will change from 'uint8' to 'bool'"
         with tm.assert_produces_warning(FutureWarning, match=msg):
             get_dummies(df)

From 472fa284273c583a07dfc8b585e831599fd4e32b Mon Sep 17 00:00:00 2001
From: Kian Eliasi <kian.elbo@gmail.com>
Date: Wed, 10 Aug 2022 18:46:18 +0430
Subject: [PATCH 03/11] Add whatsnew entry for issue #45848

---
 doc/source/whatsnew/v1.5.0.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index a0d33cb513722..91bc932054f5d 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -843,6 +843,7 @@ Other Deprecations
 - Deprecated setting a categorical's categories with ``cat.categories = ['a', 'b', 'c']``, use :meth:`Categorical.rename_categories` instead (:issue:`37643`)
 - Deprecated unused arguments ``encoding`` and ``verbose`` in :meth:`Series.to_excel` and :meth:`DataFrame.to_excel` (:issue:`47912`)
 - Deprecated producing a single element when iterating over a :class:`DataFrameGroupBy` or a :class:`SeriesGroupBy` that has been grouped by a list of length 1; A tuple of length one will be returned instead (:issue:`42795`)
+- Emit ``FutureWarning`` from :func:`get_dummies` when ``dtype`` is unspecified, indicating that its default value will be changed to ``bool`` (:issue:`45848`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_150.performance:

From 2ead75002a86d50b5651f0d370a53372c9691424 Mon Sep 17 00:00:00 2001
From: Kian Eliasi <kian.elbo@gmail.com>
Date: Wed, 10 Aug 2022 20:02:24 +0430
Subject: [PATCH 04/11] Fix dtype warning test

---
 pandas/tests/reshape/test_get_dummies.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pandas/tests/reshape/test_get_dummies.py b/pandas/tests/reshape/test_get_dummies.py
index 71c8ac42bd3f2..50f9b4f26a4d2 100644
--- a/pandas/tests/reshape/test_get_dummies.py
+++ b/pandas/tests/reshape/test_get_dummies.py
@@ -47,7 +47,7 @@ def test_get_dummies_raises_on_dtype_object(self, df):
 
     def test_get_dummies_warns_default_dtype(self, df):
         # https://github.com/pandas-dev/pandas/issues/45848
-        msg = "The default dtype will change from 'uint8' to 'bool'"
+        msg = "In a future version of pandas the default dtype will change"
         with tm.assert_produces_warning(FutureWarning, match=msg):
             get_dummies(df)
 

From ddcc7d3bc4dccf0b488b2d154fe89dac1747af6f Mon Sep 17 00:00:00 2001
From: Kian Eliasi <kian.elbo@gmail.com>
Date: Wed, 10 Aug 2022 20:03:04 +0430
Subject: [PATCH 05/11] Suppress warnings in docs

---
 doc/source/user_guide/reshaping.rst | 8 ++++++++
 doc/source/whatsnew/v0.13.0.rst     | 1 +
 doc/source/whatsnew/v0.15.0.rst     | 1 +
 doc/source/whatsnew/v0.19.0.rst     | 1 +
 doc/source/whatsnew/v0.23.0.rst     | 1 +
 doc/source/whatsnew/v0.24.0.rst     | 1 +
 6 files changed, 13 insertions(+)

diff --git a/doc/source/user_guide/reshaping.rst b/doc/source/user_guide/reshaping.rst
index adca9de6c130a..d0f305168311a 100644
--- a/doc/source/user_guide/reshaping.rst
+++ b/doc/source/user_guide/reshaping.rst
@@ -608,6 +608,7 @@ values, can derive a :class:`DataFrame` containing ``k`` columns of 1s and 0s us
 :func:`~pandas.get_dummies`:
 
 .. ipython:: python
+   :okwarning:
 
    df = pd.DataFrame({"key": list("bbacab"), "data1": range(6)})
 
@@ -617,6 +618,7 @@ Sometimes it's useful to prefix the column names, for example when merging the r
 with the original :class:`DataFrame`:
 
 .. ipython:: python
+   :okwarning:
 
    dummies = pd.get_dummies(df["key"], prefix="key")
    dummies
@@ -626,6 +628,7 @@ with the original :class:`DataFrame`:
 This function is often used along with discretization functions like :func:`~pandas.cut`:
 
 .. ipython:: python
+   :okwarning:
 
    values = np.random.randn(10)
    values
@@ -642,6 +645,7 @@ variables (categorical in the statistical sense, those with ``object`` or
 
 
 .. ipython:: python
+    :okwarning:
 
     df = pd.DataFrame({"A": ["a", "b", "a"], "B": ["c", "c", "b"], "C": [1, 2, 3]})
     pd.get_dummies(df)
@@ -650,6 +654,7 @@ All non-object columns are included untouched in the output. You can control
 the columns that are encoded with the ``columns`` keyword.
 
 .. ipython:: python
+    :okwarning:
 
     pd.get_dummies(df, columns=["A"])
 
@@ -667,6 +672,7 @@ the prefix separator. You can specify ``prefix`` and ``prefix_sep`` in 3 ways:
 * dict: Mapping column name to prefix.
 
 .. ipython:: python
+    :okwarning:
 
     simple = pd.get_dummies(df, prefix="new_prefix")
     simple
@@ -680,6 +686,7 @@ variable to avoid collinearity when feeding the result to statistical models.
 You can switch to this mode by turn on ``drop_first``.
 
 .. ipython:: python
+    :okwarning:
 
     s = pd.Series(list("abcaa"))
 
@@ -690,6 +697,7 @@ You can switch to this mode by turn on ``drop_first``.
 When a column contains only one level, it will be omitted in the result.
 
 .. ipython:: python
+    :okwarning:
 
     df = pd.DataFrame({"A": list("aaaaa"), "B": list("ababc")})
 
diff --git a/doc/source/whatsnew/v0.13.0.rst b/doc/source/whatsnew/v0.13.0.rst
index 8265ad58f7ea3..f5dc576b4420d 100644
--- a/doc/source/whatsnew/v0.13.0.rst
+++ b/doc/source/whatsnew/v0.13.0.rst
@@ -501,6 +501,7 @@ Enhancements
 - ``NaN`` handing in get_dummies (:issue:`4446`) with ``dummy_na``
 
   .. ipython:: python
+     :okwarning:
 
      # previously, nan was erroneously counted as 2 here
      # now it is not counted at all
diff --git a/doc/source/whatsnew/v0.15.0.rst b/doc/source/whatsnew/v0.15.0.rst
index 04506f1655c7d..2df21038eff46 100644
--- a/doc/source/whatsnew/v0.15.0.rst
+++ b/doc/source/whatsnew/v0.15.0.rst
@@ -1007,6 +1007,7 @@ Other:
   left untouched.
 
   .. ipython:: python
+    :okwarning:
 
     df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['c', 'c', 'b'],
                     'C': [1, 2, 3]})
diff --git a/doc/source/whatsnew/v0.19.0.rst b/doc/source/whatsnew/v0.19.0.rst
index f2fdd23af1297..145c87b71b3d1 100644
--- a/doc/source/whatsnew/v0.19.0.rst
+++ b/doc/source/whatsnew/v0.19.0.rst
@@ -431,6 +431,7 @@ The ``pd.get_dummies`` function now returns dummy-encoded columns as small integ
 **New behavior**:
 
 .. ipython:: python
+   :okwarning:
 
    pd.get_dummies(["a", "b", "a", "c"]).dtypes
 
diff --git a/doc/source/whatsnew/v0.23.0.rst b/doc/source/whatsnew/v0.23.0.rst
index 9f24bc8e8ec50..4a715a5b61a04 100644
--- a/doc/source/whatsnew/v0.23.0.rst
+++ b/doc/source/whatsnew/v0.23.0.rst
@@ -366,6 +366,7 @@ Function ``get_dummies`` now supports ``dtype`` argument
 The :func:`get_dummies` now accepts a ``dtype`` argument, which specifies a dtype for the new columns. The default remains uint8. (:issue:`18330`)
 
 .. ipython:: python
+   :okwarning:
 
    df = pd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [5, 6]})
    pd.get_dummies(df, columns=['c']).dtypes
diff --git a/doc/source/whatsnew/v0.24.0.rst b/doc/source/whatsnew/v0.24.0.rst
index f5175283cce4e..3651235bea649 100644
--- a/doc/source/whatsnew/v0.24.0.rst
+++ b/doc/source/whatsnew/v0.24.0.rst
@@ -833,6 +833,7 @@ then all the columns are dummy-encoded, and a :class:`SparseDataFrame` was retur
 Now, the return type is consistently a :class:`DataFrame`.
 
 .. ipython:: python
+   :okwarning:
 
    type(pd.get_dummies(df, sparse=True))
    type(pd.get_dummies(df[['B', 'C']], sparse=True))

From 81dbb875e5f0533d7c56eb4ce410976cc52bdc3a Mon Sep 17 00:00:00 2001
From: Kian Eliasi <kian.elbo@gmail.com>
Date: Wed, 10 Aug 2022 22:00:34 +0430
Subject: [PATCH 06/11] Edit whatsnew entry

Co-authored-by: Marco Edward Gorelli <marcogorelli@protonmail.com>
---
 doc/source/whatsnew/v1.5.0.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index 91bc932054f5d..77248684ab634 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -843,7 +843,7 @@ Other Deprecations
 - Deprecated setting a categorical's categories with ``cat.categories = ['a', 'b', 'c']``, use :meth:`Categorical.rename_categories` instead (:issue:`37643`)
 - Deprecated unused arguments ``encoding`` and ``verbose`` in :meth:`Series.to_excel` and :meth:`DataFrame.to_excel` (:issue:`47912`)
 - Deprecated producing a single element when iterating over a :class:`DataFrameGroupBy` or a :class:`SeriesGroupBy` that has been grouped by a list of length 1; A tuple of length one will be returned instead (:issue:`42795`)
-- Emit ``FutureWarning`` from :func:`get_dummies` when ``dtype`` is unspecified, indicating that its default value will be changed to ``bool`` (:issue:`45848`)
+- Deprecated ``np.uint8`` as the default ``dtype`` for :func:`get_dummies` - in a future version, it will be changed to ``bool`` (:issue:`45848`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_150.performance:

From f97df66c282e8b8fe707f9f7a2ced1e4a3580fc1 Mon Sep 17 00:00:00 2001
From: Kian Eliasi <kian.elbo@gmail.com>
Date: Tue, 23 Aug 2022 14:05:35 +0430
Subject: [PATCH 07/11] Fix find_stack_level in get_dummies dtype warning

---
 pandas/core/reshape/encoding.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pandas/core/reshape/encoding.py b/pandas/core/reshape/encoding.py
index aa2dc6f6e6a3d..e95d8b70a0bea 100644
--- a/pandas/core/reshape/encoding.py
+++ b/pandas/core/reshape/encoding.py
@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 from collections import defaultdict
+import inspect
 import itertools
 from typing import Hashable
 import warnings
@@ -234,7 +235,7 @@ def _get_dummies_1d(
             "In a future version of pandas the default dtype will change from "
             "'uint8' to 'bool', please specify a dtype to silence this warning",
             FutureWarning,
-            stacklevel=find_stack_level(),
+            stacklevel=find_stack_level(inspect.currentframe()),
         )
         dtype = np.dtype(np.uint8)
     # error: Argument 1 to "dtype" has incompatible type "Union[ExtensionDtype, str,

From 15aeb3e018af148c7abfcce9d6331bc16c13e8c4 Mon Sep 17 00:00:00 2001
From: Kian Eliasi <kian.elbo@gmail.com>
Date: Thu, 22 Sep 2022 13:01:38 +0200
Subject: [PATCH 08/11] Change the default dtype of get_dummies to bool

---
 doc/source/user_guide/reshaping.rst           |   8 -
 doc/source/whatsnew/v0.13.0.rst               |   1 -
 doc/source/whatsnew/v0.15.0.rst               |   1 -
 doc/source/whatsnew/v0.19.0.rst               |   1 -
 doc/source/whatsnew/v0.23.0.rst               |   1 -
 doc/source/whatsnew/v0.24.0.rst               |   1 -
 doc/source/whatsnew/v1.5.0.rst                |   1 -
 doc/source/whatsnew/v1.5.1.rst                |   2 +-
 pandas/core/reshape/encoding.py               |  72 ++++---
 pandas/tests/frame/indexing/test_getitem.py   |   4 +-
 .../tests/frame/methods/test_sort_values.py   |   2 +-
 pandas/tests/reshape/test_get_dummies.py      | 178 ++++++++----------
 12 files changed, 116 insertions(+), 156 deletions(-)

diff --git a/doc/source/user_guide/reshaping.rst b/doc/source/user_guide/reshaping.rst
index d0f305168311a..adca9de6c130a 100644
--- a/doc/source/user_guide/reshaping.rst
+++ b/doc/source/user_guide/reshaping.rst
@@ -608,7 +608,6 @@ values, can derive a :class:`DataFrame` containing ``k`` columns of 1s and 0s us
 :func:`~pandas.get_dummies`:
 
 .. ipython:: python
-   :okwarning:
 
    df = pd.DataFrame({"key": list("bbacab"), "data1": range(6)})
 
@@ -618,7 +617,6 @@ Sometimes it's useful to prefix the column names, for example when merging the r
 with the original :class:`DataFrame`:
 
 .. ipython:: python
-   :okwarning:
 
    dummies = pd.get_dummies(df["key"], prefix="key")
    dummies
@@ -628,7 +626,6 @@ with the original :class:`DataFrame`:
 This function is often used along with discretization functions like :func:`~pandas.cut`:
 
 .. ipython:: python
-   :okwarning:
 
    values = np.random.randn(10)
    values
@@ -645,7 +642,6 @@ variables (categorical in the statistical sense, those with ``object`` or
 
 
 .. ipython:: python
-    :okwarning:
 
     df = pd.DataFrame({"A": ["a", "b", "a"], "B": ["c", "c", "b"], "C": [1, 2, 3]})
     pd.get_dummies(df)
@@ -654,7 +650,6 @@ All non-object columns are included untouched in the output. You can control
 the columns that are encoded with the ``columns`` keyword.
 
 .. ipython:: python
-    :okwarning:
 
     pd.get_dummies(df, columns=["A"])
 
@@ -672,7 +667,6 @@ the prefix separator. You can specify ``prefix`` and ``prefix_sep`` in 3 ways:
 * dict: Mapping column name to prefix.
 
 .. ipython:: python
-    :okwarning:
 
     simple = pd.get_dummies(df, prefix="new_prefix")
     simple
@@ -686,7 +680,6 @@ variable to avoid collinearity when feeding the result to statistical models.
 You can switch to this mode by turn on ``drop_first``.
 
 .. ipython:: python
-    :okwarning:
 
     s = pd.Series(list("abcaa"))
 
@@ -697,7 +690,6 @@ You can switch to this mode by turn on ``drop_first``.
 When a column contains only one level, it will be omitted in the result.
 
 .. ipython:: python
-    :okwarning:
 
     df = pd.DataFrame({"A": list("aaaaa"), "B": list("ababc")})
 
diff --git a/doc/source/whatsnew/v0.13.0.rst b/doc/source/whatsnew/v0.13.0.rst
index f5dc576b4420d..8265ad58f7ea3 100644
--- a/doc/source/whatsnew/v0.13.0.rst
+++ b/doc/source/whatsnew/v0.13.0.rst
@@ -501,7 +501,6 @@ Enhancements
 - ``NaN`` handing in get_dummies (:issue:`4446`) with ``dummy_na``
 
   .. ipython:: python
-     :okwarning:
 
      # previously, nan was erroneously counted as 2 here
      # now it is not counted at all
diff --git a/doc/source/whatsnew/v0.15.0.rst b/doc/source/whatsnew/v0.15.0.rst
index 2df21038eff46..04506f1655c7d 100644
--- a/doc/source/whatsnew/v0.15.0.rst
+++ b/doc/source/whatsnew/v0.15.0.rst
@@ -1007,7 +1007,6 @@ Other:
   left untouched.
 
   .. ipython:: python
-    :okwarning:
 
     df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['c', 'c', 'b'],
                     'C': [1, 2, 3]})
diff --git a/doc/source/whatsnew/v0.19.0.rst b/doc/source/whatsnew/v0.19.0.rst
index 145c87b71b3d1..f2fdd23af1297 100644
--- a/doc/source/whatsnew/v0.19.0.rst
+++ b/doc/source/whatsnew/v0.19.0.rst
@@ -431,7 +431,6 @@ The ``pd.get_dummies`` function now returns dummy-encoded columns as small integ
 **New behavior**:
 
 .. ipython:: python
-   :okwarning:
 
    pd.get_dummies(["a", "b", "a", "c"]).dtypes
 
diff --git a/doc/source/whatsnew/v0.23.0.rst b/doc/source/whatsnew/v0.23.0.rst
index 4a715a5b61a04..9f24bc8e8ec50 100644
--- a/doc/source/whatsnew/v0.23.0.rst
+++ b/doc/source/whatsnew/v0.23.0.rst
@@ -366,7 +366,6 @@ Function ``get_dummies`` now supports ``dtype`` argument
 The :func:`get_dummies` now accepts a ``dtype`` argument, which specifies a dtype for the new columns. The default remains uint8. (:issue:`18330`)
 
 .. ipython:: python
-   :okwarning:
 
    df = pd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [5, 6]})
    pd.get_dummies(df, columns=['c']).dtypes
diff --git a/doc/source/whatsnew/v0.24.0.rst b/doc/source/whatsnew/v0.24.0.rst
index 3651235bea649..f5175283cce4e 100644
--- a/doc/source/whatsnew/v0.24.0.rst
+++ b/doc/source/whatsnew/v0.24.0.rst
@@ -833,7 +833,6 @@ then all the columns are dummy-encoded, and a :class:`SparseDataFrame` was retur
 Now, the return type is consistently a :class:`DataFrame`.
 
 .. ipython:: python
-   :okwarning:
 
    type(pd.get_dummies(df, sparse=True))
    type(pd.get_dummies(df[['B', 'C']], sparse=True))
diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index c35297c1bcca7..7f968694693f9 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -932,7 +932,6 @@ Other Deprecations
 - Deprecated unused arguments ``encoding`` and ``verbose`` in :meth:`Series.to_excel` and :meth:`DataFrame.to_excel` (:issue:`47912`)
 - Deprecated the ``inplace`` keyword in :meth:`DataFrame.set_axis` and :meth:`Series.set_axis`, use ``obj = obj.set_axis(..., copy=False)`` instead (:issue:`48130`)
 - Deprecated producing a single element when iterating over a :class:`DataFrameGroupBy` or a :class:`SeriesGroupBy` that has been grouped by a list of length 1; A tuple of length one will be returned instead (:issue:`42795`)
-- Deprecated ``np.uint8`` as the default ``dtype`` for :func:`get_dummies` - in a future version, it will be changed to ``bool`` (:issue:`45848`)
 - Fixed up warning message of deprecation of :meth:`MultiIndex.lesort_depth` as public method, as the message previously referred to :meth:`MultiIndex.is_lexsorted` instead (:issue:`38701`)
 - Deprecated the ``sort_columns`` argument in :meth:`DataFrame.plot` and :meth:`Series.plot` (:issue:`47563`).
 - Deprecated positional arguments for all but the first argument of :meth:`DataFrame.to_stata` and :func:`read_stata`, use keyword arguments instead (:issue:`48128`).
diff --git a/doc/source/whatsnew/v1.5.1.rst b/doc/source/whatsnew/v1.5.1.rst
index 9d40d9118db32..e5e12bea47689 100644
--- a/doc/source/whatsnew/v1.5.1.rst
+++ b/doc/source/whatsnew/v1.5.1.rst
@@ -23,7 +23,7 @@ Fixed regressions
 
 Bug fixes
 ~~~~~~~~~
--
+- Bug in :func:`get_dummies` with default ``dtype`` being ``uint8`` - the default ``dtype`` is now changed to ``bool`` (:issue:`45848`)
 -
 
 .. ---------------------------------------------------------------------------
diff --git a/pandas/core/reshape/encoding.py b/pandas/core/reshape/encoding.py
index 19b1a2ed96fea..ce449675c8db8 100644
--- a/pandas/core/reshape/encoding.py
+++ b/pandas/core/reshape/encoding.py
@@ -1,16 +1,13 @@
 from __future__ import annotations
 
 from collections import defaultdict
-import inspect
 import itertools
 from typing import Hashable
-import warnings
 
 import numpy as np
 
 from pandas._libs.sparse import IntIndex
 from pandas._typing import Dtype
-from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.common import (
     is_integer_dtype,
@@ -66,7 +63,7 @@ def get_dummies(
     drop_first : bool, default False
         Whether to get k-1 dummies out of k categorical levels by removing the
         first level.
-    dtype : dtype, default np.uint8
+    dtype : dtype, default bool
         Data type for new columns. Only a single dtype is allowed.
 
     Returns
@@ -89,50 +86,50 @@ def get_dummies(
     >>> s = pd.Series(list('abca'))
 
     >>> pd.get_dummies(s)
-       a  b  c
-    0  1  0  0
-    1  0  1  0
-    2  0  0  1
-    3  1  0  0
+           a      b      c
+    0   True  False  False
+    1  False   True  False
+    2  False  False   True
+    3   True  False  False
 
     >>> s1 = ['a', 'b', np.nan]
 
     >>> pd.get_dummies(s1)
-       a  b
-    0  1  0
-    1  0  1
-    2  0  0
+           a      b
+    0   True  False
+    1  False   True
+    2  False  False
 
     >>> pd.get_dummies(s1, dummy_na=True)
-       a  b  NaN
-    0  1  0    0
-    1  0  1    0
-    2  0  0    1
+           a      b    NaN
+    0   True  False  False
+    1  False   True  False
+    2  False  False   True
 
     >>> df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'],
     ...                    'C': [1, 2, 3]})
 
     >>> pd.get_dummies(df, prefix=['col1', 'col2'])
        C  col1_a  col1_b  col2_a  col2_b  col2_c
-    0  1       1       0       0       1       0
-    1  2       0       1       1       0       0
-    2  3       1       0       0       0       1
+    0  1    True   False   False    True   False
+    1  2   False    True    True   False   False
+    2  3    True   False   False   False    True
 
     >>> pd.get_dummies(pd.Series(list('abcaa')))
-       a  b  c
-    0  1  0  0
-    1  0  1  0
-    2  0  0  1
-    3  1  0  0
-    4  1  0  0
+           a      b      c
+    0   True  False  False
+    1  False   True  False
+    2  False  False   True
+    3   True  False  False
+    4   True  False  False
 
     >>> pd.get_dummies(pd.Series(list('abcaa')), drop_first=True)
-       b  c
-    0  0  0
-    1  1  0
-    2  0  1
-    3  0  0
-    4  0  0
+           b      c
+    0  False  False
+    1   True  False
+    2  False   True
+    3  False  False
+    4  False  False
 
     >>> pd.get_dummies(pd.Series(list('abc')), dtype=float)
          a    b    c
@@ -236,16 +233,11 @@ def _get_dummies_1d(
     codes, levels = factorize_from_iterable(Series(data))
 
     if dtype is None:
-        warnings.warn(
-            "In a future version of pandas the default dtype will change from "
-            "'uint8' to 'bool', please specify a dtype to silence this warning",
-            FutureWarning,
-            stacklevel=find_stack_level(inspect.currentframe()),
-        )
-        dtype = np.dtype(np.uint8)
+        dtype = bool
     # error: Argument 1 to "dtype" has incompatible type "Union[ExtensionDtype, str,
     # dtype[Any], Type[object]]"; expected "Type[Any]"
-    dtype = np.dtype(dtype)  # type: ignore[arg-type]
+    else:
+        dtype = np.dtype(dtype)  # type: ignore[arg-type]
 
     if is_object_dtype(dtype):
         raise ValueError("dtype=object is not a valid dtype for get_dummies")
diff --git a/pandas/tests/frame/indexing/test_getitem.py b/pandas/tests/frame/indexing/test_getitem.py
index f5c85bd98d8ad..0c1b206cc39bb 100644
--- a/pandas/tests/frame/indexing/test_getitem.py
+++ b/pandas/tests/frame/indexing/test_getitem.py
@@ -52,9 +52,7 @@ def test_getitem_list_of_labels_categoricalindex_cols(self):
         # GH#16115
         cats = Categorical([Timestamp("12-31-1999"), Timestamp("12-31-2000")])
 
-        expected = DataFrame(
-            [[1, 0], [0, 1]], dtype="uint8", index=[0, 1], columns=cats
-        )
+        expected = DataFrame([[1, 0], [0, 1]], dtype="bool", index=[0, 1], columns=cats)
         dummies = get_dummies(cats)
         result = dummies[list(dummies.columns)]
         tm.assert_frame_equal(result, expected)
diff --git a/pandas/tests/frame/methods/test_sort_values.py b/pandas/tests/frame/methods/test_sort_values.py
index 9f3fcb1db546d..26d684bb2fa3a 100644
--- a/pandas/tests/frame/methods/test_sort_values.py
+++ b/pandas/tests/frame/methods/test_sort_values.py
@@ -19,7 +19,7 @@ def test_sort_values_sparse_no_warning(self):
         # GH#45618
         # TODO(2.0): test will be unnecessary
         ser = pd.Series(Categorical(["a", "b", "a"], categories=["a", "b", "c"]))
-        df = pd.get_dummies(ser, sparse=True)
+        df = pd.get_dummies(ser, dtype=np.uint8, sparse=True)
 
         with tm.assert_produces_warning(None):
             # No warnings about constructing Index from SparseArray
diff --git a/pandas/tests/reshape/test_get_dummies.py b/pandas/tests/reshape/test_get_dummies.py
index 50f9b4f26a4d2..4345a357a0ba8 100644
--- a/pandas/tests/reshape/test_get_dummies.py
+++ b/pandas/tests/reshape/test_get_dummies.py
@@ -45,12 +45,6 @@ def test_get_dummies_raises_on_dtype_object(self, df):
         with pytest.raises(ValueError, match=msg):
             get_dummies(df, dtype="object")
 
-    def test_get_dummies_warns_default_dtype(self, df):
-        # https://github.com/pandas-dev/pandas/issues/45848
-        msg = "In a future version of pandas the default dtype will change"
-        with tm.assert_produces_warning(FutureWarning, match=msg):
-            get_dummies(df)
-
     def test_get_dummies_basic(self, sparse, dtype):
         s_list = list("abc")
         s_series = Series(s_list)
@@ -127,11 +121,9 @@ def test_get_dummies_just_na(self, sparse):
         just_na_series = Series(just_na_list)
         just_na_series_index = Series(just_na_list, index=["A"])
 
-        res_list = get_dummies(just_na_list, dtype=np.uint8, sparse=sparse)
-        res_series = get_dummies(just_na_series, dtype=np.uint8, sparse=sparse)
-        res_series_index = get_dummies(
-            just_na_series_index, dtype=np.uint8, sparse=sparse
-        )
+        res_list = get_dummies(just_na_list, sparse=sparse)
+        res_series = get_dummies(just_na_series, sparse=sparse)
+        res_series_index = get_dummies(just_na_series_index, sparse=sparse)
 
         assert res_list.empty
         assert res_series.empty
@@ -177,9 +169,9 @@ def test_get_dummies_unicode(self, sparse):
         e = "e"
         eacute = unicodedata.lookup("LATIN SMALL LETTER E WITH ACUTE")
         s = [e, eacute, eacute]
-        res = get_dummies(s, dtype=np.uint8, prefix="letter", sparse=sparse)
+        res = get_dummies(s, prefix="letter", sparse=sparse)
         exp = DataFrame(
-            {"letter_e": [1, 0, 0], f"letter_{eacute}": [0, 1, 1]}, dtype=np.uint8
+            {"letter_e": [True, False, False], f"letter_{eacute}": [False, True, True]}
         )
         if sparse:
             exp = exp.apply(SparseArray, fill_value=0)
@@ -187,18 +179,18 @@ def test_get_dummies_unicode(self, sparse):
 
     def test_dataframe_dummies_all_obj(self, df, sparse):
         df = df[["A", "B"]]
-        result = get_dummies(df, dtype=np.uint8, sparse=sparse)
+        result = get_dummies(df, sparse=sparse)
         expected = DataFrame(
             {"A_a": [1, 0, 1], "A_b": [0, 1, 0], "B_b": [1, 1, 0], "B_c": [0, 0, 1]},
-            dtype=np.uint8,
+            dtype=bool,
         )
         if sparse:
             expected = DataFrame(
                 {
-                    "A_a": SparseArray([1, 0, 1], dtype="uint8"),
-                    "A_b": SparseArray([0, 1, 0], dtype="uint8"),
-                    "B_b": SparseArray([1, 1, 0], dtype="uint8"),
-                    "B_c": SparseArray([0, 0, 1], dtype="uint8"),
+                    "A_a": SparseArray([1, 0, 1], dtype="bool"),
+                    "A_b": SparseArray([0, 1, 0], dtype="bool"),
+                    "B_b": SparseArray([1, 1, 0], dtype="bool"),
+                    "B_c": SparseArray([0, 0, 1], dtype="bool"),
                 }
             )
 
@@ -208,7 +200,7 @@ def test_dataframe_dummies_string_dtype(self, df):
         # GH44965
         df = df[["A", "B"]]
         df = df.astype({"A": "object", "B": "string"})
-        result = get_dummies(df, dtype=np.uint8)
+        result = get_dummies(df)
         expected = DataFrame(
             {
                 "A_a": [1, 0, 1],
@@ -216,7 +208,7 @@ def test_dataframe_dummies_string_dtype(self, df):
                 "B_b": [1, 1, 0],
                 "B_c": [0, 0, 1],
             },
-            dtype=np.uint8,
+            dtype=bool,
         )
         tm.assert_frame_equal(result, expected)
 
@@ -242,16 +234,15 @@ def test_dataframe_dummies_mix_default(self, df, sparse, dtype):
 
     def test_dataframe_dummies_prefix_list(self, df, sparse):
         prefixes = ["from_A", "from_B"]
-        result = get_dummies(df, dtype=np.uint8, prefix=prefixes, sparse=sparse)
+        result = get_dummies(df, prefix=prefixes, sparse=sparse)
         expected = DataFrame(
             {
                 "C": [1, 2, 3],
-                "from_A_a": [1, 0, 1],
-                "from_A_b": [0, 1, 0],
-                "from_B_b": [1, 1, 0],
-                "from_B_c": [0, 0, 1],
+                "from_A_a": [True, False, True],
+                "from_A_b": [False, True, False],
+                "from_B_b": [True, True, False],
+                "from_B_c": [False, False, True],
             },
-            dtype=np.uint8,
         )
         expected[["C"]] = df[["C"]]
         cols = ["from_A_a", "from_A_b", "from_B_b", "from_B_c"]
@@ -263,12 +254,15 @@ def test_dataframe_dummies_prefix_list(self, df, sparse):
 
     def test_dataframe_dummies_prefix_str(self, df, sparse):
         # not that you should do this...
-        result = get_dummies(df, dtype=np.uint8, prefix="bad", sparse=sparse)
+        result = get_dummies(df, prefix="bad", sparse=sparse)
         bad_columns = ["bad_a", "bad_b", "bad_b", "bad_c"]
         expected = DataFrame(
-            [[1, 1, 0, 1, 0], [2, 0, 1, 1, 0], [3, 1, 0, 0, 1]],
+            [
+                [1, True, False, True, False],
+                [2, False, True, True, False],
+                [3, True, False, False, True],
+            ],
             columns=["C"] + bad_columns,
-            dtype=np.uint8,
         )
         expected = expected.astype({"C": np.int64})
         if sparse:
@@ -277,10 +271,10 @@ def test_dataframe_dummies_prefix_str(self, df, sparse):
             expected = pd.concat(
                 [
                     Series([1, 2, 3], name="C"),
-                    Series([1, 0, 1], name="bad_a", dtype="Sparse[uint8]"),
-                    Series([0, 1, 0], name="bad_b", dtype="Sparse[uint8]"),
-                    Series([1, 1, 0], name="bad_b", dtype="Sparse[uint8]"),
-                    Series([0, 0, 1], name="bad_c", dtype="Sparse[uint8]"),
+                    Series([True, False, True], name="bad_a", dtype="Sparse[bool]"),
+                    Series([False, True, False], name="bad_b", dtype="Sparse[bool]"),
+                    Series([True, True, False], name="bad_b", dtype="Sparse[bool]"),
+                    Series([False, False, True], name="bad_c", dtype="Sparse[bool]"),
                 ],
                 axis=1,
             )
@@ -288,9 +282,7 @@ def test_dataframe_dummies_prefix_str(self, df, sparse):
         tm.assert_frame_equal(result, expected)
 
     def test_dataframe_dummies_subset(self, df, sparse):
-        result = get_dummies(
-            df, dtype=np.uint8, prefix=["from_A"], columns=["A"], sparse=sparse
-        )
+        result = get_dummies(df, prefix=["from_A"], columns=["A"], sparse=sparse)
         expected = DataFrame(
             {
                 "B": ["b", "b", "c"],
@@ -300,40 +292,37 @@ def test_dataframe_dummies_subset(self, df, sparse):
             },
         )
         cols = expected.columns
-        expected[cols[1:]] = expected[cols[1:]].astype(np.uint8)
+        expected[cols[1:]] = expected[cols[1:]].astype(bool)
         expected[["C"]] = df[["C"]]
         if sparse:
             cols = ["from_A_a", "from_A_b"]
-            expected[cols] = expected[cols].astype(SparseDtype("uint8", 0))
+            expected[cols] = expected[cols].astype(SparseDtype("bool", 0))
         tm.assert_frame_equal(result, expected)
 
     def test_dataframe_dummies_prefix_sep(self, df, sparse):
-        result = get_dummies(df, dtype=np.uint8, prefix_sep="..", sparse=sparse)
+        result = get_dummies(df, prefix_sep="..", sparse=sparse)
         expected = DataFrame(
             {
                 "C": [1, 2, 3],
-                "A..a": [1, 0, 1],
-                "A..b": [0, 1, 0],
-                "B..b": [1, 1, 0],
-                "B..c": [0, 0, 1],
+                "A..a": [True, False, True],
+                "A..b": [False, True, False],
+                "B..b": [True, True, False],
+                "B..c": [False, False, True],
             },
-            dtype=np.uint8,
         )
         expected[["C"]] = df[["C"]]
         expected = expected[["C", "A..a", "A..b", "B..b", "B..c"]]
         if sparse:
             cols = ["A..a", "A..b", "B..b", "B..c"]
-            expected[cols] = expected[cols].astype(SparseDtype("uint8", 0))
+            expected[cols] = expected[cols].astype(SparseDtype("bool", 0))
 
         tm.assert_frame_equal(result, expected)
 
-        result = get_dummies(df, dtype=np.uint8, prefix_sep=["..", "__"], sparse=sparse)
+        result = get_dummies(df, prefix_sep=["..", "__"], sparse=sparse)
         expected = expected.rename(columns={"B..b": "B__b", "B..c": "B__c"})
         tm.assert_frame_equal(result, expected)
 
-        result = get_dummies(
-            df, dtype=np.uint8, prefix_sep={"A": "..", "B": "__"}, sparse=sparse
-        )
+        result = get_dummies(df, prefix_sep={"A": "..", "B": "__"}, sparse=sparse)
         tm.assert_frame_equal(result, expected)
 
     def test_dataframe_dummies_prefix_bad_length(self, df, sparse):
@@ -342,7 +331,7 @@ def test_dataframe_dummies_prefix_bad_length(self, df, sparse):
             "encoded (2)"
         )
         with pytest.raises(ValueError, match=msg):
-            get_dummies(df, dtype=np.uint8, prefix=["too few"], sparse=sparse)
+            get_dummies(df, prefix=["too few"], sparse=sparse)
 
     def test_dataframe_dummies_prefix_sep_bad_length(self, df, sparse):
         msg = re.escape(
@@ -350,12 +339,12 @@ def test_dataframe_dummies_prefix_sep_bad_length(self, df, sparse):
             "encoded (2)"
         )
         with pytest.raises(ValueError, match=msg):
-            get_dummies(df, dtype=np.uint8, prefix_sep=["bad"], sparse=sparse)
+            get_dummies(df, prefix_sep=["bad"], sparse=sparse)
 
     def test_dataframe_dummies_prefix_dict(self, sparse):
         prefixes = {"A": "from_A", "B": "from_B"}
         df = DataFrame({"C": [1, 2, 3], "A": ["a", "b", "a"], "B": ["b", "b", "c"]})
-        result = get_dummies(df, dtype=np.uint8, prefix=prefixes, sparse=sparse)
+        result = get_dummies(df, prefix=prefixes, sparse=sparse)
 
         expected = DataFrame(
             {
@@ -368,9 +357,9 @@ def test_dataframe_dummies_prefix_dict(self, sparse):
         )
 
         columns = ["from_A_a", "from_A_b", "from_B_b", "from_B_c"]
-        expected[columns] = expected[columns].astype(np.uint8)
+        expected[columns] = expected[columns].astype(bool)
         if sparse:
-            expected[columns] = expected[columns].astype(SparseDtype("uint8", 0))
+            expected[columns] = expected[columns].astype(SparseDtype("bool", 0))
 
         tm.assert_frame_equal(result, expected)
 
@@ -434,19 +423,19 @@ def test_dataframe_dummies_with_categorical(self, df, sparse, dtype):
         [
             (
                 {"data": DataFrame({"ä": ["a"]})},
-                DataFrame({"ä_a": [1]}, dtype=np.uint8),
+                DataFrame({"ä_a": [True]}),
             ),
             (
                 {"data": DataFrame({"x": ["ä"]})},
-                DataFrame({"x_ä": [1]}, dtype=np.uint8),
+                DataFrame({"x_ä": [True]}),
             ),
             (
                 {"data": DataFrame({"x": ["a"]}), "prefix": "ä"},
-                DataFrame({"ä_a": [1]}, dtype=np.uint8),
+                DataFrame({"ä_a": [True]}),
             ),
             (
                 {"data": DataFrame({"x": ["a"]}), "prefix_sep": "ä"},
-                DataFrame({"xäa": [1]}, dtype=np.uint8),
+                DataFrame({"xäa": [True]}),
             ),
         ],
     )
@@ -463,20 +452,18 @@ def test_get_dummies_basic_drop_first(self, sparse):
         s_series = Series(s_list)
         s_series_index = Series(s_list, list("ABC"))
 
-        expected = DataFrame({"b": [0, 1, 0], "c": [0, 0, 1]}, dtype=np.uint8)
+        expected = DataFrame({"b": [0, 1, 0], "c": [0, 0, 1]}, dtype=bool)
 
-        result = get_dummies(s_list, dtype=np.uint8, drop_first=True, sparse=sparse)
+        result = get_dummies(s_list, drop_first=True, sparse=sparse)
         if sparse:
             expected = expected.apply(SparseArray, fill_value=0)
         tm.assert_frame_equal(result, expected)
 
-        result = get_dummies(s_series, dtype=np.uint8, drop_first=True, sparse=sparse)
+        result = get_dummies(s_series, drop_first=True, sparse=sparse)
         tm.assert_frame_equal(result, expected)
 
         expected.index = list("ABC")
-        result = get_dummies(
-            s_series_index, dtype=np.uint8, drop_first=True, sparse=sparse
-        )
+        result = get_dummies(s_series_index, drop_first=True, sparse=sparse)
         tm.assert_frame_equal(result, expected)
 
     def test_get_dummies_basic_drop_first_one_level(self, sparse):
@@ -487,32 +474,28 @@ def test_get_dummies_basic_drop_first_one_level(self, sparse):
 
         expected = DataFrame(index=np.arange(3))
 
-        result = get_dummies(s_list, dtype=np.uint8, drop_first=True, sparse=sparse)
+        result = get_dummies(s_list, drop_first=True, sparse=sparse)
         tm.assert_frame_equal(result, expected)
 
-        result = get_dummies(s_series, dtype=np.uint8, drop_first=True, sparse=sparse)
+        result = get_dummies(s_series, drop_first=True, sparse=sparse)
         tm.assert_frame_equal(result, expected)
 
         expected = DataFrame(index=list("ABC"))
-        result = get_dummies(
-            s_series_index, dtype=np.uint8, drop_first=True, sparse=sparse
-        )
+        result = get_dummies(s_series_index, drop_first=True, sparse=sparse)
         tm.assert_frame_equal(result, expected)
 
     def test_get_dummies_basic_drop_first_NA(self, sparse):
         # Test NA handling together with drop_first
         s_NA = ["a", "b", np.nan]
-        res = get_dummies(s_NA, dtype=np.uint8, drop_first=True, sparse=sparse)
-        exp = DataFrame({"b": [0, 1, 0]}, dtype=np.uint8)
+        res = get_dummies(s_NA, drop_first=True, sparse=sparse)
+        exp = DataFrame({"b": [0, 1, 0]}, dtype=bool)
         if sparse:
             exp = exp.apply(SparseArray, fill_value=0)
 
         tm.assert_frame_equal(res, exp)
 
-        res_na = get_dummies(
-            s_NA, dtype=np.uint8, dummy_na=True, drop_first=True, sparse=sparse
-        )
-        exp_na = DataFrame({"b": [0, 1, 0], np.nan: [0, 0, 1]}, dtype=np.uint8).reindex(
+        res_na = get_dummies(s_NA, dummy_na=True, drop_first=True, sparse=sparse)
+        exp_na = DataFrame({"b": [0, 1, 0], np.nan: [0, 0, 1]}, dtype=bool).reindex(
             ["b", np.nan], axis=1
         )
         if sparse:
@@ -520,27 +503,27 @@ def test_get_dummies_basic_drop_first_NA(self, sparse):
         tm.assert_frame_equal(res_na, exp_na)
 
         res_just_na = get_dummies(
-            [np.nan], dtype=np.uint8, dummy_na=True, drop_first=True, sparse=sparse
+            [np.nan], dummy_na=True, drop_first=True, sparse=sparse
         )
         exp_just_na = DataFrame(index=np.arange(1))
         tm.assert_frame_equal(res_just_na, exp_just_na)
 
     def test_dataframe_dummies_drop_first(self, df, sparse):
         df = df[["A", "B"]]
-        result = get_dummies(df, dtype=np.uint8, drop_first=True, sparse=sparse)
-        expected = DataFrame({"A_b": [0, 1, 0], "B_c": [0, 0, 1]}, dtype=np.uint8)
+        result = get_dummies(df, drop_first=True, sparse=sparse)
+        expected = DataFrame({"A_b": [0, 1, 0], "B_c": [0, 0, 1]}, dtype=bool)
         if sparse:
             expected = expected.apply(SparseArray, fill_value=0)
         tm.assert_frame_equal(result, expected)
 
     def test_dataframe_dummies_drop_first_with_categorical(self, df, sparse, dtype):
         df["cat"] = Categorical(["x", "y", "y"])
-        result = get_dummies(df, dtype=np.uint8, drop_first=True, sparse=sparse)
+        result = get_dummies(df, drop_first=True, sparse=sparse)
         expected = DataFrame(
             {"C": [1, 2, 3], "A_b": [0, 1, 0], "B_c": [0, 0, 1], "cat_y": [0, 1, 1]}
         )
         cols = ["A_b", "B_c", "cat_y"]
-        expected[cols] = expected[cols].astype(np.uint8)
+        expected[cols] = expected[cols].astype(bool)
         expected = expected[["C", "A_b", "B_c", "cat_y"]]
         if sparse:
             for col in cols:
@@ -550,7 +533,7 @@ def test_dataframe_dummies_drop_first_with_categorical(self, df, sparse, dtype):
     def test_dataframe_dummies_drop_first_with_na(self, df, sparse):
         df.loc[3, :] = [np.nan, np.nan, np.nan]
         result = get_dummies(
-            df, dtype=np.uint8, dummy_na=True, drop_first=True, sparse=sparse
+            df, dummy_na=True, drop_first=True, sparse=sparse
         ).sort_index(axis=1)
         expected = DataFrame(
             {
@@ -562,7 +545,7 @@ def test_dataframe_dummies_drop_first_with_na(self, df, sparse):
             }
         )
         cols = ["A_b", "A_nan", "B_c", "B_nan"]
-        expected[cols] = expected[cols].astype(np.uint8)
+        expected[cols] = expected[cols].astype(bool)
         expected = expected.sort_index(axis=1)
         if sparse:
             for col in cols:
@@ -570,22 +553,20 @@ def test_dataframe_dummies_drop_first_with_na(self, df, sparse):
 
         tm.assert_frame_equal(result, expected)
 
-        result = get_dummies(
-            df, dtype=np.uint8, dummy_na=False, drop_first=True, sparse=sparse
-        )
+        result = get_dummies(df, dummy_na=False, drop_first=True, sparse=sparse)
         expected = expected[["C", "A_b", "B_c"]]
         tm.assert_frame_equal(result, expected)
 
     def test_get_dummies_int_int(self):
         data = Series([1, 2, 1])
-        result = get_dummies(data, dtype=np.uint8)
-        expected = DataFrame([[1, 0], [0, 1], [1, 0]], columns=[1, 2], dtype=np.uint8)
+        result = get_dummies(data)
+        expected = DataFrame([[1, 0], [0, 1], [1, 0]], columns=[1, 2], dtype=bool)
         tm.assert_frame_equal(result, expected)
 
         data = Series(Categorical(["a", "b", "a"]))
-        result = get_dummies(data, dtype=np.uint8)
+        result = get_dummies(data)
         expected = DataFrame(
-            [[1, 0], [0, 1], [1, 0]], columns=Categorical(["a", "b"]), dtype=np.uint8
+            [[1, 0], [0, 1], [1, 0]], columns=Categorical(["a", "b"]), dtype=bool
         )
         tm.assert_frame_equal(result, expected)
 
@@ -625,7 +606,7 @@ def test_dataframe_dummies_preserve_categorical_dtype(self, dtype, ordered):
     def test_get_dummies_dont_sparsify_all_columns(self, sparse):
         # GH18914
         df = DataFrame.from_dict({"GDP": [1, 2], "Nation": ["AB", "CD"]})
-        df = get_dummies(df, dtype=np.uint8, columns=["Nation"], sparse=sparse)
+        df = get_dummies(df, columns=["Nation"], sparse=sparse)
         df2 = df.reindex(columns=["GDP"])
 
         tm.assert_frame_equal(df[["GDP"]], df2)
@@ -633,12 +614,15 @@ def test_get_dummies_dont_sparsify_all_columns(self, sparse):
     def test_get_dummies_duplicate_columns(self, df):
         # GH20839
         df.columns = ["A", "A", "A"]
-        result = get_dummies(df, dtype=np.uint8).sort_index(axis=1)
+        result = get_dummies(df).sort_index(axis=1)
 
         expected = DataFrame(
-            [[1, 1, 0, 1, 0], [2, 0, 1, 1, 0], [3, 1, 0, 0, 1]],
+            [
+                [1, True, False, True, False],
+                [2, False, True, True, False],
+                [3, True, False, False, True],
+            ],
             columns=["A", "A_a", "A_b", "A_b", "A_c"],
-            dtype=np.uint8,
         ).sort_index(axis=1)
 
         expected = expected.astype({"A": np.int64})
@@ -647,8 +631,8 @@ def test_get_dummies_duplicate_columns(self, df):
 
     def test_get_dummies_all_sparse(self):
         df = DataFrame({"A": [1, 2]})
-        result = get_dummies(df, dtype=np.uint8, columns=["A"], sparse=True)
-        dtype = SparseDtype("uint8", 0)
+        result = get_dummies(df, columns=["A"], sparse=True)
+        dtype = SparseDtype("bool", 0)
         expected = DataFrame(
             {
                 "A_1": SparseArray([1, 0], dtype=dtype),
@@ -672,4 +656,4 @@ def test_get_dummies_with_string_values(self, values):
         msg = "Input must be a list-like for parameter `columns`"
 
         with pytest.raises(TypeError, match=msg):
-            get_dummies(df, dtype=np.uint8, columns=values)
+            get_dummies(df, columns=values)

From a246b8c2edb6c1d2f7edef41961989592b4f4600 Mon Sep 17 00:00:00 2001
From: Kian Eliasi <kian.elbo@gmail.com>
Date: Sun, 25 Sep 2022 16:32:10 +0200
Subject: [PATCH 09/11] Revert dtype(bool) change

---
 pandas/core/reshape/encoding.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/pandas/core/reshape/encoding.py b/pandas/core/reshape/encoding.py
index a89ac050ce18e..a39e3c1f10956 100644
--- a/pandas/core/reshape/encoding.py
+++ b/pandas/core/reshape/encoding.py
@@ -236,11 +236,10 @@ def _get_dummies_1d(
     codes, levels = factorize_from_iterable(Series(data))
 
     if dtype is None:
-        dtype = bool
+        dtype = np.dtype(bool)
     # error: Argument 1 to "dtype" has incompatible type "Union[ExtensionDtype, str,
     # dtype[Any], Type[object]]"; expected "Type[Any]"
-    else:
-        dtype = np.dtype(dtype)  # type: ignore[arg-type]
+    dtype = np.dtype(dtype)  # type: ignore[arg-type]
 
     if is_object_dtype(dtype):
         raise ValueError("dtype=object is not a valid dtype for get_dummies")

From 7cef2fcde8c01fb4d6010a2f9c9e8ccd3320bd27 Mon Sep 17 00:00:00 2001
From: Kian Eliasi <kian.elbo@gmail.com>
Date: Fri, 7 Oct 2022 12:45:38 +0200
Subject: [PATCH 10/11] Move the changelog entry to v1.6.0.rst

---
 doc/source/whatsnew/v1.5.1.rst | 1 -
 doc/source/whatsnew/v1.6.0.rst | 1 +
 2 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/source/whatsnew/v1.5.1.rst b/doc/source/whatsnew/v1.5.1.rst
index 99db171a624dc..4d7576c013fd6 100644
--- a/doc/source/whatsnew/v1.5.1.rst
+++ b/doc/source/whatsnew/v1.5.1.rst
@@ -92,7 +92,6 @@ Fixed regressions
 
 Bug fixes
 ~~~~~~~~~
-- Bug in :func:`get_dummies` with default ``dtype`` being ``uint8`` - the default ``dtype`` is now changed to ``bool`` (:issue:`45848`)
 - Bug in :meth:`Series.__getitem__` not falling back to positional for integer keys and boolean :class:`Index` (:issue:`48653`)
 - Bug in :meth:`DataFrame.to_hdf` raising ``AssertionError`` with boolean index (:issue:`48667`)
 - Bug in :func:`assert_index_equal` for extension arrays with non matching ``NA`` raising ``ValueError`` (:issue:`48608`)
diff --git a/doc/source/whatsnew/v1.6.0.rst b/doc/source/whatsnew/v1.6.0.rst
index 0bc91d3cd9637..6b7fa004eadf5 100644
--- a/doc/source/whatsnew/v1.6.0.rst
+++ b/doc/source/whatsnew/v1.6.0.rst
@@ -256,6 +256,7 @@ Reshaping
 ^^^^^^^^^
 - Bug in :meth:`DataFrame.pivot_table` raising ``TypeError`` for nullable dtype and ``margins=True`` (:issue:`48681`)
 - Bug in :meth:`DataFrame.pivot` not respecting ``None`` as column name (:issue:`48293`)
+- Bug in :func:`get_dummies` with default ``dtype`` being ``uint8`` - the default ``dtype`` is now changed to ``bool`` (:issue:`45848`)
 - Bug in :func:`join` when ``left_on`` or ``right_on`` is or includes a :class:`CategoricalIndex` incorrectly raising ``AttributeError`` (:issue:`48464`)
 -
 

From 8a93cc948cc4b03f4bcc9b9dfb3d8392faeedb91 Mon Sep 17 00:00:00 2001
From: Kian Eliasi <kian.elbo@gmail.com>
Date: Tue, 11 Oct 2022 10:58:15 +0200
Subject: [PATCH 11/11] Move whatsnew entry to 'Other API changes'

---
 doc/source/whatsnew/v1.6.0.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/source/whatsnew/v1.6.0.rst b/doc/source/whatsnew/v1.6.0.rst
index eb0a3807c0c2f..0cad6f3caaf91 100644
--- a/doc/source/whatsnew/v1.6.0.rst
+++ b/doc/source/whatsnew/v1.6.0.rst
@@ -118,6 +118,7 @@ Other API changes
 ^^^^^^^^^^^^^^^^^
 - Passing ``nanoseconds`` greater than 999 or less than 0 in :class:`Timestamp` now raises a ``ValueError`` (:issue:`48538`, :issue:`48255`)
 - :func:`read_csv`: specifying an incorrect number of columns with ``index_col`` of now raises ``ParserError`` instead of ``IndexError`` when using the c parser.
+- Default value of ``dtype`` in :func:`get_dummies` is changed to ``bool`` from ``uint8`` (:issue:`45848`)
 - :meth:`DataFrame.astype`, :meth:`Series.astype`, and :meth:`DatetimeIndex.astype` casting datetime64 data to any of "datetime64[s]", "datetime64[ms]", "datetime64[us]" will return an object with the given resolution instead of coercing back to "datetime64[ns]" (:issue:`48928`)
 - :meth:`DataFrame.astype`, :meth:`Series.astype`, and :meth:`DatetimeIndex.astype` casting timedelta64 data to any of "timedelta64[s]", "timedelta64[ms]", "timedelta64[us]" will return an object with the given resolution instead of coercing to "float64" dtype (:issue:`48963`)
 -
@@ -260,7 +261,6 @@ Reshaping
 ^^^^^^^^^
 - Bug in :meth:`DataFrame.pivot_table` raising ``TypeError`` for nullable dtype and ``margins=True`` (:issue:`48681`)
 - Bug in :meth:`DataFrame.pivot` not respecting ``None`` as column name (:issue:`48293`)
-- Bug in :func:`get_dummies` with default ``dtype`` being ``uint8`` - the default ``dtype`` is now changed to ``bool`` (:issue:`45848`)
 - Bug in :func:`join` when ``left_on`` or ``right_on`` is or includes a :class:`CategoricalIndex` incorrectly raising ``AttributeError`` (:issue:`48464`)
 -