Repository: pandas-dev/pandas
Issue category: Feature Request

Issue:
ENH: allow get_dummies to accept dtype argument
- [x] closes #18330 (there's no issue for this one)
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry

Update in version 0.19.0 made `get_dummies` return uint8 values instead of floats (#8725). While I agree with the argument that `get_dummies` should output integers __by default__ (to save some memory), in many cases it would be beneficial for user to choose other dtype.

In my case there was serious performance degradation between versions 0.18 and 0.19. After investigation, reason behind it turned out to be the change to `get_dummies` output type. DataFrame with dummy values was used as an argument to np.dot in an optimization function (second argument was matrix of floats). Since there were lots of iterations involved, and on each iteration np.dot was converting all uint8 values to float64, conversion overhead took unreasonably long time. It is possible to work around this issue by converting dummy columns "manually" afterwards, but it adds unnecessary complexity to the code and is clearly less convenient than calling `get_dummies` with `dtype=float`.

Apart from performance considerations, I can imagine `dtype=bool` to be a common use case.

`get_dummies(data, dtype=None)` is allowed and will return uint8 values to match the [DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html) interface (where None allows inferring datatype, which is default behavior).

I've extended the test suite to run all the `get_dummies` tests (except for those that don't deal with internal dtypes, like `test_just_na`) twice, once with `uint8` and once with `float64`.


Hints:

Patch:
diff --git a/doc/source/reshaping.rst b/doc/source/reshaping.rst
index 1209c4a8d6be8..1b81d83bb76c7 100644
--- a/doc/source/reshaping.rst
+++ b/doc/source/reshaping.rst
@@ -240,7 +240,7 @@ values will be set to ``NaN``.
    df3
    df3.unstack()
 
-.. versionadded: 0.18.0
+.. versionadded:: 0.18.0
 
 Alternatively, unstack takes an optional ``fill_value`` argument, for specifying
 the value of missing data.
@@ -634,6 +634,17 @@ When a column contains only one level, it will be omitted in the result.
 
     pd.get_dummies(df, drop_first=True)
 
+By default new columns will have ``np.uint8`` dtype. To choose another dtype use ``dtype`` argument:
+
+.. ipython:: python
+
+    df = pd.DataFrame({'A': list('abc'), 'B': [1.1, 2.2, 3.3]})
+
+    pd.get_dummies(df, dtype=bool).dtypes
+
+.. versionadded:: 0.22.0
+
+
 .. _reshaping.factorize:
 
 Factorizing values
diff --git a/doc/source/whatsnew/v0.22.0.txt b/doc/source/whatsnew/v0.22.0.txt
index 4f403ff8053a7..162a1ca2bfcb3 100644
--- a/doc/source/whatsnew/v0.22.0.txt
+++ b/doc/source/whatsnew/v0.22.0.txt
@@ -17,6 +17,21 @@ New features
 -
 -
 
+
+.. _whatsnew_0210.enhancements.get_dummies_dtype:
+
+``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
+
+   df = pd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [5, 6]})
+   pd.get_dummies(df, columns=['c']).dtypes
+   pd.get_dummies(df, columns=['c'], dtype=bool).dtypes
+
+
 .. _whatsnew_0220.enhancements.other:
 
 Other Enhancements
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index cbc259ba7bceb..782971a742b54 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -965,7 +965,7 @@ def _set_axis_name(self, name, axis=0, inplace=False):
         inplace : bool
             whether to modify `self` directly or return a copy
 
-            .. versionadded: 0.21.0
+            .. versionadded:: 0.21.0
 
         Returns
         -------
diff --git a/pandas/core/reshape/reshape.py b/pandas/core/reshape/reshape.py
index 8b656d8ba25e9..5bb86885c0875 100644
--- a/pandas/core/reshape/reshape.py
+++ b/pandas/core/reshape/reshape.py
@@ -10,7 +10,7 @@
 from pandas.core.dtypes.common import (
     _ensure_platform_int,
     is_list_like, is_bool_dtype,
-    needs_i8_conversion, is_sparse)
+    needs_i8_conversion, is_sparse, is_object_dtype)
 from pandas.core.dtypes.cast import maybe_promote
 from pandas.core.dtypes.missing import notna
 
@@ -697,7 +697,7 @@ def _convert_level_number(level_num, columns):
 
 
 def get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False,
-                columns=None, sparse=False, drop_first=False):
+                columns=None, sparse=False, drop_first=False, dtype=None):
     """
     Convert categorical variable into dummy/indicator variables
 
@@ -728,6 +728,11 @@ def get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False,
 
         .. versionadded:: 0.18.0
 
+    dtype : dtype, default np.uint8
+        Data type for new columns. Only a single dtype is allowed.
+
+        .. versionadded:: 0.22.0
+
     Returns
     -------
     dummies : DataFrame or SparseDataFrame
@@ -783,6 +788,12 @@ def get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False,
     3  0  0
     4  0  0
 
+    >>> pd.get_dummies(pd.Series(list('abc')), dtype=float)
+         a    b    c
+    0  1.0  0.0  0.0
+    1  0.0  1.0  0.0
+    2  0.0  0.0  1.0
+
     See Also
     --------
     Series.str.get_dummies
@@ -835,20 +846,29 @@ def check_len(item, name):
 
             dummy = _get_dummies_1d(data[col], prefix=pre, prefix_sep=sep,
                                     dummy_na=dummy_na, sparse=sparse,
-                                    drop_first=drop_first)
+                                    drop_first=drop_first, dtype=dtype)
             with_dummies.append(dummy)
         result = concat(with_dummies, axis=1)
     else:
         result = _get_dummies_1d(data, prefix, prefix_sep, dummy_na,
-                                 sparse=sparse, drop_first=drop_first)
+                                 sparse=sparse,
+                                 drop_first=drop_first,
+                                 dtype=dtype)
     return result
 
 
 def _get_dummies_1d(data, prefix, prefix_sep='_', dummy_na=False,
-                    sparse=False, drop_first=False):
+                    sparse=False, drop_first=False, dtype=None):
     # Series avoids inconsistent NaN handling
     codes, levels = _factorize_from_iterable(Series(data))
 
+    if dtype is None:
+        dtype = np.uint8
+    dtype = np.dtype(dtype)
+
+    if is_object_dtype(dtype):
+        raise ValueError("dtype=object is not a valid dtype for get_dummies")
+
     def get_empty_Frame(data, sparse):
         if isinstance(data, Series):
             index = data.index
@@ -903,18 +923,18 @@ def get_empty_Frame(data, sparse):
             sp_indices = sp_indices[1:]
             dummy_cols = dummy_cols[1:]
         for col, ixs in zip(dummy_cols, sp_indices):
-            sarr = SparseArray(np.ones(len(ixs), dtype=np.uint8),
+            sarr = SparseArray(np.ones(len(ixs), dtype=dtype),
                                sparse_index=IntIndex(N, ixs), fill_value=0,
-                               dtype=np.uint8)
+                               dtype=dtype)
             sparse_series[col] = SparseSeries(data=sarr, index=index)
 
         out = SparseDataFrame(sparse_series, index=index, columns=dummy_cols,
                               default_fill_value=0,
-                              dtype=np.uint8)
+                              dtype=dtype)
         return out
 
     else:
-        dummy_mat = np.eye(number_of_cols, dtype=np.uint8).take(codes, axis=0)
+        dummy_mat = np.eye(number_of_cols, dtype=dtype).take(codes, axis=0)
 
         if not dummy_na:
             # reset NaN GH4446


Test patch:
diff --git a/pandas/tests/reshape/test_reshape.py b/pandas/tests/reshape/test_reshape.py
index 2722c3e92d85a..5d4aa048ae303 100644
--- a/pandas/tests/reshape/test_reshape.py
+++ b/pandas/tests/reshape/test_reshape.py
@@ -218,35 +218,51 @@ def test_multiindex(self):
 
 class TestGetDummies(object):
 
-    sparse = False
-
-    def setup_method(self, method):
-        self.df = DataFrame({'A': ['a', 'b', 'a'],
-                             'B': ['b', 'b', 'c'],
-                             'C': [1, 2, 3]})
+    @pytest.fixture
+    def df(self):
+        return DataFrame({'A': ['a', 'b', 'a'],
+                          'B': ['b', 'b', 'c'],
+                          'C': [1, 2, 3]})
+
+    @pytest.fixture(params=['uint8', 'i8', np.float64, bool, None])
+    def dtype(self, request):
+        return np.dtype(request.param)
+
+    @pytest.fixture(params=['dense', 'sparse'])
+    def sparse(self, request):
+        # params are strings to simplify reading test results,
+        # e.g. TestGetDummies::test_basic[uint8-sparse] instead of [uint8-True]
+        return request.param == 'sparse'
+
+    def effective_dtype(self, dtype):
+        if dtype is None:
+            return np.uint8
+        return dtype
+
+    def test_raises_on_dtype_object(self, df):
+        with pytest.raises(ValueError):
+            get_dummies(df, dtype='object')
 
-    def test_basic(self):
+    def test_basic(self, sparse, dtype):
         s_list = list('abc')
         s_series = Series(s_list)
         s_series_index = Series(s_list, list('ABC'))
 
-        expected = DataFrame({'a': {0: 1,
-                                    1: 0,
-                                    2: 0},
-                              'b': {0: 0,
-                                    1: 1,
-                                    2: 0},
-                              'c': {0: 0,
-                                    1: 0,
-                                    2: 1}}, dtype=np.uint8)
-        assert_frame_equal(get_dummies(s_list, sparse=self.sparse), expected)
-        assert_frame_equal(get_dummies(s_series, sparse=self.sparse), expected)
+        expected = DataFrame({'a': [1, 0, 0],
+                              'b': [0, 1, 0],
+                              'c': [0, 0, 1]},
+                             dtype=self.effective_dtype(dtype))
+        result = get_dummies(s_list, sparse=sparse, dtype=dtype)
+        assert_frame_equal(result, expected)
+
+        result = get_dummies(s_series, sparse=sparse, dtype=dtype)
+        assert_frame_equal(result, expected)
 
         expected.index = list('ABC')
-        assert_frame_equal(
-            get_dummies(s_series_index, sparse=self.sparse), expected)
+        result = get_dummies(s_series_index, sparse=sparse, dtype=dtype)
+        assert_frame_equal(result, expected)
 
-    def test_basic_types(self):
+    def test_basic_types(self, sparse, dtype):
         # GH 10531
         s_list = list('abc')
         s_series = Series(s_list)
@@ -257,38 +273,43 @@ def test_basic_types(self):
         expected = DataFrame({'a': [1, 0, 0],
                               'b': [0, 1, 0],
                               'c': [0, 0, 1]},
-                             dtype='uint8',
+                             dtype=self.effective_dtype(dtype),
                              columns=list('abc'))
-        if not self.sparse:
+        if not sparse:
             compare = tm.assert_frame_equal
         else:
             expected = expected.to_sparse(fill_value=0, kind='integer')
             compare = tm.assert_sp_frame_equal
 
-        result = get_dummies(s_list, sparse=self.sparse)
+        result = get_dummies(s_list, sparse=sparse, dtype=dtype)
         compare(result, expected)
 
-        result = get_dummies(s_series, sparse=self.sparse)
+        result = get_dummies(s_series, sparse=sparse, dtype=dtype)
         compare(result, expected)
 
-        result = get_dummies(s_df, sparse=self.sparse, columns=s_df.columns)
+        result = get_dummies(s_df, columns=s_df.columns,
+                             sparse=sparse, dtype=dtype)
         tm.assert_series_equal(result.get_dtype_counts(),
-                               Series({'uint8': 8}))
+                               Series({dtype.name: 8}))
 
-        result = get_dummies(s_df, sparse=self.sparse, columns=['a'])
-        expected = Series({'uint8': 3, 'int64': 1, 'object': 1}).sort_values()
+        result = get_dummies(s_df, columns=['a'], sparse=sparse, dtype=dtype)
+        dtype_name = self.effective_dtype(dtype).name
+
+        expected_counts = {'int64': 1, 'object': 1}
+        expected_counts[dtype_name] = 3 + expected_counts.get(dtype_name, 0)
+
+        expected = Series(expected_counts).sort_values()
         tm.assert_series_equal(result.get_dtype_counts().sort_values(),
                                expected)
 
-    def test_just_na(self):
+    def test_just_na(self, sparse):
         just_na_list = [np.nan]
         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=self.sparse)
-        res_series = get_dummies(just_na_series, sparse=self.sparse)
-        res_series_index = get_dummies(just_na_series_index,
-                                       sparse=self.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
@@ -298,216 +319,209 @@ def test_just_na(self):
         assert res_series.index.tolist() == [0]
         assert res_series_index.index.tolist() == ['A']
 
-    def test_include_na(self):
+    def test_include_na(self, sparse, dtype):
+        if sparse:
+            pytest.xfail(reason='nan in index is problematic (GH 16894)')
+
         s = ['a', 'b', np.nan]
-        res = get_dummies(s, sparse=self.sparse)
-        exp = DataFrame({'a': {0: 1, 1: 0, 2: 0},
-                         'b': {0: 0, 1: 1, 2: 0}}, dtype=np.uint8)
+        res = get_dummies(s, sparse=sparse, dtype=dtype)
+        exp = DataFrame({'a': [1, 0, 0],
+                         'b': [0, 1, 0]},
+                        dtype=self.effective_dtype(dtype))
         assert_frame_equal(res, exp)
 
         # Sparse dataframes do not allow nan labelled columns, see #GH8822
-        res_na = get_dummies(s, dummy_na=True, sparse=self.sparse)
-        exp_na = DataFrame({nan: {0: 0, 1: 0, 2: 1},
-                            'a': {0: 1, 1: 0, 2: 0},
-                            'b': {0: 0, 1: 1, 2: 0}},
-                           dtype=np.uint8)
+        res_na = get_dummies(s, dummy_na=True, sparse=sparse, dtype=dtype)
+        exp_na = DataFrame({nan: [0, 0, 1],
+                            'a': [1, 0, 0],
+                            'b': [0, 1, 0]},
+                           dtype=self.effective_dtype(dtype))
         exp_na = exp_na.reindex(['a', 'b', nan], axis=1)
         # hack (NaN handling in assert_index_equal)
         exp_na.columns = res_na.columns
         assert_frame_equal(res_na, exp_na)
 
-        res_just_na = get_dummies([nan], dummy_na=True, sparse=self.sparse)
+        res_just_na = get_dummies([nan], dummy_na=True,
+                                  sparse=sparse, dtype=dtype)
         exp_just_na = DataFrame(Series(1, index=[0]), columns=[nan],
-                                dtype=np.uint8)
+                                dtype=self.effective_dtype(dtype))
         tm.assert_numpy_array_equal(res_just_na.values, exp_just_na.values)
 
-    def test_unicode(self
-                     ):  # See GH 6885 - get_dummies chokes on unicode values
+    def test_unicode(self, sparse):
+        # See GH 6885 - get_dummies chokes on unicode values
         import unicodedata
         e = 'e'
         eacute = unicodedata.lookup('LATIN SMALL LETTER E WITH ACUTE')
         s = [e, eacute, eacute]
-        res = get_dummies(s, prefix='letter', sparse=self.sparse)
-        exp = DataFrame({'letter_e': {0: 1,
-                                      1: 0,
-                                      2: 0},
-                         u('letter_%s') % eacute: {0: 0,
-                                                   1: 1,
-                                                   2: 1}},
+        res = get_dummies(s, prefix='letter', sparse=sparse)
+        exp = DataFrame({'letter_e': [1, 0, 0],
+                         u('letter_%s') % eacute: [0, 1, 1]},
                         dtype=np.uint8)
         assert_frame_equal(res, exp)
 
-    def test_dataframe_dummies_all_obj(self):
-        df = self.df[['A', 'B']]
-        result = get_dummies(df, sparse=self.sparse)
+    def test_dataframe_dummies_all_obj(self, df, sparse):
+        df = df[['A', 'B']]
+        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)
+                              'B_c': [0, 0, 1]},
+                             dtype=np.uint8)
         assert_frame_equal(result, expected)
 
-    def test_dataframe_dummies_mix_default(self):
-        df = self.df
-        result = get_dummies(df, sparse=self.sparse)
+    def test_dataframe_dummies_mix_default(self, df, sparse, dtype):
+        result = get_dummies(df, sparse=sparse, dtype=dtype)
         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]})
         cols = ['A_a', 'A_b', 'B_b', 'B_c']
-        expected[cols] = expected[cols].astype(np.uint8)
+        expected[cols] = expected[cols].astype(dtype)
         expected = expected[['C', 'A_a', 'A_b', 'B_b', 'B_c']]
         assert_frame_equal(result, expected)
 
-    def test_dataframe_dummies_prefix_list(self):
+    def test_dataframe_dummies_prefix_list(self, df, sparse):
         prefixes = ['from_A', 'from_B']
-        df = DataFrame({'A': ['a', 'b', 'a'],
-                        'B': ['b', 'b', 'c'],
-                        'C': [1, 2, 3]})
-        result = get_dummies(df, prefix=prefixes, sparse=self.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]})
-        cols = expected.columns[1:]
-        expected[cols] = expected[cols].astype(np.uint8)
-        expected = expected[['C', 'from_A_a', 'from_A_b', 'from_B_b',
-                             'from_B_c']]
+                              'from_B_c': [0, 0, 1]},
+                             dtype=np.uint8)
+        expected[['C']] = df[['C']]
+        expected = expected[['C', 'from_A_a', 'from_A_b',
+                             'from_B_b', 'from_B_c']]
         assert_frame_equal(result, expected)
 
-    def test_dataframe_dummies_prefix_str(self):
+    def test_dataframe_dummies_prefix_str(self, df, sparse):
         # not that you should do this...
-        df = self.df
-        result = get_dummies(df, prefix='bad', sparse=self.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]],
-                             columns=['C', 'bad_a', 'bad_b', 'bad_b', 'bad_c'],
+                             columns=['C'] + bad_columns,
                              dtype=np.uint8)
         expected = expected.astype({"C": np.int64})
         assert_frame_equal(result, expected)
 
-    def test_dataframe_dummies_subset(self):
-        df = self.df
+    def test_dataframe_dummies_subset(self, df, sparse):
         result = get_dummies(df, prefix=['from_A'], columns=['A'],
-                             sparse=self.sparse)
+                             sparse=sparse)
         expected = DataFrame({'from_A_a': [1, 0, 1],
                               'from_A_b': [0, 1, 0],
                               'B': ['b', 'b', 'c'],
-                              'C': [1, 2, 3]})
-        cols = ['from_A_a', 'from_A_b']
-        expected[cols] = expected[cols].astype(np.uint8)
+                              'C': [1, 2, 3]}, dtype=np.uint8)
+        expected[['C']] = df[['C']]
         assert_frame_equal(result, expected)
 
-    def test_dataframe_dummies_prefix_sep(self):
-        df = self.df
-        result = get_dummies(df, prefix_sep='..', sparse=self.sparse)
+    def test_dataframe_dummies_prefix_sep(self, df, 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]})
+                              'B..c': [0, 0, 1]},
+                             dtype=np.uint8)
+        expected[['C']] = df[['C']]
         expected = expected[['C', 'A..a', 'A..b', 'B..b', 'B..c']]
-        cols = expected.columns[1:]
-        expected[cols] = expected[cols].astype(np.uint8)
         assert_frame_equal(result, expected)
 
-        result = get_dummies(df, prefix_sep=['..', '__'], sparse=self.sparse)
+        result = get_dummies(df, prefix_sep=['..', '__'], sparse=sparse)
         expected = expected.rename(columns={'B..b': 'B__b', 'B..c': 'B__c'})
         assert_frame_equal(result, expected)
 
-        result = get_dummies(df, prefix_sep={'A': '..',
-                                             'B': '__'}, sparse=self.sparse)
+        result = get_dummies(df, prefix_sep={'A': '..', 'B': '__'},
+                             sparse=sparse)
         assert_frame_equal(result, expected)
 
-    def test_dataframe_dummies_prefix_bad_length(self):
+    def test_dataframe_dummies_prefix_bad_length(self, df, sparse):
         with pytest.raises(ValueError):
-            get_dummies(self.df, prefix=['too few'], sparse=self.sparse)
+            get_dummies(df, prefix=['too few'], sparse=sparse)
 
-    def test_dataframe_dummies_prefix_sep_bad_length(self):
+    def test_dataframe_dummies_prefix_sep_bad_length(self, df, sparse):
         with pytest.raises(ValueError):
-            get_dummies(self.df, prefix_sep=['bad'], sparse=self.sparse)
+            get_dummies(df, prefix_sep=['bad'], sparse=sparse)
 
-    def test_dataframe_dummies_prefix_dict(self):
+    def test_dataframe_dummies_prefix_dict(self, sparse):
         prefixes = {'A': 'from_A', 'B': 'from_B'}
         df = DataFrame({'A': ['a', 'b', 'a'],
                         'B': ['b', 'b', 'c'],
                         'C': [1, 2, 3]})
-        result = get_dummies(df, prefix=prefixes, sparse=self.sparse)
+        result = get_dummies(df, prefix=prefixes, sparse=sparse)
+
         expected = DataFrame({'from_A_a': [1, 0, 1],
                               'from_A_b': [0, 1, 0],
                               'from_B_b': [1, 1, 0],
                               'from_B_c': [0, 0, 1],
                               'C': [1, 2, 3]})
-        cols = ['from_A_a', 'from_A_b', 'from_B_b', 'from_B_c']
-        expected[cols] = expected[cols].astype(np.uint8)
+
+        columns = ['from_A_a', 'from_A_b', 'from_B_b', 'from_B_c']
+        expected[columns] = expected[columns].astype(np.uint8)
         assert_frame_equal(result, expected)
 
-    def test_dataframe_dummies_with_na(self):
-        df = self.df
+    def test_dataframe_dummies_with_na(self, df, sparse, dtype):
         df.loc[3, :] = [np.nan, np.nan, np.nan]
-        result = get_dummies(df, dummy_na=True, sparse=self.sparse)
+        result = get_dummies(df, dummy_na=True,
+                             sparse=sparse, dtype=dtype).sort_index(axis=1)
         expected = DataFrame({'C': [1, 2, 3, np.nan],
                               'A_a': [1, 0, 1, 0],
                               'A_b': [0, 1, 0, 0],
                               'A_nan': [0, 0, 0, 1],
                               'B_b': [1, 1, 0, 0],
                               'B_c': [0, 0, 1, 0],
-                              'B_nan': [0, 0, 0, 1]})
-        cols = ['A_a', 'A_b', 'A_nan', 'B_b', 'B_c', 'B_nan']
-        expected[cols] = expected[cols].astype(np.uint8)
-        expected = expected[['C', 'A_a', 'A_b', 'A_nan',
-                             'B_b', 'B_c', 'B_nan']]
+                              'B_nan': [0, 0, 0, 1]}).sort_index(axis=1)
+
+        e_dtype = self.effective_dtype(dtype)
+        columns = ['A_a', 'A_b', 'A_nan', 'B_b', 'B_c', 'B_nan']
+        expected[columns] = expected[columns].astype(e_dtype)
         assert_frame_equal(result, expected)
 
-        result = get_dummies(df, dummy_na=False, sparse=self.sparse)
+        result = get_dummies(df, dummy_na=False, sparse=sparse, dtype=dtype)
         expected = expected[['C', 'A_a', 'A_b', 'B_b', 'B_c']]
         assert_frame_equal(result, expected)
 
-    def test_dataframe_dummies_with_categorical(self):
-        df = self.df
+    def test_dataframe_dummies_with_categorical(self, df, sparse, dtype):
         df['cat'] = pd.Categorical(['x', 'y', 'y'])
-        result = get_dummies(df, sparse=self.sparse)
+        result = get_dummies(df, sparse=sparse, dtype=dtype).sort_index(axis=1)
         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],
                               'cat_x': [1, 0, 0],
-                              'cat_y': [0, 1, 1]})
-        cols = ['A_a', 'A_b', 'B_b', 'B_c', 'cat_x', 'cat_y']
-        expected[cols] = expected[cols].astype(np.uint8)
-        expected = expected[['C', 'A_a', 'A_b', 'B_b', 'B_c',
-                             'cat_x', 'cat_y']]
+                              'cat_y': [0, 1, 1]}).sort_index(axis=1)
+
+        columns = ['A_a', 'A_b', 'B_b', 'B_c', 'cat_x', 'cat_y']
+        effective_dtype = self.effective_dtype(dtype)
+        expected[columns] = expected[columns].astype(effective_dtype)
+        expected.sort_index(axis=1)
         assert_frame_equal(result, expected)
 
-    def test_basic_drop_first(self):
+    def test_basic_drop_first(self, sparse):
         # GH12402 Add a new parameter `drop_first` to avoid collinearity
         # Basic case
         s_list = list('abc')
         s_series = Series(s_list)
         s_series_index = Series(s_list, list('ABC'))
 
-        expected = DataFrame({'b': {0: 0,
-                                    1: 1,
-                                    2: 0},
-                              'c': {0: 0,
-                                    1: 0,
-                                    2: 1}}, dtype=np.uint8)
+        expected = DataFrame({'b': [0, 1, 0],
+                              'c': [0, 0, 1]},
+                             dtype=np.uint8)
 
-        result = get_dummies(s_list, sparse=self.sparse, drop_first=True)
+        result = get_dummies(s_list, drop_first=True, sparse=sparse)
         assert_frame_equal(result, expected)
 
-        result = get_dummies(s_series, sparse=self.sparse, drop_first=True)
+        result = get_dummies(s_series, drop_first=True, sparse=sparse)
         assert_frame_equal(result, expected)
 
         expected.index = list('ABC')
-        result = get_dummies(s_series_index, sparse=self.sparse,
-                             drop_first=True)
+        result = get_dummies(s_series_index, drop_first=True, sparse=sparse)
         assert_frame_equal(result, expected)
 
-    def test_basic_drop_first_one_level(self):
+    def test_basic_drop_first_one_level(self, sparse):
         # Test the case that categorical variable only has one level.
         s_list = list('aaa')
         s_series = Series(s_list)
@@ -515,53 +529,48 @@ def test_basic_drop_first_one_level(self):
 
         expected = DataFrame(index=np.arange(3))
 
-        result = get_dummies(s_list, sparse=self.sparse, drop_first=True)
+        result = get_dummies(s_list, drop_first=True, sparse=sparse)
         assert_frame_equal(result, expected)
 
-        result = get_dummies(s_series, sparse=self.sparse, drop_first=True)
+        result = get_dummies(s_series, drop_first=True, sparse=sparse)
         assert_frame_equal(result, expected)
 
         expected = DataFrame(index=list('ABC'))
-        result = get_dummies(s_series_index, sparse=self.sparse,
-                             drop_first=True)
+        result = get_dummies(s_series_index, drop_first=True, sparse=sparse)
         assert_frame_equal(result, expected)
 
-    def test_basic_drop_first_NA(self):
+    def test_basic_drop_first_NA(self, sparse):
         # Test NA hadling together with drop_first
         s_NA = ['a', 'b', np.nan]
-        res = get_dummies(s_NA, sparse=self.sparse, drop_first=True)
-        exp = DataFrame({'b': {0: 0,
-                               1: 1,
-                               2: 0}}, dtype=np.uint8)
+        res = get_dummies(s_NA, drop_first=True, sparse=sparse)
+        exp = DataFrame({'b': [0, 1, 0]}, dtype=np.uint8)
         assert_frame_equal(res, exp)
 
-        res_na = get_dummies(s_NA, dummy_na=True, sparse=self.sparse,
-                             drop_first=True)
-        exp_na = DataFrame({'b': {0: 0,
-                                  1: 1,
-                                  2: 0},
-                            nan: {0: 0,
-                                  1: 0,
-                                  2: 1}}, dtype=np.uint8).reindex(
-                                      ['b', nan], axis=1)
+        res_na = get_dummies(s_NA, dummy_na=True, drop_first=True,
+                             sparse=sparse)
+        exp_na = DataFrame(
+            {'b': [0, 1, 0],
+             nan: [0, 0, 1]},
+            dtype=np.uint8).reindex(['b', nan], axis=1)
         assert_frame_equal(res_na, exp_na)
 
-        res_just_na = get_dummies([nan], dummy_na=True, sparse=self.sparse,
-                                  drop_first=True)
+        res_just_na = get_dummies([nan], dummy_na=True, drop_first=True,
+                                  sparse=sparse)
         exp_just_na = DataFrame(index=np.arange(1))
         assert_frame_equal(res_just_na, exp_just_na)
 
-    def test_dataframe_dummies_drop_first(self):
-        df = self.df[['A', 'B']]
-        result = get_dummies(df, sparse=self.sparse, drop_first=True)
+    def test_dataframe_dummies_drop_first(self, df, sparse):
+        df = df[['A', 'B']]
+        result = get_dummies(df, drop_first=True, sparse=sparse)
         expected = DataFrame({'A_b': [0, 1, 0],
-                              'B_c': [0, 0, 1]}, dtype=np.uint8)
+                              'B_c': [0, 0, 1]},
+                             dtype=np.uint8)
         assert_frame_equal(result, expected)
 
-    def test_dataframe_dummies_drop_first_with_categorical(self):
-        df = self.df
+    def test_dataframe_dummies_drop_first_with_categorical(
+            self, df, sparse, dtype):
         df['cat'] = pd.Categorical(['x', 'y', 'y'])
-        result = get_dummies(df, sparse=self.sparse, drop_first=True)
+        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],
@@ -571,11 +580,10 @@ def test_dataframe_dummies_drop_first_with_categorical(self):
         expected = expected[['C', 'A_b', 'B_c', 'cat_y']]
         assert_frame_equal(result, expected)
 
-    def test_dataframe_dummies_drop_first_with_na(self):
-        df = self.df
+    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, sparse=self.sparse,
-                             drop_first=True)
+        result = get_dummies(df, dummy_na=True, drop_first=True,
+                             sparse=sparse).sort_index(axis=1)
         expected = DataFrame({'C': [1, 2, 3, np.nan],
                               'A_b': [0, 1, 0, 0],
                               'A_nan': [0, 0, 0, 1],
@@ -583,30 +591,34 @@ def test_dataframe_dummies_drop_first_with_na(self):
                               'B_nan': [0, 0, 0, 1]})
         cols = ['A_b', 'A_nan', 'B_c', 'B_nan']
         expected[cols] = expected[cols].astype(np.uint8)
-
-        expected = expected[['C', 'A_b', 'A_nan', 'B_c', 'B_nan']]
+        expected = expected.sort_index(axis=1)
         assert_frame_equal(result, expected)
 
-        result = get_dummies(df, dummy_na=False, sparse=self.sparse,
-                             drop_first=True)
+        result = get_dummies(df, dummy_na=False, drop_first=True,
+                             sparse=sparse)
         expected = expected[['C', 'A_b', 'B_c']]
         assert_frame_equal(result, expected)
 
     def test_int_int(self):
         data = Series([1, 2, 1])
         result = pd.get_dummies(data)
-        expected = DataFrame([[1, 0], [0, 1], [1, 0]], columns=[1, 2],
+        expected = DataFrame([[1, 0],
+                              [0, 1],
+                              [1, 0]],
+                             columns=[1, 2],
                              dtype=np.uint8)
         tm.assert_frame_equal(result, expected)
 
         data = Series(pd.Categorical(['a', 'b', 'a']))
         result = pd.get_dummies(data)
-        expected = DataFrame([[1, 0], [0, 1], [1, 0]],
+        expected = DataFrame([[1, 0],
+                              [0, 1],
+                              [1, 0]],
                              columns=pd.Categorical(['a', 'b']),
                              dtype=np.uint8)
         tm.assert_frame_equal(result, expected)
 
-    def test_int_df(self):
+    def test_int_df(self, dtype):
         data = DataFrame(
             {'A': [1, 2, 1],
              'B': pd.Categorical(['a', 'b', 'a']),
@@ -620,34 +632,28 @@ def test_int_df(self):
             [2, 2., 0, 1, 0, 1],
             [1, 1., 1, 0, 1, 0]
         ], columns=columns)
-        expected[columns[2:]] = expected[columns[2:]].astype(np.uint8)
-        result = pd.get_dummies(data, columns=['A', 'B'])
+        expected[columns[2:]] = expected[columns[2:]].astype(dtype)
+        result = pd.get_dummies(data, columns=['A', 'B'], dtype=dtype)
         tm.assert_frame_equal(result, expected)
 
-    def test_dataframe_dummies_preserve_categorical_dtype(self):
+    def test_dataframe_dummies_preserve_categorical_dtype(self, dtype):
         # GH13854
         for ordered in [False, True]:
             cat = pd.Categorical(list("xy"), categories=list("xyz"),
                                  ordered=ordered)
-            result = get_dummies(cat)
+            result = get_dummies(cat, dtype=dtype)
 
-            data = np.array([[1, 0, 0], [0, 1, 0]], dtype=np.uint8)
+            data = np.array([[1, 0, 0], [0, 1, 0]],
+                            dtype=self.effective_dtype(dtype))
             cols = pd.CategoricalIndex(cat.categories,
                                        categories=cat.categories,
                                        ordered=ordered)
-            expected = DataFrame(data, columns=cols)
+            expected = DataFrame(data, columns=cols,
+                                 dtype=self.effective_dtype(dtype))
 
             tm.assert_frame_equal(result, expected)
 
 
-class TestGetDummiesSparse(TestGetDummies):
-    sparse = True
-
-    @pytest.mark.xfail(reason='nan in index is problematic (GH 16894)')
-    def test_include_na(self):
-        super(TestGetDummiesSparse, self).test_include_na()
-
-
 class TestMakeAxisDummies(object):
 
     def test_preserve_categorical_dtype(self):