ENH: allow get_dummies to accept dtype argument

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

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

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
         -------

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