BUG: get_dummies not returning SparseDataFrame

Tests redo

Author: Artemy Kolchinsky

doc/source/whatsnew/v0.17.0.txt

@@ -377,16 +377,12 @@ Bug Fixes
 - Bug in ``Series.plot(kind='hist')`` Y Label not informative (:issue:`10485`)
 
 
-
-
-
-
-
-
-
-
 - Bug in operator equal on Index not being consistent with Series (:issue:`9947`)
 
 - Reading "famafrench" data via ``DataReader`` results in HTTP 404 error because of the website url is changed (:issue:`10591`).
 
 - Bug in `read_msgpack` where DataFrame to decode has duplicate column names (:issue:`9618`)
+
+
+- Bug in `get_dummies` with `sparse=True` not returning SparseDataFrame (:issue:`10531`)
+

pandas/core/reshape.py

@@ -957,13 +957,15 @@ def get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False,
         If `columns` is None then all the columns with
         `object` or `category` dtype will be converted.
     sparse : bool, default False
-        Whether the returned DataFrame should be sparse or not.
+        Whether the dummy columns should be sparse or not.  Returns
+        SparseDataFrame if `data` is a Series or if all columns are included.
+        Otherwise returns a DataFrame with some SparseBlocks.
 
         .. versionadded:: 0.16.1
 
     Returns
     -------
-    dummies : DataFrame
+    dummies : DataFrame or SparseDataFrame
 
     Examples
     --------
@@ -1042,8 +1044,11 @@ def check_len(item, name):
         elif isinstance(prefix_sep, dict):
             prefix_sep = [prefix_sep[col] for col in columns_to_encode]
 
-        result = data.drop(columns_to_encode, axis=1)
-        with_dummies = [result]
+        if set(columns_to_encode) == set(data.columns):
+            with_dummies = []
+        else:
+            with_dummies = [data.drop(columns_to_encode, axis=1)]
+
         for (col, pre, sep) in zip(columns_to_encode, prefix, prefix_sep):
 
             dummy = _get_dummies_1d(data[col], prefix=pre, prefix_sep=sep,

pandas/tests/test_reshape.py

@@ -8,6 +8,7 @@
 import nose
 
 from pandas import DataFrame, Series
+from pandas.core.sparse import SparseDataFrame
 import pandas as pd
 
 from numpy import nan
@@ -171,6 +172,33 @@ def test_basic(self):
         expected.index = list('ABC')
         assert_frame_equal(get_dummies(s_series_index, sparse=self.sparse), expected)
 
+    def test_basic_types(self):
+        # GH 10531
+        s_list = list('abc')
+        s_series = Series(s_list)
+        s_df = DataFrame({'a': [0, 1, 0, 1, 2],
+                          'b': ['A', 'A', 'B', 'C', 'C'],
+                          'c': [2, 3, 3, 3, 2]})
+
+        if not self.sparse:
+            exp_df_type = DataFrame 
+            exp_blk_type = pd.core.internals.FloatBlock
+        else:
+            exp_df_type = SparseDataFrame
+            exp_blk_type = pd.core.internals.SparseBlock
+
+        self.assertEqual(type(get_dummies(s_list, sparse=self.sparse)), exp_df_type)
+        self.assertEqual(type(get_dummies(s_series, sparse=self.sparse)), exp_df_type)
+
+        r = get_dummies(s_df, sparse=self.sparse, columns=s_df.columns)
+        self.assertEqual(type(r), exp_df_type)
+
+        r = get_dummies(s_df, sparse=self.sparse, columns=['a'])
+        self.assertEqual(type(r[['a_0']]._data.blocks[0]), exp_blk_type)
+        self.assertEqual(type(r[['a_1']]._data.blocks[0]), exp_blk_type)
+        self.assertEqual(type(r[['a_2']]._data.blocks[0]), exp_blk_type)
+
+
     def test_just_na(self):
         just_na_list = [np.nan]
         just_na_series = Series(just_na_list)