BUG/PERF: Sparse get_dummies uses concat

asv_bench/benchmarks/join_merge.py

@@ -50,7 +50,7 @@ def setup(self, axis):
         self.empty_right = [df, DataFrame()]
 
     def time_concat_series(self, axis):
-        concat(self.series, axis=axis)
+        concat(self.series, axis=axis, sort=False)
 
     def time_concat_small_frames(self, axis):
         concat(self.small_frames, axis=axis)

asv_bench/benchmarks/panel_ctor.py

@@ -1,7 +1,7 @@
 import warnings
 from datetime import datetime, timedelta
 
-from pandas import DataFrame, Panel, DatetimeIndex, date_range
+from pandas import DataFrame, Panel, date_range
 
 
 class DifferentIndexes(object):
@@ -23,9 +23,9 @@ def time_from_dict(self):
 class SameIndexes(object):
 
     def setup(self):
-        idx = DatetimeIndex(start=datetime(1990, 1, 1),
-                            end=datetime(2012, 1, 1),
-                            freq='D')
+        idx = date_range(start=datetime(1990, 1, 1),
+                         end=datetime(2012, 1, 1),
+                         freq='D')
         df = DataFrame({'a': 0, 'b': 1, 'c': 2}, index=idx)
         self.data_frames = dict(enumerate([df] * 100))
 
@@ -40,10 +40,10 @@ def setup(self):
         start = datetime(1990, 1, 1)
         end = datetime(2012, 1, 1)
         df1 = DataFrame({'a': 0, 'b': 1, 'c': 2},
-                        index=DatetimeIndex(start=start, end=end, freq='D'))
+                        index=date_range(start=start, end=end, freq='D'))
         end += timedelta(days=1)
         df2 = DataFrame({'a': 0, 'b': 1, 'c': 2},
-                        index=DatetimeIndex(start=start, end=end, freq='D'))
+                        index=date_range(start=start, end=end, freq='D'))
         dfs = [df1] * 50 + [df2] * 50
         self.data_frames = dict(enumerate(dfs))
 

asv_bench/benchmarks/reindex.py

@@ -1,14 +1,14 @@
 import numpy as np
 import pandas.util.testing as tm
-from pandas import (DataFrame, Series, DatetimeIndex, MultiIndex, Index,
+from pandas import (DataFrame, Series, MultiIndex, Index,
                     date_range)
 from .pandas_vb_common import lib
 
 
 class Reindex(object):
 
     def setup(self):
-        rng = DatetimeIndex(start='1/1/1970', periods=10000, freq='1min')
+        rng = date_range(start='1/1/1970', periods=10000, freq='1min')
         self.df = DataFrame(np.random.rand(10000, 10), index=rng,
                             columns=range(10))
         self.df['foo'] = 'bar'

asv_bench/benchmarks/timedelta.py

@@ -1,8 +1,9 @@
 import datetime
 
 import numpy as np
-from pandas import Series, timedelta_range, to_timedelta, Timestamp, \
-    Timedelta, TimedeltaIndex, DataFrame
+
+from pandas import (
+    DataFrame, Series, Timedelta, Timestamp, timedelta_range, to_timedelta)
 
 
 class TimedeltaConstructor(object):
@@ -122,8 +123,8 @@ def time_timedelta_nanoseconds(self, series):
 class TimedeltaIndexing(object):
 
     def setup(self):
-        self.index = TimedeltaIndex(start='1985', periods=1000, freq='D')
-        self.index2 = TimedeltaIndex(start='1986', periods=1000, freq='D')
+        self.index = timedelta_range(start='1985', periods=1000, freq='D')
+        self.index2 = timedelta_range(start='1986', periods=1000, freq='D')
         self.series = Series(range(1000), index=self.index)
         self.timedelta = self.index[500]
 

asv_bench/benchmarks/timestamp.py

@@ -1,8 +1,9 @@
 import datetime
 
-from pandas import Timestamp
-import pytz
 import dateutil
+import pytz
+
+from pandas import Timestamp
 
 
 class TimestampConstruction(object):
@@ -46,7 +47,7 @@ def time_dayofweek(self, tz, freq):
         self.ts.dayofweek
 
     def time_weekday_name(self, tz, freq):
-        self.ts.weekday_name
+        self.ts.day_name
 
     def time_dayofyear(self, tz, freq):
         self.ts.dayofyear

doc/source/whatsnew/v0.24.0.rst

@@ -647,7 +647,7 @@ changes were made:
   * The ``out`` and ``mode`` parameters are now longer accepted (previously, this raised if they were specified).
   * Passing a scalar for ``indices`` is no longer allowed.
 
-- The result of concatenating a mix of sparse and dense Series is a Series with sparse values, rather than a ``SparseSeries``.
+- The result of :func:`concat` with a mix of sparse and dense Series is a Series with sparse values, rather than a ``SparseSeries``.
 - ``SparseDataFrame.combine`` and ``DataFrame.combine_first`` no longer supports combining a sparse column with a dense column while preserving the sparse subtype. The result will be an object-dtype SparseArray.
 - Setting :attr:`SparseArray.fill_value` to a fill value with a different dtype is now allowed.
 - ``DataFrame[column]`` is now a :class:`Series` with sparse values, rather than a :class:`SparseSeries`, when slicing a single column with sparse values (:issue:`23559`).
@@ -1103,6 +1103,7 @@ Other API Changes
 - The order of the arguments of :func:`DataFrame.to_html` and :func:`DataFrame.to_string` is rearranged to be consistent with each other. (:issue:`23614`)
 - :meth:`CategoricalIndex.reindex` now raises a ``ValueError`` if the target index is non-unique and not equal to the current index. It previously only raised if the target index was not of a categorical dtype (:issue:`23963`).
 - :func:`Series.to_list` and :func:`Index.to_list` are now aliases of ``Series.tolist`` respectively ``Index.tolist`` (:issue:`8826`)
+- The result of ``SparseSeries.unstack`` is now a :class:`DataFrame` with sparse values, rather than a :class:`SparseDataFrame` (issue:`24372`).
 
 .. _whatsnew_0240.deprecations:
 
@@ -1613,6 +1614,7 @@ Sparse
 - Bug in :meth:`SparseArary.unique` not returning the unique values (:issue:`19595`)
 - Bug in :meth:`SparseArray.nonzero` and :meth:`SparseDataFrame.dropna` returning shifted/incorrect results (:issue:`21172`)
 - Bug in :meth:`DataFrame.apply` where dtypes would lose sparseness (:issue:`23744`)
+- Bug in :func:`concat` when concatenating a list of :class:`Series` with all-sparse values changing the ``fill_value`` and converting to a dense Series (:issue:`24371`)
 
 Style
 ^^^^^

pandas/core/dtypes/concat.py

@@ -66,19 +66,19 @@ def _get_series_result_type(result, objs=None):
     return appropriate class of Series concat
     input is either dict or array-like
     """
+    from pandas import SparseSeries, SparseDataFrame, DataFrame
+
     # concat Series with axis 1
     if isinstance(result, dict):
         # concat Series with axis 1
-        if all(is_sparse(c) for c in compat.itervalues(result)):
-            from pandas.core.sparse.api import SparseDataFrame
+        if all(isinstance(c, (SparseSeries, SparseDataFrame))
+               for c in compat.itervalues(result)):
             return SparseDataFrame
         else:
-            from pandas.core.frame import DataFrame
             return DataFrame
 
     # otherwise it is a SingleBlockManager (axis = 0)
     if result._block.is_sparse:
-        from pandas.core.sparse.api import SparseSeries
         return SparseSeries
     else:
         return objs[0]._constructor

pandas/core/reshape/reshape.py

@@ -11,8 +11,8 @@
 
 from pandas.core.dtypes.cast import maybe_promote
 from pandas.core.dtypes.common import (
-    ensure_platform_int, is_bool_dtype, is_extension_array_dtype, is_list_like,
-    is_object_dtype, needs_i8_conversion)
+    ensure_platform_int, is_bool_dtype, is_extension_array_dtype,
+    is_integer_dtype, is_list_like, is_object_dtype, needs_i8_conversion)
 from pandas.core.dtypes.missing import notna
 
 from pandas import compat
@@ -853,6 +853,7 @@ def check_len(item, name):
 
 def _get_dummies_1d(data, prefix, prefix_sep='_', dummy_na=False,
                     sparse=False, drop_first=False, dtype=None):
+    from pandas.core.reshape.concat import concat
     # Series avoids inconsistent NaN handling
     codes, levels = _factorize_from_iterable(Series(data))
 
@@ -909,7 +910,15 @@ def _make_col_name(prefix, prefix_sep, level):
         index = None
 
     if sparse:
-        sparse_series = {}
+
+        if is_integer_dtype(dtype):
+            fill_value = 0
+        elif dtype == bool:
+            fill_value = False
+        else:
+            fill_value = 0.0
+
+        sparse_series = []
         N = len(data)
         sp_indices = [[] for _ in range(len(dummy_cols))]
         mask = codes != -1
@@ -926,12 +935,12 @@ def _make_col_name(prefix, prefix_sep, level):
             dummy_cols = dummy_cols[1:]
         for col, ixs in zip(dummy_cols, sp_indices):
             sarr = SparseArray(np.ones(len(ixs), dtype=dtype),
-                               sparse_index=IntIndex(N, ixs), fill_value=0,
+                               sparse_index=IntIndex(N, ixs),
+                               fill_value=fill_value,
                                dtype=dtype)
-            sparse_series[col] = Series(data=sarr, index=index)
+            sparse_series.append(Series(data=sarr, index=index, name=col))
 
-        out = DataFrame(sparse_series, index=index, columns=dummy_cols,
-                        dtype=dtype)
+        out = concat(sparse_series, axis=1, copy=False)
         return out
 
     else:

pandas/tests/sparse/test_reshape.py

@@ -35,4 +35,8 @@ def test_sparse_frame_unstack(sparse_df):
 
 def test_sparse_series_unstack(sparse_df, multi_index3):
     frame = pd.SparseSeries(np.ones(3), index=multi_index3).unstack()
-    tm.assert_sp_frame_equal(frame, sparse_df)
+
+    arr = np.array([1, np.nan, np.nan])
+    arrays = {i: pd.SparseArray(np.roll(arr, i)) for i in range(3)}
+    expected = pd.DataFrame(arrays)
+    tm.assert_frame_equal(frame, expected)