BUG: Fix concat(Series[sparse], axis=1)

* Preserve sparsity
* Preserve fill value

Author: TomAugspurger

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`).
@@ -1613,6 +1613,7 @@ 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
@@ -910,6 +910,14 @@ def _make_col_name(prefix, prefix_sep, level):
         index = None
 
     if sparse:
+
+        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))]
@@ -927,7 +935,8 @@ 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.append(Series(data=sarr, index=index, name=col))