[API/REF]: SparseArray is an ExtensionArray (pandas-dev#22325)

Makes SparseArray an ExtensionArray.

* Fixed DataFrame.__setitem__ for updating to sparse.

Closes pandas-dev#22367

* Fixed Series[sparse].to_sparse

Closes pandas-dev#22389

Closes pandas-dev#21978
Closes pandas-dev#19506
Closes pandas-dev#22835

doc/source/whatsnew/v0.24.0.txt

@@ -381,6 +381,37 @@ is the case with :attr:`Period.end_time`, for example
 
    p.end_time
 
+.. _whatsnew_0240.api_breaking.sparse_values:
+
+Sparse Data Structure Refactor
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+``SparseArray``, the array backing ``SparseSeries`` and the columns in a ``SparseDataFrame``,
+is now an extension array (:issue:`21978`, :issue:`19056`, :issue:`22835`).
+To conform to this interface and for consistency with the rest of pandas, some API breaking
+changes were made:
+
+- ``SparseArray`` is no longer a subclass of :class:`numpy.ndarray`. To convert a SparseArray to a NumPy array, use :meth:`numpy.asarray`.
+- ``SparseArray.dtype`` and ``SparseSeries.dtype`` are now instances of :class:`SparseDtype`, rather than ``np.dtype``. Access the underlying dtype with ``SparseDtype.subtype``.
+- :meth:`numpy.asarray(sparse_array)` now returns a dense array with all the values, not just the non-fill-value values (:issue:`14167`)
+- ``SparseArray.take`` now matches the API of :meth:`pandas.api.extensions.ExtensionArray.take` (:issue:`19506`):
+
+  * The default value of ``allow_fill`` has changed from ``False`` to ``True``.
+  * 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``.
+- ``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.
+
+
+Some new warnings are issued for operations that require or are likely to materialize a large dense array:
+
+- A :class:`errors.PerformanceWarning` is issued when using fillna with a ``method``, as a dense array is constructed to create the filled array. Filling with a ``value`` is the efficient way to fill a sparse array.
+- A :class:`errors.PerformanceWarning` is now issued when concatenating sparse Series with differing fill values. The fill value from the first sparse array continues to be used.
+
+In addition to these API breaking changes, many :ref:`performance improvements and bug fixes have been made <whatsnew_0240.bug_fixes.sparse>`.
+
 .. _whatsnew_0240.api_breaking.frame_to_dict_index_orient:
 
 Raise ValueError in ``DataFrame.to_dict(orient='index')``
@@ -574,6 +605,7 @@ update the ``ExtensionDtype._metadata`` tuple to match the signature of your
 - Added :meth:`pandas.api.types.register_extension_dtype` to register an extension type with pandas (:issue:`22664`)
 - Series backed by an ``ExtensionArray`` now work with :func:`util.hash_pandas_object` (:issue:`23066`)
 - Updated the ``.type`` attribute for ``PeriodDtype``, ``DatetimeTZDtype``, and ``IntervalDtype`` to be instances of the dtype (``Period``, ``Timestamp``, and ``Interval`` respectively) (:issue:`22938`)
+- :func:`ExtensionArray.isna` is allowed to return an ``ExtensionArray`` (:issue:`22325`).
 - Support for reduction operations such as ``sum``, ``mean`` via opt-in base class method override (:issue:`22762`)
 
 .. _whatsnew_0240.api.incompatibilities:
@@ -656,6 +688,7 @@ Other API Changes
 - :class:`pandas.io.formats.style.Styler` supports a ``number-format`` property when using :meth:`~pandas.io.formats.style.Styler.to_excel` (:issue:`22015`)
 - :meth:`DataFrame.corr` and :meth:`Series.corr` now raise a ``ValueError`` along with a helpful error message instead of a ``KeyError`` when supplied with an invalid method (:issue:`22298`)
 - :meth:`shift` will now always return a copy, instead of the previous behaviour of returning self when shifting by 0 (:issue:`22397`)
+- Slicing a single row of a DataFrame with multiple ExtensionArrays of the same type now preserves the dtype, rather than coercing to object (:issue:`22784`)
 
 .. _whatsnew_0240.deprecations:
 
@@ -897,13 +930,6 @@ Groupby/Resample/Rolling
 - :func:`RollingGroupby.agg` and :func:`ExpandingGroupby.agg` now support multiple aggregation functions as parameters (:issue:`15072`)
 - Bug in :meth:`DataFrame.resample` and :meth:`Series.resample` when resampling by a weekly offset (``'W'``) across a DST transition (:issue:`9119`, :issue:`21459`)
 
-Sparse
-^^^^^^
-
--
--
--
-
 Reshaping
 ^^^^^^^^^
 
@@ -922,6 +948,19 @@ Reshaping
 - Bug in :func:`merge_asof` when merging on float values within defined tolerance (:issue:`22981`)
 - Bug in :func:`pandas.concat` when concatenating a multicolumn DataFrame with tz-aware data against a DataFrame with a different number of columns (:issue`22796`)
 
+.. _whatsnew_0240.bug_fixes.sparse:
+
+Sparse
+^^^^^^
+
+- Updating a boolean, datetime, or timedelta column to be Sparse now works (:issue:`22367`)
+- Bug in :meth:`Series.to_sparse` with Series already holding sparse data not constructing properly (:issue:`22389`)
+- Providing a ``sparse_index`` to the SparseArray constructor no longer defaults the na-value to ``np.nan`` for all dtypes. The correct na_value for ``data.dtype`` is now used.
+- Bug in ``SparseArray.nbytes`` under-reporting its memory usage by not including the size of its sparse index.
+- Improved performance of :meth:`Series.shift` for non-NA ``fill_value``, as values are no longer converted to a dense array.
+- Bug in ``DataFrame.groupby`` not including ``fill_value`` in the groups for non-NA ``fill_value`` when grouping by a sparse column (:issue:`5078`)
+- Bug in unary inversion operator (``~``) on a ``SparseSeries`` with boolean values. The performance of this has also been improved (:issue:`22835`)
+
 Build Changes
 ^^^^^^^^^^^^^
 

pandas/_libs/sparse.pyx

@@ -68,6 +68,10 @@ cdef class IntIndex(SparseIndex):
         output += 'Indices: %s\n' % repr(self.indices)
         return output
 
+    @property
+    def nbytes(self):
+        return self.indices.nbytes
+
     def check_integrity(self):
         """
         Checks the following:
@@ -359,6 +363,10 @@ cdef class BlockIndex(SparseIndex):
 
         return output
 
+    @property
+    def nbytes(self):
+        return self.blocs.nbytes + self.blengths.nbytes
+
     @property
     def ngaps(self):
         return self.length - self.npoints

pandas/core/arrays/base.py

@@ -287,10 +287,25 @@ def astype(self, dtype, copy=True):
         return np.array(self, dtype=dtype, copy=copy)
 
     def isna(self):
-        # type: () -> np.ndarray
-        """Boolean NumPy array indicating if each value is missing.
+        # type: () -> Union[ExtensionArray, np.ndarray]
+        """
+        A 1-D array indicating if each value is missing.
+
+        Returns
+        -------
+        na_values : Union[np.ndarray, ExtensionArray]
+            In most cases, this should return a NumPy ndarray. For
+            exceptional cases like ``SparseArray``, where returning
+            an ndarray would be expensive, an ExtensionArray may be
+            returned.
+
+        Notes
+        -----
+        If returning an ExtensionArray, then
 
-        This should return a 1-D array the same length as 'self'.
+        * ``na_values._is_boolean`` should be True
+        * `na_values` should implement :func:`ExtensionArray._reduce`
+        * ``na_values.any`` and ``na_values.all`` should be implemented
         """
         raise AbstractMethodError(self)
 

pandas/core/common.py

@@ -14,7 +14,9 @@
 
 from pandas import compat
 from pandas.compat import iteritems, PY36, OrderedDict
-from pandas.core.dtypes.generic import ABCSeries, ABCIndex, ABCIndexClass
+from pandas.core.dtypes.generic import (
+    ABCSeries, ABCIndex, ABCIndexClass
+)
 from pandas.core.dtypes.common import (
     is_integer, is_bool_dtype, is_extension_array_dtype, is_array_like
 )

pandas/core/dtypes/common.py

@@ -12,6 +12,7 @@
     PeriodDtype, IntervalDtype,
     PandasExtensionDtype, ExtensionDtype,
     _pandas_registry)
+from pandas.core.sparse.dtype import SparseDtype
 from pandas.core.dtypes.generic import (
     ABCCategorical, ABCPeriodIndex, ABCDatetimeIndex, ABCSeries,
     ABCSparseArray, ABCSparseSeries, ABCCategoricalIndex, ABCIndexClass,
@@ -180,8 +181,10 @@ def is_sparse(arr):
     >>> is_sparse(bsr_matrix([1, 2, 3]))
     False
     """
+    from pandas.core.sparse.dtype import SparseDtype
 
-    return isinstance(arr, (ABCSparseArray, ABCSparseSeries))
+    dtype = getattr(arr, 'dtype', arr)
+    return isinstance(dtype, SparseDtype)
 
 
 def is_scipy_sparse(arr):
@@ -1643,8 +1646,9 @@ def is_bool_dtype(arr_or_dtype):
     True
     >>> is_bool_dtype(pd.Categorical([True, False]))
     True
+    >>> is_bool_dtype(pd.SparseArray([True, False]))
+    True
     """
-
     if arr_or_dtype is None:
         return False
     try:
@@ -1751,6 +1755,8 @@ def is_extension_array_dtype(arr_or_dtype):
     array interface. In pandas, this includes:
 
     * Categorical
+    * Sparse
+    * Interval
 
     Third-party libraries may implement arrays or types satisfying
     this interface as well.
@@ -1873,7 +1879,8 @@ def _get_dtype(arr_or_dtype):
             return PeriodDtype.construct_from_string(arr_or_dtype)
         elif is_interval_dtype(arr_or_dtype):
             return IntervalDtype.construct_from_string(arr_or_dtype)
-    elif isinstance(arr_or_dtype, (ABCCategorical, ABCCategoricalIndex)):
+    elif isinstance(arr_or_dtype, (ABCCategorical, ABCCategoricalIndex,
+                                   ABCSparseArray, ABCSparseSeries)):
         return arr_or_dtype.dtype
 
     if hasattr(arr_or_dtype, 'dtype'):
@@ -1921,6 +1928,10 @@ def _get_dtype_type(arr_or_dtype):
         elif is_interval_dtype(arr_or_dtype):
             return Interval
         return _get_dtype_type(np.dtype(arr_or_dtype))
+    elif isinstance(arr_or_dtype, (ABCSparseSeries, ABCSparseArray,
+                                   SparseDtype)):
+        dtype = getattr(arr_or_dtype, 'dtype', arr_or_dtype)
+        return dtype.type
     try:
         return arr_or_dtype.dtype.type
     except AttributeError:

pandas/core/dtypes/concat.py

@@ -93,11 +93,13 @@ def _get_series_result_type(result, objs=None):
 def _get_frame_result_type(result, objs):
     """
     return appropriate class of DataFrame-like concat
-    if all blocks are SparseBlock, return SparseDataFrame
+    if all blocks are sparse, return SparseDataFrame
     otherwise, return 1st obj
     """
 
-    if result.blocks and all(b.is_sparse for b in result.blocks):
+    if (result.blocks and (
+            all(is_sparse(b) for b in result.blocks) or
+            all(isinstance(obj, ABCSparseDataFrame) for obj in objs))):
         from pandas.core.sparse.api import SparseDataFrame
         return SparseDataFrame
     else:
@@ -554,61 +556,23 @@ def _concat_sparse(to_concat, axis=0, typs=None):
     a single array, preserving the combined dtypes
     """
 
-    from pandas.core.sparse.array import SparseArray, _make_index
+    from pandas.core.sparse.array import SparseArray
 
-    def convert_sparse(x, axis):
-        # coerce to native type
-        if isinstance(x, SparseArray):
-            x = x.get_values()
-        else:
-            x = np.asarray(x)
-        x = x.ravel()
-        if axis > 0:
-            x = np.atleast_2d(x)
-        return x
+    fill_values = [x.fill_value for x in to_concat
+                   if isinstance(x, SparseArray)]
 
-    if typs is None:
-        typs = get_dtype_kinds(to_concat)
+    if len(set(fill_values)) > 1:
+        raise ValueError("Cannot concatenate SparseArrays with different "
+                         "fill values")
 
-    if len(typs) == 1:
-        # concat input as it is if all inputs are sparse
-        # and have the same fill_value
-        fill_values = {c.fill_value for c in to_concat}
-        if len(fill_values) == 1:
-            sp_values = [c.sp_values for c in to_concat]
-            indexes = [c.sp_index.to_int_index() for c in to_concat]
-
-            indices = []
-            loc = 0
-            for idx in indexes:
-                indices.append(idx.indices + loc)
-                loc += idx.length
-            sp_values = np.concatenate(sp_values)
-            indices = np.concatenate(indices)
-            sp_index = _make_index(loc, indices, kind=to_concat[0].sp_index)
-
-            return SparseArray(sp_values, sparse_index=sp_index,
-                               fill_value=to_concat[0].fill_value)
-
-    # input may be sparse / dense mixed and may have different fill_value
-    # input must contain sparse at least 1
-    sparses = [c for c in to_concat if is_sparse(c)]
-    fill_values = [c.fill_value for c in sparses]
-    sp_indexes = [c.sp_index for c in sparses]
-
-    # densify and regular concat
-    to_concat = [convert_sparse(x, axis) for x in to_concat]
-    result = np.concatenate(to_concat, axis=axis)
-
-    if not len(typs - {'sparse', 'f', 'i'}):
-        # sparsify if inputs are sparse and dense numerics
-        # first sparse input's fill_value and SparseIndex is used
-        result = SparseArray(result.ravel(), fill_value=fill_values[0],
-                             kind=sp_indexes[0])
-    else:
-        # coerce to object if needed
-        result = result.astype('object')
-    return result
+    fill_value = fill_values[0]
+
+    # TODO: Fix join unit generation so we aren't passed this.
+    to_concat = [x if isinstance(x, SparseArray)
+                 else SparseArray(x.squeeze(), fill_value=fill_value)
+                 for x in to_concat]
+
+    return SparseArray._concat_same_type(to_concat)
 
 
 def _concat_rangeindex_same_dtype(indexes):

pandas/core/dtypes/missing.py

@@ -499,6 +499,19 @@ def na_value_for_dtype(dtype, compat=True):
     Returns
     -------
     np.dtype or a pandas dtype
+
+    Examples
+    --------
+    >>> na_value_for_dtype(np.dtype('int64'))
+    0
+    >>> na_value_for_dtype(np.dtype('int64'), compat=False)
+    nan
+    >>> na_value_for_dtype(np.dtype('float64'))
+    nan
+    >>> na_value_for_dtype(np.dtype('bool'))
+    False
+    >>> na_value_for_dtype(np.dtype('datetime64[ns]'))
+    NaT
     """
     dtype = pandas_dtype(dtype)
 

pandas/core/internals/__init__.py

@@ -5,7 +5,7 @@
     make_block,     # io.pytables, io.packers
     FloatBlock, IntBlock, ComplexBlock, BoolBlock, ObjectBlock,
     TimeDeltaBlock, DatetimeBlock, DatetimeTZBlock,
-    CategoricalBlock, ExtensionBlock, SparseBlock, ScalarBlock,
+    CategoricalBlock, ExtensionBlock, ScalarBlock,
     Block)
 from .managers import (  # noqa:F401
     BlockManager, SingleBlockManager,