content: """Sparse accessor"""
  
  import numpy as np
  
  from pandas.compat._optional import import_optional_dependency
  
  from pandas.core.dtypes.cast import find_common_type
  
  from pandas.core.accessor import (
      PandasDelegate,
      delegate_names,
  )
  from pandas.core.arrays.sparse.array import SparseArray
  from pandas.core.arrays.sparse.dtype import SparseDtype
  
  
  class BaseAccessor:
      _validation_msg = "Can only use the '.sparse' accessor with Sparse data."
  
      def __init__(self, data=None):
          self._parent = data
          self._validate(data)
  
      def _validate(self, data):
          raise NotImplementedError
  
  
  @delegate_names(
      SparseArray, ["npoints", "density", "fill_value", "sp_values"], typ="property"
  )
  class SparseAccessor(BaseAccessor, PandasDelegate):
      """
      Accessor for SparseSparse from other sparse matrix data types.
      """
  
      def _validate(self, data):
          if not isinstance(data.dtype, SparseDtype):
              raise AttributeError(self._validation_msg)
  
      def _delegate_property_get(self, name, *args, **kwargs):
          return getattr(self._parent.array, name)
  
      def _delegate_method(self, name, *args, **kwargs):
          if name == "from_coo":
              return self.from_coo(*args, **kwargs)
          elif name == "to_coo":
              return self.to_coo(*args, **kwargs)
          else:
              raise ValueError
  
      @classmethod
      def from_coo(cls, A, dense_index=False):
          """
          Create a Series with sparse values from a scipy.sparse.coo_matrix.
  
          Parameters
          ----------
          A : scipy.sparse.coo_matrix
          dense_index : bool, default False
              If False (default), the SparseSeries index consists of only the
              coords of the non-null entries of the original coo_matrix.
              If True, the SparseSeries index consists of the full sorted
              (row, col) coordinates of the coo_matrix.
  
          Returns
          -------
          s : Series
              A Series with sparse values.
  
          Examples
          --------
          >>> from scipy import sparse
  
          >>> A = sparse.coo_matrix(
          ...     ([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(3, 4)
          ... )
          >>> A
          <3x4 sparse matrix of type '<class 'numpy.float64'>'
          with 3 stored elements in COOrdinate format>
  
          >>> A.todense()
          matrix([[0., 0., 1., 2.],
          [3., 0., 0., 0.],
          [0., 0., 0., 0.]])
  
          >>> ss = pd.Series.sparse.from_coo(A)
          >>> ss
          0  2    1.0
             3    2.0
          1  0    3.0
          dtype: Sparse[float64, nan]
          """
          from pandas import Series
          from pandas.core.arrays.sparse.scipy_sparse import coo_to_sparse_series
  
          result = coo_to_sparse_series(A, dense_index=dense_index)
          result = Series(result.array, index=result.index, copy=False)
  
          return result
  
      def to_coo(self, row_levels=(0,), column_levels=(1,), sort_labels=False):
          """
          Create a scipy.sparse.coo_matrix from a Series with MultiIndex.
  
          Use row_levels and column_levels to determine the row and column
          coordinates respectively. row_levels and column_levels are the names
          (labels) or numbers of the levels. {row_levels, column_levels} must be
          a partition of the MultiIndex level names (or numbers).
  
          Parameters
          ----------
          row_levels : tuple/list
          column_levels : tuple/list
          sort_labels : bool, default False
              Sort the row and column labels before forming the sparse matrix.
  
          Returns
          -------
          y : scipy.sparse.coo_matrix
          rows : list (row labels)
          columns : list (column labels)
  
          Examples
          --------
          >>> s = pd.Series([3.0, np.nan, 1.0, 3.0, np.nan, np.nan])
          >>> s.index = pd.MultiIndex.from_tuples(
          ...     [
          ...         (1, 2, "a", 0),
          ...         (1, 2, "a", 1),
          ...         (1, 1, "b", 0),
          ...         (1, 1, "b", 1),
          ...         (2, 1, "b", 0),
          ...         (2, 1, "b", 1)
          ...     ],
          ...     names=["A", "B", "C", "D"],
          ... )
          >>> s
          A  B  C  D
          1  2  a  0    3.0
                   1    NaN
             1  b  0    1.0
                   1    3.0
          2  1  b  0    NaN
                   1    NaN
          dtype: float64
  
          >>> ss = s.astype("Sparse")
          >>> ss
          A  B  C  D
          1  2  a  0    3.0
                   1    NaN
             1  b  0    1.0
                   1    3.0
          2  1  b  0    NaN
                   1    NaN
          dtype: Sparse[float64, nan]
  
          >>> A, rows, columns = ss.sparse.to_coo(
          ...     row_levels=["A", "B"], column_levels=["C", "D"], sort_labels=True
          ... )
          >>> A
          <3x4 sparse matrix of type '<class 'numpy.float64'>'
          with 3 stored elements in COOrdinate format>
          >>> A.todense()
          matrix([[0., 0., 1., 3.],
          [3., 0., 0., 0.],
          [0., 0., 0., 0.]])
  
          >>> rows
          [(1, 1), (1, 2), (2, 1)]
          >>> columns
          [('a', 0), ('a', 1), ('b', 0), ('b', 1)]
          """
          from pandas.core.arrays.sparse.scipy_sparse import sparse_series_to_coo
  
          A, rows, columns = sparse_series_to_coo(
              self._parent, row_levels, column_levels, sort_labels=sort_labels
          )
          return A, rows, columns
  
      def to_dense(self):
          """
          Convert a Series from sparse values to dense.
  
          .. versionadded:: 0.25.0
  
          Returns
          -------
          Series:
              A Series with the same values, stored as a dense array.
  
          Examples
          --------
          >>> series = pd.Series(pd.arrays.SparseArray([0, 1, 0]))
          >>> series
          0    0
          1    1
          2    0
          dtype: Sparse[int64, 0]
  
          >>> series.sparse.to_dense()
          0    0
          1    1
          2    0
          dtype: int64
          """
          from pandas import Series
  
          return Series(
              self._parent.array.to_dense(),
              index=self._parent.index,
              name=self._parent.name,
          )
  
  
  class SparseFrameAccessor(BaseAccessor, PandasDelegate):
      """
      DataFrame accessor for sparse data.
  
      .. versionadded:: 0.25.0
      """
  
      def _validate(self, data):
          dtypes = data.dtypes
          if not all(isinstance(t, SparseDtype) for t in dtypes):
              raise AttributeError(self._validation_msg)
  
      @classmethod
      def from_spmatrix(cls, data, index=None, columns=None):
          """
          Create a new DataFrame from a scipy sparse matrix.
  
          .. versionadded:: 0.25.0
  
          Parameters
          ----------
          data : scipy.sparse.spmatrix
              Must be convertible to csc format.
          index, columns : Index, optional
              Row and column labels to use for the resulting DataFrame.
              Defaults to a RangeIndex.
  
          Returns
          -------
          DataFrame
              Each column of the DataFrame is stored as a
              :class:`arrays.SparseArray`.
  
          Examples
          --------
          >>> import scipy.sparse
          >>> mat = scipy.sparse.eye(3)
          >>> pd.DataFrame.sparse.from_spmatrix(mat)
               0    1    2
          0  1.0  0.0  0.0
          1  0.0  1.0  0.0
          2  0.0  0.0  1.0
          """
          from pandas._libs.sparse import IntIndex
  
          from pandas import DataFrame
  
          data = data.tocsc()
          index, columns = cls._prep_index(data, index, columns)
          n_rows, n_columns = data.shape
          # We need to make sure indices are sorted, as we create
          # IntIndex with no input validation (i.e. check_integrity=False ).
          # Indices may already be sorted in scipy in which case this adds
          # a small overhead.
          data.sort_indices()
          indices = data.indices
          indptr = data.indptr
          array_data = data.data
          dtype = SparseDtype(array_data.dtype, 0)
          arrays = []
          for i in range(n_columns):
              sl = slice(indptr[i], indptr[i + 1])
              idx = IntIndex(n_rows, indices[sl], check_integrity=False)
              arr = SparseArray._simple_new(array_data[sl], idx, dtype)
              arrays.append(arr)
          return DataFrame._from_arrays(
              arrays, columns=columns, index=index, verify_integrity=False
          )
  
      def to_dense(self):
          """
          Convert a DataFrame with sparse values to dense.
  
          .. versionadded:: 0.25.0
  
          Returns
          -------
          DataFrame
              A DataFrame with the same values stored as dense arrays.
  
          Examples
          --------
          >>> df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0])})
          >>> df.sparse.to_dense()
             A
          0  0
          1  1
          2  0
          """
          from pandas import DataFrame
  
          data = {k: v.array.to_dense() for k, v in self._parent.items()}
          return DataFrame(data, index=self._parent.index, columns=self._parent.columns)
  
      def to_coo(self):
          """
          Return the contents of the frame as a sparse SciPy COO matrix.
  
          .. versionadded:: 0.25.0
  
          Returns
          -------
          coo_matrix : scipy.sparse.spmatrix
              If the caller is heterogeneous and contains booleans or objects,
              the result will be of dtype=object. See Notes.
  
          Notes
          -----
          The dtype will be the lowest-common-denominator type (implicit
          upcasting); that is to say if the dtypes (even of numeric types)
          are mixed, the one that accommodates all will be chosen.
  
          e.g. If the dtypes are float16 and float32, dtype will be upcast to
          float32. By numpy.find_common_type convention, mixing int64 and
          and uint64 will result in a float64 dtype.
          """
          import_optional_dependency("scipy")
          from scipy.sparse import coo_matrix
  
          dtype = find_common_type(self._parent.dtypes.to_list())
          if isinstance(dtype, SparseDtype):
              dtype = dtype.subtype
  
          cols, rows, data = [], [], []
          for col, name in enumerate(self._parent):
              s = self._parent[name]
              row = s.array.sp_index.to_int_index().indices
              cols.append(np.repeat(col, len(row)))
              rows.append(row)
              data.append(s.array.sp_values.astype(dtype, copy=False))
  
          cols = np.concatenate(cols)
          rows = np.concatenate(rows)
          data = np.concatenate(data)
          return coo_matrix((data, (rows, cols)), shape=self._parent.shape)
  
      @property
      def density(self) -> float:
          """
          Ratio of non-sparse points to total (dense) data points.
          """
          tmp = np.mean([column.array.density for _, column in self._parent.items()])
          return tmp
  
      @staticmethod
      def _prep_index(data, index, columns):
          from pandas.core.indexes.api import ensure_index
          import pandas.core.indexes.base as ibase
  
          N, K = data.shape
          if index is None:
              index = ibase.default_index(N)
          else:
              index = ensure_index(index)
          if columns is None:
              columns = ibase.default_index(K)
          else:
              columns = ensure_index(columns)
  
          if len(columns) != K:
              raise ValueError(f"Column length mismatch: {len(columns)} vs. {K}")
          if len(index) != N:
              raise ValueError(f"Index length mismatch: {len(index)} vs. {N}")
          return index, columns
  