def to_coo(self, row_levels=(0, ), column_levels=(1, ), sort_labels=False):
"""
Create a scipy.sparse.coo_matrix from a SparseSeries 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).
.. versionadded:: 0.16.0
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
--------
>>> from numpy import nan
>>> s = Series([3.0, nan, 1.0, 3.0, nan, nan])
>>> s.index = 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'])
>>> ss = s.to_sparse()
>>> A, rows, columns = ss.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)]
"""
A, rows, columns = _sparse_series_to_coo(self,
row_levels,
column_levels,
sort_labels=sort_labels)
return A, rows, columns
def to_coo(self, row_levels=(0, ), column_levels=(1, ), sort_labels=False):
"""
Create a scipy.sparse.coo_matrix from a SparseSeries 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).
.. versionadded:: 0.16.0
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
--------
>>> from numpy import nan
>>> s = Series([3.0, nan, 1.0, 3.0, nan, nan])
>>> s.index = 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'])
>>> ss = s.to_sparse()
>>> A, rows, columns = ss.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)]
"""
A, rows, columns = _sparse_series_to_coo(self, row_levels,
column_levels,
sort_labels=sort_labels)
return A, rows, columns
def to_coo(self, row_levels=(0, ), column_levels=(1, ), sort_labels=False):
A, rows, columns = _sparse_series_to_coo(self,
row_levels,
column_levels,
sort_labels=sort_labels)
return A, rows, columns
def to_coo(self, row_levels=(0, ), column_levels=(1, ), sort_labels=False):
A, rows, columns = _sparse_series_to_coo(self, row_levels,
column_levels,
sort_labels=sort_labels)
return A, rows, columns
