@@ -92,8 +92,8 @@ def from_coo(cls, A, dense_index: bool = False) -> Series:
9292 ... ([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(3, 4)
9393 ... )
9494 >>> A
95- <3x4 sparse matrix of type '<class 'numpy. float64'> '
96- with 3 stored elements in COOrdinate format >
95+ <COOrdinate sparse matrix of dtype ' float64'
96+ with 3 stored elements and shape (3, 4) >
9797
9898 >>> A.todense()
9999 matrix([[0., 0., 1., 2.],
@@ -178,8 +178,8 @@ def to_coo(self, row_levels=(0,), column_levels=(1,), sort_labels: bool = False)
178178 ... row_levels=["A", "B"], column_levels=["C", "D"], sort_labels=True
179179 ... )
180180 >>> A
181- <3x4 sparse matrix of type '<class 'numpy. float64'> '
182- with 3 stored elements in COOrdinate format >
181+ <COOrdinate sparse matrix of dtype ' float64'
182+ with 3 stored elements and shape (3, 4) >
183183 >>> A.todense()
184184 matrix([[0., 0., 1., 3.],
185185 [3., 0., 0., 0.],
@@ -350,8 +350,8 @@ def to_coo(self):
350350 --------
351351 >>> df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0, 1])})
352352 >>> df.sparse.to_coo()
353- <4x1 sparse matrix of type '<class 'numpy. int64'> '
354- with 2 stored elements in COOrdinate format >
353+ <COOrdinate sparse matrix of dtype ' int64'
354+ with 2 stored elements and shape (4, 1) >
355355 """
356356 import_optional_dependency ("scipy" )
357357 from scipy .sparse import coo_matrix
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