TYP: Typing fixes and improvements related to scipy.sparse

pandas/_libs/sparse.pyi

@@ -1,9 +1,11 @@
-from collections.abc import Sequence
 from typing import Self
 
 import numpy as np
 
-from pandas._typing import npt
+from pandas._typing import (
+    TakeIndexer,
+    npt,
+)
 
 class SparseIndex:
     length: int
@@ -26,7 +28,7 @@ class SparseIndex:
 class IntIndex(SparseIndex):
     indices: npt.NDArray[np.int32]
     def __init__(
-        self, length: int, indices: Sequence[int], check_integrity: bool = ...
+        self, length: int, indices: TakeIndexer, check_integrity: bool = ...
     ) -> None: ...
 
 class BlockIndex(SparseIndex):

pandas/core/arrays/sparse/accessor.py

@@ -440,10 +440,10 @@ def to_coo(self) -> spmatrix:
             rows.append(row)
             data.append(sp_arr.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)
+        cols_arr = np.concatenate(cols)
+        rows_arr = np.concatenate(rows)
+        data_arr = np.concatenate(data)
+        return coo_matrix((data_arr, (rows_arr, cols_arr)), shape=self._parent.shape)
 
     @property
     def density(self) -> float:

pandas/core/arrays/sparse/array.py

@@ -92,13 +92,26 @@
         Sequence,
     )
     from enum import Enum
+    from typing import (
+        Protocol,
+        type_check_only,
+    )
 
     class ellipsis(Enum):
         Ellipsis = "..."
 
     Ellipsis = ellipsis.Ellipsis
 
-    from scipy.sparse import spmatrix
+    from scipy.sparse import (
+        csc_array,
+        csc_matrix,
+    )
+
+    @type_check_only
+    class _SparseMatrixLike(Protocol):
+        @property
+        def shape(self, /) -> tuple[int, int]: ...
+        def tocsc(self, /) -> csc_array | csc_matrix: ...
 
     from pandas._typing import NumpySorter
 
@@ -120,6 +133,7 @@ class ellipsis(Enum):
 
     from pandas import Series
 
+
 else:
     ellipsis = type(Ellipsis)
 
@@ -511,7 +525,7 @@ def _simple_new(
         return new
 
     @classmethod
-    def from_spmatrix(cls, data: spmatrix) -> Self:
+    def from_spmatrix(cls, data: _SparseMatrixLike) -> Self:
         """
         Create a SparseArray from a scipy.sparse matrix.
 
@@ -543,10 +557,10 @@ def from_spmatrix(cls, data: spmatrix) -> Self:
 
         # our sparse index classes require that the positions be strictly
         # increasing. So we need to sort loc, and arr accordingly.
-        data = data.tocsc()
-        data.sort_indices()
-        arr = data.data
-        idx = data.indices
+        data_csc = data.tocsc()
+        data_csc.sort_indices()
+        arr = data_csc.data
+        idx = data_csc.indices
 
         zero = np.array(0, dtype=arr.dtype).item()
         dtype = SparseDtype(arr.dtype, zero)
@@ -1217,10 +1231,7 @@ def _concat_same_type(cls, to_concat: Sequence[Self]) -> Self:
 
             data = np.concatenate(values)
             indices_arr = np.concatenate(indices)
-            # error: Argument 2 to "IntIndex" has incompatible type
-            # "ndarray[Any, dtype[signedinteger[_32Bit]]]";
-            # expected "Sequence[int]"
-            sp_index = IntIndex(length, indices_arr)  # type: ignore[arg-type]
+            sp_index = IntIndex(length, indices_arr)
 
         else:
             # when concatenating block indices, we don't claim that you'll

pandas/tests/arrays/sparse/test_libsparse.py

@@ -209,12 +209,10 @@ def test_intersect_empty(self):
     @pytest.mark.parametrize(
         "case",
         [
-            # Argument 2 to "IntIndex" has incompatible type "ndarray[Any,
-            # dtype[signedinteger[_32Bit]]]"; expected "Sequence[int]"
-            IntIndex(5, np.array([1, 2], dtype=np.int32)),  # type: ignore[arg-type]
-            IntIndex(5, np.array([0, 2, 4], dtype=np.int32)),  # type: ignore[arg-type]
-            IntIndex(0, np.array([], dtype=np.int32)),  # type: ignore[arg-type]
-            IntIndex(5, np.array([], dtype=np.int32)),  # type: ignore[arg-type]
+            IntIndex(5, np.array([1, 2], dtype=np.int32)),
+            IntIndex(5, np.array([0, 2, 4], dtype=np.int32)),
+            IntIndex(0, np.array([], dtype=np.int32)),
+            IntIndex(5, np.array([], dtype=np.int32)),
         ],
     )
     def test_intersect_identical(self, case):