PERF: optimize DataFrame.sparse.from_spmatrix performance

doc/source/whatsnew/v1.1.0.rst

@@ -224,6 +224,9 @@ Performance improvements
 - The internal index method :meth:`~Index._shallow_copy` now copies cached attributes over to the new index,
   avoiding creating these again on the new index. This can speed up many operations that depend on creating copies of
   existing indexes (:issue:`28584`, :issue:`32640`, :issue:`32669`)
+- Performance improvement when creating sparse :class:`DataFrame` from
+  ``scipy.sparse`` matrices using the :meth:`DataFrame.sparse.from_spmatrix`
+  constructor (:issue:`32196`).
 
 .. ---------------------------------------------------------------------------
 

pandas/_libs/sparse.pyx

@@ -34,18 +34,21 @@ cdef class IntIndex(SparseIndex):
     length : integer
     indices : array-like
         Contains integers corresponding to the indices.
+    check_integrity : bool, default=True
+        Check integrity of the input.
     """
 
     cdef readonly:
         Py_ssize_t length, npoints
         ndarray indices
 
-    def __init__(self, Py_ssize_t length, indices):
+    def __init__(self, Py_ssize_t length, indices, check_integrity=True):
         self.length = length
         self.indices = np.ascontiguousarray(indices, dtype=np.int32)
         self.npoints = len(self.indices)
 
-        self.check_integrity()
+        if check_integrity:
+            self.check_integrity()
 
     def __reduce__(self):
         args = (self.length, self.indices)

pandas/core/arrays/sparse/accessor.py

@@ -227,15 +227,24 @@ def from_spmatrix(cls, data, index=None, columns=None):
         1  0.0  1.0  0.0
         2  0.0  0.0  1.0
         """
-        from pandas import DataFrame
+        from pandas import DataFrame, SparseDtype
+        from . import IntIndex, SparseArray
 
         data = data.tocsc()
         index, columns = cls._prep_index(data, index, columns)
-        sparrays = [SparseArray.from_spmatrix(data[:, i]) for i in range(data.shape[1])]
-        data = dict(enumerate(sparrays))
-        result = DataFrame(data, index=index)
-        result.columns = columns
-        return result
+        n_rows, n_columns = data.shape
+        data.sort_indices()
+        indices = data.indices
+        indptr = data.indptr
+        data = data.data
+        dtype = SparseDtype(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(data[sl], idx, dtype)
+            arrays.append(arr)
+        return DataFrame._from_arrays(arrays, columns=columns, index=index)
 
     def to_dense(self):
         """