BUG: to_coo silently converting non-zero fill values (#43763)

* BUG: to_coo silently converting non-zero fill values

* fix to_coo benchmark to have 0 fill value

Author: saehuihwang

asv_bench/benchmarks/sparse.py

@@ -95,11 +95,11 @@ class ToCooFrame:
     def setup(self):
         N = 10000
         k = 10
-        arr = np.full((N, k), np.nan)
+        arr = np.zeros((N, k), dtype=float)
         arr[0, 0] = 3.0
         arr[12, 7] = -1.0
         arr[0, 9] = 11.2
-        self.df = pd.DataFrame(arr, dtype=pd.SparseDtype("float"))
+        self.df = pd.DataFrame(arr, dtype=pd.SparseDtype("float", fill_value=0.0))
 
     def time_to_coo(self):
         self.df.sparse.to_coo()

doc/source/whatsnew/v1.4.0.rst

@@ -499,7 +499,7 @@ Sparse
 ^^^^^^
 - Bug in :meth:`DataFrame.sparse.to_coo` raising ``AttributeError`` when column names are not unique (:issue:`29564`)
 - Bug in :meth:`SparseArray.max` and :meth:`SparseArray.min` raising ``ValueError`` for arrays with 0 non-null elements (:issue:`43527`)
--
+- Bug in :meth:`DataFrame.sparse.to_coo` silently converting non-zero fill values to zero (:issue:`24817`)
 -
 
 ExtensionArray

pandas/core/arrays/sparse/accessor.py

@@ -340,10 +340,14 @@ def to_coo(self):
 
         cols, rows, data = [], [], []
         for col, (_, ser) in enumerate(self._parent.iteritems()):
-            row = ser.array.sp_index.to_int_index().indices
+            sp_arr = ser.array
+            if sp_arr.fill_value != 0:
+                raise ValueError("fill value must be 0 when converting to COO matrix")
+
+            row = sp_arr.sp_index.to_int_index().indices
             cols.append(np.repeat(col, len(row)))
             rows.append(row)
-            data.append(ser.array.sp_values.astype(dtype, copy=False))
+            data.append(sp_arr.sp_values.astype(dtype, copy=False))
 
         cols = np.concatenate(cols)
         rows = np.concatenate(rows)

pandas/tests/arrays/sparse/test_accessor.py

@@ -85,6 +85,22 @@ def test_to_coo(self, colnames):
         expected = scipy.sparse.coo_matrix(np.asarray(df))
         assert (result != expected).nnz == 0
 
+    @pytest.mark.parametrize("fill_value", [1, np.nan])
+    @td.skip_if_no_scipy
+    def test_to_coo_nonzero_fill_val_raises(self, fill_value):
+        df = pd.DataFrame(
+            {
+                "A": SparseArray(
+                    [fill_value, fill_value, fill_value, 2], fill_value=fill_value
+                ),
+                "B": SparseArray(
+                    [fill_value, 2, fill_value, fill_value], fill_value=fill_value
+                ),
+            }
+        )
+        with pytest.raises(ValueError, match="fill value must be 0"):
+            df.sparse.to_coo()
+
     def test_to_dense(self):
         df = pd.DataFrame(
             {