Adding default value overrides to Sparse dtype

Author: kushagr96

pandas/core/arrays/sparse/accessor.py

@@ -314,17 +314,7 @@ def from_spmatrix(cls, data, index=None, columns=None) -> DataFrame:
         indptr = data.indptr
         array_data = data.data
 
-        default_fill_value_override = None
-
-        # Default values for float type is NaN. Hence, in order to create a
-        # Sparse matrix of type float, we need override this default value as 0.0.
-        if array_data.dtype.kind in "f":
-            default_fill_value_override = 0.0
-        # Similarly, default value needs to be overridden for complex type arrays.
-        elif array_data.dtype.kind in "c":
-            default_fill_value_override = 0.0 + 0.0j
-
-        dtype = SparseDtype(array_data.dtype, fill_value=default_fill_value_override)
+        dtype = SparseDtype(array_data.dtype)
         arrays = []
         for i in range(n_columns):
             sl = slice(indptr[i], indptr[i + 1])

pandas/core/dtypes/dtypes.py

@@ -1747,6 +1747,14 @@ def __init__(self, dtype: Dtype = np.float64, fill_value: Any = None) -> None:
         if fill_value is None:
             fill_value = na_value_for_dtype(dtype)
 
+            # Default values for float type is NaN. Hence, in order to create a Sparse
+            # matrix of type float, we need to override this default value as 0.0.
+            if dtype.kind in "f":
+                fill_value = 0.0
+            # Similarly, default value needs to be overridden for complex type arrays.
+            elif dtype.kind in "c":
+                fill_value = 0.0 + 0.0j
+
         self._dtype = dtype
         self._fill_value = fill_value
         self._check_fill_value()