DOCS: fix docstring validation errors for pandas.Series

Fixes:
-i "pandas.Series.sparse.fill_value SA01" \
-i "pandas.Series.sparse.from_coo PR07,SA01" \
-i "pandas.Series.sparse.npoints SA01" \
-i "pandas.Series.sparse.sp_values SA01" \

Author: ivonastojanovic

ci/code_checks.sh

@@ -147,10 +147,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.sem PR01,RT03,SA01" \
         -i "pandas.Series.sparse PR01,SA01" \
         -i "pandas.Series.sparse.density SA01" \
-        -i "pandas.Series.sparse.fill_value SA01" \
-        -i "pandas.Series.sparse.from_coo PR07,SA01" \
-        -i "pandas.Series.sparse.npoints SA01" \
-        -i "pandas.Series.sparse.sp_values SA01" \
         -i "pandas.Series.sparse.to_coo PR07,RT03,SA01" \
         -i "pandas.Series.std PR01,RT03,SA01" \
         -i "pandas.Series.str.match RT03" \

pandas/core/arrays/sparse/accessor.py

@@ -79,6 +79,10 @@ def from_coo(cls, A, dense_index: bool = False) -> Series:
         Parameters
         ----------
         A : scipy.sparse.coo_matrix
+            A sparse matrix in COOrdinate format. The matrix should have
+            non-zero elements that will be used to create a Series with sparse
+            values. The row and column indices of these elements form the index
+            of the Series.
         dense_index : bool, default False
             If False (default), the index consists of only the
             coords of the non-null entries of the original coo_matrix.
@@ -90,6 +94,14 @@ def from_coo(cls, A, dense_index: bool = False) -> Series:
         s : Series
             A Series with sparse values.
 
+        See Also
+        --------
+        scipy.sparse.coo_matrix : Sparse matrix in COOrdinate format.
+        pd.Series.sparse : Accessor object for sparse data in a Series.
+        pd.DataFrame.sparse.from_spmatrix : Create a DataFrame with sparse
+            values from a scipy sparse matrix.
+        pd.SparseArray : Array type for storing sparse data.
+
         Examples
         --------
         >>> from scipy import sparse

pandas/core/arrays/sparse/array.py

@@ -603,6 +603,15 @@ def sp_values(self) -> np.ndarray:
         """
         An ndarray containing the non- ``fill_value`` values.
 
+        See Also
+        --------
+        SparseArray : Array type for storing sparse data.
+        SparseArray.sp_index : The index object storing the positions of
+            non-``fill_value`` values.
+        SparseArray.fill_value : The value considered as "missing" or not stored.
+        SparseArray.sp_values : Returns the non-``fill_value`` values in the sparse
+        array.
+
         Examples
         --------
         >>> from pandas.arrays import SparseArray
@@ -623,6 +632,11 @@ def fill_value(self):
 
         For memory savings, this should be the most common value in the array.
 
+        See Also
+        --------
+        pd.SparseDtype : Type for sparse data which holds the dtype and fill_value.
+        pd.Series.sparse : Accessor object for sparse data in a Series.
+
         Examples
         --------
         >>> ser = pd.Series([0, 0, 2, 2, 2], dtype="Sparse[int]")
@@ -685,6 +699,12 @@ def npoints(self) -> int:
         """
         The number of non- ``fill_value`` points.
 
+        See Also
+        --------
+        SparseArray : Array type for storing sparse data.
+        SparseIndex : Stores the locations and counts of non-``fill_value`` data.
+        SparseArray.fill_value : The value considered as "missing" or not stored.
+
         Examples
         --------
         >>> from pandas.arrays import SparseArray