diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index f662b4781e84b..149c5c0326733 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -97,7 +97,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.dt.unit GL08" \
         -i "pandas.Series.pad PR01,SA01" \
         -i "pandas.Series.sparse.from_coo PR07,SA01" \
-        -i "pandas.Series.sparse.npoints SA01" \
         -i "pandas.Timedelta.max PR02" \
         -i "pandas.Timedelta.min PR02" \
         -i "pandas.Timedelta.resolution PR02" \
diff --git a/pandas/core/arrays/sparse/array.py b/pandas/core/arrays/sparse/array.py
index c8ec4068ca199..0c76280e7fdb4 100644
--- a/pandas/core/arrays/sparse/array.py
+++ b/pandas/core/arrays/sparse/array.py
@@ -708,6 +708,18 @@ def npoints(self) -> int:
         """
         The number of non- ``fill_value`` points.
 
+        This property returns the number of elements in the sparse series that are
+        not equal to the ``fill_value``. Sparse data structures store only the
+        non-``fill_value`` elements, reducing memory usage when the majority of
+        values are the same.
+
+        See Also
+        --------
+        Series.sparse.to_dense : Convert a Series from sparse values to dense.
+        Series.sparse.fill_value : Elements in ``data`` that are ``fill_value`` are
+            not stored.
+        Series.sparse.density : The percent of non- ``fill_value`` points, as decimal.
+
         Examples
         --------
         >>> from pandas.arrays import SparseArray