Add a fill_nan method to dataframe and column

spec/API_specification/dataframe_api/__init__.py

@@ -12,6 +12,16 @@
 from ._types import DType
 
 
+__all__ = [
+    "__dataframe_api_version",
+    "column_from_sequence",
+    "concat",
+    "dataframe_from_dict",
+    "isnull",
+    "null",
+]
+
+
 __dataframe_api_version__: str = "YYYY.MM"
 """
 String representing the version of the DataFrame API specification to which

spec/API_specification/dataframe_api/column_object.py

@@ -456,3 +456,17 @@ def unique_indices(self, *, skip_nulls: bool = True) -> Column[int]:
         To get the unique values, you can do ``col.get_rows(col.unique_indices())``.
         """
         ...
+
+    def fill_nan(self, value: float | 'null', /) -> Column:
+        """
+        Fill floating point ``nan`` values with the given fill value.
+
+        Parameters
+        ----------
+        value : float or `null`
+            Value used to replace any ``nan`` in the column with. Must be
+            of the Python scalar type matching the dtype of the column (or
+            be `null`).
+
+        """
+        ...

spec/API_specification/dataframe_api/dataframe_object.py

@@ -706,3 +706,20 @@ def isnan(self) -> DataFrame:
         In particular, does not check for `np.timedelta64('NaT')`.
         """
         ...
+
+    def fill_nan(self, value: float | 'null', /) -> DataFrame:
+        """
+        Fill ``nan`` values with the given fill value.
+
+        The fill operation will apply to all columns with a floating-point
+        dtype. Other columns remain unchanged.
+
+        Parameters
+        ----------
+        value : float or `null`
+            Value used to replace any ``nan`` in the column with. Must be
+            of the Python scalar type matching the dtype of the column (or
+            be `null`).
+
+        """
+        ...