Backport PR #35776: Changed 'int' type to 'integer' in to_numeric docstring (#35806)

Co-authored-by: edwardkong <33737404+edwardkong@users.noreply.github.com>

Author: meeseeksmachine

pandas/core/arrays/sparse/array.py

@@ -234,7 +234,7 @@ class SparseArray(PandasObject, ExtensionArray, ExtensionOpsMixin):
         3. ``data.dtype.fill_value`` if `fill_value` is None and `dtype`
            is not a ``SparseDtype`` and `data` is a ``SparseArray``.
 
-    kind : {'int', 'block'}, default 'int'
+    kind : {'integer', 'block'}, default 'integer'
         The type of storage for sparse locations.
 
         * 'block': Stores a `block` and `block_length` for each

pandas/core/tools/numeric.py

@@ -40,13 +40,13 @@ def to_numeric(arg, errors="raise", downcast=None):
         - If 'raise', then invalid parsing will raise an exception.
         - If 'coerce', then invalid parsing will be set as NaN.
         - If 'ignore', then invalid parsing will return the input.
-    downcast : {'int', 'signed', 'unsigned', 'float'}, default None
+    downcast : {'integer', 'signed', 'unsigned', 'float'}, default None
         If not None, and if the data has been successfully cast to a
         numerical dtype (or if the data was numeric to begin with),
         downcast that resulting data to the smallest numerical dtype
         possible according to the following rules:
 
-        - 'int' or 'signed': smallest signed int dtype (min.: np.int8)
+        - 'integer' or 'signed': smallest signed int dtype (min.: np.int8)
         - 'unsigned': smallest unsigned int dtype (min.: np.uint8)
         - 'float': smallest float dtype (min.: np.float32)