DOC: Add note to user guide that SettingWithCopyWarning won't be necessary anymore

doc/source/user_guide/copy_on_write.rst

@@ -6,6 +6,12 @@
 Copy-on-Write (CoW)
 *******************
 
+.. note::
+
+    Copy-on-Write will become the default in pandas 3.0 We recommend
+    :ref:`turning it on now <copy_on_write_enabling>`
+    to benefit from all improvements.
+
 Copy-on-Write was first introduced in version 1.5.0. Starting from version 2.0 most of the
 optimizations that become possible through CoW are implemented and supported. All possible
 optimizations are supported starting from pandas 2.1.
@@ -123,6 +129,8 @@ CoW triggers a copy when ``df`` is changed to avoid mutating ``view`` as well:
     df
     view
 
+.. _copy_on_write_chained_assignment:
+
 Chained Assignment
 ------------------
 
@@ -238,6 +246,8 @@ and :meth:`DataFrame.rename`.
 These methods return views when Copy-on-Write is enabled, which provides a significant
 performance improvement compared to the regular execution.
 
+.. _copy_on_write_enabling:
+
 How to enable CoW
 -----------------
 

doc/source/user_guide/indexing.rst

@@ -1727,6 +1727,22 @@ You can assign a custom index to the ``index`` attribute:
 Returning a view versus a copy
 ------------------------------
 
+.. warning::
+
+    :ref:`Copy-on-Write <copy_on_write>`
+    will become the new default in pandas 3.0. This means than chained indexing will
+    never work. As a consequence, the ``SettingWithCopyWarning`` won't be necessary
+    anymore.
+    See :ref:`this section <copy_on_write_chained_assignment>`
+    for more context.
+    We recommend turning Copy-on-Write on to leverage the improvements with
+
+    ```
+    pd.options.mode.copy_on_write = True
+    ```
+
+    even before pandas 3.0 is available.
+
 When setting values in a pandas object, care must be taken to avoid what is called
 ``chained indexing``. Here is an example.
 
@@ -1765,6 +1781,22 @@ faster, and allows one to index *both* axes if so desired.
 Why does assignment fail when using chained indexing?
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+.. warning::
+
+    :ref:`Copy-on-Write <copy_on_write>`
+    will become the new default in pandas 3.0. This means than chained indexing will
+    never work. As a consequence, the ``SettingWithCopyWarning`` won't be necessary
+    anymore.
+    See :ref:`this section <copy_on_write_chained_assignment>`
+    for more context.
+    We recommend turning Copy-on-Write on to leverage the improvements with
+
+    ```
+    pd.options.mode.copy_on_write = True
+    ```
+
+    even before pandas 3.0 is available.
+
 The problem in the previous section is just a performance issue. What's up with
 the ``SettingWithCopy`` warning? We don't **usually** throw warnings around when
 you do something that might cost a few extra milliseconds!
@@ -1821,6 +1853,22 @@ Yikes!
 Evaluation order matters
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
+.. warning::
+
+    :ref:`Copy-on-Write <copy_on_write>`
+    will become the new default in pandas 3.0. This means than chained indexing will
+    never work. As a consequence, the ``SettingWithCopyWarning`` won't be necessary
+    anymore.
+    See :ref:`this section <copy_on_write_chained_assignment>`
+    for more context.
+    We recommend turning Copy-on-Write on to leverage the improvements with
+
+    ```
+    pd.options.mode.copy_on_write = True
+    ```
+
+    even before pandas 3.0 is available.
+
 When you use chained indexing, the order and type of the indexing operation
 partially determine whether the result is a slice into the original object, or
 a copy of the slice.

pandas/io/parsers/arrow_parser_wrapper.py

@@ -267,7 +267,15 @@ def read(self) -> DataFrame:
             dtype_mapping[pa.null()] = pd.Int64Dtype()
             frame = table.to_pandas(types_mapper=dtype_mapping.get)
         elif using_pyarrow_string_dtype():
-            frame = table.to_pandas(types_mapper=arrow_string_types_mapper())
+
+            def types_mapper(dtype):
+                dtype_dict = self.kwds["dtype"]
+                if dtype_dict is not None and dtype_dict.get(dtype, None) is not None:
+                    return dtype_dict.get(dtype)
+                return arrow_string_types_mapper()(dtype)
+
+            frame = table.to_pandas(types_mapper=types_mapper)
+
         else:
             if isinstance(self.kwds.get("dtype"), dict):
                 frame = table.to_pandas(types_mapper=self.kwds["dtype"].get)