DOC: Add PyArrow user guide

doc/source/reference/arrays.rst

@@ -113,6 +113,8 @@ values.
 
    ArrowDtype
 
+For more information, please see the :ref:`PyArrow user guide <pyarrow>`
+
 .. _api.arrays.datetime:
 
 Datetimes

doc/source/user_guide/index.rst

@@ -64,6 +64,7 @@ Guides
     dsintro
     basics
     io
+    pyarrow
     indexing
     advanced
     merging

doc/source/user_guide/pyarrow.rst

@@ -0,0 +1,162 @@
+.. _pyarrow:
+
+{{ header }}
+
+*********************
+PyArrow Functionality
+*********************
+
+pandas can utilize `PyArrow <https://arrow.apache.org/docs/python/index.html>`__ to extend functionality and improve the performance
+of various APIs. This includes:
+
+* More extensive `data types <https://arrow.apache.org/docs/python/api/datatypes.html>`__ compared to NumPy
+* Missing data support (NA) for all data types
+* Performant IO reader integration
+* Facilitate interoperability with other dataframe libraries based on the Apache Arrow specification (e.g. polars, cuDF)
+
+To use this functionality, please ensure you have :ref:`installed the minimum supported PyArrow version. <install.optional_dependencies>`
+
+
+Data Structure Integration
+--------------------------
+
+A :class:`Series`, :class:`Index`, or the columns of a :class:`DataFrame` can be directly backed by a :external+pyarrow:py:class:`pyarrow.ChunkedArray`
+which is similar to a NumPy array. To construct these from the main pandas data structures, you can pass in a string of the type followed by
+``[pyarrow]``, e.g. ``"int64[pyarrow]""`` into the ``dtype`` parameter
+
+.. ipython:: python
+
+   ser = pd.Series([-1.5, 0.2, None], dtype="float32[pyarrow]")
+   ser
+
+   idx = pd.Index([True, None], dtype="bool[pyarrow]")
+   idx
+
+   df = pd.DataFrame([[1, 2], [3, 4]], dtype="uint64[pyarrow]")
+   df
+
+For PyArrow types that accept parameters, you can pass in a PyArrow type with those parameters
+into :class:`ArrowDtype` to use in the ``dtype`` parameter.
+
+.. ipython:: python
+
+   import pyarrow as pa
+   list_str_type = pa.list_(pa.string())
+   ser = pd.Series([["hello"], ["there"]], dtype=pd.ArrowDtype(list_str_type))
+   ser
+
+   from datetime import time
+   idx = pd.Index([time(12, 30), None], dtype=pd.ArrowDtype(pa.time64("us")))
+   idx
+
+   from decimal import Decimal
+   decimal_type = pd.ArrowDtype(pa.decimal128(3, scale=2))
+   data = [[Decimal("3.19"), None], [None, Decimal("-1.23")]]
+   df = pd.DataFrame(data, dtype=decimal_type)
+   df
+
+If you already have an :external+pyarrow:py:class:`pyarrow.Array` or :external+pyarrow:py:class:`pyarrow.ChunkedArray`,
+you can pass it into :class:`.arrays.ArrowExtensionArray` to construct the associated :class:`Series`, :class:`Index`
+or :class:`DataFrame` object.
+
+.. ipython:: python
+
+   pa_array = pa.array([{"1": "2"}, {"10": "20"}, None])
+   ser = pd.Series(pd.arrays.ArrowExtensionArray(pa_array))
+   ser
+
+To retrieve a pyarrow :external+pyarrow:py:class:`pyarrow.ChunkedArray` from a :class:`Series` or :class:`Index`, you can call
+the pyarrow array constructor on the :class:`Series` or :class:`Index`.
+
+.. ipython:: python
+
+   ser = pd.Series([1, 2, None], dtype="uint8[pyarrow]")
+   pa.array(ser)
+
+   idx = pd.Index(ser)
+   pa.array(idx)
+
+Operations
+----------
+
+PyArrow data structure integration is implemented through pandas' :class:`~pandas.api.extensions.ExtensionArray` :ref:`interface <extending.extension-types>`;
+therefore, supported functionality exists where this interface is integrated within the pandas API. Additionally, this functionality
+is accelerated with PyArrow `compute functions <https://arrow.apache.org/docs/python/api/compute.html>`__ where available. This includes:
+
+* Numeric aggregations
+* Numeric arithmetic
+* Numeric rounding
+* Logical and comparison functions
+* String functionality
+* Datetime functionality
+
+The following are just some examples of operations that are accelerated by native PyArrow compute functions.
+
+.. ipython:: python
+
+   ser = pd.Series([-1.545, 0.211, None], dtype="float32[pyarrow]")
+   ser.mean()
+   ser + ser
+   ser > (ser + 1)
+
+   ser.dropna()
+   ser.isna()
+   ser.fillna(0)
+
+   ser_str = pd.Series(["a", "b", None], dtype="string[pyarrow]")
+   ser_str.str.startswith("a")
+
+   from datetime import datetime
+   pa_type = pd.ArrowDtype(pa.timestamp("ns"))
+   ser_dt = pd.Series([datetime(2022, 1, 1), None], dtype=pa_type)
+   ser_dt.dt.strftime("%Y-%m")
+
+I/O Reading
+-----------
+
+PyArrow also provides IO reading functionality that has been integrated into several pandas IO readers. The following
+functions provide an ``engine`` keyword that can dispatch to PyArrow to accelerate reading from an IO source.
+
+* :func:`read_csv`
+* :func:`read_json`
+* :func:`read_orc`
+* :func:`read_feather`
+
+.. ipython:: python
+
+   import io
+   data = io.StringIO("""a,b,c
+      1,2.5,True
+      3,4.5,False
+   """)
+   df = pd.read_csv(data, engine="pyarrow")
+   df
+
+By default, these functions and all other IO reader functions return NumPy-backed data. These readers can return
+PyArrow-backed data by specifying the parameter ``use_nullable_dtypes=True`` **and** the global configuration option ``"mode.dtype_backend"``
+set to ``"pyarrow"``. A reader does not need to set ``engine="pyarrow"`` to necessarily return PyArrow-backed data.
+
+.. ipython:: python
+
+    import io
+    data = io.StringIO("""a,b,c,d,e,f,g,h,i
+        1,2.5,True,a,,,,,
+        3,4.5,False,b,6,7.5,True,a,
+    """)
+    with pd.option_context("mode.dtype_backend", "pyarrow"):
+        df_pyarrow = pd.read_csv(data, use_nullable_dtypes=True)
+    df_pyarrow.dtypes
+
+To simplify specifying ``use_nullable_dtypes=True`` in several functions, you can set a global option ``nullable_dtypes``
+to ``True``. You will still need to set the global configuration option ``"mode.dtype_backend"`` to ``pyarrow``.
+
+.. ipython:: python
+
+    pd.set_option("mode.dtype_backend", "pyarrow")
+    pd.options.mode.nullable_dtypes = True
+
+Several non-IO reader functions can also use the ``"mode.dtype_backend"`` option to return PyArrow-backed data including:
+
+* :func:`to_numeric`
+* :meth:`DataFrame.convert_dtypes`
+* :meth:`Series.convert_dtypes`