DOC: Add missing API item to reference docs (pandas-dev#48455)

* DOC: Add missing API item to reference docs

* Address review

* Fix formatting

Author: mroeschke

doc/source/reference/arrays.rst

@@ -19,20 +19,21 @@ objects contained with a :class:`Index`, :class:`Series`, or
 For some data types, pandas extends NumPy's type system. String aliases for these types
 can be found at :ref:`basics.dtypes`.
 
-=================== ========================= ============================= =============================
-Kind of Data        pandas Data Type          Scalar                        Array
-=================== ========================= ============================= =============================
-TZ-aware datetime   :class:`DatetimeTZDtype`  :class:`Timestamp`            :ref:`api.arrays.datetime`
-Timedeltas          (none)                    :class:`Timedelta`            :ref:`api.arrays.timedelta`
-Period (time spans) :class:`PeriodDtype`      :class:`Period`               :ref:`api.arrays.period`
-Intervals           :class:`IntervalDtype`    :class:`Interval`             :ref:`api.arrays.interval`
-Nullable Integer    :class:`Int64Dtype`, ...  (none)                        :ref:`api.arrays.integer_na`
-Categorical         :class:`CategoricalDtype` (none)                        :ref:`api.arrays.categorical`
-Sparse              :class:`SparseDtype`      (none)                        :ref:`api.arrays.sparse`
-Strings             :class:`StringDtype`      :class:`str`                  :ref:`api.arrays.string`
-Boolean (with NA)   :class:`BooleanDtype`     :class:`bool`                 :ref:`api.arrays.bool`
-PyArrow             :class:`ArrowDtype`       Python Scalars or :class:`NA` :ref:`api.arrays.arrow`
-=================== ========================= ============================= =============================
+=================== ========================== ============================= =============================
+Kind of Data        pandas Data Type           Scalar                        Array
+=================== ========================== ============================= =============================
+TZ-aware datetime   :class:`DatetimeTZDtype`   :class:`Timestamp`            :ref:`api.arrays.datetime`
+Timedeltas          (none)                     :class:`Timedelta`            :ref:`api.arrays.timedelta`
+Period (time spans) :class:`PeriodDtype`       :class:`Period`               :ref:`api.arrays.period`
+Intervals           :class:`IntervalDtype`     :class:`Interval`             :ref:`api.arrays.interval`
+Nullable Integer    :class:`Int64Dtype`, ...   (none)                        :ref:`api.arrays.integer_na`
+Nullable Float      :class:`Float64Dtype`, ... (none)                        :ref:`api.arrays.float_na`
+Categorical         :class:`CategoricalDtype`  (none)                        :ref:`api.arrays.categorical`
+Sparse              :class:`SparseDtype`       (none)                        :ref:`api.arrays.sparse`
+Strings             :class:`StringDtype`       :class:`str`                  :ref:`api.arrays.string`
+Nullable Boolean    :class:`BooleanDtype`      :class:`bool`                 :ref:`api.arrays.bool`
+PyArrow             :class:`ArrowDtype`        Python Scalars or :class:`NA` :ref:`api.arrays.arrow`
+=================== ========================== ============================= =============================
 
 pandas and third-party libraries can extend NumPy's type system (see :ref:`extending.extension-types`).
 The top-level :meth:`array` method can be used to create a new array, which may be
@@ -91,13 +92,20 @@ with the :class:`arrays.DatetimeArray` extension array, which can hold timezone-
 or timezone-aware values.
 
 :class:`Timestamp`, a subclass of :class:`datetime.datetime`, is pandas'
-scalar type for timezone-naive or timezone-aware datetime data.
+scalar type for timezone-naive or timezone-aware datetime data. :class:`NaT`
+is the missing value for datetime data.
 
 .. autosummary::
    :toctree: api/
 
    Timestamp
 
+.. autosummary::
+   :toctree: api/
+   :template: autosummary/class_without_autosummary.rst
+
+   NaT
+
 Properties
 ~~~~~~~~~~
 .. autosummary::
@@ -208,13 +216,20 @@ Timedeltas
 ----------
 
 NumPy can natively represent timedeltas. pandas provides :class:`Timedelta`
-for symmetry with :class:`Timestamp`.
+for symmetry with :class:`Timestamp`. :class:`NaT`
+is the missing value for timedelta data.
 
 .. autosummary::
    :toctree: api/
 
    Timedelta
 
+.. autosummary::
+   :toctree: api/
+   :template: autosummary/class_without_autosummary.rst
+
+   NaT
+
 Properties
 ~~~~~~~~~~
 .. autosummary::
@@ -419,6 +434,26 @@ pandas provides this through :class:`arrays.IntegerArray`.
    UInt16Dtype
    UInt32Dtype
    UInt64Dtype
+   NA
+
+.. _api.arrays.float_na:
+
+Nullable float
+--------------
+
+.. autosummary::
+   :toctree: api/
+   :template: autosummary/class_without_autosummary.rst
+
+   arrays.FloatingArray
+
+.. autosummary::
+   :toctree: api/
+   :template: autosummary/class_without_autosummary.rst
+
+   Float32Dtype
+   Float64Dtype
+   NA
 
 .. _api.arrays.categorical:
 
@@ -555,6 +590,7 @@ with a bool :class:`numpy.ndarray`.
    :template: autosummary/class_without_autosummary.rst
 
    BooleanDtype
+   NA
 
 
 .. Dtype attributes which are manually listed in their docstrings: including

doc/source/reference/general_functions.rst

@@ -26,6 +26,7 @@ Data manipulations
    from_dummies
    factorize
    unique
+   lreshape
    wide_to_long
 
 Top-level missing data

doc/source/reference/groupby.rst

@@ -27,6 +27,13 @@ Indexing, iteration
 
    Grouper
 
+Function application helper
+---------------------------
+.. autosummary::
+   :toctree: api/
+
+   NamedAgg
+
 .. currentmodule:: pandas.core.groupby
 
 Function application

doc/source/reference/options.rst

@@ -19,3 +19,10 @@ Working with options
    get_option
    set_option
    option_context
+
+Numeric formatting
+------------------
+.. autosummary::
+   :toctree: api/
+
+   set_eng_float_format

pandas/core/groupby/generic.py

@@ -115,6 +115,31 @@
 
 
 class NamedAgg(NamedTuple):
+    """
+    Helper for column specific aggregation with control over output column names.
+
+    Subclass of typing.NamedTuple.
+
+    Parameters
+    ----------
+    column : Hashable
+        Column label in the DataFrame to apply aggfunc.
+    aggfunc : function or str
+        Function to apply to the provided column. If string, the name of a built-in
+        pandas function.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({"key": [1, 1, 2], "a": [-1, 0, 1], 1: [10, 11, 12]})
+    >>> agg_a = pd.NamedAgg(column="a", aggfunc="min")
+    >>> agg_1 = pd.NamedAgg(column=1, aggfunc=np.mean)
+    >>> df.groupby("key").agg(result_a=agg_a, result_1=agg_1)
+         result_a  result_1
+    key
+    1          -1      10.5
+    2           1      12.0
+    """
+
     column: Hashable
     aggfunc: AggScalar
 

pandas/io/formats/format.py

@@ -2116,11 +2116,56 @@ def __call__(self, num: float) -> str:
 
 def set_eng_float_format(accuracy: int = 3, use_eng_prefix: bool = False) -> None:
     """
-    Alter default behavior on how float is formatted in DataFrame.
-    Format float in engineering format. By accuracy, we mean the number of
-    decimal digits after the floating point.
+    Format float representation in DataFrame with SI notation.
 
-    See also EngFormatter.
+    Parameters
+    ----------
+    accuracy : int, default 3
+        Number of decimal digits after the floating point.
+    use_eng_prefix : bool, default False
+        Whether to represent a value with SI prefixes.
+
+    Returns
+    -------
+    None
+
+    Examples
+    --------
+    >>> df = pd.DataFrame([1e-9, 1e-3, 1, 1e3, 1e6])
+    >>> df
+                  0
+    0  1.000000e-09
+    1  1.000000e-03
+    2  1.000000e+00
+    3  1.000000e+03
+    4  1.000000e+06
+
+    >>> pd.set_eng_float_format(accuracy=1)
+    >>> df
+             0
+    0  1.0E-09
+    1  1.0E-03
+    2  1.0E+00
+    3  1.0E+03
+    4  1.0E+06
+
+    >>> pd.set_eng_float_format(use_eng_prefix=True)
+    >>> df
+            0
+    0  1.000n
+    1  1.000m
+    2   1.000
+    3  1.000k
+    4  1.000M
+
+    >>> pd.set_eng_float_format(accuracy=1, use_eng_prefix=True)
+    >>> df
+          0
+    0  1.0n
+    1  1.0m
+    2   1.0
+    3  1.0k
+    4  1.0M
     """
     set_option("display.float_format", EngFormatter(accuracy, use_eng_prefix))