From 14d49735d4af03854f153b0913185479546e7ef2 Mon Sep 17 00:00:00 2001
From: tomytp <thomazrebellomiranda@gmail.com>
Date: Tue, 28 May 2024 17:41:47 -0300
Subject: [PATCH 01/13] Named agg allow kwargs

---
 pandas/core/groupby/generic.py | 31 ++++++++++++++++++++++++++++---
 1 file changed, 28 insertions(+), 3 deletions(-)

diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index a20577e8d3df9..8dadd74415f1c 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -108,7 +108,12 @@
 ScalarResult = TypeVar("ScalarResult")
 
 
-class NamedAgg(NamedTuple):
+class _BaseNamedAgg(NamedTuple):
+    column: Hashable
+    aggfunc: AggScalar
+
+
+def NamedAgg(column: Hashable, aggfunc: AggScalar, *args, **kwargs):
     """
     Helper for column specific aggregation with control over output column names.
 
@@ -134,8 +139,28 @@ class NamedAgg(NamedTuple):
     2           1      12.0
     """
 
-    column: Hashable
-    aggfunc: AggScalar
+    class NamedAggWrapper(_BaseNamedAgg):
+        def __new__(cls, _column, _aggfunc, *_args, **_kwargs):
+            original_aggfunc = _aggfunc
+            if not isinstance(_aggfunc, str):
+                _aggfunc = cls._get_wrapped_aggfunc(_aggfunc, *_args, **_kwargs)
+
+            self = _BaseNamedAgg.__new__(_column, _aggfunc)
+            self.original_aggfunc = original_aggfunc
+            return self
+
+        @staticmethod
+        def _get_wrapped_aggfunc(function, *initial_args, **initial_kwargs):
+            def wrapped_aggfunc(*new_args, **new_kwargs):
+                final_args = new_args + initial_args
+                final_kwargs = {**initial_kwargs, **new_kwargs}
+                return function(*final_args, **final_kwargs)
+            return wrapped_aggfunc
+
+        def __repr__(self):
+            return f"NamedAgg(column='{self.column}', aggfunc={self.original_aggfunc})"
+
+    return NamedAggWrapper(column, aggfunc, *args, **kwargs)
 
 
 class SeriesGroupBy(GroupBy[Series]):

From 8ba87acb9ff064bc7cebdaffe9aaf39304817b49 Mon Sep 17 00:00:00 2001
From: tomytp <thomazrebellomiranda@gmail.com>
Date: Tue, 28 May 2024 17:54:02 -0300
Subject: [PATCH 02/13] fixed NamedAgg initialization

---
 pandas/core/groupby/generic.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 8dadd74415f1c..bb01ed088ff14 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -145,7 +145,7 @@ def __new__(cls, _column, _aggfunc, *_args, **_kwargs):
             if not isinstance(_aggfunc, str):
                 _aggfunc = cls._get_wrapped_aggfunc(_aggfunc, *_args, **_kwargs)
 
-            self = _BaseNamedAgg.__new__(_column, _aggfunc)
+            self = _BaseNamedAgg.__new__(cls, _column, _aggfunc)
             self.original_aggfunc = original_aggfunc
             return self
 

From 816aa1768a49bd492f1b53c6411868a423f344ad Mon Sep 17 00:00:00 2001
From: tomytp <thomazrebellomiranda@gmail.com>
Date: Tue, 28 May 2024 19:24:15 -0300
Subject: [PATCH 03/13] Included NammedAgg changes in whatsnew

---
 doc/source/whatsnew/v3.0.0.rst                |  3 +-
 .../tests/groupby/aggregate/test_aggregate.py | 28 +++++++++++++++++++
 2 files changed, 30 insertions(+), 1 deletion(-)

diff --git a/doc/source/whatsnew/v3.0.0.rst b/doc/source/whatsnew/v3.0.0.rst
index 6a6abcf2d48fe..b797e8a92dd40 100644
--- a/doc/source/whatsnew/v3.0.0.rst
+++ b/doc/source/whatsnew/v3.0.0.rst
@@ -31,6 +31,7 @@ Other enhancements
 - :class:`pandas.api.typing.FrozenList` is available for typing the outputs of :attr:`MultiIndex.names`, :attr:`MultiIndex.codes` and :attr:`MultiIndex.levels` (:issue:`58237`)
 - :class:`pandas.api.typing.SASReader` is available for typing the output of :func:`read_sas` (:issue:`55689`)
 - :func:`DataFrame.to_excel` now raises an ``UserWarning`` when the character count in a cell exceeds Excel's limitation of 32767 characters (:issue:`56954`)
+- :func:`pandas.NamedAgg` now forwards any *args and **kwargs to calls of ``aggfunc`` (:issue:`58283`)
 - :func:`read_stata` now returns ``datetime64`` resolutions better matching those natively stored in the stata format (:issue:`55642`)
 - :meth:`Styler.set_tooltips` provides alternative method to storing tooltips by using title attribute of td elements. (:issue:`56981`)
 - Allow dictionaries to be passed to :meth:`pandas.Series.str.replace` via ``pat`` parameter (:issue:`51748`)
@@ -43,7 +44,7 @@ Other enhancements
 - :meth:`DataFrame.cummin`, :meth:`DataFrame.cummax`, :meth:`DataFrame.cumprod` and :meth:`DataFrame.cumsum` methods now have a ``numeric_only`` parameter (:issue:`53072`)
 - :meth:`DataFrame.fillna` and :meth:`Series.fillna` can now accept ``value=None``; for non-object dtype the corresponding NA value will be used (:issue:`57723`)
 - :meth:`Series.cummin` and :meth:`Series.cummax` now supports :class:`CategoricalDtype` (:issue:`52335`)
-- :meth:`Series.plot` now correctly handle the ``ylabel`` parameter for pie charts, allowing for explicit control over the y-axis label (:issue:`58239`)
+- :meth:`Series.plot` now correctly handle the ``ylabel`` parameter for pie chgit aarts, allowing for explicit control over the y-axis label (:issue:`58239`)
 - Support reading Stata 110-format (Stata 7) dta files (:issue:`47176`)
 -
 
diff --git a/pandas/tests/groupby/aggregate/test_aggregate.py b/pandas/tests/groupby/aggregate/test_aggregate.py
index 3362d6209af6d..39357fa2b9547 100644
--- a/pandas/tests/groupby/aggregate/test_aggregate.py
+++ b/pandas/tests/groupby/aggregate/test_aggregate.py
@@ -827,6 +827,34 @@ def test_agg_namedtuple(self):
         expected = df.groupby("A").agg(b=("B", "sum"), c=("B", "count"))
         tm.assert_frame_equal(result, expected)
 
+    def test_single_named_agg_with_args_and_kwargs(self):
+        df = DataFrame({"A": [0, 1, 2, 3], "B": [1, 2, 3, 4]})
+
+        def n_between(ser, low, high):
+            return ser.between(low, high).sum()
+
+        result = df.groupby("A").agg(n_between=pd.NamedAgg("B", n_between, 0, high=2))
+        expected = df.groupby("A").agg(n_between=("B", lambda x: x.between(0, 2).sum()))
+        tm.assert_frame_equal(result, expected)
+
+    def test_multiple_named_agg_with_args_and_kwargs(self):
+        df = DataFrame({"A": [0, 1, 2, 3], "B": [1, 2, 3, 4]})
+
+        def n_between(ser, low, high):
+            return ser.between(low, high).sum()
+
+        result = df.groupby("A").agg(
+            n_between01=pd.NamedAgg("B", n_between, 0, 1),
+            n_between13=pd.NamedAgg("B", n_between, 1, 3),
+            n_between02=pd.NamedAgg("B", n_between, 0, 2),
+        )
+        expected = df.groupby("A").agg(
+            n_between01=("B", lambda x: x.between(0, 1).sum()),
+            n_between13=("B", lambda x: x.between(0, 3).sum()),
+            n_between02=("B", lambda x: x.between(0, 2).sum()),
+        )
+        tm.assert_frame_equal(result, expected)
+
     def test_mangled(self):
         df = DataFrame({"A": [0, 1], "B": [1, 2], "C": [3, 4]})
         result = df.groupby("A").agg(b=("B", lambda x: 0), c=("C", lambda x: 1))

From ed6b6509616cb3bf6321b9899c488bfddf41c552 Mon Sep 17 00:00:00 2001
From: tomytp <thomazrebellomiranda@gmail.com>
Date: Tue, 28 May 2024 19:40:40 -0300
Subject: [PATCH 04/13] simplified NamedAgg class

---
 doc/source/user_guide/style.ipynb | 352 +++++++++++++++---------------
 doc/source/whatsnew/v3.0.0.rst    |   2 +-
 pandas/core/groupby/generic.py    |  39 ++--
 3 files changed, 197 insertions(+), 196 deletions(-)

diff --git a/doc/source/user_guide/style.ipynb b/doc/source/user_guide/style.ipynb
index 04ba3e5be8ff7..a6aa3e8847850 100644
--- a/doc/source/user_guide/style.ipynb
+++ b/doc/source/user_guide/style.ipynb
@@ -44,12 +44,12 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
-   "outputs": [],
    "source": [
     "import matplotlib.pyplot\n",
     "# We have this here to trigger matplotlib's font cache stuff.\n",
     "# This cell is hidden from the output"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -74,7 +74,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -89,7 +88,8 @@
     "  .format(precision=3, thousands=\".\", decimal=\",\") \\\n",
     "  .format_index(str.upper, axis=1) \\\n",
     "  .relabel_index([\"row 1\", \"row 2\"], axis=0)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -102,7 +102,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "weather_df = pd.DataFrame(np.random.rand(10,2)*5, \n",
     "                          index=pd.date_range(start=\"2021-01-01\", periods=10),\n",
@@ -123,16 +122,17 @@
     "    return styler\n",
     "\n",
     "weather_df"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "weather_df.loc[\"2021-01-04\":\"2021-01-08\"].style.pipe(make_pretty)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -155,13 +155,13 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df = pd.DataFrame(np.random.randn(5, 5))\n",
     "df.style \\\n",
     "  .hide(subset=[0, 2, 4], axis=0) \\\n",
     "  .hide(subset=[0, 2, 4], axis=1)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -174,13 +174,13 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "show = [0, 2, 4]\n",
     "df.style \\\n",
     "  .hide([row for row in df.index if row not in show], axis=0) \\\n",
     "  .hide([col for col in df.columns if col not in show], axis=1)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -197,13 +197,13 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "summary_styler = df.agg([\"sum\", \"mean\"]).style \\\n",
     "                   .format(precision=3) \\\n",
     "                   .relabel_index([\"Sum\", \"Average\"])\n",
     "df.style.format(precision=1).concat(summary_styler)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -225,13 +225,13 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]], \n",
     "                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'), \n",
     "                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))\n",
     "df.style"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -239,7 +239,6 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
-   "outputs": [],
    "source": [
     "# Hidden cell to just create the below example: code is covered throughout the guide.\n",
     "s = df.style\\\n",
@@ -299,16 +298,17 @@
     "    'visibility: hidden; position: absolute; z-index: 1; border: 1px solid #000066;'\n",
     "    'background-color: white; color: #000066; font-size: 0.8em;' \n",
     "    'transform: translate(0px, -24px); padding: 0.6em; border-radius: 0.5em;')\n"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "s"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -323,11 +323,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "s = df.style.format('{:.0f}').hide([('Random', 'Tumour'), ('Random', 'Non-Tumour')], axis=\"columns\")\n",
     "s"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -335,11 +335,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
-   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s.set_uuid('after_hide')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -392,7 +392,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "cell_hover = {  # for row hover use <tr> instead of <td>\n",
     "    'selector': 'td:hover',\n",
@@ -407,7 +406,8 @@
     "    'props': 'background-color: #000066; color: white;'\n",
     "}\n",
     "s.set_table_styles([cell_hover, index_names, headers])"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -415,11 +415,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
-   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s.set_uuid('after_tab_styles1')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -432,14 +432,14 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "s.set_table_styles([\n",
     "    {'selector': 'th.col_heading', 'props': 'text-align: center;'},\n",
     "    {'selector': 'th.col_heading.level0', 'props': 'font-size: 1.5em;'},\n",
     "    {'selector': 'td', 'props': 'text-align: center; font-weight: bold;'},\n",
     "], overwrite=False)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -447,11 +447,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
-   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s.set_uuid('after_tab_styles2')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -466,13 +466,13 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "s.set_table_styles({\n",
     "    ('Regression', 'Tumour'): [{'selector': 'th', 'props': 'border-left: 1px solid white'},\n",
     "                               {'selector': 'td', 'props': 'border-left: 1px solid #000066'}]\n",
     "}, overwrite=False, axis=0)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -480,11 +480,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
-   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s.set_uuid('xyz01')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -505,11 +505,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "out = s.set_table_attributes('class=\"my-table-cls\"').to_html()\n",
     "print(out[out.find('<table'):][:109])"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -529,7 +529,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "s.set_table_styles([  # create internal CSS classes\n",
     "    {'selector': '.true', 'props': 'background-color: #e6ffe6;'},\n",
@@ -540,7 +539,8 @@
     "                          index=df.index, \n",
     "                          columns=df.columns[:4])\n",
     "s.set_td_classes(cell_color)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -548,11 +548,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
-   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s.set_uuid('after_classes')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -577,12 +577,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "np.random.seed(0)\n",
     "df2 = pd.DataFrame(np.random.randn(10,4), columns=['A','B','C','D'])\n",
     "df2.style"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -595,14 +595,14 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "def style_negative(v, props=''):\n",
     "    return props if v < 0 else None\n",
     "s2 = df2.style.map(style_negative, props='color:red;')\\\n",
     "              .map(lambda v: 'opacity: 20%;' if (v < 0.3) and (v > -0.3) else None)\n",
     "s2"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -610,11 +610,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
-   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s2.set_uuid('after_applymap')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -627,12 +627,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "def highlight_max(s, props=''):\n",
     "    return np.where(s == np.nanmax(s.values), props, '')\n",
     "s2.apply(highlight_max, props='color:white;background-color:darkblue', axis=0)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -640,11 +640,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
-   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s2.set_uuid('after_apply')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -657,11 +657,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "s2.apply(highlight_max, props='color:white;background-color:pink;', axis=1)\\\n",
     "  .apply(highlight_max, props='color:white;background-color:purple', axis=None)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -669,11 +669,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
-   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s2.set_uuid('after_apply_again')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -711,11 +711,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "s2.map_index(lambda v: \"color:pink;\" if v>4 else \"color:darkblue;\", axis=0)\n",
     "s2.apply_index(lambda s: np.where(s.isin([\"A\", \"B\"]), \"color:pink;\", \"color:darkblue;\"), axis=1)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -732,14 +732,14 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "s.set_caption(\"Confusion matrix for multiple cancer prediction models.\")\\\n",
     " .set_table_styles([{\n",
     "     'selector': 'caption',\n",
     "     'props': 'caption-side: bottom; font-size:1.25em;'\n",
     " }], overwrite=False)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -747,11 +747,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
-   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s.set_uuid('after_caption')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -766,7 +766,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "tt = pd.DataFrame([['This model has a very strong true positive rate', \n",
     "                    \"This model's total number of false negatives is too high\"]], \n",
@@ -774,7 +773,8 @@
     "s.set_tooltips(tt, props='visibility: hidden; position: absolute; z-index: 1; border: 1px solid #000066;'\n",
     "                         'background-color: white; color: #000066; font-size: 0.8em;' \n",
     "                         'transform: translate(0px, -24px); padding: 0.6em; border-radius: 0.5em;')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -782,11 +782,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
-   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s.set_uuid('after_tooltips')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -799,7 +799,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "s.set_table_styles([  # create internal CSS classes\n",
     "    {'selector': '.border-red', 'props': 'border: 2px dashed red;'},\n",
@@ -810,7 +809,8 @@
     "                          index=df.index, \n",
     "                          columns=df.columns[:4])\n",
     "s.set_td_classes(cell_color + cell_border)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -818,11 +818,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
-   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s.set_uuid('after_borders')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -845,13 +845,13 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df3 = pd.DataFrame(np.random.randn(4,4), \n",
     "                   pd.MultiIndex.from_product([['A', 'B'], ['r1', 'r2']]),\n",
     "                   columns=['c1','c2','c3','c4'])\n",
     "df3"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -864,12 +864,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "slice_ = ['c3', 'c4']\n",
     "df3.style.apply(highlight_max, props='color:red;', axis=0, subset=slice_)\\\n",
     "         .set_properties(**{'background-color': '#ffffb3'}, subset=slice_)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -882,13 +882,13 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "idx = pd.IndexSlice\n",
     "slice_ = idx[idx[:,'r1'], idx['c2':'c4']]\n",
     "df3.style.apply(highlight_max, props='color:red;', axis=0, subset=slice_)\\\n",
     "         .set_properties(**{'background-color': '#ffffb3'}, subset=slice_)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -901,12 +901,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "slice_ = idx[idx[:,'r2'], :]\n",
     "df3.style.apply(highlight_max, props='color:red;', axis=1, subset=slice_)\\\n",
     "         .set_properties(**{'background-color': '#ffffb3'}, subset=slice_)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -921,12 +921,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "slice_ = idx[idx[(df3['c1'] + df3['c3']) < -2.0], ['c2', 'c4']]\n",
     "df3.style.apply(highlight_max, props='color:red;', axis=1, subset=slice_)\\\n",
     "         .set_properties(**{'background-color': '#ffffb3'}, subset=slice_)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -979,11 +979,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df4 = pd.DataFrame([[1,2],[3,4]])\n",
     "s4 = df4.style"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1000,11 +1000,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "from pandas.io.formats.style import Styler\n",
     "s4 = Styler(df4, uuid_len=0, cell_ids=False)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1030,11 +1030,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "props = 'font-family: \"Times New Roman\", Times, serif; color: #e83e8c; font-size:1.3em;'\n",
     "df4.style.map(lambda x: props, subset=[1])"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1051,10 +1051,10 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df4.style.set_table_styles([{'selector': 'td.col1', 'props': props}])"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1080,12 +1080,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df2.style.apply(highlight_max, props='color:white;background-color:darkblue;', axis=0)\\\n",
     "         .apply(highlight_max, props='color:white;background-color:pink;', axis=1)\\\n",
     "         .apply(highlight_max, props='color:white;background-color:purple', axis=None)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1102,7 +1102,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "build = lambda x: pd.DataFrame(x, index=df2.index, columns=df2.columns)\n",
     "cls1 = build(df2.apply(highlight_max, props='cls-1 ', axis=0))\n",
@@ -1113,7 +1112,8 @@
     "    {'selector': '.cls-2', 'props': 'color:white;background-color:pink;'},\n",
     "    {'selector': '.cls-3', 'props': 'color:white;background-color:purple;'}\n",
     "]).set_td_classes(cls1 + cls2 + cls3)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1137,7 +1137,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "my_css = {\n",
     "    \"row_heading\": \"\",\n",
@@ -1157,16 +1156,17 @@
     "                       {'selector': '.l0', 'props': 'color:blue;'}],\n",
     "                      css_class_names=my_css)\n",
     "print(html.to_html())"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "html"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1211,12 +1211,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df2.iloc[0,2] = np.nan\n",
     "df2.iloc[4,3] = np.nan\n",
     "df2.loc[:4].style.highlight_null(color='yellow')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1229,10 +1229,10 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df2.loc[:4].style.highlight_max(axis=1, props='color:white; font-weight:bold; background-color:darkblue;')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1246,11 +1246,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "left = pd.Series([1.0, 0.0, 1.0], index=[\"A\", \"B\", \"D\"])\n",
     "df2.loc[:4].style.highlight_between(left=left, right=1.5, axis=1, props='color:white; background-color:purple;')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1264,10 +1264,10 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df2.loc[:4].style.highlight_quantile(q_left=0.85, axis=None, color='yellow')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1287,22 +1287,22 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "import seaborn as sns\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
     "\n",
     "df2.style.background_gradient(cmap=cm)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df2.style.text_gradient(cmap=cm)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1327,12 +1327,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df2.loc[:4].style.set_properties(**{'background-color': 'black',\n",
     "                           'color': 'lawngreen',\n",
     "                           'border-color': 'white'})"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1352,10 +1352,10 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df2.style.bar(subset=['A', 'B'], color='#d65f5f')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1370,13 +1370,13 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df2.style.format('{:.3f}', na_rep=\"\")\\\n",
     "         .bar(align=0, vmin=-2.5, vmax=2.5, cmap=\"bwr\", height=50,\n",
     "              width=60, props=\"width: 120px; border-right: 1px solid black;\")\\\n",
     "         .text_gradient(cmap=\"bwr\", vmin=-2.5, vmax=2.5)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1391,7 +1391,6 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
-   "outputs": [],
    "source": [
     "# Hide the construction of the display chart from the user\n",
     "import pandas as pd\n",
@@ -1432,16 +1431,17 @@
     "head+= \"\"\"\n",
     "</tbody>\n",
     "</table>\"\"\""
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "HTML(head)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1461,7 +1461,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "style1 = df2.style\\\n",
     "            .map(style_negative, props='color:red;')\\\n",
@@ -1469,18 +1468,19 @@
     "            .set_table_styles([{\"selector\": \"th\", \"props\": \"color: blue;\"}])\\\n",
     "            .hide(axis=\"index\")\n",
     "style1"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "style2 = df3.style\n",
     "style2.use(style1.export())\n",
     "style2"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1523,7 +1523,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "from ipywidgets import widgets\n",
     "@widgets.interact\n",
@@ -1532,7 +1531,8 @@
     "        cmap=sns.palettes.diverging_palette(h_neg=h_neg, h_pos=h_pos, s=s, l=l,\n",
     "                                            as_cmap=True)\n",
     "    )"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1545,7 +1545,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "def magnify():\n",
     "    return [dict(selector=\"th\",\n",
@@ -1558,13 +1557,13 @@
     "                 props=[('max-width', '200px'),\n",
     "                        ('font-size', '12pt')])\n",
     "]"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "np.random.seed(25)\n",
     "cmap = cmap=sns.diverging_palette(5, 250, as_cmap=True)\n",
@@ -1575,7 +1574,8 @@
     "    .set_caption(\"Hover to magnify\")\\\n",
     "    .format(precision=2)\\\n",
     "    .set_table_styles(magnify())"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1592,11 +1592,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "bigdf = pd.DataFrame(np.random.randn(16, 100))\n",
     "bigdf.style.set_sticky(axis=\"index\")"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1609,11 +1609,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "bigdf.index = pd.MultiIndex.from_product([[\"A\",\"B\"],[0,1],[0,1,2,3]])\n",
     "bigdf.style.set_sticky(axis=\"index\", pixel_size=18, levels=[1,2])"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1630,29 +1630,29 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df4 = pd.DataFrame([['<div></div>', '\"&other\"', '<span></span>']])\n",
     "df4.style"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df4.style.format(escape=\"html\")"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df4.style.format('<a href=\"https://pandas.pydata.org\" target=\"_blank\">{}</a>', escape=\"html\")"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1691,13 +1691,13 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df2.style.\\\n",
     "    map(style_negative, props='color:red;').\\\n",
     "    highlight_max(axis=0).\\\n",
     "    to_excel('styled.xlsx', engine='openpyxl')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1763,10 +1763,10 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "print(pd.DataFrame([[1,2],[3,4]], index=['i1', 'i2'], columns=['c1', 'c2']).style.to_html())"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1781,22 +1781,22 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df4 = pd.DataFrame([['text']])\n",
     "df4.style.map(lambda x: 'color:green;')\\\n",
     "         .map(lambda x: 'color:red;')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df4.style.map(lambda x: 'color:red;')\\\n",
     "         .map(lambda x: 'color:green;')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1818,12 +1818,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df4.style.set_uuid('a_')\\\n",
     "         .set_table_styles([{'selector': 'td', 'props': 'color:red;'}])\\\n",
     "         .map(lambda x: 'color:green;')"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1836,14 +1836,14 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df4.style.set_uuid('b_')\\\n",
     "         .set_table_styles([{'selector': 'td', 'props': 'color:red;'},\n",
     "                            {'selector': '.cls-1', 'props': 'color:blue;'}])\\\n",
     "         .map(lambda x: 'color:green;')\\\n",
     "         .set_td_classes(pd.DataFrame([['cls-1']]))"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1856,7 +1856,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df4.style.set_uuid('c_')\\\n",
     "         .set_table_styles([{'selector': 'td', 'props': 'color:red;'},\n",
@@ -1864,7 +1863,8 @@
     "                            {'selector': 'td.data', 'props': 'color:yellow;'}])\\\n",
     "         .map(lambda x: 'color:green;')\\\n",
     "         .set_td_classes(pd.DataFrame([['cls-1']]))"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1879,7 +1879,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "df4.style.set_uuid('d_')\\\n",
     "         .set_table_styles([{'selector': 'td', 'props': 'color:red;'},\n",
@@ -1887,7 +1886,8 @@
     "                            {'selector': 'td.data', 'props': 'color:yellow;'}])\\\n",
     "         .map(lambda x: 'color:green !important;')\\\n",
     "         .set_td_classes(pd.DataFrame([['cls-1']]))"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1920,12 +1920,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "from jinja2 import Environment, ChoiceLoader, FileSystemLoader\n",
     "from IPython.display import HTML\n",
     "from pandas.io.formats.style import Styler"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1938,11 +1938,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "with open(\"templates/myhtml.tpl\") as f:\n",
     "    print(f.read())"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1956,7 +1956,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "class MyStyler(Styler):\n",
     "    env = Environment(\n",
@@ -1966,7 +1965,8 @@
     "        ])\n",
     "    )\n",
     "    template_html_table = env.get_template(\"myhtml.tpl\")"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1983,10 +1983,10 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "MyStyler(df3)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -1999,10 +1999,10 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "HTML(MyStyler(df3).to_html(table_title=\"Extending Example\"))"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -2015,11 +2015,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "EasyStyler = Styler.from_custom_template(\"templates\", \"myhtml.tpl\")\n",
     "HTML(EasyStyler(df3).to_html(table_title=\"Another Title\"))"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -2043,20 +2043,20 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
-   "outputs": [],
    "source": [
     "with open(\"templates/html_style_structure.html\") as f:\n",
     "    style_structure = f.read()"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "HTML(style_structure)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -2071,20 +2071,20 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
-   "outputs": [],
    "source": [
     "with open(\"templates/html_table_structure.html\") as f:\n",
     "    table_structure = f.read()"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "HTML(table_structure)"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -2099,7 +2099,6 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
-   "outputs": [],
    "source": [
     "# # Hack to get the same style in the notebook as the\n",
     "# # main site. This is hidden in the docs.\n",
@@ -2108,7 +2107,8 @@
     "#     css = f.read()\n",
     "    \n",
     "# HTML('<style>{}</style>'.format(css))"
-   ]
+   ],
+   "outputs": []
   }
  ],
  "metadata": {
diff --git a/doc/source/whatsnew/v3.0.0.rst b/doc/source/whatsnew/v3.0.0.rst
index b797e8a92dd40..405c8ec13a4e1 100644
--- a/doc/source/whatsnew/v3.0.0.rst
+++ b/doc/source/whatsnew/v3.0.0.rst
@@ -28,10 +28,10 @@ enhancement2
 
 Other enhancements
 ^^^^^^^^^^^^^^^^^^
+- :class:`pandas.NamedAgg` now forwards any *args and **kwargs to calls of ``aggfunc`` (:issue:`58283`)
 - :class:`pandas.api.typing.FrozenList` is available for typing the outputs of :attr:`MultiIndex.names`, :attr:`MultiIndex.codes` and :attr:`MultiIndex.levels` (:issue:`58237`)
 - :class:`pandas.api.typing.SASReader` is available for typing the output of :func:`read_sas` (:issue:`55689`)
 - :func:`DataFrame.to_excel` now raises an ``UserWarning`` when the character count in a cell exceeds Excel's limitation of 32767 characters (:issue:`56954`)
-- :func:`pandas.NamedAgg` now forwards any *args and **kwargs to calls of ``aggfunc`` (:issue:`58283`)
 - :func:`read_stata` now returns ``datetime64`` resolutions better matching those natively stored in the stata format (:issue:`55642`)
 - :meth:`Styler.set_tooltips` provides alternative method to storing tooltips by using title attribute of td elements. (:issue:`56981`)
 - Allow dictionaries to be passed to :meth:`pandas.Series.str.replace` via ``pat`` parameter (:issue:`51748`)
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index bb01ed088ff14..624f9bf42ddd5 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -113,7 +113,7 @@ class _BaseNamedAgg(NamedTuple):
     aggfunc: AggScalar
 
 
-def NamedAgg(column: Hashable, aggfunc: AggScalar, *args, **kwargs):
+class NamedAgg(_BaseNamedAgg):
     """
     Helper for column specific aggregation with control over output column names.
 
@@ -126,6 +126,9 @@ def NamedAgg(column: Hashable, aggfunc: AggScalar, *args, **kwargs):
     aggfunc : function or str
         Function to apply to the provided column. If string, the name of a built-in
         pandas function.
+    *args : args passed to aggfunc
+    **kwargs : kwargs passed to aggfunc
+
 
     Examples
     --------
@@ -139,28 +142,26 @@ def NamedAgg(column: Hashable, aggfunc: AggScalar, *args, **kwargs):
     2           1      12.0
     """
 
-    class NamedAggWrapper(_BaseNamedAgg):
-        def __new__(cls, _column, _aggfunc, *_args, **_kwargs):
-            original_aggfunc = _aggfunc
-            if not isinstance(_aggfunc, str):
-                _aggfunc = cls._get_wrapped_aggfunc(_aggfunc, *_args, **_kwargs)
+    def __new__(cls, column, aggfunc, *args, **kwargs):
+        original_aggfunc = aggfunc
+        if not isinstance(aggfunc, str):
+            aggfunc = cls._get_wrapped_aggfunc(aggfunc, *args, **kwargs)
 
-            self = _BaseNamedAgg.__new__(cls, _column, _aggfunc)
-            self.original_aggfunc = original_aggfunc
-            return self
+        self = _BaseNamedAgg.__new__(cls, column, aggfunc)
+        self.original_aggfunc = original_aggfunc
+        return self
 
-        @staticmethod
-        def _get_wrapped_aggfunc(function, *initial_args, **initial_kwargs):
-            def wrapped_aggfunc(*new_args, **new_kwargs):
-                final_args = new_args + initial_args
-                final_kwargs = {**initial_kwargs, **new_kwargs}
-                return function(*final_args, **final_kwargs)
-            return wrapped_aggfunc
+    @staticmethod
+    def _get_wrapped_aggfunc(function, *initial_args, **initial_kwargs):
+        def wrapped_aggfunc(*new_args, **new_kwargs):
+            final_args = new_args + initial_args
+            final_kwargs = {**initial_kwargs, **new_kwargs}
+            return function(*final_args, **final_kwargs)
 
-        def __repr__(self):
-            return f"NamedAgg(column='{self.column}', aggfunc={self.original_aggfunc})"
+        return wrapped_aggfunc
 
-    return NamedAggWrapper(column, aggfunc, *args, **kwargs)
+    def __repr__(self):
+        return f"NamedAgg(column='{self.column}', aggfunc={self.original_aggfunc})"
 
 
 class SeriesGroupBy(GroupBy[Series]):

From 31611a26ab808052efa777af44c6654368906920 Mon Sep 17 00:00:00 2001
From: tomytp <thomazrebellomiranda@gmail.com>
Date: Tue, 28 May 2024 19:54:30 -0300
Subject: [PATCH 05/13] changed to callsuper

---
 pandas/core/groupby/generic.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 624f9bf42ddd5..185301a2e214d 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -147,7 +147,7 @@ def __new__(cls, column, aggfunc, *args, **kwargs):
         if not isinstance(aggfunc, str):
             aggfunc = cls._get_wrapped_aggfunc(aggfunc, *args, **kwargs)
 
-        self = _BaseNamedAgg.__new__(cls, column, aggfunc)
+        self = super(_BaseNamedAgg, cls).__new__(cls, column, aggfunc)
         self.original_aggfunc = original_aggfunc
         return self
 

From 3ecf296df6a32510a26edece31f0508500080f22 Mon Sep 17 00:00:00 2001
From: tomytp <thomazrebellomiranda@gmail.com>
Date: Tue, 28 May 2024 20:03:54 -0300
Subject: [PATCH 06/13] fix typo in whatsnew

---
 doc/source/whatsnew/v3.0.0.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/source/whatsnew/v3.0.0.rst b/doc/source/whatsnew/v3.0.0.rst
index 405c8ec13a4e1..ce1df2552f897 100644
--- a/doc/source/whatsnew/v3.0.0.rst
+++ b/doc/source/whatsnew/v3.0.0.rst
@@ -44,7 +44,7 @@ Other enhancements
 - :meth:`DataFrame.cummin`, :meth:`DataFrame.cummax`, :meth:`DataFrame.cumprod` and :meth:`DataFrame.cumsum` methods now have a ``numeric_only`` parameter (:issue:`53072`)
 - :meth:`DataFrame.fillna` and :meth:`Series.fillna` can now accept ``value=None``; for non-object dtype the corresponding NA value will be used (:issue:`57723`)
 - :meth:`Series.cummin` and :meth:`Series.cummax` now supports :class:`CategoricalDtype` (:issue:`52335`)
-- :meth:`Series.plot` now correctly handle the ``ylabel`` parameter for pie chgit aarts, allowing for explicit control over the y-axis label (:issue:`58239`)
+- :meth:`Series.plot` now correctly handle the ``ylabel`` parameter for pie charts, allowing for explicit control over the y-axis label (:issue:`58239`)
 - Support reading Stata 110-format (Stata 7) dta files (:issue:`47176`)
 -
 

From 240cc96b9ad0d774d775b1bf8482bd93d3255510 Mon Sep 17 00:00:00 2001
From: tomytp <thomazrebellomiranda@gmail.com>
Date: Tue, 28 May 2024 20:13:34 -0300
Subject: [PATCH 07/13] Fix __new__ error in NamedAgg

---
 pandas/core/groupby/generic.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 185301a2e214d..624f9bf42ddd5 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -147,7 +147,7 @@ def __new__(cls, column, aggfunc, *args, **kwargs):
         if not isinstance(aggfunc, str):
             aggfunc = cls._get_wrapped_aggfunc(aggfunc, *args, **kwargs)
 
-        self = super(_BaseNamedAgg, cls).__new__(cls, column, aggfunc)
+        self = _BaseNamedAgg.__new__(cls, column, aggfunc)
         self.original_aggfunc = original_aggfunc
         return self
 

From 7f752d54b9d25b8e93e14274631b51466a3a487d Mon Sep 17 00:00:00 2001
From: tomytp <thomazrebellomiranda@gmail.com>
Date: Tue, 28 May 2024 20:41:32 -0300
Subject: [PATCH 08/13] fix attribute error in NamedAgg

---
 pandas/core/groupby/generic.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 624f9bf42ddd5..dfd76ae49c01e 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -143,12 +143,9 @@ class NamedAgg(_BaseNamedAgg):
     """
 
     def __new__(cls, column, aggfunc, *args, **kwargs):
-        original_aggfunc = aggfunc
         if not isinstance(aggfunc, str):
             aggfunc = cls._get_wrapped_aggfunc(aggfunc, *args, **kwargs)
-
         self = _BaseNamedAgg.__new__(cls, column, aggfunc)
-        self.original_aggfunc = original_aggfunc
         return self
 
     @staticmethod
@@ -161,7 +158,7 @@ def wrapped_aggfunc(*new_args, **new_kwargs):
         return wrapped_aggfunc
 
     def __repr__(self):
-        return f"NamedAgg(column='{self.column}', aggfunc={self.original_aggfunc})"
+        return f"NamedAgg(column='{self.column}', aggfunc={self.aggfunc})"
 
 
 class SeriesGroupBy(GroupBy[Series]):

From ae11c8e715abeb482e84fd05c3337dc6531ecd0f Mon Sep 17 00:00:00 2001
From: tomytp <thomazrebellomiranda@gmail.com>
Date: Tue, 28 May 2024 21:10:49 -0300
Subject: [PATCH 09/13] fix NamedAgg docstring

---
 pandas/core/groupby/generic.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index dfd76ae49c01e..ec47c08164d4c 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -126,9 +126,10 @@ class NamedAgg(_BaseNamedAgg):
     aggfunc : function or str
         Function to apply to the provided column. If string, the name of a built-in
         pandas function.
-    *args : args passed to aggfunc
-    **kwargs : kwargs passed to aggfunc
-
+    *args : tuple, optional
+        args passed to aggfunc
+    **kwargs : dict, optional
+        kwargs passed to aggfunc
 
     Examples
     --------

From f30f13d7f80bbbb52673f2ea14c38ecace0614f1 Mon Sep 17 00:00:00 2001
From: tomytp <thomazrebellomiranda@gmail.com>
Date: Tue, 28 May 2024 21:28:13 -0300
Subject: [PATCH 10/13] fix docstring NamedAgg

---
 pandas/core/groupby/generic.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index ec47c08164d4c..3d51413ffc9b0 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -127,9 +127,9 @@ class NamedAgg(_BaseNamedAgg):
         Function to apply to the provided column. If string, the name of a built-in
         pandas function.
     *args : tuple, optional
-        args passed to aggfunc
+        Args passed to aggfunc.
     **kwargs : dict, optional
-        kwargs passed to aggfunc
+        Kwargs passed to aggfunc.
 
     Examples
     --------

From 1c0649193ef8b7b107a3c50434ad067297934bbf Mon Sep 17 00:00:00 2001
From: tomytp <thomazrebellomiranda@gmail.com>
Date: Tue, 28 May 2024 22:15:34 -0300
Subject: [PATCH 11/13] Fix whatsnew formatting

---
 doc/source/whatsnew/v3.0.0.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/source/whatsnew/v3.0.0.rst b/doc/source/whatsnew/v3.0.0.rst
index ce1df2552f897..23bce184f6948 100644
--- a/doc/source/whatsnew/v3.0.0.rst
+++ b/doc/source/whatsnew/v3.0.0.rst
@@ -28,7 +28,7 @@ enhancement2
 
 Other enhancements
 ^^^^^^^^^^^^^^^^^^
-- :class:`pandas.NamedAgg` now forwards any *args and **kwargs to calls of ``aggfunc`` (:issue:`58283`)
+- :class:`pandas.NamedAgg` now forwards any ``*args`` and ``**kwargs`` to calls of ``aggfunc`` (:issue:`58283`)
 - :class:`pandas.api.typing.FrozenList` is available for typing the outputs of :attr:`MultiIndex.names`, :attr:`MultiIndex.codes` and :attr:`MultiIndex.levels` (:issue:`58237`)
 - :class:`pandas.api.typing.SASReader` is available for typing the output of :func:`read_sas` (:issue:`55689`)
 - :func:`DataFrame.to_excel` now raises an ``UserWarning`` when the character count in a cell exceeds Excel's limitation of 32767 characters (:issue:`56954`)

From 7202de9c860c9c19b5813b6daa1332ad7cda7a8c Mon Sep 17 00:00:00 2001
From: tomytp <thomazrebellomiranda@gmail.com>
Date: Fri, 31 May 2024 15:10:30 -0300
Subject: [PATCH 12/13] rollback changes from doc

---
 doc/source/user_guide/style.ipynb | 352 +++++++++++++++---------------
 1 file changed, 176 insertions(+), 176 deletions(-)

diff --git a/doc/source/user_guide/style.ipynb b/doc/source/user_guide/style.ipynb
index a6aa3e8847850..04ba3e5be8ff7 100644
--- a/doc/source/user_guide/style.ipynb
+++ b/doc/source/user_guide/style.ipynb
@@ -44,12 +44,12 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot\n",
     "# We have this here to trigger matplotlib's font cache stuff.\n",
     "# This cell is hidden from the output"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -74,6 +74,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -88,8 +89,7 @@
     "  .format(precision=3, thousands=\".\", decimal=\",\") \\\n",
     "  .format_index(str.upper, axis=1) \\\n",
     "  .relabel_index([\"row 1\", \"row 2\"], axis=0)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -102,6 +102,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "weather_df = pd.DataFrame(np.random.rand(10,2)*5, \n",
     "                          index=pd.date_range(start=\"2021-01-01\", periods=10),\n",
@@ -122,17 +123,16 @@
     "    return styler\n",
     "\n",
     "weather_df"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "weather_df.loc[\"2021-01-04\":\"2021-01-08\"].style.pipe(make_pretty)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -155,13 +155,13 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df = pd.DataFrame(np.random.randn(5, 5))\n",
     "df.style \\\n",
     "  .hide(subset=[0, 2, 4], axis=0) \\\n",
     "  .hide(subset=[0, 2, 4], axis=1)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -174,13 +174,13 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "show = [0, 2, 4]\n",
     "df.style \\\n",
     "  .hide([row for row in df.index if row not in show], axis=0) \\\n",
     "  .hide([col for col in df.columns if col not in show], axis=1)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -197,13 +197,13 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "summary_styler = df.agg([\"sum\", \"mean\"]).style \\\n",
     "                   .format(precision=3) \\\n",
     "                   .relabel_index([\"Sum\", \"Average\"])\n",
     "df.style.format(precision=1).concat(summary_styler)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -225,13 +225,13 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]], \n",
     "                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'), \n",
     "                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))\n",
     "df.style"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
@@ -239,6 +239,7 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
+   "outputs": [],
    "source": [
     "# Hidden cell to just create the below example: code is covered throughout the guide.\n",
     "s = df.style\\\n",
@@ -298,17 +299,16 @@
     "    'visibility: hidden; position: absolute; z-index: 1; border: 1px solid #000066;'\n",
     "    'background-color: white; color: #000066; font-size: 0.8em;' \n",
     "    'transform: translate(0px, -24px); padding: 0.6em; border-radius: 0.5em;')\n"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "s"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -323,11 +323,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "s = df.style.format('{:.0f}').hide([('Random', 'Tumour'), ('Random', 'Non-Tumour')], axis=\"columns\")\n",
     "s"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
@@ -335,11 +335,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
+   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s.set_uuid('after_hide')"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -392,6 +392,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "cell_hover = {  # for row hover use <tr> instead of <td>\n",
     "    'selector': 'td:hover',\n",
@@ -406,8 +407,7 @@
     "    'props': 'background-color: #000066; color: white;'\n",
     "}\n",
     "s.set_table_styles([cell_hover, index_names, headers])"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
@@ -415,11 +415,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
+   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s.set_uuid('after_tab_styles1')"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -432,14 +432,14 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "s.set_table_styles([\n",
     "    {'selector': 'th.col_heading', 'props': 'text-align: center;'},\n",
     "    {'selector': 'th.col_heading.level0', 'props': 'font-size: 1.5em;'},\n",
     "    {'selector': 'td', 'props': 'text-align: center; font-weight: bold;'},\n",
     "], overwrite=False)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
@@ -447,11 +447,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
+   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s.set_uuid('after_tab_styles2')"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -466,13 +466,13 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "s.set_table_styles({\n",
     "    ('Regression', 'Tumour'): [{'selector': 'th', 'props': 'border-left: 1px solid white'},\n",
     "                               {'selector': 'td', 'props': 'border-left: 1px solid #000066'}]\n",
     "}, overwrite=False, axis=0)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
@@ -480,11 +480,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
+   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s.set_uuid('xyz01')"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -505,11 +505,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "out = s.set_table_attributes('class=\"my-table-cls\"').to_html()\n",
     "print(out[out.find('<table'):][:109])"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -529,6 +529,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "s.set_table_styles([  # create internal CSS classes\n",
     "    {'selector': '.true', 'props': 'background-color: #e6ffe6;'},\n",
@@ -539,8 +540,7 @@
     "                          index=df.index, \n",
     "                          columns=df.columns[:4])\n",
     "s.set_td_classes(cell_color)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
@@ -548,11 +548,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
+   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s.set_uuid('after_classes')"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -577,12 +577,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "np.random.seed(0)\n",
     "df2 = pd.DataFrame(np.random.randn(10,4), columns=['A','B','C','D'])\n",
     "df2.style"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -595,14 +595,14 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "def style_negative(v, props=''):\n",
     "    return props if v < 0 else None\n",
     "s2 = df2.style.map(style_negative, props='color:red;')\\\n",
     "              .map(lambda v: 'opacity: 20%;' if (v < 0.3) and (v > -0.3) else None)\n",
     "s2"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
@@ -610,11 +610,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
+   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s2.set_uuid('after_applymap')"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -627,12 +627,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "def highlight_max(s, props=''):\n",
     "    return np.where(s == np.nanmax(s.values), props, '')\n",
     "s2.apply(highlight_max, props='color:white;background-color:darkblue', axis=0)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
@@ -640,11 +640,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
+   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s2.set_uuid('after_apply')"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -657,11 +657,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "s2.apply(highlight_max, props='color:white;background-color:pink;', axis=1)\\\n",
     "  .apply(highlight_max, props='color:white;background-color:purple', axis=None)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
@@ -669,11 +669,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
+   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s2.set_uuid('after_apply_again')"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -711,11 +711,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "s2.map_index(lambda v: \"color:pink;\" if v>4 else \"color:darkblue;\", axis=0)\n",
     "s2.apply_index(lambda s: np.where(s.isin([\"A\", \"B\"]), \"color:pink;\", \"color:darkblue;\"), axis=1)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -732,14 +732,14 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "s.set_caption(\"Confusion matrix for multiple cancer prediction models.\")\\\n",
     " .set_table_styles([{\n",
     "     'selector': 'caption',\n",
     "     'props': 'caption-side: bottom; font-size:1.25em;'\n",
     " }], overwrite=False)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
@@ -747,11 +747,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
+   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s.set_uuid('after_caption')"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -766,6 +766,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "tt = pd.DataFrame([['This model has a very strong true positive rate', \n",
     "                    \"This model's total number of false negatives is too high\"]], \n",
@@ -773,8 +774,7 @@
     "s.set_tooltips(tt, props='visibility: hidden; position: absolute; z-index: 1; border: 1px solid #000066;'\n",
     "                         'background-color: white; color: #000066; font-size: 0.8em;' \n",
     "                         'transform: translate(0px, -24px); padding: 0.6em; border-radius: 0.5em;')"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
@@ -782,11 +782,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
+   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s.set_uuid('after_tooltips')"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -799,6 +799,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "s.set_table_styles([  # create internal CSS classes\n",
     "    {'selector': '.border-red', 'props': 'border: 2px dashed red;'},\n",
@@ -809,8 +810,7 @@
     "                          index=df.index, \n",
     "                          columns=df.columns[:4])\n",
     "s.set_td_classes(cell_color + cell_border)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
@@ -818,11 +818,11 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
+   "outputs": [],
    "source": [
     "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
     "s.set_uuid('after_borders')"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -845,13 +845,13 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df3 = pd.DataFrame(np.random.randn(4,4), \n",
     "                   pd.MultiIndex.from_product([['A', 'B'], ['r1', 'r2']]),\n",
     "                   columns=['c1','c2','c3','c4'])\n",
     "df3"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -864,12 +864,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "slice_ = ['c3', 'c4']\n",
     "df3.style.apply(highlight_max, props='color:red;', axis=0, subset=slice_)\\\n",
     "         .set_properties(**{'background-color': '#ffffb3'}, subset=slice_)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -882,13 +882,13 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "idx = pd.IndexSlice\n",
     "slice_ = idx[idx[:,'r1'], idx['c2':'c4']]\n",
     "df3.style.apply(highlight_max, props='color:red;', axis=0, subset=slice_)\\\n",
     "         .set_properties(**{'background-color': '#ffffb3'}, subset=slice_)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -901,12 +901,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "slice_ = idx[idx[:,'r2'], :]\n",
     "df3.style.apply(highlight_max, props='color:red;', axis=1, subset=slice_)\\\n",
     "         .set_properties(**{'background-color': '#ffffb3'}, subset=slice_)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -921,12 +921,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "slice_ = idx[idx[(df3['c1'] + df3['c3']) < -2.0], ['c2', 'c4']]\n",
     "df3.style.apply(highlight_max, props='color:red;', axis=1, subset=slice_)\\\n",
     "         .set_properties(**{'background-color': '#ffffb3'}, subset=slice_)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -979,11 +979,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df4 = pd.DataFrame([[1,2],[3,4]])\n",
     "s4 = df4.style"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1000,11 +1000,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "from pandas.io.formats.style import Styler\n",
     "s4 = Styler(df4, uuid_len=0, cell_ids=False)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1030,11 +1030,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "props = 'font-family: \"Times New Roman\", Times, serif; color: #e83e8c; font-size:1.3em;'\n",
     "df4.style.map(lambda x: props, subset=[1])"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1051,10 +1051,10 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df4.style.set_table_styles([{'selector': 'td.col1', 'props': props}])"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1080,12 +1080,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df2.style.apply(highlight_max, props='color:white;background-color:darkblue;', axis=0)\\\n",
     "         .apply(highlight_max, props='color:white;background-color:pink;', axis=1)\\\n",
     "         .apply(highlight_max, props='color:white;background-color:purple', axis=None)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1102,6 +1102,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "build = lambda x: pd.DataFrame(x, index=df2.index, columns=df2.columns)\n",
     "cls1 = build(df2.apply(highlight_max, props='cls-1 ', axis=0))\n",
@@ -1112,8 +1113,7 @@
     "    {'selector': '.cls-2', 'props': 'color:white;background-color:pink;'},\n",
     "    {'selector': '.cls-3', 'props': 'color:white;background-color:purple;'}\n",
     "]).set_td_classes(cls1 + cls2 + cls3)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1137,6 +1137,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "my_css = {\n",
     "    \"row_heading\": \"\",\n",
@@ -1156,17 +1157,16 @@
     "                       {'selector': '.l0', 'props': 'color:blue;'}],\n",
     "                      css_class_names=my_css)\n",
     "print(html.to_html())"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "html"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1211,12 +1211,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df2.iloc[0,2] = np.nan\n",
     "df2.iloc[4,3] = np.nan\n",
     "df2.loc[:4].style.highlight_null(color='yellow')"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1229,10 +1229,10 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df2.loc[:4].style.highlight_max(axis=1, props='color:white; font-weight:bold; background-color:darkblue;')"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1246,11 +1246,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "left = pd.Series([1.0, 0.0, 1.0], index=[\"A\", \"B\", \"D\"])\n",
     "df2.loc[:4].style.highlight_between(left=left, right=1.5, axis=1, props='color:white; background-color:purple;')"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1264,10 +1264,10 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df2.loc[:4].style.highlight_quantile(q_left=0.85, axis=None, color='yellow')"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1287,22 +1287,22 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "import seaborn as sns\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
     "\n",
     "df2.style.background_gradient(cmap=cm)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df2.style.text_gradient(cmap=cm)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1327,12 +1327,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df2.loc[:4].style.set_properties(**{'background-color': 'black',\n",
     "                           'color': 'lawngreen',\n",
     "                           'border-color': 'white'})"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1352,10 +1352,10 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df2.style.bar(subset=['A', 'B'], color='#d65f5f')"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1370,13 +1370,13 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df2.style.format('{:.3f}', na_rep=\"\")\\\n",
     "         .bar(align=0, vmin=-2.5, vmax=2.5, cmap=\"bwr\", height=50,\n",
     "              width=60, props=\"width: 120px; border-right: 1px solid black;\")\\\n",
     "         .text_gradient(cmap=\"bwr\", vmin=-2.5, vmax=2.5)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1391,6 +1391,7 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
+   "outputs": [],
    "source": [
     "# Hide the construction of the display chart from the user\n",
     "import pandas as pd\n",
@@ -1431,17 +1432,16 @@
     "head+= \"\"\"\n",
     "</tbody>\n",
     "</table>\"\"\""
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "HTML(head)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1461,6 +1461,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "style1 = df2.style\\\n",
     "            .map(style_negative, props='color:red;')\\\n",
@@ -1468,19 +1469,18 @@
     "            .set_table_styles([{\"selector\": \"th\", \"props\": \"color: blue;\"}])\\\n",
     "            .hide(axis=\"index\")\n",
     "style1"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "style2 = df3.style\n",
     "style2.use(style1.export())\n",
     "style2"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1523,6 +1523,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "from ipywidgets import widgets\n",
     "@widgets.interact\n",
@@ -1531,8 +1532,7 @@
     "        cmap=sns.palettes.diverging_palette(h_neg=h_neg, h_pos=h_pos, s=s, l=l,\n",
     "                                            as_cmap=True)\n",
     "    )"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1545,6 +1545,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "def magnify():\n",
     "    return [dict(selector=\"th\",\n",
@@ -1557,13 +1558,13 @@
     "                 props=[('max-width', '200px'),\n",
     "                        ('font-size', '12pt')])\n",
     "]"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "np.random.seed(25)\n",
     "cmap = cmap=sns.diverging_palette(5, 250, as_cmap=True)\n",
@@ -1574,8 +1575,7 @@
     "    .set_caption(\"Hover to magnify\")\\\n",
     "    .format(precision=2)\\\n",
     "    .set_table_styles(magnify())"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1592,11 +1592,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "bigdf = pd.DataFrame(np.random.randn(16, 100))\n",
     "bigdf.style.set_sticky(axis=\"index\")"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1609,11 +1609,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "bigdf.index = pd.MultiIndex.from_product([[\"A\",\"B\"],[0,1],[0,1,2,3]])\n",
     "bigdf.style.set_sticky(axis=\"index\", pixel_size=18, levels=[1,2])"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1630,29 +1630,29 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df4 = pd.DataFrame([['<div></div>', '\"&other\"', '<span></span>']])\n",
     "df4.style"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df4.style.format(escape=\"html\")"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df4.style.format('<a href=\"https://pandas.pydata.org\" target=\"_blank\">{}</a>', escape=\"html\")"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1691,13 +1691,13 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df2.style.\\\n",
     "    map(style_negative, props='color:red;').\\\n",
     "    highlight_max(axis=0).\\\n",
     "    to_excel('styled.xlsx', engine='openpyxl')"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1763,10 +1763,10 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "print(pd.DataFrame([[1,2],[3,4]], index=['i1', 'i2'], columns=['c1', 'c2']).style.to_html())"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1781,22 +1781,22 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df4 = pd.DataFrame([['text']])\n",
     "df4.style.map(lambda x: 'color:green;')\\\n",
     "         .map(lambda x: 'color:red;')"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df4.style.map(lambda x: 'color:red;')\\\n",
     "         .map(lambda x: 'color:green;')"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1818,12 +1818,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df4.style.set_uuid('a_')\\\n",
     "         .set_table_styles([{'selector': 'td', 'props': 'color:red;'}])\\\n",
     "         .map(lambda x: 'color:green;')"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1836,14 +1836,14 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df4.style.set_uuid('b_')\\\n",
     "         .set_table_styles([{'selector': 'td', 'props': 'color:red;'},\n",
     "                            {'selector': '.cls-1', 'props': 'color:blue;'}])\\\n",
     "         .map(lambda x: 'color:green;')\\\n",
     "         .set_td_classes(pd.DataFrame([['cls-1']]))"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1856,6 +1856,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df4.style.set_uuid('c_')\\\n",
     "         .set_table_styles([{'selector': 'td', 'props': 'color:red;'},\n",
@@ -1863,8 +1864,7 @@
     "                            {'selector': 'td.data', 'props': 'color:yellow;'}])\\\n",
     "         .map(lambda x: 'color:green;')\\\n",
     "         .set_td_classes(pd.DataFrame([['cls-1']]))"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1879,6 +1879,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df4.style.set_uuid('d_')\\\n",
     "         .set_table_styles([{'selector': 'td', 'props': 'color:red;'},\n",
@@ -1886,8 +1887,7 @@
     "                            {'selector': 'td.data', 'props': 'color:yellow;'}])\\\n",
     "         .map(lambda x: 'color:green !important;')\\\n",
     "         .set_td_classes(pd.DataFrame([['cls-1']]))"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1920,12 +1920,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "from jinja2 import Environment, ChoiceLoader, FileSystemLoader\n",
     "from IPython.display import HTML\n",
     "from pandas.io.formats.style import Styler"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1938,11 +1938,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "with open(\"templates/myhtml.tpl\") as f:\n",
     "    print(f.read())"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1956,6 +1956,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "class MyStyler(Styler):\n",
     "    env = Environment(\n",
@@ -1965,8 +1966,7 @@
     "        ])\n",
     "    )\n",
     "    template_html_table = env.get_template(\"myhtml.tpl\")"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1983,10 +1983,10 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "MyStyler(df3)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1999,10 +1999,10 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "HTML(MyStyler(df3).to_html(table_title=\"Extending Example\"))"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -2015,11 +2015,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "EasyStyler = Styler.from_custom_template(\"templates\", \"myhtml.tpl\")\n",
     "HTML(EasyStyler(df3).to_html(table_title=\"Another Title\"))"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -2043,20 +2043,20 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
+   "outputs": [],
    "source": [
     "with open(\"templates/html_style_structure.html\") as f:\n",
     "    style_structure = f.read()"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "HTML(style_structure)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -2071,20 +2071,20 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
+   "outputs": [],
    "source": [
     "with open(\"templates/html_table_structure.html\") as f:\n",
     "    table_structure = f.read()"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "HTML(table_structure)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -2099,6 +2099,7 @@
    "metadata": {
     "nbsphinx": "hidden"
    },
+   "outputs": [],
    "source": [
     "# # Hack to get the same style in the notebook as the\n",
     "# # main site. This is hidden in the docs.\n",
@@ -2107,8 +2108,7 @@
     "#     css = f.read()\n",
     "    \n",
     "# HTML('<style>{}</style>'.format(css))"
-   ],
-   "outputs": []
+   ]
   }
  ],
  "metadata": {

From a563ec100ab95ba7beefccbc8f23e68c1cb45c40 Mon Sep 17 00:00:00 2001
From: tomytp <thomazrebellomiranda@gmail.com>
Date: Fri, 31 May 2024 15:11:46 -0300
Subject: [PATCH 13/13] removed unnecessary dunder implementation

---
 pandas/core/groupby/generic.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 3d51413ffc9b0..eeb29028cc100 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -158,9 +158,6 @@ def wrapped_aggfunc(*new_args, **new_kwargs):
 
         return wrapped_aggfunc
 
-    def __repr__(self):
-        return f"NamedAgg(column='{self.column}', aggfunc={self.aggfunc})"
-
 
 class SeriesGroupBy(GroupBy[Series]):
     def _wrap_agged_manager(self, mgr: Manager) -> Series: