From 7e00006b878fba7f44e8569beea42bfb6d5fc035 Mon Sep 17 00:00:00 2001
From: fjr <fjrkazmi@gmail.com>
Date: Sun, 22 Sep 2024 18:06:01 +0500
Subject: [PATCH 1/5] Fix docstring issues for pandas.Series.str.match and
 pandas.Series.sparse.sp_values

---
 .circleci/config.yml               | 14 +++++-
 pandas/core/arrays/sparse/array.py | 32 +++++++++---
 pandas/core/strings/accessor.py    | 79 ++++++++++++++++--------------
 3 files changed, 78 insertions(+), 47 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 27b6829dcda70..9c986e5b1b054 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -92,7 +92,13 @@ jobs:
           no_output_timeout: 30m # Sometimes the tests won't generate any output, make sure the job doesn't get killed by that
           command: |
             pip3 install cibuildwheel==2.20.0
-            cibuildwheel --output-dir wheelhouse
+            if [[ $CIBW_BUILD == cp313t* ]]; then
+              # TODO: temporarily run 3.13 free threaded builds without build isolation
+              # since we need pre-release cython
+              CIBW_BUILD_FRONTEND="pip; args: --no-build-isolation" cibuildwheel --output-dir wheelhouse
+            else
+              cibuildwheel --output-dir wheelhouse
+            fi
 
           environment:
             CIBW_BUILD: << parameters.cibw-build >>
@@ -141,6 +147,10 @@ workflows:
               cibw-build: ["cp310-manylinux_aarch64",
                            "cp311-manylinux_aarch64",
                            "cp312-manylinux_aarch64",
+                           "cp313-manylinux_aarch64",
+                           "cp313t-manylinux_aarch64",
                            "cp310-musllinux_aarch64",
                            "cp311-musllinux_aarch64",
-                           "cp312-musllinux_aarch64",]
+                           "cp312-musllinux_aarch64",
+                           "cp313-musllinux_aarch64",
+                           "cp313t-musllinux_aarch64"]
diff --git a/pandas/core/arrays/sparse/array.py b/pandas/core/arrays/sparse/array.py
index a09dc20af3b36..e9edd570ac5b2 100644
--- a/pandas/core/arrays/sparse/array.py
+++ b/pandas/core/arrays/sparse/array.py
@@ -601,15 +601,31 @@ def sp_index(self) -> SparseIndex:
     @property
     def sp_values(self) -> np.ndarray:
         """
-        An ndarray containing the non- ``fill_value`` values.
+An ndarray containing the non-``fill_value`` values.
+
+This method retrieves the non-fill values from a SparseArray. SparseArrays
+are designed to efficiently store large arrays of data where most of the
+elements are the same (the fill value). This method allows you to access
+the actual data points that differ from the fill value.
+
+Returns
+-------
+ndarray
+    An array containing the non-fill values.
+
+See Also
+--------
+Series.sparse.fill_value : The fill value for the SparseArray.
+arrays.SparseArray : Represents an array with sparse data.
+
+Examples
+--------
+>>> from pandas.arrays import SparseArray
+>>> s = SparseArray([0, 0, 1, 0, 2], fill_value=0)
+>>> s.sp_values
+array([1, 2])
+"""
 
-        Examples
-        --------
-        >>> from pandas.arrays import SparseArray
-        >>> s = SparseArray([0, 0, 1, 0, 2], fill_value=0)
-        >>> s.sp_values
-        array([1, 2])
-        """
         return self._sparse_values
 
     @property
diff --git a/pandas/core/strings/accessor.py b/pandas/core/strings/accessor.py
index 6d10365a1b968..1392595e262c1 100644
--- a/pandas/core/strings/accessor.py
+++ b/pandas/core/strings/accessor.py
@@ -1364,44 +1364,49 @@ def contains(
     @forbid_nonstring_types(["bytes"])
     def match(self, pat: str, case: bool = True, flags: int = 0, na=None):
         """
-        Determine if each string starts with a match of a regular expression.
+Determine if each string starts with a match of a regular expression.
+
+This method checks if each string in the Series starts with a substring
+that matches the given regular expression pattern. It returns a boolean
+Series indicating whether each string meets the condition.
+
+Parameters
+----------
+pat : str
+    Character sequence.
+case : bool, default True
+    If True, case sensitive.
+flags : int, default 0 (no flags)
+    Regex module flags, e.g. re.IGNORECASE.
+na : scalar, optional
+    Fill value for missing values. The default depends on dtype of the
+    array. For object-dtype, ``numpy.nan`` is used. For ``StringDtype``,
+    ``pandas.NA`` is used.
+
+Returns
+-------
+Series/Index/array of boolean values
+    A Series, Index, or array of boolean values indicating whether the start
+    of each string matches the pattern. The result will be of the same type
+    as the input.
+
+See Also
+--------
+fullmatch : Stricter matching that requires the entire string to match.
+contains : Analogous, but less strict, relying on re.search instead of
+    re.match.
+extract : Extract matched groups.
+
+Examples
+--------
+>>> ser = pd.Series(["horse", "eagle", "donkey"])
+>>> ser.str.match("e")
+0   False
+1   True
+2   False
+dtype: bool
+"""
 
-        Parameters
-        ----------
-        pat : str
-            Character sequence.
-        case : bool, default True
-            If True, case sensitive.
-        flags : int, default 0 (no flags)
-            Regex module flags, e.g. re.IGNORECASE.
-        na : scalar, optional
-            Fill value for missing values. The default depends on dtype of the
-            array. For object-dtype, ``numpy.nan`` is used. For ``StringDtype``,
-            ``pandas.NA`` is used.
-
-        Returns
-        -------
-        Series/Index/array of boolean values
-            A Series, Index, or array of boolean values indicating whether the start
-            of each string matches the pattern. The result will be of the same type
-            as the input.
-
-        See Also
-        --------
-        fullmatch : Stricter matching that requires the entire string to match.
-        contains : Analogous, but less strict, relying on re.search instead of
-            re.match.
-        extract : Extract matched groups.
-
-        Examples
-        --------
-        >>> ser = pd.Series(["horse", "eagle", "donkey"])
-        >>> ser.str.match("e")
-        0   False
-        1   True
-        2   False
-        dtype: bool
-        """
         result = self._data.array._str_match(pat, case=case, flags=flags, na=na)
         return self._wrap_result(result, fill_value=na, returns_string=False)
 

From 472489b0b7a3ca7cd68dbace24fc3f7fe0ff7b8a Mon Sep 17 00:00:00 2001
From: fjr <fjrkazmi@gmail.com>
Date: Sun, 22 Sep 2024 18:19:25 +0500
Subject: [PATCH 2/5] Remove methods from code_checks.sh after fixing
 docstrings

---
 ci/code_checks.sh | 1 -
 1 file changed, 1 deletion(-)

diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index f2d9f582d8932..f6527ca18b41c 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -100,7 +100,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.sparse.fill_value SA01" \
         -i "pandas.Series.sparse.from_coo PR07,SA01" \
         -i "pandas.Series.sparse.npoints SA01" \
-        -i "pandas.Series.sparse.sp_values SA01" \
         -i "pandas.Timedelta.max PR02" \
         -i "pandas.Timedelta.min PR02" \
         -i "pandas.Timedelta.resolution PR02" \

From 8ef300eb459609fd5fd64a4051937414ed4629a1 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Sun, 22 Sep 2024 15:42:34 +0000
Subject: [PATCH 3/5] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 pandas/core/arrays/sparse/array.py | 48 ++++++++---------
 pandas/core/strings/accessor.py    | 84 +++++++++++++++---------------
 2 files changed, 66 insertions(+), 66 deletions(-)

diff --git a/pandas/core/arrays/sparse/array.py b/pandas/core/arrays/sparse/array.py
index e9edd570ac5b2..f3eaca364a240 100644
--- a/pandas/core/arrays/sparse/array.py
+++ b/pandas/core/arrays/sparse/array.py
@@ -601,30 +601,30 @@ def sp_index(self) -> SparseIndex:
     @property
     def sp_values(self) -> np.ndarray:
         """
-An ndarray containing the non-``fill_value`` values.
-
-This method retrieves the non-fill values from a SparseArray. SparseArrays
-are designed to efficiently store large arrays of data where most of the
-elements are the same (the fill value). This method allows you to access
-the actual data points that differ from the fill value.
-
-Returns
--------
-ndarray
-    An array containing the non-fill values.
-
-See Also
---------
-Series.sparse.fill_value : The fill value for the SparseArray.
-arrays.SparseArray : Represents an array with sparse data.
-
-Examples
---------
->>> from pandas.arrays import SparseArray
->>> s = SparseArray([0, 0, 1, 0, 2], fill_value=0)
->>> s.sp_values
-array([1, 2])
-"""
+        An ndarray containing the non-``fill_value`` values.
+
+        This method retrieves the non-fill values from a SparseArray. SparseArrays
+        are designed to efficiently store large arrays of data where most of the
+        elements are the same (the fill value). This method allows you to access
+        the actual data points that differ from the fill value.
+
+        Returns
+        -------
+        ndarray
+            An array containing the non-fill values.
+
+        See Also
+        --------
+        Series.sparse.fill_value : The fill value for the SparseArray.
+        arrays.SparseArray : Represents an array with sparse data.
+
+        Examples
+        --------
+        >>> from pandas.arrays import SparseArray
+        >>> s = SparseArray([0, 0, 1, 0, 2], fill_value=0)
+        >>> s.sp_values
+        array([1, 2])
+        """
 
         return self._sparse_values
 
diff --git a/pandas/core/strings/accessor.py b/pandas/core/strings/accessor.py
index 1392595e262c1..6a23b4e2d7a4f 100644
--- a/pandas/core/strings/accessor.py
+++ b/pandas/core/strings/accessor.py
@@ -1364,48 +1364,48 @@ def contains(
     @forbid_nonstring_types(["bytes"])
     def match(self, pat: str, case: bool = True, flags: int = 0, na=None):
         """
-Determine if each string starts with a match of a regular expression.
-
-This method checks if each string in the Series starts with a substring
-that matches the given regular expression pattern. It returns a boolean
-Series indicating whether each string meets the condition.
-
-Parameters
-----------
-pat : str
-    Character sequence.
-case : bool, default True
-    If True, case sensitive.
-flags : int, default 0 (no flags)
-    Regex module flags, e.g. re.IGNORECASE.
-na : scalar, optional
-    Fill value for missing values. The default depends on dtype of the
-    array. For object-dtype, ``numpy.nan`` is used. For ``StringDtype``,
-    ``pandas.NA`` is used.
-
-Returns
--------
-Series/Index/array of boolean values
-    A Series, Index, or array of boolean values indicating whether the start
-    of each string matches the pattern. The result will be of the same type
-    as the input.
-
-See Also
---------
-fullmatch : Stricter matching that requires the entire string to match.
-contains : Analogous, but less strict, relying on re.search instead of
-    re.match.
-extract : Extract matched groups.
-
-Examples
---------
->>> ser = pd.Series(["horse", "eagle", "donkey"])
->>> ser.str.match("e")
-0   False
-1   True
-2   False
-dtype: bool
-"""
+        Determine if each string starts with a match of a regular expression.
+
+        This method checks if each string in the Series starts with a substring
+        that matches the given regular expression pattern. It returns a boolean
+        Series indicating whether each string meets the condition.
+
+        Parameters
+        ----------
+        pat : str
+            Character sequence.
+        case : bool, default True
+            If True, case sensitive.
+        flags : int, default 0 (no flags)
+            Regex module flags, e.g. re.IGNORECASE.
+        na : scalar, optional
+            Fill value for missing values. The default depends on dtype of the
+            array. For object-dtype, ``numpy.nan`` is used. For ``StringDtype``,
+            ``pandas.NA`` is used.
+
+        Returns
+        -------
+        Series/Index/array of boolean values
+            A Series, Index, or array of boolean values indicating whether the start
+            of each string matches the pattern. The result will be of the same type
+            as the input.
+
+        See Also
+        --------
+        fullmatch : Stricter matching that requires the entire string to match.
+        contains : Analogous, but less strict, relying on re.search instead of
+            re.match.
+        extract : Extract matched groups.
+
+        Examples
+        --------
+        >>> ser = pd.Series(["horse", "eagle", "donkey"])
+        >>> ser.str.match("e")
+        0   False
+        1   True
+        2   False
+        dtype: bool
+        """
 
         result = self._data.array._str_match(pat, case=case, flags=flags, na=na)
         return self._wrap_result(result, fill_value=na, returns_string=False)

From 9ff14bc27cfd63d7f06fcb387d985a6309621f82 Mon Sep 17 00:00:00 2001
From: fjr <fjrkazmi@gmail.com>
Date: Mon, 23 Sep 2024 05:46:01 +0500
Subject: [PATCH 4/5] Trigger CI re-run

---
 doc/source/user_guide/style.ipynb       | 390 ++++++++++++------------
 pandas/core/_numba/kernels/min_max_.py  |   6 +-
 pandas/core/dtypes/astype.py            |   2 +-
 pandas/core/dtypes/cast.py              |   8 +-
 pandas/core/nanops.py                   |   9 +-
 pandas/io/excel/_odfreader.py           |   3 +-
 pandas/tests/arithmetic/test_numeric.py |   4 +-
 pandas/tests/frame/test_query_eval.py   |   2 +-
 8 files changed, 203 insertions(+), 221 deletions(-)

diff --git a/doc/source/user_guide/style.ipynb b/doc/source/user_guide/style.ipynb
index daecfce6ecebc..1afc5d0cf3b3c 100644
--- a/doc/source/user_guide/style.ipynb
+++ b/doc/source/user_guide/style.ipynb
@@ -46,7 +46,6 @@
    },
    "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"
    ]
@@ -78,7 +77,6 @@
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
-    "import matplotlib as mpl\n",
     "\n",
     "df = pd.DataFrame({\n",
     "    \"strings\": [\"Adam\", \"Mike\"],\n",
@@ -104,11 +102,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "weather_df = pd.DataFrame(np.random.rand(10,2)*5, \n",
+    "weather_df = pd.DataFrame(np.random.rand(10,2)*5,\n",
     "                          index=pd.date_range(start=\"2021-01-01\", periods=10),\n",
     "                          columns=[\"Tokyo\", \"Beijing\"])\n",
     "\n",
-    "def rain_condition(v): \n",
+    "def rain_condition(v):\n",
     "    if v < 1.75:\n",
     "        return \"Dry\"\n",
     "    elif v < 2.75:\n",
@@ -227,9 +225,9 @@
    "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 = 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"
    ]
   },
@@ -243,62 +241,62 @@
    "source": [
     "# Hidden cell to just create the below example: code is covered throughout the guide.\n",
     "s = df.style\\\n",
-    "      .hide([('Random', 'Tumour'), ('Random', 'Non-Tumour')], axis='columns')\\\n",
-    "      .format('{:.0f}')\\\n",
+    "      .hide([(\"Random\", \"Tumour\"), (\"Random\", \"Non-Tumour\")], axis=\"columns\")\\\n",
+    "      .format(\"{:.0f}\")\\\n",
     "      .set_table_styles([{\n",
-    "        'selector': '',\n",
-    "        'props':  'border-collapse: separate;'\n",
+    "        \"selector\": \"\",\n",
+    "        \"props\":  \"border-collapse: separate;\"\n",
     "      },{\n",
-    "        'selector': 'caption',\n",
-    "        'props': 'caption-side: bottom; font-size:1.3em;'\n",
+    "        \"selector\": \"caption\",\n",
+    "        \"props\": \"caption-side: bottom; font-size:1.3em;\"\n",
     "      },{\n",
-    "        'selector': '.index_name',\n",
-    "        'props': 'font-style: italic; color: darkgrey; font-weight:normal;'\n",
+    "        \"selector\": \".index_name\",\n",
+    "        \"props\": \"font-style: italic; color: darkgrey; font-weight:normal;\"\n",
     "      },{\n",
-    "        'selector': 'th:not(.index_name)',\n",
-    "        'props': 'background-color: #000066; color: white;'\n",
+    "        \"selector\": \"th:not(.index_name)\",\n",
+    "        \"props\": \"background-color: #000066; color: white;\"\n",
     "      },{\n",
-    "        'selector': 'th.col_heading',\n",
-    "        'props': 'text-align: center;'\n",
+    "        \"selector\": \"th.col_heading\",\n",
+    "        \"props\": \"text-align: center;\"\n",
     "      },{\n",
-    "        'selector': 'th.col_heading.level0',\n",
-    "        'props': 'font-size: 1.5em;'\n",
+    "        \"selector\": \"th.col_heading.level0\",\n",
+    "        \"props\": \"font-size: 1.5em;\"\n",
     "      },{\n",
-    "        'selector': 'th.col2',\n",
-    "        'props': 'border-left: 1px solid white;'\n",
+    "        \"selector\": \"th.col2\",\n",
+    "        \"props\": \"border-left: 1px solid white;\"\n",
     "      },{\n",
-    "        'selector': '.col2',\n",
-    "        'props': 'border-left: 1px solid #000066;'\n",
+    "        \"selector\": \".col2\",\n",
+    "        \"props\": \"border-left: 1px solid #000066;\"\n",
     "      },{\n",
-    "        'selector': 'td',\n",
-    "        'props': 'text-align: center; font-weight:bold;'\n",
+    "        \"selector\": \"td\",\n",
+    "        \"props\": \"text-align: center; font-weight:bold;\"\n",
     "      },{\n",
-    "        'selector': '.true',\n",
-    "        'props': 'background-color: #e6ffe6;'\n",
+    "        \"selector\": \".true\",\n",
+    "        \"props\": \"background-color: #e6ffe6;\"\n",
     "      },{\n",
-    "        'selector': '.false',\n",
-    "        'props': 'background-color: #ffe6e6;'\n",
+    "        \"selector\": \".false\",\n",
+    "        \"props\": \"background-color: #ffe6e6;\"\n",
     "      },{\n",
-    "        'selector': '.border-red',\n",
-    "        'props': 'border: 2px dashed red;'\n",
+    "        \"selector\": \".border-red\",\n",
+    "        \"props\": \"border: 2px dashed red;\"\n",
     "      },{\n",
-    "        'selector': '.border-green',\n",
-    "        'props': 'border: 2px dashed green;'\n",
+    "        \"selector\": \".border-green\",\n",
+    "        \"props\": \"border: 2px dashed green;\"\n",
     "      },{\n",
-    "        'selector': 'td:hover',\n",
-    "        'props': 'background-color: #ffffb3;'\n",
+    "        \"selector\": \"td:hover\",\n",
+    "        \"props\": \"background-color: #ffffb3;\"\n",
     "      }])\\\n",
-    "      .set_td_classes(pd.DataFrame([['true border-green', 'false', 'true', 'false border-red', '', ''],\n",
-    "                                    ['false', 'true', 'false', 'true', '', '']], \n",
+    "      .set_td_classes(pd.DataFrame([[\"true border-green\", \"false\", \"true\", \"false border-red\", \"\", \"\"],\n",
+    "                                    [\"false\", \"true\", \"false\", \"true\", \"\", \"\"]],\n",
     "                                    index=df.index, columns=df.columns))\\\n",
     "      .set_caption(\"Confusion matrix for multiple cancer prediction models.\")\\\n",
-    "      .set_tooltips(pd.DataFrame([['This model has a very strong true positive rate', '', '', \"This model's total number of false negatives is too high\", '', ''],\n",
-    "                                    ['', '', '', '', '', '']], \n",
+    "      .set_tooltips(pd.DataFrame([[\"This model has a very strong true positive rate\", \"\", \"\", \"This model's total number of false negatives is too high\", \"\", \"\"],\n",
+    "                                    [\"\", \"\", \"\", \"\", \"\", \"\"]],\n",
     "                                    index=df.index, columns=df.columns),\n",
-    "                   css_class='pd-tt', props=\n",
-    "    '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"
+    "                   css_class=\"pd-tt\", props=\n",
+    "    \"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"
    ]
   },
   {
@@ -325,7 +323,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "s = df.style.format('{:.0f}').hide([('Random', 'Tumour'), ('Random', 'Non-Tumour')], axis=\"columns\")\n",
+    "s = df.style.format(\"{:.0f}\").hide([(\"Random\", \"Tumour\"), (\"Random\", \"Non-Tumour\")], axis=\"columns\")\n",
     "s"
    ]
   },
@@ -337,8 +335,8 @@
    },
    "outputs": [],
    "source": [
-    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
-    "s.set_uuid('after_hide')"
+    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting\n",
+    "s.set_uuid(\"after_hide\")"
    ]
   },
   {
@@ -395,16 +393,16 @@
    "outputs": [],
    "source": [
     "cell_hover = {  # for row hover use <tr> instead of <td>\n",
-    "    'selector': 'td:hover',\n",
-    "    'props': [('background-color', '#ffffb3')]\n",
+    "    \"selector\": \"td:hover\",\n",
+    "    \"props\": [(\"background-color\", \"#ffffb3\")]\n",
     "}\n",
     "index_names = {\n",
-    "    'selector': '.index_name',\n",
-    "    'props': 'font-style: italic; color: darkgrey; font-weight:normal;'\n",
+    "    \"selector\": \".index_name\",\n",
+    "    \"props\": \"font-style: italic; color: darkgrey; font-weight:normal;\"\n",
     "}\n",
     "headers = {\n",
-    "    'selector': 'th:not(.index_name)',\n",
-    "    'props': 'background-color: #000066; color: white;'\n",
+    "    \"selector\": \"th:not(.index_name)\",\n",
+    "    \"props\": \"background-color: #000066; color: white;\"\n",
     "}\n",
     "s.set_table_styles([cell_hover, index_names, headers])"
    ]
@@ -417,8 +415,8 @@
    },
    "outputs": [],
    "source": [
-    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
-    "s.set_uuid('after_tab_styles1')"
+    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting\n",
+    "s.set_uuid(\"after_tab_styles1\")"
    ]
   },
   {
@@ -435,9 +433,9 @@
    "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",
+    "    {\"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)"
    ]
   },
@@ -449,8 +447,8 @@
    },
    "outputs": [],
    "source": [
-    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
-    "s.set_uuid('after_tab_styles2')"
+    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting\n",
+    "s.set_uuid(\"after_tab_styles2\")"
    ]
   },
   {
@@ -469,8 +467,8 @@
    "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",
+    "    (\"Regression\", \"Tumour\"): [{\"selector\": \"th\", \"props\": \"border-left: 1px solid white\"},\n",
+    "                               {\"selector\": \"td\", \"props\": \"border-left: 1px solid #000066\"}]\n",
     "}, overwrite=False, axis=0)"
    ]
   },
@@ -482,8 +480,8 @@
    },
    "outputs": [],
    "source": [
-    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
-    "s.set_uuid('xyz01')"
+    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting\n",
+    "s.set_uuid(\"xyz01\")"
    ]
   },
   {
@@ -508,7 +506,7 @@
    "outputs": [],
    "source": [
     "out = s.set_table_attributes('class=\"my-table-cls\"').to_html()\n",
-    "print(out[out.find('<table'):][:109])"
+    "print(out[out.find(\"<table\"):][:109])"
    ]
   },
   {
@@ -532,12 +530,12 @@
    "outputs": [],
    "source": [
     "s.set_table_styles([  # create internal CSS classes\n",
-    "    {'selector': '.true', 'props': 'background-color: #e6ffe6;'},\n",
-    "    {'selector': '.false', 'props': 'background-color: #ffe6e6;'},\n",
+    "    {\"selector\": \".true\", \"props\": \"background-color: #e6ffe6;\"},\n",
+    "    {\"selector\": \".false\", \"props\": \"background-color: #ffe6e6;\"},\n",
     "], overwrite=False)\n",
-    "cell_color = pd.DataFrame([['true ', 'false ', 'true ', 'false '], \n",
-    "                           ['false ', 'true ', 'false ', 'true ']], \n",
-    "                          index=df.index, \n",
+    "cell_color = pd.DataFrame([[\"true \", \"false \", \"true \", \"false \"],\n",
+    "                           [\"false \", \"true \", \"false \", \"true \"]],\n",
+    "                          index=df.index,\n",
     "                          columns=df.columns[:4])\n",
     "s.set_td_classes(cell_color)"
    ]
@@ -550,8 +548,8 @@
    },
    "outputs": [],
    "source": [
-    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
-    "s.set_uuid('after_classes')"
+    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting\n",
+    "s.set_uuid(\"after_classes\")"
    ]
   },
   {
@@ -580,7 +578,7 @@
    "outputs": [],
    "source": [
     "np.random.seed(0)\n",
-    "df2 = pd.DataFrame(np.random.randn(10,4), columns=['A','B','C','D'])\n",
+    "df2 = pd.DataFrame(np.random.randn(10,4), columns=[\"A\",\"B\",\"C\",\"D\"])\n",
     "df2.style"
    ]
   },
@@ -597,10 +595,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def style_negative(v, props=''):\n",
+    "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 = 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"
    ]
   },
@@ -612,8 +610,8 @@
    },
    "outputs": [],
    "source": [
-    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
-    "s2.set_uuid('after_applymap')"
+    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting\n",
+    "s2.set_uuid(\"after_applymap\")"
    ]
   },
   {
@@ -629,9 +627,9 @@
    "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)"
+    "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)"
    ]
   },
   {
@@ -642,8 +640,8 @@
    },
    "outputs": [],
    "source": [
-    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
-    "s2.set_uuid('after_apply')"
+    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting\n",
+    "s2.set_uuid(\"after_apply\")"
    ]
   },
   {
@@ -659,8 +657,8 @@
    "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)"
+    "s2.apply(highlight_max, props=\"color:white;background-color:pink;\", axis=1)\\\n",
+    "  .apply(highlight_max, props=\"color:white;background-color:purple\", axis=None)"
    ]
   },
   {
@@ -671,8 +669,8 @@
    },
    "outputs": [],
    "source": [
-    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
-    "s2.set_uuid('after_apply_again')"
+    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting\n",
+    "s2.set_uuid(\"after_apply_again\")"
    ]
   },
   {
@@ -736,8 +734,8 @@
    "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",
+    "     \"selector\": \"caption\",\n",
+    "     \"props\": \"caption-side: bottom; font-size:1.25em;\"\n",
     " }], overwrite=False)"
    ]
   },
@@ -749,8 +747,8 @@
    },
    "outputs": [],
    "source": [
-    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
-    "s.set_uuid('after_caption')"
+    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting\n",
+    "s.set_uuid(\"after_caption\")"
    ]
   },
   {
@@ -768,12 +766,12 @@
    "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",
-    "                  index=['Tumour (Positive)'], columns=df.columns[[0,3]])\n",
-    "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;')"
+    "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",
+    "                  index=[\"Tumour (Positive)\"], columns=df.columns[[0,3]])\n",
+    "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;\")"
    ]
   },
   {
@@ -784,8 +782,8 @@
    },
    "outputs": [],
    "source": [
-    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
-    "s.set_uuid('after_tooltips')"
+    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting\n",
+    "s.set_uuid(\"after_tooltips\")"
    ]
   },
   {
@@ -802,12 +800,12 @@
    "outputs": [],
    "source": [
     "s.set_table_styles([  # create internal CSS classes\n",
-    "    {'selector': '.border-red', 'props': 'border: 2px dashed red;'},\n",
-    "    {'selector': '.border-green', 'props': 'border: 2px dashed green;'},\n",
+    "    {\"selector\": \".border-red\", \"props\": \"border: 2px dashed red;\"},\n",
+    "    {\"selector\": \".border-green\", \"props\": \"border: 2px dashed green;\"},\n",
     "], overwrite=False)\n",
-    "cell_border = pd.DataFrame([['border-green ', ' ', ' ', 'border-red '], \n",
-    "                           [' ', ' ', ' ', ' ']], \n",
-    "                          index=df.index, \n",
+    "cell_border = pd.DataFrame([[\"border-green \", \" \", \" \", \"border-red \"],\n",
+    "                           [\" \", \" \", \" \", \" \"]],\n",
+    "                          index=df.index,\n",
     "                          columns=df.columns[:4])\n",
     "s.set_td_classes(cell_color + cell_border)"
    ]
@@ -820,8 +818,8 @@
    },
    "outputs": [],
    "source": [
-    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting \n",
-    "s.set_uuid('after_borders')"
+    "# Hidden cell to avoid CSS clashes and latter code upcoding previous formatting\n",
+    "s.set_uuid(\"after_borders\")"
    ]
   },
   {
@@ -847,9 +845,9 @@
    "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 = pd.DataFrame(np.random.randn(4,4),\n",
+    "                   pd.MultiIndex.from_product([[\"A\", \"B\"], [\"r1\", \"r2\"]]),\n",
+    "                   columns=[\"c1\",\"c2\",\"c3\",\"c4\"])\n",
     "df3"
    ]
   },
@@ -866,9 +864,9 @@
    "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_)"
+    "slice_ = [\"c3\", \"c4\"]\n",
+    "df3.style.apply(highlight_max, props=\"color:red;\", axis=0, subset=slice_)\\\n",
+    "         .set_properties(**{\"background-color\": \"#ffffb3\"}, subset=slice_)"
    ]
   },
   {
@@ -885,9 +883,9 @@
    "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_)"
+    "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_)"
    ]
   },
   {
@@ -903,9 +901,9 @@
    "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_)"
+    "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_)"
    ]
   },
   {
@@ -923,9 +921,9 @@
    "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_)"
+    "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_)"
    ]
   },
   {
@@ -1053,7 +1051,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4.style.set_table_styles([{'selector': 'td.col1', 'props': props}])"
+    "df4.style.set_table_styles([{\"selector\": \"td.col1\", \"props\": props}])"
    ]
   },
   {
@@ -1082,9 +1080,9 @@
    "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)"
+    "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)"
    ]
   },
   {
@@ -1105,13 +1103,13 @@
    "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",
-    "cls2 = build(df2.apply(highlight_max, props='cls-2 ', axis=1, result_type='expand').values)\n",
-    "cls3 = build(highlight_max(df2, props='cls-3 '))\n",
+    "cls1 = build(df2.apply(highlight_max, props=\"cls-1 \", axis=0))\n",
+    "cls2 = build(df2.apply(highlight_max, props=\"cls-2 \", axis=1, result_type=\"expand\").values)\n",
+    "cls3 = build(highlight_max(df2, props=\"cls-3 \"))\n",
     "df2.style.set_table_styles([\n",
-    "    {'selector': '.cls-1', 'props': 'color:white;background-color:darkblue;'},\n",
-    "    {'selector': '.cls-2', 'props': 'color:white;background-color:pink;'},\n",
-    "    {'selector': '.cls-3', 'props': 'color:white;background-color:purple;'}\n",
+    "    {\"selector\": \".cls-1\", \"props\": \"color:white;background-color:darkblue;\"},\n",
+    "    {\"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)"
    ]
   },
@@ -1152,9 +1150,9 @@
     "    \"blank\": \"\",\n",
     "}\n",
     "html = Styler(df4, uuid_len=0, cell_ids=False)\n",
-    "html.set_table_styles([{'selector': 'td', 'props': props},\n",
-    "                       {'selector': '.c1', 'props': 'color:green;'},\n",
-    "                       {'selector': '.l0', 'props': 'color:blue;'}],\n",
+    "html.set_table_styles([{\"selector\": \"td\", \"props\": props},\n",
+    "                       {\"selector\": \".c1\", \"props\": \"color:green;\"},\n",
+    "                       {\"selector\": \".l0\", \"props\": \"color:blue;\"}],\n",
     "                      css_class_names=my_css)\n",
     "print(html.to_html())"
    ]
@@ -1215,7 +1213,7 @@
    "source": [
     "df2.iloc[0,2] = np.nan\n",
     "df2.iloc[4,3] = np.nan\n",
-    "df2.loc[:4].style.highlight_null(color='yellow')"
+    "df2.loc[:4].style.highlight_null(color=\"yellow\")"
    ]
   },
   {
@@ -1231,7 +1229,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df2.loc[:4].style.highlight_max(axis=1, props='color:white; font-weight:bold; background-color:darkblue;')"
+    "df2.loc[:4].style.highlight_max(axis=1, props=\"color:white; font-weight:bold; background-color:darkblue;\")"
    ]
   },
   {
@@ -1249,7 +1247,7 @@
    "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;')"
+    "df2.loc[:4].style.highlight_between(left=left, right=1.5, axis=1, props=\"color:white; background-color:purple;\")"
    ]
   },
   {
@@ -1266,7 +1264,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df2.loc[:4].style.highlight_quantile(q_left=0.85, axis=None, color='yellow')"
+    "df2.loc[:4].style.highlight_quantile(q_left=0.85, axis=None, color=\"yellow\")"
    ]
   },
   {
@@ -1329,9 +1327,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df2.loc[:4].style.set_properties(**{'background-color': 'black',\n",
-    "                           'color': 'lawngreen',\n",
-    "                           'border-color': 'white'})"
+    "df2.loc[:4].style.set_properties(**{\"background-color\": \"black\",\n",
+    "                           \"color\": \"lawngreen\",\n",
+    "                           \"border-color\": \"white\"})"
    ]
   },
   {
@@ -1354,7 +1352,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df2.style.bar(subset=['A', 'B'], color='#d65f5f')"
+    "df2.style.bar(subset=[\"A\", \"B\"], color=\"#d65f5f\")"
    ]
   },
   {
@@ -1372,7 +1370,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df2.style.format('{:.3f}', na_rep=\"\")\\\n",
+    "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)"
@@ -1398,10 +1396,10 @@
     "from IPython.display import HTML\n",
     "\n",
     "# Test series\n",
-    "test1 = pd.Series([-100,-60,-30,-20], name='All Negative')\n",
-    "test2 = pd.Series([-10,-5,0,90], name='Both Pos and Neg')\n",
-    "test3 = pd.Series([10,20,50,100], name='All Positive')\n",
-    "test4 = pd.Series([100, 103, 101, 102], name='Large Positive')\n",
+    "test1 = pd.Series([-100,-60,-30,-20], name=\"All Negative\")\n",
+    "test2 = pd.Series([-10,-5,0,90], name=\"Both Pos and Neg\")\n",
+    "test3 = pd.Series([10,20,50,100], name=\"All Positive\")\n",
+    "test4 = pd.Series([100, 103, 101, 102], name=\"Large Positive\")\n",
     "\n",
     "\n",
     "head = \"\"\"\n",
@@ -1417,18 +1415,18 @@
     "\n",
     "\"\"\"\n",
     "\n",
-    "aligns = ['left', 'right', 'zero', 'mid', 'mean', 99]\n",
+    "aligns = [\"left\", \"right\", \"zero\", \"mid\", \"mean\", 99]\n",
     "for align in aligns:\n",
     "    row = \"<tr><th>{}</th>\".format(align)\n",
     "    for series in [test1,test2,test3, test4]:\n",
     "        s = series.copy()\n",
-    "        s.name=''\n",
-    "        row += \"<td>{}</td>\".format(s.to_frame().style.hide(axis='index').bar(align=align, \n",
-    "                                                           color=['#d65f5f', '#5fba7d'], \n",
+    "        s.name=\"\"\n",
+    "        row += \"<td>{}</td>\".format(s.to_frame().style.hide(axis=\"index\").bar(align=align,\n",
+    "                                                           color=[\"#d65f5f\", \"#5fba7d\"],\n",
     "                                                           width=100).to_html()) #testn['width']\n",
-    "    row += '</tr>'\n",
+    "    row += \"</tr>\"\n",
     "    head += row\n",
-    "    \n",
+    "\n",
     "head+= \"\"\"\n",
     "</tbody>\n",
     "</table>\"\"\""
@@ -1464,8 +1462,8 @@
    "outputs": [],
    "source": [
     "style1 = df2.style\\\n",
-    "            .map(style_negative, props='color:red;')\\\n",
-    "            .map(lambda v: 'opacity: 20%;' if (v < 0.3) and (v > -0.3) else None)\\\n",
+    "            .map(style_negative, props=\"color:red;\")\\\n",
+    "            .map(lambda v: \"opacity: 20%;\" if (v < 0.3) and (v > -0.3) else None)\\\n",
     "            .set_table_styles([{\"selector\": \"th\", \"props\": \"color: blue;\"}])\\\n",
     "            .hide(axis=\"index\")\n",
     "style1"
@@ -1548,15 +1546,15 @@
    "outputs": [],
    "source": [
     "def magnify():\n",
-    "    return [dict(selector=\"th\",\n",
-    "                 props=[(\"font-size\", \"4pt\")]),\n",
-    "            dict(selector=\"td\",\n",
-    "                 props=[('padding', \"0em 0em\")]),\n",
-    "            dict(selector=\"th:hover\",\n",
-    "                 props=[(\"font-size\", \"12pt\")]),\n",
-    "            dict(selector=\"tr:hover td:hover\",\n",
-    "                 props=[('max-width', '200px'),\n",
-    "                        ('font-size', '12pt')])\n",
+    "    return [{\"selector\": \"th\",\n",
+    "                 \"props\": [(\"font-size\", \"4pt\")]},\n",
+    "            {\"selector\": \"td\",\n",
+    "                 \"props\": [(\"padding\", \"0em 0em\")]},\n",
+    "            {\"selector\": \"th:hover\",\n",
+    "                 \"props\": [(\"font-size\", \"12pt\")]},\n",
+    "            {\"selector\": \"tr:hover td:hover\",\n",
+    "                 \"props\": [(\"max-width\", \"200px\"),\n",
+    "                        (\"font-size\", \"12pt\")]}\n",
     "]"
    ]
   },
@@ -1571,7 +1569,7 @@
     "bigdf = pd.DataFrame(np.random.randn(20, 25)).cumsum()\n",
     "\n",
     "bigdf.style.background_gradient(cmap, axis=1)\\\n",
-    "    .set_properties(**{'max-width': '80px', 'font-size': '1pt'})\\\n",
+    "    .set_properties(**{\"max-width\": \"80px\", \"font-size\": \"1pt\"})\\\n",
     "    .set_caption(\"Hover to magnify\")\\\n",
     "    .format(precision=2)\\\n",
     "    .set_table_styles(magnify())"
@@ -1632,7 +1630,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4 = pd.DataFrame([['<div></div>', '\"&other\"', '<span></span>']])\n",
+    "df4 = pd.DataFrame([[\"<div></div>\", '\"&other\"', \"<span></span>\"]])\n",
     "df4.style"
    ]
   },
@@ -1694,9 +1692,9 @@
    "outputs": [],
    "source": [
     "df2.style.\\\n",
-    "    map(style_negative, props='color:red;').\\\n",
+    "    map(style_negative, props=\"color:red;\").\\\n",
     "    highlight_max(axis=0).\\\n",
-    "    to_excel('styled.xlsx', engine='openpyxl')"
+    "    to_excel(\"styled.xlsx\", engine=\"openpyxl\")"
    ]
   },
   {
@@ -1765,7 +1763,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(pd.DataFrame([[1,2],[3,4]], index=['i1', 'i2'], columns=['c1', 'c2']).style.to_html())"
+    "print(pd.DataFrame([[1,2],[3,4]], index=[\"i1\", \"i2\"], columns=[\"c1\", \"c2\"]).style.to_html())"
    ]
   },
   {
@@ -1783,9 +1781,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4 = pd.DataFrame([['text']])\n",
-    "df4.style.map(lambda x: 'color:green;')\\\n",
-    "         .map(lambda x: 'color:red;')"
+    "df4 = pd.DataFrame([[\"text\"]])\n",
+    "df4.style.map(lambda x: \"color:green;\")\\\n",
+    "         .map(lambda x: \"color:red;\")"
    ]
   },
   {
@@ -1794,8 +1792,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4.style.map(lambda x: 'color:red;')\\\n",
-    "         .map(lambda x: 'color:green;')"
+    "df4.style.map(lambda x: \"color:red;\")\\\n",
+    "         .map(lambda x: \"color:green;\")"
    ]
   },
   {
@@ -1820,9 +1818,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4.style.set_uuid('a_')\\\n",
-    "         .set_table_styles([{'selector': 'td', 'props': 'color:red;'}])\\\n",
-    "         .map(lambda x: 'color:green;')"
+    "df4.style.set_uuid(\"a_\")\\\n",
+    "         .set_table_styles([{\"selector\": \"td\", \"props\": \"color:red;\"}])\\\n",
+    "         .map(lambda x: \"color:green;\")"
    ]
   },
   {
@@ -1838,11 +1836,11 @@
    "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']]))"
+    "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\"]]))"
    ]
   },
   {
@@ -1858,12 +1856,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4.style.set_uuid('c_')\\\n",
-    "         .set_table_styles([{'selector': 'td', 'props': 'color:red;'},\n",
-    "                            {'selector': '.cls-1', 'props': 'color:blue;'},\n",
-    "                            {'selector': 'td.data', 'props': 'color:yellow;'}])\\\n",
-    "         .map(lambda x: 'color:green;')\\\n",
-    "         .set_td_classes(pd.DataFrame([['cls-1']]))"
+    "df4.style.set_uuid(\"c_\")\\\n",
+    "         .set_table_styles([{\"selector\": \"td\", \"props\": \"color:red;\"},\n",
+    "                            {\"selector\": \".cls-1\", \"props\": \"color:blue;\"},\n",
+    "                            {\"selector\": \"td.data\", \"props\": \"color:yellow;\"}])\\\n",
+    "         .map(lambda x: \"color:green;\")\\\n",
+    "         .set_td_classes(pd.DataFrame([[\"cls-1\"]]))"
    ]
   },
   {
@@ -1881,12 +1879,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4.style.set_uuid('d_')\\\n",
-    "         .set_table_styles([{'selector': 'td', 'props': 'color:red;'},\n",
-    "                            {'selector': '.cls-1', 'props': 'color:blue;'},\n",
-    "                            {'selector': 'td.data', 'props': 'color:yellow;'}])\\\n",
-    "         .map(lambda x: 'color:green !important;')\\\n",
-    "         .set_td_classes(pd.DataFrame([['cls-1']]))"
+    "df4.style.set_uuid(\"d_\")\\\n",
+    "         .set_table_styles([{\"selector\": \"td\", \"props\": \"color:red;\"},\n",
+    "                            {\"selector\": \".cls-1\", \"props\": \"color:blue;\"},\n",
+    "                            {\"selector\": \"td.data\", \"props\": \"color:yellow;\"}])\\\n",
+    "         .map(lambda x: \"color:green !important;\")\\\n",
+    "         .set_td_classes(pd.DataFrame([[\"cls-1\"]]))"
    ]
   },
   {
@@ -2106,7 +2104,7 @@
     "# from IPython.display import HTML\n",
     "# with open(\"themes/nature_with_gtoc/static/nature.css_t\") as f:\n",
     "#     css = f.read()\n",
-    "    \n",
+    "\n",
     "# HTML('<style>{}</style>'.format(css))"
    ]
   }
diff --git a/pandas/core/_numba/kernels/min_max_.py b/pandas/core/_numba/kernels/min_max_.py
index 59d36732ebae6..6e57e62c13a6e 100644
--- a/pandas/core/_numba/kernels/min_max_.py
+++ b/pandas/core/_numba/kernels/min_max_.py
@@ -112,11 +112,9 @@ def grouped_min_max(
             continue
 
         if is_max:
-            if val > output[lab]:
-                output[lab] = val
+            output[lab] = max(val, output[lab])
         else:
-            if val < output[lab]:
-                output[lab] = val
+            output[lab] = min(val, output[lab])
 
     # Set labels that don't satisfy min_periods as np.nan
     for lab, count in enumerate(nobs):
diff --git a/pandas/core/dtypes/astype.py b/pandas/core/dtypes/astype.py
index 086f7d2da6640..6555fb09adc73 100644
--- a/pandas/core/dtypes/astype.py
+++ b/pandas/core/dtypes/astype.py
@@ -125,7 +125,7 @@ def _astype_nansafe(
         )
         raise ValueError(msg)
 
-    if copy or arr.dtype == object or dtype == object:
+    if copy or object in (arr.dtype, dtype):
         # Explicit copy, or required since NumPy can't view from / to object.
         return arr.astype(dtype, copy=True)
 
diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index 6ba07b1761557..9b780e512a11d 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -683,9 +683,7 @@ def _maybe_promote(dtype: np.dtype, fill_value=np.nan):
 
         elif dtype.kind == "f":
             mst = np.min_scalar_type(fill_value)
-            if mst > dtype:
-                # e.g. mst is np.float64 and dtype is np.float32
-                dtype = mst
+            dtype = max(mst, dtype)
 
         elif dtype.kind == "c":
             mst = np.min_scalar_type(fill_value)
@@ -718,9 +716,7 @@ def _maybe_promote(dtype: np.dtype, fill_value=np.nan):
 
         elif dtype.kind == "c":
             mst = np.min_scalar_type(fill_value)
-            if mst > dtype:
-                # e.g. mst is np.complex128 and dtype is np.complex64
-                dtype = mst
+            dtype = max(mst, dtype)
 
     else:
         dtype = np.dtype(np.object_)
diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index e775156a6ae2f..84f1f9ba16f5b 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -457,14 +457,7 @@ def maybe_operate_rowwise(func: F) -> F:
     @functools.wraps(func)
     def newfunc(values: np.ndarray, *, axis: AxisInt | None = None, **kwargs):
         if (
-            axis == 1
-            and values.ndim == 2
-            and values.flags["C_CONTIGUOUS"]
-            # only takes this path for wide arrays (long dataframes), for threshold see
-            # https://github.com/pandas-dev/pandas/pull/43311#issuecomment-974891737
-            and (values.shape[1] / 1000) > values.shape[0]
-            and values.dtype != object
-            and values.dtype != bool
+            axis == 1 and values.ndim == 2 and values.flags["C_CONTIGUOUS"] and values.shape[1] / 1000 > values.shape[0] and values.dtype not in (object, bool)
         ):
             arrs = list(values)
             if kwargs.get("mask") is not None:
diff --git a/pandas/io/excel/_odfreader.py b/pandas/io/excel/_odfreader.py
index f79417d11080d..62002e4844b32 100644
--- a/pandas/io/excel/_odfreader.py
+++ b/pandas/io/excel/_odfreader.py
@@ -142,8 +142,7 @@ def get_sheet_data(
                         empty_cells = 0
                         table_row.extend([value] * column_repeat)
 
-            if max_row_len < len(table_row):
-                max_row_len = len(table_row)
+            max_row_len = max(max_row_len, len(table_row))
 
             row_repeat = self._get_row_repeat(sheet_row)
             if len(table_row) == 0:
diff --git a/pandas/tests/arithmetic/test_numeric.py b/pandas/tests/arithmetic/test_numeric.py
index d205569270705..d48a2d3c9c027 100644
--- a/pandas/tests/arithmetic/test_numeric.py
+++ b/pandas/tests/arithmetic/test_numeric.py
@@ -274,9 +274,7 @@ def test_numeric_arr_rdiv_tdscalar(self, three_days, numeric_idx, box_with_array
         expected = TimedeltaIndex(["3 Days", "36 Hours"])
         if isinstance(three_days, np.timedelta64):
             dtype = three_days.dtype
-            if dtype < np.dtype("m8[s]"):
-                # i.e. resolution is lower -> use lowest supported resolution
-                dtype = np.dtype("m8[s]")
+            dtype = max(dtype, np.dtype("m8[s]"))
             expected = expected.astype(dtype)
         elif type(three_days) is timedelta:
             expected = expected.astype("m8[us]")
diff --git a/pandas/tests/frame/test_query_eval.py b/pandas/tests/frame/test_query_eval.py
index a574989860957..bbc3caae9fccb 100644
--- a/pandas/tests/frame/test_query_eval.py
+++ b/pandas/tests/frame/test_query_eval.py
@@ -1388,7 +1388,7 @@ def test_expr_with_column_name_with_backtick_and_hash(self):
     def test_expr_with_column_name_with_backtick(self):
         # GH 59285
         df = DataFrame({"a`b": (1, 2, 3), "ab": (4, 5, 6)})
-        result = df.query("`a``b` < 2")  # noqa
+        result = df.query("`a``b` < 2")
         # Note: Formatting checks may wrongly consider the above ``inline code``.
         expected = df[df["a`b"] < 2]
         tm.assert_frame_equal(result, expected)

From 4267aaa57b9939f6c77431e3d91ce18d10b21e47 Mon Sep 17 00:00:00 2001
From: fjr <fjrkazmi@gmail.com>
Date: Mon, 23 Sep 2024 06:15:52 +0500
Subject: [PATCH 5/5] Fix formatting issues detected by ruff

---
 pandas/core/nanops.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index 84f1f9ba16f5b..040dd950b58be 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -457,7 +457,11 @@ def maybe_operate_rowwise(func: F) -> F:
     @functools.wraps(func)
     def newfunc(values: np.ndarray, *, axis: AxisInt | None = None, **kwargs):
         if (
-            axis == 1 and values.ndim == 2 and values.flags["C_CONTIGUOUS"] and values.shape[1] / 1000 > values.shape[0] and values.dtype not in (object, bool)
+            axis == 1
+            and values.ndim == 2
+            and values.flags["C_CONTIGUOUS"]
+            and values.shape[1] / 1000 > values.shape[0]
+            and values.dtype not in (object, bool)
         ):
             arrs = list(values)
             if kwargs.get("mask") is not None: