From 6d05c42de83d141d0dce610bded59dfcfcd85c58 Mon Sep 17 00:00:00 2001
From: Victor Momodu <victoramomodu@gmail.com>
Date: Thu, 16 Feb 2023 17:11:19 +0100
Subject: [PATCH 1/8] Ran pre-commit and fixed all errors

---
 .pre-commit-config.yaml                       | 1 +
 pandas/conftest.py                            | 4 +---
 pandas/core/dtypes/dtypes.py                  | 6 +++---
 pandas/core/series.py                         | 2 --
 pandas/io/formats/css.py                      | 6 +++---
 pandas/io/json/_json.py                       | 3 +--
 pandas/io/pytables.py                         | 4 ++--
 pandas/io/stata.py                            | 1 -
 pandas/tests/frame/methods/test_to_records.py | 2 +-
 pandas/tests/io/formats/style/test_style.py   | 2 +-
 pandas/tseries/frequencies.py                 | 2 +-
 pyproject.toml                                | 2 ++
 scripts/validate_exception_location.py        | 3 +--
 13 files changed, 17 insertions(+), 21 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 2f13ac4c73fa0..a6364d51cab41 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -19,6 +19,7 @@ repos:
     rev: v0.0.244
     hooks:
     -   id: ruff
+        args: [--fix, --exit-non-zero-on-fix]
 -   repo: https://github.com/MarcoGorelli/absolufy-imports
     rev: v0.3.1
     hooks:
diff --git a/pandas/conftest.py b/pandas/conftest.py
index 50951532364d1..4284a03f20d3c 100644
--- a/pandas/conftest.py
+++ b/pandas/conftest.py
@@ -660,9 +660,7 @@ def index_flat(request):
         key
         for key, value in indices_dict.items()
         if not (
-            key.startswith("int")
-            or key.startswith("uint")
-            or key.startswith("float")
+            key.startswith(("int", "uint", "float"))
             or key in ["range", "empty", "repeats", "bool-dtype"]
         )
         and not isinstance(value, MultiIndex)
diff --git a/pandas/core/dtypes/dtypes.py b/pandas/core/dtypes/dtypes.py
index 123e20d6258bf..2909b24840831 100644
--- a/pandas/core/dtypes/dtypes.py
+++ b/pandas/core/dtypes/dtypes.py
@@ -897,7 +897,7 @@ def freq(self):
     @classmethod
     def _parse_dtype_strict(cls, freq: str_type) -> BaseOffset:
         if isinstance(freq, str):  # note: freq is already of type str!
-            if freq.startswith("period[") or freq.startswith("Period["):
+            if freq.startswith(("Period[", "period[")):
                 m = cls._match.search(freq)
                 if m is not None:
                     freq = m.group("freq")
@@ -916,7 +916,7 @@ def construct_from_string(cls, string: str_type) -> PeriodDtype:
         """
         if (
             isinstance(string, str)
-            and (string.startswith("period[") or string.startswith("Period["))
+            and (string.startswith(("period[", "Period[")))
             or isinstance(string, BaseOffset)
         ):
             # do not parse string like U as period[U]
@@ -980,7 +980,7 @@ def is_dtype(cls, dtype: object) -> bool:
         if isinstance(dtype, str):
             # PeriodDtype can be instantiated from freq string like "U",
             # but doesn't regard freq str like "U" as dtype.
-            if dtype.startswith("period[") or dtype.startswith("Period["):
+            if dtype.startswith(("period[", "Period[")):
                 try:
                     return cls._parse_dtype_strict(dtype) is not None
                 except ValueError:
diff --git a/pandas/core/series.py b/pandas/core/series.py
index e4c7c4d3b3d73..9b40b586a6814 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -836,8 +836,6 @@ def view(self, dtype: Dtype | None = None) -> Series:
 
     # ----------------------------------------------------------------------
     # NDArray Compat
-    _HANDLED_TYPES = (Index, ExtensionArray, np.ndarray)
-
     def __array__(self, dtype: npt.DTypeLike | None = None) -> np.ndarray:
         """
         Return the values as a NumPy array.
diff --git a/pandas/io/formats/css.py b/pandas/io/formats/css.py
index f2f808a6e2081..d757b0ec18a31 100644
--- a/pandas/io/formats/css.py
+++ b/pandas/io/formats/css.py
@@ -200,15 +200,15 @@ class CSSResolver:
     SIDES = ("top", "right", "bottom", "left")
 
     CSS_EXPANSIONS = {
-        **{
+        {
             (f"border-{prop}" if prop else "border"): _border_expander(prop)
             for prop in ["", "top", "right", "bottom", "left"]
         },
-        **{
+        {
             f"border-{prop}": _side_expander(f"border-{{:s}}-{prop}")
             for prop in ["color", "style", "width"]
         },
-        **{
+        {
             "margin": _side_expander("margin-{:s}"),
             "padding": _side_expander("padding-{:s}"),
         },
diff --git a/pandas/io/json/_json.py b/pandas/io/json/_json.py
index bdc070d04bd69..335d510666a1f 100644
--- a/pandas/io/json/_json.py
+++ b/pandas/io/json/_json.py
@@ -1415,8 +1415,7 @@ def is_ok(col) -> bool:
 
             col_lower = col.lower()
             if (
-                col_lower.endswith("_at")
-                or col_lower.endswith("_time")
+                col_lower.endswith(("_at", "_time"))
                 or col_lower == "modified"
                 or col_lower == "date"
                 or col_lower == "datetime"
diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py
index 4a95daafd82a9..ac464264d98d9 100644
--- a/pandas/io/pytables.py
+++ b/pandas/io/pytables.py
@@ -5119,13 +5119,13 @@ def _dtype_to_kind(dtype_str: str) -> str:
     """
     dtype_str = _ensure_decoded(dtype_str)
 
-    if dtype_str.startswith("string") or dtype_str.startswith("bytes"):
+    if dtype_str.startswith(("string", "bytes")):
         kind = "string"
     elif dtype_str.startswith("float"):
         kind = "float"
     elif dtype_str.startswith("complex"):
         kind = "complex"
-    elif dtype_str.startswith("int") or dtype_str.startswith("uint"):
+    elif dtype_str.startswith(("int", "uint")):
         kind = "integer"
     elif dtype_str.startswith("datetime64"):
         kind = "datetime64"
diff --git a/pandas/io/stata.py b/pandas/io/stata.py
index 155f989eb0634..fd6e98a6466c8 100644
--- a/pandas/io/stata.py
+++ b/pandas/io/stata.py
@@ -875,7 +875,6 @@ class StataMissingValue:
         float32_base = struct.pack("<i", int_value)
 
     float64_base: bytes = b"\x00\x00\x00\x00\x00\x00\xe0\x7f"
-    increment = struct.unpack("q", b"\x00\x00\x00\x00\x00\x01\x00\x00")[0]
     for i in range(27):
         key = struct.unpack("<d", float64_base)[0]
         MISSING_VALUES[key] = "."
diff --git a/pandas/tests/frame/methods/test_to_records.py b/pandas/tests/frame/methods/test_to_records.py
index 393d304024bb6..ebf99d3cf2e57 100644
--- a/pandas/tests/frame/methods/test_to_records.py
+++ b/pandas/tests/frame/methods/test_to_records.py
@@ -489,7 +489,7 @@ def keys(self):
         df = DataFrame({"A": [1, 2], "B": [0.2, 1.5], "C": ["a", "bc"]})
 
         dtype_mappings = {
-            "column_dtypes": DictLike(**{"A": np.int8, "B": np.float32}),
+            "column_dtypes": DictLike({"A": np.int8, "B": np.float32}),
             "index_dtypes": f"{tm.ENDIAN}U2",
         }
 
diff --git a/pandas/tests/io/formats/style/test_style.py b/pandas/tests/io/formats/style/test_style.py
index 46fb614d96633..0387f317c5c0d 100644
--- a/pandas/tests/io/formats/style/test_style.py
+++ b/pandas/tests/io/formats/style/test_style.py
@@ -46,7 +46,7 @@ def mi_styler(mi_df):
 def mi_styler_comp(mi_styler):
     # comprehensively add features to mi_styler
     mi_styler = mi_styler._copy(deepcopy=True)
-    mi_styler.css = {**mi_styler.css, **{"row": "ROW", "col": "COL"}}
+    mi_styler.css = {**mi_styler.css, "row": "ROW", "col": "COL"}
     mi_styler.uuid_len = 5
     mi_styler.uuid = "abcde"
     mi_styler.set_caption("capt")
diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py
index 34e0c111360fd..e1af8c0b48c2f 100644
--- a/pandas/tseries/frequencies.py
+++ b/pandas/tseries/frequencies.py
@@ -596,7 +596,7 @@ def _is_annual(rule: str) -> bool:
 
 def _is_quarterly(rule: str) -> bool:
     rule = rule.upper()
-    return rule == "Q" or rule.startswith("Q-") or rule.startswith("BQ")
+    return rule == "Q" or rule.startswith(("Q-", "BQ"))
 
 
 def _is_monthly(rule: str) -> bool:
diff --git a/pyproject.toml b/pyproject.toml
index 8cd0155aca09e..725ca0a6951de 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -212,6 +212,8 @@ select = [
   "Q",
   # pylint
   "PLE", "PLR", "PLW",
+  # misc lints
+  "PIE",
 ]
 
 ignore = [
diff --git a/scripts/validate_exception_location.py b/scripts/validate_exception_location.py
index ebbe6c95a3ec9..b067527826de7 100644
--- a/scripts/validate_exception_location.py
+++ b/scripts/validate_exception_location.py
@@ -53,8 +53,7 @@ def visit_ClassDef(self, node) -> None:
         def is_an_exception_subclass(base_id: str) -> bool:
             return (
                 base_id == "Exception"
-                or base_id.endswith("Warning")
-                or base_id.endswith("Error")
+                or base_id.endswith(("Warning", "Error"))
             )
 
         exception_classes = []

From 7bb6483fa7edd8a72fb065fcd0f692c3f6a6bef2 Mon Sep 17 00:00:00 2001
From: Victor Momodu <victoramomodu@gmail.com>
Date: Thu, 16 Feb 2023 18:02:55 +0100
Subject: [PATCH 2/8] Formatted using black

---
 doc/source/user_guide/style.ipynb      | 652 ++++++++++++++-----------
 scripts/validate_exception_location.py |   5 +-
 2 files changed, 372 insertions(+), 285 deletions(-)

diff --git a/doc/source/user_guide/style.ipynb b/doc/source/user_guide/style.ipynb
index 90e57ad4ad90f..539885e1f6f03 100644
--- a/doc/source/user_guide/style.ipynb
+++ b/doc/source/user_guide/style.ipynb
@@ -47,6 +47,7 @@
    "outputs": [],
    "source": [
     "import matplotlib.pyplot\n",
+    "\n",
     "# We have this here to trigger matplotlib's font cache stuff.\n",
     "# This cell is hidden from the output"
    ]
@@ -80,15 +81,12 @@
     "import numpy as np\n",
     "import matplotlib as mpl\n",
     "\n",
-    "df = pd.DataFrame({\n",
-    "    \"strings\": [\"Adam\", \"Mike\"],\n",
-    "    \"ints\": [1, 3],\n",
-    "    \"floats\": [1.123, 1000.23]\n",
-    "})\n",
-    "df.style \\\n",
-    "  .format(precision=3, thousands=\".\", decimal=\",\") \\\n",
-    "  .format_index(str.upper, axis=1) \\\n",
-    "  .relabel_index([\"row 1\", \"row 2\"], axis=0)"
+    "df = pd.DataFrame(\n",
+    "    {\"strings\": [\"Adam\", \"Mike\"], \"ints\": [1, 3], \"floats\": [1.123, 1000.23]}\n",
+    ")\n",
+    "df.style.format(precision=3, thousands=\".\", decimal=\",\").format_index(\n",
+    "    str.upper, axis=1\n",
+    ").relabel_index([\"row 1\", \"row 2\"], axis=0)"
    ]
   },
   {
@@ -104,17 +102,21 @@
    "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",
-    "                          columns=[\"Tokyo\", \"Beijing\"])\n",
+    "weather_df = pd.DataFrame(\n",
+    "    np.random.rand(10, 2) * 5,\n",
+    "    index=pd.date_range(start=\"2021-01-01\", periods=10),\n",
+    "    columns=[\"Tokyo\", \"Beijing\"],\n",
+    ")\n",
+    "\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",
     "        return \"Rain\"\n",
     "    return \"Heavy Rain\"\n",
     "\n",
+    "\n",
     "def make_pretty(styler):\n",
     "    styler.set_caption(\"Weather Conditions\")\n",
     "    styler.format(rain_condition)\n",
@@ -122,6 +124,7 @@
     "    styler.background_gradient(axis=None, vmin=1, vmax=5, cmap=\"YlGnBu\")\n",
     "    return styler\n",
     "\n",
+    "\n",
     "weather_df"
    ]
   },
@@ -158,9 +161,7 @@
    "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)"
+    "df.style.hide(subset=[0, 2, 4], axis=0).hide(subset=[0, 2, 4], axis=1)"
    ]
   },
   {
@@ -177,9 +178,9 @@
    "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)"
+    "df.style.hide([row for row in df.index if row not in show], axis=0).hide(\n",
+    "    [col for col in df.columns if col not in show], axis=1\n",
+    ")"
    ]
   },
   {
@@ -199,9 +200,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "summary_styler = df.agg([\"sum\", \"mean\"]).style \\\n",
-    "                   .format(precision=3) \\\n",
-    "                   .relabel_index([\"Sum\", \"Average\"])\n",
+    "summary_styler = (\n",
+    "    df.agg([\"sum\", \"mean\"]).style.format(precision=3).relabel_index([\"Sum\", \"Average\"])\n",
+    ")\n",
     "df.style.format(precision=1).concat(summary_styler)"
    ]
   },
@@ -227,9 +228,16 @@
    "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(\n",
+    "    [[38.0, 2.0, 18.0, 22.0, 21, np.nan], [19, 439, 6, 452, 226, 232]],\n",
+    "    index=pd.Index(\n",
+    "        [\"Tumour (Positive)\", \"Non-Tumour (Negative)\"], name=\"Actual Label:\"\n",
+    "    ),\n",
+    "    columns=pd.MultiIndex.from_product(\n",
+    "        [[\"Decision Tree\", \"Regression\", \"Random\"], [\"Tumour\", \"Non-Tumour\"]],\n",
+    "        names=[\"Model:\", \"Predicted:\"],\n",
+    "    ),\n",
+    ")\n",
     "df.style"
    ]
   },
@@ -242,63 +250,66 @@
    "outputs": [],
    "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",
-    "      .set_table_styles([{\n",
-    "        'selector': '',\n",
-    "        'props':  'border-collapse: separate;'\n",
-    "      },{\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",
-    "      },{\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",
-    "      },{\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",
-    "      },{\n",
-    "        'selector': '.col2',\n",
-    "        'props': 'border-left: 1px solid #000066;'\n",
-    "      },{\n",
-    "        'selector': 'td',\n",
-    "        'props': 'text-align: center; font-weight:bold;'\n",
-    "      },{\n",
-    "        'selector': '.true',\n",
-    "        'props': 'background-color: #e6ffe6;'\n",
-    "      },{\n",
-    "        'selector': '.false',\n",
-    "        'props': 'background-color: #ffe6e6;'\n",
-    "      },{\n",
-    "        'selector': '.border-red',\n",
-    "        'props': 'border: 2px dashed red;'\n",
-    "      },{\n",
-    "        'selector': '.border-green',\n",
-    "        'props': 'border: 2px dashed green;'\n",
-    "      },{\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",
-    "                                    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",
-    "                                    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"
+    "s = (\n",
+    "    df.style.hide([(\"Random\", \"Tumour\"), (\"Random\", \"Non-Tumour\")], axis=\"columns\")\n",
+    "    .format(\"{:.0f}\")\n",
+    "    .set_table_styles(\n",
+    "        [\n",
+    "            {\"selector\": \"\", \"props\": \"border-collapse: separate;\"},\n",
+    "            {\"selector\": \"caption\", \"props\": \"caption-side: bottom; font-size:1.3em;\"},\n",
+    "            {\n",
+    "                \"selector\": \".index_name\",\n",
+    "                \"props\": \"font-style: italic; color: darkgrey; font-weight:normal;\",\n",
+    "            },\n",
+    "            {\n",
+    "                \"selector\": \"th:not(.index_name)\",\n",
+    "                \"props\": \"background-color: #000066; color: white;\",\n",
+    "            },\n",
+    "            {\"selector\": \"th.col_heading\", \"props\": \"text-align: center;\"},\n",
+    "            {\"selector\": \"th.col_heading.level0\", \"props\": \"font-size: 1.5em;\"},\n",
+    "            {\"selector\": \"th.col2\", \"props\": \"border-left: 1px solid white;\"},\n",
+    "            {\"selector\": \".col2\", \"props\": \"border-left: 1px solid #000066;\"},\n",
+    "            {\"selector\": \"td\", \"props\": \"text-align: center; font-weight:bold;\"},\n",
+    "            {\"selector\": \".true\", \"props\": \"background-color: #e6ffe6;\"},\n",
+    "            {\"selector\": \".false\", \"props\": \"background-color: #ffe6e6;\"},\n",
+    "            {\"selector\": \".border-red\", \"props\": \"border: 2px dashed red;\"},\n",
+    "            {\"selector\": \".border-green\", \"props\": \"border: 2px dashed green;\"},\n",
+    "            {\"selector\": \"td:hover\", \"props\": \"background-color: #ffffb3;\"},\n",
+    "        ]\n",
+    "    )\n",
+    "    .set_td_classes(\n",
+    "        pd.DataFrame(\n",
+    "            [\n",
+    "                [\"true border-green\", \"false\", \"true\", \"false border-red\", \"\", \"\"],\n",
+    "                [\"false\", \"true\", \"false\", \"true\", \"\", \"\"],\n",
+    "            ],\n",
+    "            index=df.index,\n",
+    "            columns=df.columns,\n",
+    "        )\n",
+    "    )\n",
+    "    .set_caption(\"Confusion matrix for multiple cancer prediction models.\")\n",
+    "    .set_tooltips(\n",
+    "        pd.DataFrame(\n",
+    "            [\n",
+    "                [\n",
+    "                    \"This model has a very strong true positive rate\",\n",
+    "                    \"\",\n",
+    "                    \"\",\n",
+    "                    \"This model's total number of false negatives is too high\",\n",
+    "                    \"\",\n",
+    "                    \"\",\n",
+    "                ],\n",
+    "                [\"\", \"\", \"\", \"\", \"\", \"\"],\n",
+    "            ],\n",
+    "            index=df.index,\n",
+    "            columns=df.columns,\n",
+    "        ),\n",
+    "        css_class=\"pd-tt\",\n",
+    "        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;\",\n",
+    "    )\n",
+    ")"
    ]
   },
   {
@@ -325,7 +336,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "s = df.style.format('{:.0f}').hide([('Random', 'Tumour'), ('Random', 'Non-Tumour')], axis=\"columns\")\n",
+    "s = df.style.format(\"{:.0f}\").hide(\n",
+    "    [(\"Random\", \"Tumour\"), (\"Random\", \"Non-Tumour\")], axis=\"columns\"\n",
+    ")\n",
     "s"
    ]
   },
@@ -337,8 +350,8 @@
    },
    "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 +408,16 @@
    "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 +430,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -434,11 +447,14 @@
    "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)"
+    "s.set_table_styles(\n",
+    "    [\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",
+    "    ],\n",
+    "    overwrite=False,\n",
+    ")"
    ]
   },
   {
@@ -449,8 +465,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -468,10 +484,16 @@
    "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)"
+    "s.set_table_styles(\n",
+    "    {\n",
+    "        (\"Regression\", \"Tumour\"): [\n",
+    "            {\"selector\": \"th\", \"props\": \"border-left: 1px solid white\"},\n",
+    "            {\"selector\": \"td\", \"props\": \"border-left: 1px solid #000066\"},\n",
+    "        ]\n",
+    "    },\n",
+    "    overwrite=False,\n",
+    "    axis=0,\n",
+    ")"
    ]
   },
   {
@@ -482,8 +504,8 @@
    },
    "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 +530,7 @@
    "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])"
    ]
   },
   {
@@ -531,14 +553,18 @@
    "metadata": {},
    "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",
-    "], overwrite=False)\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_table_styles(\n",
+    "    [  # create internal CSS classes\n",
+    "        {\"selector\": \".true\", \"props\": \"background-color: #e6ffe6;\"},\n",
+    "        {\"selector\": \".false\", \"props\": \"background-color: #ffe6e6;\"},\n",
+    "    ],\n",
+    "    overwrite=False,\n",
+    ")\n",
+    "cell_color = pd.DataFrame(\n",
+    "    [[\"true \", \"false \", \"true \", \"false \"], [\"false \", \"true \", \"false \", \"true \"]],\n",
+    "    index=df.index,\n",
+    "    columns=df.columns[:4],\n",
+    ")\n",
     "s.set_td_classes(cell_color)"
    ]
   },
@@ -550,8 +576,8 @@
    },
    "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 +606,7 @@
    "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 +623,13 @@
    "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.applymap(style_negative, props='color:red;')\\\n",
-    "              .applymap(lambda v: 'opacity: 20%;' if (v < 0.3) and (v > -0.3) else None)\n",
+    "\n",
+    "\n",
+    "s2 = df2.style.applymap(style_negative, props=\"color:red;\").applymap(\n",
+    "    lambda v: \"opacity: 20%;\" if (v < 0.3) and (v > -0.3) else None\n",
+    ")\n",
     "s2"
    ]
   },
@@ -612,8 +641,8 @@
    },
    "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 +658,11 @@
    "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",
+    "\n",
+    "\n",
+    "s2.apply(highlight_max, props=\"color:white;background-color:darkblue\", axis=0)"
    ]
   },
   {
@@ -642,8 +673,8 @@
    },
    "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 +690,9 @@
    "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).apply(\n",
+    "    highlight_max, props=\"color:white;background-color:purple\", axis=None\n",
+    ")"
    ]
   },
   {
@@ -671,8 +703,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -713,8 +745,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "s2.applymap_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)"
+    "s2.applymap_index(lambda v: \"color:pink;\" if v > 4 else \"color:darkblue;\", axis=0)\n",
+    "s2.apply_index(\n",
+    "    lambda s: np.where(s.isin([\"A\", \"B\"]), \"color:pink;\", \"color:darkblue;\"), axis=1\n",
+    ")"
    ]
   },
   {
@@ -734,11 +768,12 @@
    "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)"
+    "s.set_caption(\n",
+    "    \"Confusion matrix for multiple cancer prediction models.\"\n",
+    ").set_table_styles(\n",
+    "    [{\"selector\": \"caption\", \"props\": \"caption-side: bottom; font-size:1.25em;\"}],\n",
+    "    overwrite=False,\n",
+    ")"
    ]
   },
   {
@@ -749,8 +784,8 @@
    },
    "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 +803,22 @@
    "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(\n",
+    "    [\n",
+    "        [\n",
+    "            \"This model has a very strong true positive rate\",\n",
+    "            \"This model's total number of false negatives is too high\",\n",
+    "        ]\n",
+    "    ],\n",
+    "    index=[\"Tumour (Positive)\"],\n",
+    "    columns=df.columns[[0, 3]],\n",
+    ")\n",
+    "s.set_tooltips(\n",
+    "    tt,\n",
+    "    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;\",\n",
+    ")"
    ]
   },
   {
@@ -784,8 +829,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -801,14 +846,18 @@
    "metadata": {},
    "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",
-    "], overwrite=False)\n",
-    "cell_border = pd.DataFrame([['border-green ', ' ', ' ', 'border-red '], \n",
-    "                           [' ', ' ', ' ', ' ']], \n",
-    "                          index=df.index, \n",
-    "                          columns=df.columns[:4])\n",
+    "s.set_table_styles(\n",
+    "    [  # create internal CSS classes\n",
+    "        {\"selector\": \".border-red\", \"props\": \"border: 2px dashed red;\"},\n",
+    "        {\"selector\": \".border-green\", \"props\": \"border: 2px dashed green;\"},\n",
+    "    ],\n",
+    "    overwrite=False,\n",
+    ")\n",
+    "cell_border = pd.DataFrame(\n",
+    "    [[\"border-green \", \" \", \" \", \"border-red \"], [\" \", \" \", \" \", \" \"]],\n",
+    "    index=df.index,\n",
+    "    columns=df.columns[:4],\n",
+    ")\n",
     "s.set_td_classes(cell_color + cell_border)"
    ]
   },
@@ -820,8 +869,8 @@
    },
    "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 +896,11 @@
    "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(\n",
+    "    np.random.randn(4, 4),\n",
+    "    pd.MultiIndex.from_product([[\"A\", \"B\"], [\"r1\", \"r2\"]]),\n",
+    "    columns=[\"c1\", \"c2\", \"c3\", \"c4\"],\n",
+    ")\n",
     "df3"
    ]
   },
@@ -866,9 +917,10 @@
    "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(\n",
+    "    highlight_max, props=\"color:red;\", axis=0, subset=slice_\n",
+    ").set_properties(**{\"background-color\": \"#ffffb3\"}, subset=slice_)"
    ]
   },
   {
@@ -885,9 +937,10 @@
    "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(\n",
+    "    highlight_max, props=\"color:red;\", axis=0, subset=slice_\n",
+    ").set_properties(**{\"background-color\": \"#ffffb3\"}, subset=slice_)"
    ]
   },
   {
@@ -903,9 +956,10 @@
    "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(\n",
+    "    highlight_max, props=\"color:red;\", axis=1, subset=slice_\n",
+    ").set_properties(**{\"background-color\": \"#ffffb3\"}, subset=slice_)"
    ]
   },
   {
@@ -923,9 +977,10 @@
    "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(\n",
+    "    highlight_max, props=\"color:red;\", axis=1, subset=slice_\n",
+    ").set_properties(**{\"background-color\": \"#ffffb3\"}, subset=slice_)"
    ]
   },
   {
@@ -981,7 +1036,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4 = pd.DataFrame([[1,2],[3,4]])\n",
+    "df4 = pd.DataFrame([[1, 2], [3, 4]])\n",
     "s4 = df4.style"
    ]
   },
@@ -1003,6 +1058,7 @@
    "outputs": [],
    "source": [
     "from pandas.io.formats.style import Styler\n",
+    "\n",
     "s4 = Styler(df4, uuid_len=0, cell_ids=False)"
    ]
   },
@@ -1053,7 +1109,7 @@
    "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 +1138,11 @@
    "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(\n",
+    "    highlight_max, props=\"color:white;background-color:darkblue;\", axis=0\n",
+    ").apply(highlight_max, props=\"color:white;background-color:pink;\", axis=1).apply(\n",
+    "    highlight_max, props=\"color:white;background-color:purple\", axis=None\n",
+    ")"
    ]
   },
   {
@@ -1105,14 +1163,18 @@
    "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",
-    "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",
-    "]).set_td_classes(cls1 + cls2 + cls3)"
+    "cls1 = build(df2.apply(highlight_max, props=\"cls-1 \", axis=0))\n",
+    "cls2 = build(\n",
+    "    df2.apply(highlight_max, props=\"cls-2 \", axis=1, result_type=\"expand\").values\n",
+    ")\n",
+    "cls3 = build(highlight_max(df2, props=\"cls-3 \"))\n",
+    "df2.style.set_table_styles(\n",
+    "    [\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",
+    "    ]\n",
+    ").set_td_classes(cls1 + cls2 + cls3)"
    ]
   },
   {
@@ -1152,10 +1214,14 @@
     "    \"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",
-    "                      css_class_names=my_css)\n",
+    "html.set_table_styles(\n",
+    "    [\n",
+    "        {\"selector\": \"td\", \"props\": props},\n",
+    "        {\"selector\": \".c1\", \"props\": \"color:green;\"},\n",
+    "        {\"selector\": \".l0\", \"props\": \"color:blue;\"},\n",
+    "    ],\n",
+    "    css_class_names=my_css,\n",
+    ")\n",
     "print(html.to_html())"
    ]
   },
@@ -1213,9 +1279,9 @@
    "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')"
+    "df2.iloc[0, 2] = np.nan\n",
+    "df2.iloc[4, 3] = np.nan\n",
+    "df2.loc[:4].style.highlight_null(color=\"yellow\")"
    ]
   },
   {
@@ -1231,7 +1297,9 @@
    "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(\n",
+    "    axis=1, props=\"color:white; font-weight:bold; background-color:darkblue;\"\n",
+    ")"
    ]
   },
   {
@@ -1249,7 +1317,9 @@
    "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(\n",
+    "    left=left, right=1.5, axis=1, props=\"color:white; background-color:purple;\"\n",
+    ")"
    ]
   },
   {
@@ -1266,7 +1336,7 @@
    "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\")"
    ]
   },
   {
@@ -1290,6 +1360,7 @@
    "outputs": [],
    "source": [
     "import seaborn as sns\n",
+    "\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
     "\n",
     "df2.style.background_gradient(cmap=cm)"
@@ -1329,9 +1400,9 @@
    "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(\n",
+    "    **{\"background-color\": \"black\", \"color\": \"lawngreen\", \"border-color\": \"white\"}\n",
+    ")"
    ]
   },
   {
@@ -1354,7 +1425,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df2.style.bar(subset=['A', 'B'], color='#d65f5f')"
+    "df2.style.bar(subset=[\"A\", \"B\"], color=\"#d65f5f\")"
    ]
   },
   {
@@ -1372,10 +1443,15 @@
    "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)"
+    "df2.style.format(\"{:.3f}\", na_rep=\"\").bar(\n",
+    "    align=0,\n",
+    "    vmin=-2.5,\n",
+    "    vmax=2.5,\n",
+    "    cmap=\"bwr\",\n",
+    "    height=50,\n",
+    "    width=60,\n",
+    "    props=\"width: 120px; border-right: 1px solid black;\",\n",
+    ").text_gradient(cmap=\"bwr\", vmin=-2.5, vmax=2.5)"
    ]
   },
   {
@@ -1398,10 +1474,10 @@
     "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",
@@ -1417,19 +1493,22 @@
     "\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",
+    "    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",
-    "                                                           width=100).to_html()) #testn['width']\n",
-    "    row += '</tr>'\n",
+    "        s.name = \"\"\n",
+    "        row += \"<td>{}</td>\".format(\n",
+    "            s.to_frame()\n",
+    "            .style.hide(axis=\"index\")\n",
+    "            .bar(align=align, color=[\"#d65f5f\", \"#5fba7d\"], width=100)\n",
+    "            .to_html()\n",
+    "        )  # testn['width']\n",
+    "    row += \"</tr>\"\n",
     "    head += row\n",
-    "    \n",
-    "head+= \"\"\"\n",
+    "\n",
+    "head += \"\"\"\n",
     "</tbody>\n",
     "</table>\"\"\""
    ]
@@ -1463,11 +1542,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "style1 = df2.style\\\n",
-    "            .applymap(style_negative, props='color:red;')\\\n",
-    "            .applymap(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 = (\n",
+    "    df2.style.applymap(style_negative, props=\"color:red;\")\n",
+    "    .applymap(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",
+    ")\n",
     "style1"
    ]
   },
@@ -1526,11 +1606,14 @@
    "outputs": [],
    "source": [
     "from ipywidgets import widgets\n",
+    "\n",
+    "\n",
     "@widgets.interact\n",
-    "def f(h_neg=(0, 359, 1), h_pos=(0, 359), s=(0., 99.9), l=(0., 99.9)):\n",
+    "def f(h_neg=(0, 359, 1), h_pos=(0, 359), s=(0.0, 99.9), l=(0.0, 99.9)):\n",
     "    return df2.style.background_gradient(\n",
-    "        cmap=sns.palettes.diverging_palette(h_neg=h_neg, h_pos=h_pos, s=s, l=l,\n",
-    "                                            as_cmap=True)\n",
+    "        cmap=sns.palettes.diverging_palette(\n",
+    "            h_neg=h_neg, h_pos=h_pos, s=s, l=l, as_cmap=True\n",
+    "        )\n",
     "    )"
    ]
   },
@@ -1548,16 +1631,15 @@
    "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 [\n",
+    "        dict(selector=\"th\", props=[(\"font-size\", \"4pt\")]),\n",
+    "        dict(selector=\"td\", props=[(\"padding\", \"0em 0em\")]),\n",
+    "        dict(selector=\"th:hover\", props=[(\"font-size\", \"12pt\")]),\n",
+    "        dict(\n",
+    "            selector=\"tr:hover td:hover\",\n",
+    "            props=[(\"max-width\", \"200px\"), (\"font-size\", \"12pt\")],\n",
+    "        ),\n",
+    "    ]"
    ]
   },
   {
@@ -1567,14 +1649,12 @@
    "outputs": [],
    "source": [
     "np.random.seed(25)\n",
-    "cmap = cmap=sns.diverging_palette(5, 250, as_cmap=True)\n",
+    "cmap = cmap = sns.diverging_palette(5, 250, as_cmap=True)\n",
     "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_caption(\"Hover to magnify\")\\\n",
-    "    .format(precision=2)\\\n",
-    "    .set_table_styles(magnify())"
+    "bigdf.style.background_gradient(cmap, axis=1).set_properties(\n",
+    "    **{\"max-width\": \"80px\", \"font-size\": \"1pt\"}\n",
+    ").set_caption(\"Hover to magnify\").format(precision=2).set_table_styles(magnify())"
    ]
   },
   {
@@ -1611,8 +1691,8 @@
    "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])"
+    "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])"
    ]
   },
   {
@@ -1630,7 +1710,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4 = pd.DataFrame([['<div></div>', '\"&other\"', '<span></span>']])\n",
+    "df4 = pd.DataFrame([[\"<div></div>\", '\"&other\"', \"<span></span>\"]])\n",
     "df4.style"
    ]
   },
@@ -1649,7 +1729,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4.style.format('<a href=\"https://pandas.pydata.org\" target=\"_blank\">{}</a>', escape=\"html\")"
+    "df4.style.format(\n",
+    "    '<a href=\"https://pandas.pydata.org\" target=\"_blank\">{}</a>', escape=\"html\"\n",
+    ")"
    ]
   },
   {
@@ -1691,10 +1773,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df2.style.\\\n",
-    "    applymap(style_negative, props='color:red;').\\\n",
-    "    highlight_max(axis=0).\\\n",
-    "    to_excel('styled.xlsx', engine='openpyxl')"
+    "df2.style.applymap(style_negative, props=\"color:red;\").highlight_max(axis=0).to_excel(\n",
+    "    \"styled.xlsx\", engine=\"openpyxl\"\n",
+    ")"
    ]
   },
   {
@@ -1763,7 +1844,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(pd.DataFrame([[1,2],[3,4]], index=['i1', 'i2'], columns=['c1', 'c2']).style.to_html())"
+    "print(\n",
+    "    pd.DataFrame(\n",
+    "        [[1, 2], [3, 4]], index=[\"i1\", \"i2\"], columns=[\"c1\", \"c2\"]\n",
+    "    ).style.to_html()\n",
+    ")"
    ]
   },
   {
@@ -1781,9 +1866,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4 = pd.DataFrame([['text']])\n",
-    "df4.style.applymap(lambda x: 'color:green;')\\\n",
-    "         .applymap(lambda x: 'color:red;')"
+    "df4 = pd.DataFrame([[\"text\"]])\n",
+    "df4.style.applymap(lambda x: \"color:green;\").applymap(lambda x: \"color:red;\")"
    ]
   },
   {
@@ -1792,8 +1876,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4.style.applymap(lambda x: 'color:red;')\\\n",
-    "         .applymap(lambda x: 'color:green;')"
+    "df4.style.applymap(lambda x: \"color:red;\").applymap(lambda x: \"color:green;\")"
    ]
   },
   {
@@ -1818,9 +1901,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4.style.set_uuid('a_')\\\n",
-    "         .set_table_styles([{'selector': 'td', 'props': 'color:red;'}])\\\n",
-    "         .applymap(lambda x: 'color:green;')"
+    "df4.style.set_uuid(\"a_\").set_table_styles(\n",
+    "    [{\"selector\": \"td\", \"props\": \"color:red;\"}]\n",
+    ").applymap(lambda x: \"color:green;\")"
    ]
   },
   {
@@ -1836,11 +1919,12 @@
    "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",
-    "         .applymap(lambda x: 'color:green;')\\\n",
-    "         .set_td_classes(pd.DataFrame([['cls-1']]))"
+    "df4.style.set_uuid(\"b_\").set_table_styles(\n",
+    "    [\n",
+    "        {\"selector\": \"td\", \"props\": \"color:red;\"},\n",
+    "        {\"selector\": \".cls-1\", \"props\": \"color:blue;\"},\n",
+    "    ]\n",
+    ").applymap(lambda x: \"color:green;\").set_td_classes(pd.DataFrame([[\"cls-1\"]]))"
    ]
   },
   {
@@ -1856,12 +1940,13 @@
    "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",
-    "         .applymap(lambda x: 'color:green;')\\\n",
-    "         .set_td_classes(pd.DataFrame([['cls-1']]))"
+    "df4.style.set_uuid(\"c_\").set_table_styles(\n",
+    "    [\n",
+    "        {\"selector\": \"td\", \"props\": \"color:red;\"},\n",
+    "        {\"selector\": \".cls-1\", \"props\": \"color:blue;\"},\n",
+    "        {\"selector\": \"td.data\", \"props\": \"color:yellow;\"},\n",
+    "    ]\n",
+    ").applymap(lambda x: \"color:green;\").set_td_classes(pd.DataFrame([[\"cls-1\"]]))"
    ]
   },
   {
@@ -1879,12 +1964,15 @@
    "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",
-    "         .applymap(lambda x: 'color:green !important;')\\\n",
-    "         .set_td_classes(pd.DataFrame([['cls-1']]))"
+    "df4.style.set_uuid(\"d_\").set_table_styles(\n",
+    "    [\n",
+    "        {\"selector\": \"td\", \"props\": \"color:red;\"},\n",
+    "        {\"selector\": \".cls-1\", \"props\": \"color:blue;\"},\n",
+    "        {\"selector\": \"td.data\", \"props\": \"color:yellow;\"},\n",
+    "    ]\n",
+    ").applymap(lambda x: \"color:green !important;\").set_td_classes(\n",
+    "    pd.DataFrame([[\"cls-1\"]])\n",
+    ")"
    ]
   },
   {
@@ -1958,10 +2046,12 @@
    "source": [
     "class MyStyler(Styler):\n",
     "    env = Environment(\n",
-    "        loader=ChoiceLoader([\n",
-    "            FileSystemLoader(\"templates\"),  # contains ours\n",
-    "            Styler.loader,  # the default\n",
-    "        ])\n",
+    "        loader=ChoiceLoader(\n",
+    "            [\n",
+    "                FileSystemLoader(\"templates\"),  # contains ours\n",
+    "                Styler.loader,  # the default\n",
+    "            ]\n",
+    "        )\n",
     "    )\n",
     "    template_html_table = env.get_template(\"myhtml.tpl\")"
    ]
@@ -2104,7 +2194,7 @@
     "# 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/scripts/validate_exception_location.py b/scripts/validate_exception_location.py
index b067527826de7..7af5e749b4b96 100644
--- a/scripts/validate_exception_location.py
+++ b/scripts/validate_exception_location.py
@@ -51,10 +51,7 @@ def __init__(self, path: str, exception_set: set[str]) -> None:
 
     def visit_ClassDef(self, node) -> None:
         def is_an_exception_subclass(base_id: str) -> bool:
-            return (
-                base_id == "Exception"
-                or base_id.endswith(("Warning", "Error"))
-            )
+            return base_id == "Exception" or base_id.endswith(("Warning", "Error"))
 
         exception_classes = []
 

From e24c898a330027faa8813bcd78f60160b0058cd2 Mon Sep 17 00:00:00 2001
From: Victor Momodu <victoramomodu@gmail.com>
Date: Thu, 16 Feb 2023 18:25:04 +0100
Subject: [PATCH 3/8] Update .pre-commit-config.yaml

Removed the fix arguments.
---
 .pre-commit-config.yaml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index a6364d51cab41..2f13ac4c73fa0 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -19,7 +19,6 @@ repos:
     rev: v0.0.244
     hooks:
     -   id: ruff
-        args: [--fix, --exit-non-zero-on-fix]
 -   repo: https://github.com/MarcoGorelli/absolufy-imports
     rev: v0.3.1
     hooks:

From 73b39513d019f83bfce9052e20915477a73b09c2 Mon Sep 17 00:00:00 2001
From: Victor Momodu <victoramomodu@gmail.com>
Date: Thu, 16 Feb 2023 18:33:51 +0100
Subject: [PATCH 4/8] Update stata.py

---
 pandas/io/stata.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/pandas/io/stata.py b/pandas/io/stata.py
index fd6e98a6466c8..3738a133cec0a 100644
--- a/pandas/io/stata.py
+++ b/pandas/io/stata.py
@@ -865,22 +865,23 @@ class StataMissingValue:
             MISSING_VALUES[i + b] = "." + chr(96 + i)
 
     float32_base: bytes = b"\x00\x00\x00\x7f"
-    increment: int = struct.unpack("<i", b"\x00\x08\x00\x00")[0]
+    increment_32: int = struct.unpack("<i", b"\x00\x08\x00\x00")[0]
     for i in range(27):
         key = struct.unpack("<f", float32_base)[0]
         MISSING_VALUES[key] = "."
         if i > 0:
             MISSING_VALUES[key] += chr(96 + i)
-        int_value = struct.unpack("<i", struct.pack("<f", key))[0] + increment
+        int_value = struct.unpack("<i", struct.pack("<f", key))[0] + increment_32
         float32_base = struct.pack("<i", int_value)
 
     float64_base: bytes = b"\x00\x00\x00\x00\x00\x00\xe0\x7f"
+    increment_64 = struct.unpack("q", b"\x00\x00\x00\x00\x00\x01\x00\x00")[0]
     for i in range(27):
         key = struct.unpack("<d", float64_base)[0]
         MISSING_VALUES[key] = "."
         if i > 0:
             MISSING_VALUES[key] += chr(96 + i)
-        int_value = struct.unpack("q", struct.pack("<d", key))[0] + increment
+        int_value = struct.unpack("q", struct.pack("<d", key))[0] + increment_64
         float64_base = struct.pack("q", int_value)
 
     BASE_MISSING_VALUES: Final = {

From b4de25dcc67cd4fa0c78162059409f5ed34ade5f Mon Sep 17 00:00:00 2001
From: Victor Momodu <victoramomodu@gmail.com>
Date: Thu, 16 Feb 2023 23:16:38 +0100
Subject: [PATCH 5/8] revert changes to doc/source/user_guide/style.ipynb

---
 doc/source/user_guide/style.ipynb | 652 +++++++++++++-----------------
 1 file changed, 281 insertions(+), 371 deletions(-)

diff --git a/doc/source/user_guide/style.ipynb b/doc/source/user_guide/style.ipynb
index 539885e1f6f03..90e57ad4ad90f 100644
--- a/doc/source/user_guide/style.ipynb
+++ b/doc/source/user_guide/style.ipynb
@@ -47,7 +47,6 @@
    "outputs": [],
    "source": [
     "import matplotlib.pyplot\n",
-    "\n",
     "# We have this here to trigger matplotlib's font cache stuff.\n",
     "# This cell is hidden from the output"
    ]
@@ -81,12 +80,15 @@
     "import numpy as np\n",
     "import matplotlib as mpl\n",
     "\n",
-    "df = pd.DataFrame(\n",
-    "    {\"strings\": [\"Adam\", \"Mike\"], \"ints\": [1, 3], \"floats\": [1.123, 1000.23]}\n",
-    ")\n",
-    "df.style.format(precision=3, thousands=\".\", decimal=\",\").format_index(\n",
-    "    str.upper, axis=1\n",
-    ").relabel_index([\"row 1\", \"row 2\"], axis=0)"
+    "df = pd.DataFrame({\n",
+    "    \"strings\": [\"Adam\", \"Mike\"],\n",
+    "    \"ints\": [1, 3],\n",
+    "    \"floats\": [1.123, 1000.23]\n",
+    "})\n",
+    "df.style \\\n",
+    "  .format(precision=3, thousands=\".\", decimal=\",\") \\\n",
+    "  .format_index(str.upper, axis=1) \\\n",
+    "  .relabel_index([\"row 1\", \"row 2\"], axis=0)"
    ]
   },
   {
@@ -102,21 +104,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "weather_df = pd.DataFrame(\n",
-    "    np.random.rand(10, 2) * 5,\n",
-    "    index=pd.date_range(start=\"2021-01-01\", periods=10),\n",
-    "    columns=[\"Tokyo\", \"Beijing\"],\n",
-    ")\n",
-    "\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",
     "        return \"Rain\"\n",
     "    return \"Heavy Rain\"\n",
     "\n",
-    "\n",
     "def make_pretty(styler):\n",
     "    styler.set_caption(\"Weather Conditions\")\n",
     "    styler.format(rain_condition)\n",
@@ -124,7 +122,6 @@
     "    styler.background_gradient(axis=None, vmin=1, vmax=5, cmap=\"YlGnBu\")\n",
     "    return styler\n",
     "\n",
-    "\n",
     "weather_df"
    ]
   },
@@ -161,7 +158,9 @@
    "outputs": [],
    "source": [
     "df = pd.DataFrame(np.random.randn(5, 5))\n",
-    "df.style.hide(subset=[0, 2, 4], axis=0).hide(subset=[0, 2, 4], axis=1)"
+    "df.style \\\n",
+    "  .hide(subset=[0, 2, 4], axis=0) \\\n",
+    "  .hide(subset=[0, 2, 4], axis=1)"
    ]
   },
   {
@@ -178,9 +177,9 @@
    "outputs": [],
    "source": [
     "show = [0, 2, 4]\n",
-    "df.style.hide([row for row in df.index if row not in show], axis=0).hide(\n",
-    "    [col for col in df.columns if col not in show], axis=1\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)"
    ]
   },
   {
@@ -200,9 +199,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "summary_styler = (\n",
-    "    df.agg([\"sum\", \"mean\"]).style.format(precision=3).relabel_index([\"Sum\", \"Average\"])\n",
-    ")\n",
+    "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)"
    ]
   },
@@ -228,16 +227,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = pd.DataFrame(\n",
-    "    [[38.0, 2.0, 18.0, 22.0, 21, np.nan], [19, 439, 6, 452, 226, 232]],\n",
-    "    index=pd.Index(\n",
-    "        [\"Tumour (Positive)\", \"Non-Tumour (Negative)\"], name=\"Actual Label:\"\n",
-    "    ),\n",
-    "    columns=pd.MultiIndex.from_product(\n",
-    "        [[\"Decision Tree\", \"Regression\", \"Random\"], [\"Tumour\", \"Non-Tumour\"]],\n",
-    "        names=[\"Model:\", \"Predicted:\"],\n",
-    "    ),\n",
-    ")\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"
    ]
   },
@@ -250,66 +242,63 @@
    "outputs": [],
    "source": [
     "# Hidden cell to just create the below example: code is covered throughout the guide.\n",
-    "s = (\n",
-    "    df.style.hide([(\"Random\", \"Tumour\"), (\"Random\", \"Non-Tumour\")], axis=\"columns\")\n",
-    "    .format(\"{:.0f}\")\n",
-    "    .set_table_styles(\n",
-    "        [\n",
-    "            {\"selector\": \"\", \"props\": \"border-collapse: separate;\"},\n",
-    "            {\"selector\": \"caption\", \"props\": \"caption-side: bottom; font-size:1.3em;\"},\n",
-    "            {\n",
-    "                \"selector\": \".index_name\",\n",
-    "                \"props\": \"font-style: italic; color: darkgrey; font-weight:normal;\",\n",
-    "            },\n",
-    "            {\n",
-    "                \"selector\": \"th:not(.index_name)\",\n",
-    "                \"props\": \"background-color: #000066; color: white;\",\n",
-    "            },\n",
-    "            {\"selector\": \"th.col_heading\", \"props\": \"text-align: center;\"},\n",
-    "            {\"selector\": \"th.col_heading.level0\", \"props\": \"font-size: 1.5em;\"},\n",
-    "            {\"selector\": \"th.col2\", \"props\": \"border-left: 1px solid white;\"},\n",
-    "            {\"selector\": \".col2\", \"props\": \"border-left: 1px solid #000066;\"},\n",
-    "            {\"selector\": \"td\", \"props\": \"text-align: center; font-weight:bold;\"},\n",
-    "            {\"selector\": \".true\", \"props\": \"background-color: #e6ffe6;\"},\n",
-    "            {\"selector\": \".false\", \"props\": \"background-color: #ffe6e6;\"},\n",
-    "            {\"selector\": \".border-red\", \"props\": \"border: 2px dashed red;\"},\n",
-    "            {\"selector\": \".border-green\", \"props\": \"border: 2px dashed green;\"},\n",
-    "            {\"selector\": \"td:hover\", \"props\": \"background-color: #ffffb3;\"},\n",
-    "        ]\n",
-    "    )\n",
-    "    .set_td_classes(\n",
-    "        pd.DataFrame(\n",
-    "            [\n",
-    "                [\"true border-green\", \"false\", \"true\", \"false border-red\", \"\", \"\"],\n",
-    "                [\"false\", \"true\", \"false\", \"true\", \"\", \"\"],\n",
-    "            ],\n",
-    "            index=df.index,\n",
-    "            columns=df.columns,\n",
-    "        )\n",
-    "    )\n",
-    "    .set_caption(\"Confusion matrix for multiple cancer prediction models.\")\n",
-    "    .set_tooltips(\n",
-    "        pd.DataFrame(\n",
-    "            [\n",
-    "                [\n",
-    "                    \"This model has a very strong true positive rate\",\n",
-    "                    \"\",\n",
-    "                    \"\",\n",
-    "                    \"This model's total number of false negatives is too high\",\n",
-    "                    \"\",\n",
-    "                    \"\",\n",
-    "                ],\n",
-    "                [\"\", \"\", \"\", \"\", \"\", \"\"],\n",
-    "            ],\n",
-    "            index=df.index,\n",
-    "            columns=df.columns,\n",
-    "        ),\n",
-    "        css_class=\"pd-tt\",\n",
-    "        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;\",\n",
-    "    )\n",
-    ")"
+    "s = df.style\\\n",
+    "      .hide([('Random', 'Tumour'), ('Random', 'Non-Tumour')], axis='columns')\\\n",
+    "      .format('{:.0f}')\\\n",
+    "      .set_table_styles([{\n",
+    "        'selector': '',\n",
+    "        'props':  'border-collapse: separate;'\n",
+    "      },{\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",
+    "      },{\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",
+    "      },{\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",
+    "      },{\n",
+    "        'selector': '.col2',\n",
+    "        'props': 'border-left: 1px solid #000066;'\n",
+    "      },{\n",
+    "        'selector': 'td',\n",
+    "        'props': 'text-align: center; font-weight:bold;'\n",
+    "      },{\n",
+    "        'selector': '.true',\n",
+    "        'props': 'background-color: #e6ffe6;'\n",
+    "      },{\n",
+    "        'selector': '.false',\n",
+    "        'props': 'background-color: #ffe6e6;'\n",
+    "      },{\n",
+    "        'selector': '.border-red',\n",
+    "        'props': 'border: 2px dashed red;'\n",
+    "      },{\n",
+    "        'selector': '.border-green',\n",
+    "        'props': 'border: 2px dashed green;'\n",
+    "      },{\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",
+    "                                    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",
+    "                                    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"
    ]
   },
   {
@@ -336,9 +325,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "s = df.style.format(\"{:.0f}\").hide(\n",
-    "    [(\"Random\", \"Tumour\"), (\"Random\", \"Non-Tumour\")], axis=\"columns\"\n",
-    ")\n",
+    "s = df.style.format('{:.0f}').hide([('Random', 'Tumour'), ('Random', 'Non-Tumour')], axis=\"columns\")\n",
     "s"
    ]
   },
@@ -350,8 +337,8 @@
    },
    "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')"
    ]
   },
   {
@@ -408,16 +395,16 @@
    "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])"
    ]
@@ -430,8 +417,8 @@
    },
    "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')"
    ]
   },
   {
@@ -447,14 +434,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "s.set_table_styles(\n",
-    "    [\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",
-    "    ],\n",
-    "    overwrite=False,\n",
-    ")"
+    "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)"
    ]
   },
   {
@@ -465,8 +449,8 @@
    },
    "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')"
    ]
   },
   {
@@ -484,16 +468,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "s.set_table_styles(\n",
-    "    {\n",
-    "        (\"Regression\", \"Tumour\"): [\n",
-    "            {\"selector\": \"th\", \"props\": \"border-left: 1px solid white\"},\n",
-    "            {\"selector\": \"td\", \"props\": \"border-left: 1px solid #000066\"},\n",
-    "        ]\n",
-    "    },\n",
-    "    overwrite=False,\n",
-    "    axis=0,\n",
-    ")"
+    "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)"
    ]
   },
   {
@@ -504,8 +482,8 @@
    },
    "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')"
    ]
   },
   {
@@ -530,7 +508,7 @@
    "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])"
    ]
   },
   {
@@ -553,18 +531,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "s.set_table_styles(\n",
-    "    [  # create internal CSS classes\n",
-    "        {\"selector\": \".true\", \"props\": \"background-color: #e6ffe6;\"},\n",
-    "        {\"selector\": \".false\", \"props\": \"background-color: #ffe6e6;\"},\n",
-    "    ],\n",
-    "    overwrite=False,\n",
-    ")\n",
-    "cell_color = pd.DataFrame(\n",
-    "    [[\"true \", \"false \", \"true \", \"false \"], [\"false \", \"true \", \"false \", \"true \"]],\n",
-    "    index=df.index,\n",
-    "    columns=df.columns[:4],\n",
-    ")\n",
+    "s.set_table_styles([  # create internal CSS classes\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",
+    "                          columns=df.columns[:4])\n",
     "s.set_td_classes(cell_color)"
    ]
   },
@@ -576,8 +550,8 @@
    },
    "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')"
    ]
   },
   {
@@ -606,7 +580,7 @@
    "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"
    ]
   },
@@ -623,13 +597,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def style_negative(v, props=\"\"):\n",
+    "def style_negative(v, props=''):\n",
     "    return props if v < 0 else None\n",
-    "\n",
-    "\n",
-    "s2 = df2.style.applymap(style_negative, props=\"color:red;\").applymap(\n",
-    "    lambda v: \"opacity: 20%;\" if (v < 0.3) and (v > -0.3) else None\n",
-    ")\n",
+    "s2 = df2.style.applymap(style_negative, props='color:red;')\\\n",
+    "              .applymap(lambda v: 'opacity: 20%;' if (v < 0.3) and (v > -0.3) else None)\n",
     "s2"
    ]
   },
@@ -641,8 +612,8 @@
    },
    "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')"
    ]
   },
   {
@@ -658,11 +629,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def highlight_max(s, props=\"\"):\n",
-    "    return np.where(s == np.nanmax(s.values), props, \"\")\n",
-    "\n",
-    "\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)"
    ]
   },
   {
@@ -673,8 +642,8 @@
    },
    "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')"
    ]
   },
   {
@@ -690,9 +659,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "s2.apply(highlight_max, props=\"color:white;background-color:pink;\", axis=1).apply(\n",
-    "    highlight_max, props=\"color:white;background-color:purple\", axis=None\n",
-    ")"
+    "s2.apply(highlight_max, props='color:white;background-color:pink;', axis=1)\\\n",
+    "  .apply(highlight_max, props='color:white;background-color:purple', axis=None)"
    ]
   },
   {
@@ -703,8 +671,8 @@
    },
    "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')"
    ]
   },
   {
@@ -745,10 +713,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "s2.applymap_index(lambda v: \"color:pink;\" if v > 4 else \"color:darkblue;\", axis=0)\n",
-    "s2.apply_index(\n",
-    "    lambda s: np.where(s.isin([\"A\", \"B\"]), \"color:pink;\", \"color:darkblue;\"), axis=1\n",
-    ")"
+    "s2.applymap_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)"
    ]
   },
   {
@@ -768,12 +734,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "s.set_caption(\n",
-    "    \"Confusion matrix for multiple cancer prediction models.\"\n",
-    ").set_table_styles(\n",
-    "    [{\"selector\": \"caption\", \"props\": \"caption-side: bottom; font-size:1.25em;\"}],\n",
-    "    overwrite=False,\n",
-    ")"
+    "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)"
    ]
   },
   {
@@ -784,8 +749,8 @@
    },
    "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')"
    ]
   },
   {
@@ -803,22 +768,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tt = pd.DataFrame(\n",
-    "    [\n",
-    "        [\n",
-    "            \"This model has a very strong true positive rate\",\n",
-    "            \"This model's total number of false negatives is too high\",\n",
-    "        ]\n",
-    "    ],\n",
-    "    index=[\"Tumour (Positive)\"],\n",
-    "    columns=df.columns[[0, 3]],\n",
-    ")\n",
-    "s.set_tooltips(\n",
-    "    tt,\n",
-    "    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;\",\n",
-    ")"
+    "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;')"
    ]
   },
   {
@@ -829,8 +784,8 @@
    },
    "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')"
    ]
   },
   {
@@ -846,18 +801,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "s.set_table_styles(\n",
-    "    [  # create internal CSS classes\n",
-    "        {\"selector\": \".border-red\", \"props\": \"border: 2px dashed red;\"},\n",
-    "        {\"selector\": \".border-green\", \"props\": \"border: 2px dashed green;\"},\n",
-    "    ],\n",
-    "    overwrite=False,\n",
-    ")\n",
-    "cell_border = pd.DataFrame(\n",
-    "    [[\"border-green \", \" \", \" \", \"border-red \"], [\" \", \" \", \" \", \" \"]],\n",
-    "    index=df.index,\n",
-    "    columns=df.columns[:4],\n",
-    ")\n",
+    "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",
+    "], overwrite=False)\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)"
    ]
   },
@@ -869,8 +820,8 @@
    },
    "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')"
    ]
   },
   {
@@ -896,11 +847,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df3 = pd.DataFrame(\n",
-    "    np.random.randn(4, 4),\n",
-    "    pd.MultiIndex.from_product([[\"A\", \"B\"], [\"r1\", \"r2\"]]),\n",
-    "    columns=[\"c1\", \"c2\", \"c3\", \"c4\"],\n",
-    ")\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"
    ]
   },
@@ -917,10 +866,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "slice_ = [\"c3\", \"c4\"]\n",
-    "df3.style.apply(\n",
-    "    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_)"
    ]
   },
   {
@@ -937,10 +885,9 @@
    "outputs": [],
    "source": [
     "idx = pd.IndexSlice\n",
-    "slice_ = idx[idx[:, \"r1\"], idx[\"c2\":\"c4\"]]\n",
-    "df3.style.apply(\n",
-    "    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_)"
    ]
   },
   {
@@ -956,10 +903,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "slice_ = idx[idx[:, \"r2\"], :]\n",
-    "df3.style.apply(\n",
-    "    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_)"
    ]
   },
   {
@@ -977,10 +923,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "slice_ = idx[idx[(df3[\"c1\"] + df3[\"c3\"]) < -2.0], [\"c2\", \"c4\"]]\n",
-    "df3.style.apply(\n",
-    "    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_)"
    ]
   },
   {
@@ -1036,7 +981,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4 = pd.DataFrame([[1, 2], [3, 4]])\n",
+    "df4 = pd.DataFrame([[1,2],[3,4]])\n",
     "s4 = df4.style"
    ]
   },
@@ -1058,7 +1003,6 @@
    "outputs": [],
    "source": [
     "from pandas.io.formats.style import Styler\n",
-    "\n",
     "s4 = Styler(df4, uuid_len=0, cell_ids=False)"
    ]
   },
@@ -1109,7 +1053,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4.style.set_table_styles([{\"selector\": \"td.col1\", \"props\": props}])"
+    "df4.style.set_table_styles([{'selector': 'td.col1', 'props': props}])"
    ]
   },
   {
@@ -1138,11 +1082,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df2.style.apply(\n",
-    "    highlight_max, props=\"color:white;background-color:darkblue;\", axis=0\n",
-    ").apply(highlight_max, props=\"color:white;background-color:pink;\", axis=1).apply(\n",
-    "    highlight_max, props=\"color:white;background-color:purple\", axis=None\n",
-    ")"
+    "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)"
    ]
   },
   {
@@ -1163,18 +1105,14 @@
    "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(\n",
-    "    df2.apply(highlight_max, props=\"cls-2 \", axis=1, result_type=\"expand\").values\n",
-    ")\n",
-    "cls3 = build(highlight_max(df2, props=\"cls-3 \"))\n",
-    "df2.style.set_table_styles(\n",
-    "    [\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",
-    "    ]\n",
-    ").set_td_classes(cls1 + cls2 + cls3)"
+    "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",
+    "]).set_td_classes(cls1 + cls2 + cls3)"
    ]
   },
   {
@@ -1214,14 +1152,10 @@
     "    \"blank\": \"\",\n",
     "}\n",
     "html = Styler(df4, uuid_len=0, cell_ids=False)\n",
-    "html.set_table_styles(\n",
-    "    [\n",
-    "        {\"selector\": \"td\", \"props\": props},\n",
-    "        {\"selector\": \".c1\", \"props\": \"color:green;\"},\n",
-    "        {\"selector\": \".l0\", \"props\": \"color:blue;\"},\n",
-    "    ],\n",
-    "    css_class_names=my_css,\n",
-    ")\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())"
    ]
   },
@@ -1279,9 +1213,9 @@
    "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\")"
+    "df2.iloc[0,2] = np.nan\n",
+    "df2.iloc[4,3] = np.nan\n",
+    "df2.loc[:4].style.highlight_null(color='yellow')"
    ]
   },
   {
@@ -1297,9 +1231,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df2.loc[:4].style.highlight_max(\n",
-    "    axis=1, props=\"color:white; font-weight:bold; background-color:darkblue;\"\n",
-    ")"
+    "df2.loc[:4].style.highlight_max(axis=1, props='color:white; font-weight:bold; background-color:darkblue;')"
    ]
   },
   {
@@ -1317,9 +1249,7 @@
    "outputs": [],
    "source": [
     "left = pd.Series([1.0, 0.0, 1.0], index=[\"A\", \"B\", \"D\"])\n",
-    "df2.loc[:4].style.highlight_between(\n",
-    "    left=left, right=1.5, axis=1, props=\"color:white; background-color:purple;\"\n",
-    ")"
+    "df2.loc[:4].style.highlight_between(left=left, right=1.5, axis=1, props='color:white; background-color:purple;')"
    ]
   },
   {
@@ -1336,7 +1266,7 @@
    "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')"
    ]
   },
   {
@@ -1360,7 +1290,6 @@
    "outputs": [],
    "source": [
     "import seaborn as sns\n",
-    "\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
     "\n",
     "df2.style.background_gradient(cmap=cm)"
@@ -1400,9 +1329,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df2.loc[:4].style.set_properties(\n",
-    "    **{\"background-color\": \"black\", \"color\": \"lawngreen\", \"border-color\": \"white\"}\n",
-    ")"
+    "df2.loc[:4].style.set_properties(**{'background-color': 'black',\n",
+    "                           'color': 'lawngreen',\n",
+    "                           'border-color': 'white'})"
    ]
   },
   {
@@ -1425,7 +1354,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df2.style.bar(subset=[\"A\", \"B\"], color=\"#d65f5f\")"
+    "df2.style.bar(subset=['A', 'B'], color='#d65f5f')"
    ]
   },
   {
@@ -1443,15 +1372,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df2.style.format(\"{:.3f}\", na_rep=\"\").bar(\n",
-    "    align=0,\n",
-    "    vmin=-2.5,\n",
-    "    vmax=2.5,\n",
-    "    cmap=\"bwr\",\n",
-    "    height=50,\n",
-    "    width=60,\n",
-    "    props=\"width: 120px; border-right: 1px solid black;\",\n",
-    ").text_gradient(cmap=\"bwr\", vmin=-2.5, vmax=2.5)"
+    "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)"
    ]
   },
   {
@@ -1474,10 +1398,10 @@
     "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",
@@ -1493,22 +1417,19 @@
     "\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",
+    "    for series in [test1,test2,test3, test4]:\n",
     "        s = series.copy()\n",
-    "        s.name = \"\"\n",
-    "        row += \"<td>{}</td>\".format(\n",
-    "            s.to_frame()\n",
-    "            .style.hide(axis=\"index\")\n",
-    "            .bar(align=align, color=[\"#d65f5f\", \"#5fba7d\"], width=100)\n",
-    "            .to_html()\n",
-    "        )  # testn['width']\n",
-    "    row += \"</tr>\"\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",
     "    head += row\n",
-    "\n",
-    "head += \"\"\"\n",
+    "    \n",
+    "head+= \"\"\"\n",
     "</tbody>\n",
     "</table>\"\"\""
    ]
@@ -1542,12 +1463,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "style1 = (\n",
-    "    df2.style.applymap(style_negative, props=\"color:red;\")\n",
-    "    .applymap(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",
-    ")\n",
+    "style1 = df2.style\\\n",
+    "            .applymap(style_negative, props='color:red;')\\\n",
+    "            .applymap(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"
    ]
   },
@@ -1606,14 +1526,11 @@
    "outputs": [],
    "source": [
     "from ipywidgets import widgets\n",
-    "\n",
-    "\n",
     "@widgets.interact\n",
-    "def f(h_neg=(0, 359, 1), h_pos=(0, 359), s=(0.0, 99.9), l=(0.0, 99.9)):\n",
+    "def f(h_neg=(0, 359, 1), h_pos=(0, 359), s=(0., 99.9), l=(0., 99.9)):\n",
     "    return df2.style.background_gradient(\n",
-    "        cmap=sns.palettes.diverging_palette(\n",
-    "            h_neg=h_neg, h_pos=h_pos, s=s, l=l, as_cmap=True\n",
-    "        )\n",
+    "        cmap=sns.palettes.diverging_palette(h_neg=h_neg, h_pos=h_pos, s=s, l=l,\n",
+    "                                            as_cmap=True)\n",
     "    )"
    ]
   },
@@ -1631,15 +1548,16 @@
    "outputs": [],
    "source": [
     "def magnify():\n",
-    "    return [\n",
-    "        dict(selector=\"th\", props=[(\"font-size\", \"4pt\")]),\n",
-    "        dict(selector=\"td\", props=[(\"padding\", \"0em 0em\")]),\n",
-    "        dict(selector=\"th:hover\", props=[(\"font-size\", \"12pt\")]),\n",
-    "        dict(\n",
-    "            selector=\"tr:hover td:hover\",\n",
-    "            props=[(\"max-width\", \"200px\"), (\"font-size\", \"12pt\")],\n",
-    "        ),\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",
+    "]"
    ]
   },
   {
@@ -1649,12 +1567,14 @@
    "outputs": [],
    "source": [
     "np.random.seed(25)\n",
-    "cmap = cmap = sns.diverging_palette(5, 250, as_cmap=True)\n",
+    "cmap = cmap=sns.diverging_palette(5, 250, as_cmap=True)\n",
     "bigdf = pd.DataFrame(np.random.randn(20, 25)).cumsum()\n",
     "\n",
-    "bigdf.style.background_gradient(cmap, axis=1).set_properties(\n",
-    "    **{\"max-width\": \"80px\", \"font-size\": \"1pt\"}\n",
-    ").set_caption(\"Hover to magnify\").format(precision=2).set_table_styles(magnify())"
+    "bigdf.style.background_gradient(cmap, axis=1)\\\n",
+    "    .set_properties(**{'max-width': '80px', 'font-size': '1pt'})\\\n",
+    "    .set_caption(\"Hover to magnify\")\\\n",
+    "    .format(precision=2)\\\n",
+    "    .set_table_styles(magnify())"
    ]
   },
   {
@@ -1691,8 +1611,8 @@
    "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])"
+    "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])"
    ]
   },
   {
@@ -1710,7 +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"
    ]
   },
@@ -1729,9 +1649,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4.style.format(\n",
-    "    '<a href=\"https://pandas.pydata.org\" target=\"_blank\">{}</a>', escape=\"html\"\n",
-    ")"
+    "df4.style.format('<a href=\"https://pandas.pydata.org\" target=\"_blank\">{}</a>', escape=\"html\")"
    ]
   },
   {
@@ -1773,9 +1691,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df2.style.applymap(style_negative, props=\"color:red;\").highlight_max(axis=0).to_excel(\n",
-    "    \"styled.xlsx\", engine=\"openpyxl\"\n",
-    ")"
+    "df2.style.\\\n",
+    "    applymap(style_negative, props='color:red;').\\\n",
+    "    highlight_max(axis=0).\\\n",
+    "    to_excel('styled.xlsx', engine='openpyxl')"
    ]
   },
   {
@@ -1844,11 +1763,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(\n",
-    "    pd.DataFrame(\n",
-    "        [[1, 2], [3, 4]], index=[\"i1\", \"i2\"], columns=[\"c1\", \"c2\"]\n",
-    "    ).style.to_html()\n",
-    ")"
+    "print(pd.DataFrame([[1,2],[3,4]], index=['i1', 'i2'], columns=['c1', 'c2']).style.to_html())"
    ]
   },
   {
@@ -1866,8 +1781,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4 = pd.DataFrame([[\"text\"]])\n",
-    "df4.style.applymap(lambda x: \"color:green;\").applymap(lambda x: \"color:red;\")"
+    "df4 = pd.DataFrame([['text']])\n",
+    "df4.style.applymap(lambda x: 'color:green;')\\\n",
+    "         .applymap(lambda x: 'color:red;')"
    ]
   },
   {
@@ -1876,7 +1792,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4.style.applymap(lambda x: \"color:red;\").applymap(lambda x: \"color:green;\")"
+    "df4.style.applymap(lambda x: 'color:red;')\\\n",
+    "         .applymap(lambda x: 'color:green;')"
    ]
   },
   {
@@ -1901,9 +1818,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4.style.set_uuid(\"a_\").set_table_styles(\n",
-    "    [{\"selector\": \"td\", \"props\": \"color:red;\"}]\n",
-    ").applymap(lambda x: \"color:green;\")"
+    "df4.style.set_uuid('a_')\\\n",
+    "         .set_table_styles([{'selector': 'td', 'props': 'color:red;'}])\\\n",
+    "         .applymap(lambda x: 'color:green;')"
    ]
   },
   {
@@ -1919,12 +1836,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4.style.set_uuid(\"b_\").set_table_styles(\n",
-    "    [\n",
-    "        {\"selector\": \"td\", \"props\": \"color:red;\"},\n",
-    "        {\"selector\": \".cls-1\", \"props\": \"color:blue;\"},\n",
-    "    ]\n",
-    ").applymap(lambda x: \"color:green;\").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",
+    "         .applymap(lambda x: 'color:green;')\\\n",
+    "         .set_td_classes(pd.DataFrame([['cls-1']]))"
    ]
   },
   {
@@ -1940,13 +1856,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4.style.set_uuid(\"c_\").set_table_styles(\n",
-    "    [\n",
-    "        {\"selector\": \"td\", \"props\": \"color:red;\"},\n",
-    "        {\"selector\": \".cls-1\", \"props\": \"color:blue;\"},\n",
-    "        {\"selector\": \"td.data\", \"props\": \"color:yellow;\"},\n",
-    "    ]\n",
-    ").applymap(lambda x: \"color:green;\").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",
+    "         .applymap(lambda x: 'color:green;')\\\n",
+    "         .set_td_classes(pd.DataFrame([['cls-1']]))"
    ]
   },
   {
@@ -1964,15 +1879,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4.style.set_uuid(\"d_\").set_table_styles(\n",
-    "    [\n",
-    "        {\"selector\": \"td\", \"props\": \"color:red;\"},\n",
-    "        {\"selector\": \".cls-1\", \"props\": \"color:blue;\"},\n",
-    "        {\"selector\": \"td.data\", \"props\": \"color:yellow;\"},\n",
-    "    ]\n",
-    ").applymap(lambda x: \"color:green !important;\").set_td_classes(\n",
-    "    pd.DataFrame([[\"cls-1\"]])\n",
-    ")"
+    "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",
+    "         .applymap(lambda x: 'color:green !important;')\\\n",
+    "         .set_td_classes(pd.DataFrame([['cls-1']]))"
    ]
   },
   {
@@ -2046,12 +1958,10 @@
    "source": [
     "class MyStyler(Styler):\n",
     "    env = Environment(\n",
-    "        loader=ChoiceLoader(\n",
-    "            [\n",
-    "                FileSystemLoader(\"templates\"),  # contains ours\n",
-    "                Styler.loader,  # the default\n",
-    "            ]\n",
-    "        )\n",
+    "        loader=ChoiceLoader([\n",
+    "            FileSystemLoader(\"templates\"),  # contains ours\n",
+    "            Styler.loader,  # the default\n",
+    "        ])\n",
     "    )\n",
     "    template_html_table = env.get_template(\"myhtml.tpl\")"
    ]
@@ -2194,7 +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))"
    ]
   }

From da52ef774c9e5b45da09cd2e7c5bb1f3d4e3b8e5 Mon Sep 17 00:00:00 2001
From: Victor Momodu <victoramomodu@gmail.com>
Date: Tue, 21 Feb 2023 14:22:48 +0100
Subject: [PATCH 6/8] Update test_indexing.py

---
 pandas/tests/copy_view/test_indexing.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pandas/tests/copy_view/test_indexing.py b/pandas/tests/copy_view/test_indexing.py
index 7567ca27b8a97..ca2954f0d390e 100644
--- a/pandas/tests/copy_view/test_indexing.py
+++ b/pandas/tests/copy_view/test_indexing.py
@@ -583,8 +583,8 @@ def test_subset_chained_getitem(
         # with ArrayManager, it doesn't matter whether we have
         # single vs mixed block or numpy vs nullable dtypes
         subset_is_view = test_callspec.endswith(
-            "column-iloc-slice"
-        ) or test_callspec.endswith("column-loc-slice")
+            ("column-iloc-slice", "column-loc-slice")
+        )
 
     # modify subset -> don't modify parent
     subset = method(df)

From 2cdc0914eacf842b893c37635a4f46056bb83245 Mon Sep 17 00:00:00 2001
From: Victor Momodu <victoramomodu@gmail.com>
Date: Tue, 21 Feb 2023 16:07:45 +0100
Subject: [PATCH 7/8] Update test_to_records.py and css.py

Changed dtype_mappings and CSS_EXPANSIONS respectively.
---
 pandas/io/formats/css.py                      | 12 +++++-------
 pandas/tests/frame/methods/test_to_records.py |  2 +-
 2 files changed, 6 insertions(+), 8 deletions(-)

diff --git a/pandas/io/formats/css.py b/pandas/io/formats/css.py
index d757b0ec18a31..1763147b0c3e3 100644
--- a/pandas/io/formats/css.py
+++ b/pandas/io/formats/css.py
@@ -200,20 +200,18 @@ class CSSResolver:
     SIDES = ("top", "right", "bottom", "left")
 
     CSS_EXPANSIONS = {
-        {
+        **{
             (f"border-{prop}" if prop else "border"): _border_expander(prop)
             for prop in ["", "top", "right", "bottom", "left"]
         },
-        {
+        **{
             f"border-{prop}": _side_expander(f"border-{{:s}}-{prop}")
             for prop in ["color", "style", "width"]
         },
-        {
-            "margin": _side_expander("margin-{:s}"),
-            "padding": _side_expander("padding-{:s}"),
-        },
+        "margin": _side_expander("margin-{:s}"),
+        "padding": _side_expander("padding-{:s}"),
     }
-
+    
     def __call__(
         self,
         declarations: str | Iterable[tuple[str, str]],
diff --git a/pandas/tests/frame/methods/test_to_records.py b/pandas/tests/frame/methods/test_to_records.py
index ebf99d3cf2e57..f2eea452764a6 100644
--- a/pandas/tests/frame/methods/test_to_records.py
+++ b/pandas/tests/frame/methods/test_to_records.py
@@ -489,7 +489,7 @@ def keys(self):
         df = DataFrame({"A": [1, 2], "B": [0.2, 1.5], "C": ["a", "bc"]})
 
         dtype_mappings = {
-            "column_dtypes": DictLike({"A": np.int8, "B": np.float32}),
+            "column_dtypes": DictLike(A=np.int8, B=np.float32),
             "index_dtypes": f"{tm.ENDIAN}U2",
         }
 

From 6cd00ab97dad50bec7e4aea4e506f28fbdaafed6 Mon Sep 17 00:00:00 2001
From: Victor Momodu <victoramomodu@gmail.com>
Date: Tue, 21 Feb 2023 16:36:32 +0100
Subject: [PATCH 8/8] Update css.py

Ran black pre-commit hook.
---
 pandas/io/formats/css.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pandas/io/formats/css.py b/pandas/io/formats/css.py
index 1763147b0c3e3..c3a87e04c1158 100644
--- a/pandas/io/formats/css.py
+++ b/pandas/io/formats/css.py
@@ -211,7 +211,7 @@ class CSSResolver:
         "margin": _side_expander("margin-{:s}"),
         "padding": _side_expander("padding-{:s}"),
     }
-    
+
     def __call__(
         self,
         declarations: str | Iterable[tuple[str, str]],