From f6fcdf8363e11544d18f6264b6052141bc73ddb4 Mon Sep 17 00:00:00 2001
From: Gaurav Sheni <gvsheni@gmail.com>
Date: Tue, 10 May 2022 18:37:35 -0400
Subject: [PATCH 01/19] Update __init__.py

---
 pandas/__init__.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/pandas/__init__.py b/pandas/__init__.py
index 3645e8744d8af..7f1d3732ed008 100644
--- a/pandas/__init__.py
+++ b/pandas/__init__.py
@@ -57,6 +57,8 @@
     UInt64Dtype,
     Float32Dtype,
     Float64Dtype,
+    Float32ArrowDtype,
+    Float64ArrowDtype,
     CategoricalDtype,
     PeriodDtype,
     IntervalDtype,

From 8c39928246cec320ad379858c1de888e3e9f6b6d Mon Sep 17 00:00:00 2001
From: Gaurav Sheni <gvsheni@gmail.com>
Date: Tue, 10 May 2022 18:38:36 -0400
Subject: [PATCH 02/19] Update api.py

---
 pandas/core/api.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/pandas/core/api.py b/pandas/core/api.py
index cf082d2013d3b..1c72f2fb6f214 100644
--- a/pandas/core/api.py
+++ b/pandas/core/api.py
@@ -31,6 +31,8 @@
 from pandas.core.arrays.floating import (
     Float32Dtype,
     Float64Dtype,
+    Float32ArrowDtype,
+    Float64ArrowDtype,
 )
 from pandas.core.arrays.integer import (
     Int8Dtype,

From c7add0b6fe53d5ce5316d196a294376fab7cb70d Mon Sep 17 00:00:00 2001
From: Gaurav Sheni <gvsheni@gmail.com>
Date: Wed, 11 May 2022 00:20:41 -0400
Subject: [PATCH 03/19] added floatingdtype and lfloatingarrowarray

---
 pandas/core/api.py                   |  2 +
 pandas/core/arrays/arrow/floating.py | 69 ++++++++++++++++++++++++++++
 2 files changed, 71 insertions(+)
 create mode 100644 pandas/core/arrays/arrow/floating.py

diff --git a/pandas/core/api.py b/pandas/core/api.py
index 1c72f2fb6f214..cbea384b7ad8e 100644
--- a/pandas/core/api.py
+++ b/pandas/core/api.py
@@ -31,6 +31,8 @@
 from pandas.core.arrays.floating import (
     Float32Dtype,
     Float64Dtype,
+)
+from pandas.core.arrays.arrow.floating import (
     Float32ArrowDtype,
     Float64ArrowDtype,
 )
diff --git a/pandas/core/arrays/arrow/floating.py b/pandas/core/arrays/arrow/floating.py
new file mode 100644
index 0000000000000..dc3d8c4131724
--- /dev/null
+++ b/pandas/core/arrays/arrow/floating.py
@@ -0,0 +1,69 @@
+from __future__ import annotations
+
+from typing import (
+    Any,
+    Callable,
+    TypeVar,
+)
+
+import pyarrow as pa
+
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import cache_readonly
+
+from pandas.core.arrays.arrow.array import ArrowExtensionArray
+from pandas.core.arrays.arrow.dtype import ArrowDtype
+
+T = TypeVar("T", bound="FloatingArrowArray")
+
+
+class FloatingArrowDtype(ArrowDtype):
+    _default_pa_dtype: pa.null()
+    _dtype_checker: Callable[[Any], bool]  # pa.types.is_<type>
+
+    @property
+    def _is_numeric(self) -> bool:
+        return False
+
+    @property
+    def _is_float(self) -> bool:
+        return True
+
+    @classmethod
+    def _str_to_dtype_mapping(cls):
+        raise AbstractMethodError(cls)
+
+
+class FloatingArrowArray(ArrowExtensionArray):
+    """
+    Base class for Floating dtypes.
+    """
+
+    _dtype_cls: type[FloatingArrowDtype]
+
+    def __init__(self, values: pa.ChunkedArray) -> None:
+        checker = self._dtype_cls._dtype_checker
+        if not (isinstance(values, pa.ChunkedArray) and checker(values.type)):
+            descr = (
+                "floating"
+            )
+            raise TypeError(f"values should be {descr} arrow array.")
+        super().__init__(values)
+
+    @cache_readonly
+    def dtype(self) -> FloatingArrowDtype:
+        mapping = self._dtype_cls._str_to_dtype_mapping()
+        return mapping[str(self._data.type)]
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype=None, copy: bool = False):
+        if dtype is None:
+            dtype = cls._dtype_cls._default_pa_dtype
+        return cls(pa.chunked_array([scalars], type=dtype.type))
+
+    @classmethod
+    def _from_sequence_of_strings(cls, strings, *, dtype=None, copy: bool = False):
+        from pandas.core.tools.numeric import to_numeric
+
+        scalars = to_numeric(strings, errors="raise")
+        return cls._from_sequence(scalars, dtype=dtype, copy=copy)
\ No newline at end of file

From 1b38b5fed4d5cfc728e33f456a73d137f0a3f152 Mon Sep 17 00:00:00 2001
From: Gaurav Sheni <gvsheni@gmail.com>
Date: Wed, 11 May 2022 00:28:12 -0400
Subject: [PATCH 04/19] use numeric array

---
 pandas/core/arrays/arrow/floating.py | 124 +++++++++++++++++----------
 pandas/core/arrays/arrow/numeric.py  |  69 +++++++++++++++
 2 files changed, 146 insertions(+), 47 deletions(-)
 create mode 100644 pandas/core/arrays/arrow/numeric.py

diff --git a/pandas/core/arrays/arrow/floating.py b/pandas/core/arrays/arrow/floating.py
index dc3d8c4131724..aa1e82eeaa79b 100644
--- a/pandas/core/arrays/arrow/floating.py
+++ b/pandas/core/arrays/arrow/floating.py
@@ -1,69 +1,99 @@
 from __future__ import annotations
 
-from typing import (
-    Any,
-    Callable,
-    TypeVar,
-)
-
 import pyarrow as pa
 
-from pandas.errors import AbstractMethodError
-from pandas.util._decorators import cache_readonly
+from pandas.core.dtypes.base import register_extension_dtype
 
-from pandas.core.arrays.arrow.array import ArrowExtensionArray
-from pandas.core.arrays.arrow.dtype import ArrowDtype
-
-T = TypeVar("T", bound="FloatingArrowArray")
+from pandas.core.arrays.arrow.numeric import (
+    FloatingArrowArray,
+    FloatingArrowDtype,
+)
 
 
-class FloatingArrowDtype(ArrowDtype):
-    _default_pa_dtype: pa.null()
-    _dtype_checker: Callable[[Any], bool]  # pa.types.is_<type>
+class FloatingArrowDtype(NumericArrowDtype):
+    """
+    An ExtensionDtype to hold a single size & kind of integer Arrow dtype.
+    These specific implementations are subclasses of the non-public
+    FloatingArrowDtype. 
+    """
 
-    @property
-    def _is_numeric(self) -> bool:
-        return False
+    _default_pa_dtype = pa.float64()
+    _dtype_checker = pa.types.is_floating
 
-    @property
-    def _is_float(self) -> bool:
-        return True
+    @classmethod
+    def construct_array_type(cls) -> type[FloatingArrowArray]:
+        """
+        Return the array type associated with this dtype.
+        Returns
+        -------
+        type
+        """
+        return FloatingArrowArray
 
     @classmethod
     def _str_to_dtype_mapping(cls):
-        raise AbstractMethodError(cls)
+        return INT_STR_TO_DTYPE
 
 
-class FloatingArrowArray(ArrowExtensionArray):
+class FloatingArrowArray(NumericArrowArray):
     """
-    Base class for Floating dtypes.
+    Array of pyarrow integer values.
+    To construct an IntegerArray from generic array-like ipaut, use
+    :func:`pandas.array` with one of the integer dtypes (see examples).
+    Parameters
+    ----------
+    values : pa.ChunkedArray
+        A 1-d integer-dtype array.
+    Attributes
+    ----------
+    None
+    Methods
+    -------
+    None
+    Returns
+    -------
+    FloatingArrowArray
     """
 
-    _dtype_cls: type[FloatingArrowDtype]
+    _dtype_cls = FloatingArrowDtype
 
-    def __init__(self, values: pa.ChunkedArray) -> None:
-        checker = self._dtype_cls._dtype_checker
-        if not (isinstance(values, pa.ChunkedArray) and checker(values.type)):
-            descr = (
-                "floating"
-            )
-            raise TypeError(f"values should be {descr} arrow array.")
-        super().__init__(values)
 
-    @cache_readonly
-    def dtype(self) -> FloatingArrowDtype:
-        mapping = self._dtype_cls._str_to_dtype_mapping()
-        return mapping[str(self._data.type)]
+_dtype_docstring = """
+An ExtensionDtype for {dtype} integer pyarrow data.
+Attributes
+----------
+None
+Methods
+-------
+None
+"""
 
-    @classmethod
-    def _from_sequence(cls, scalars, *, dtype=None, copy: bool = False):
-        if dtype is None:
-            dtype = cls._dtype_cls._default_pa_dtype
-        return cls(pa.chunked_array([scalars], type=dtype.type))
+# create the Dtype
+
+
+@register_extension_dtype
+class Float16ArrowDtype(FloatingArrowDtype):
+    type = pa.float16()
+    name = "float16"
+    __doc__ = _dtype_docstring.format(dtype="float16")
+
+
+@register_extension_dtype
+class Float32ArrowDtype(FloatingArrowDtype):
+    type = pa.float32()
+    name = "float32"
+    __doc__ = _dtype_docstring.format(dtype="float32")
+
+
+@register_extension_dtype
+class Float64ArrowDtype(FloatingArrowDtype):
+    type = pa.float64()
+    name = "float64"
+    __doc__ = _dtype_docstring.format(dtype="float64")
 
-    @classmethod
-    def _from_sequence_of_strings(cls, strings, *, dtype=None, copy: bool = False):
-        from pandas.core.tools.numeric import to_numeric
 
-        scalars = to_numeric(strings, errors="raise")
-        return cls._from_sequence(scalars, dtype=dtype, copy=copy)
\ No newline at end of file
+INT_STR_TO_DTYPE: dict[str, FloatingArrowDtype] = {
+    "float16": Float16ArrowDtype(),
+    "float32": Float32ArrowDtype(),
+    "float64": Float64ArrowDtype(),
+}
\ No newline at end of file
diff --git a/pandas/core/arrays/arrow/numeric.py b/pandas/core/arrays/arrow/numeric.py
new file mode 100644
index 0000000000000..dc3d8c4131724
--- /dev/null
+++ b/pandas/core/arrays/arrow/numeric.py
@@ -0,0 +1,69 @@
+from __future__ import annotations
+
+from typing import (
+    Any,
+    Callable,
+    TypeVar,
+)
+
+import pyarrow as pa
+
+from pandas.errors import AbstractMethodError
+from pandas.util._decorators import cache_readonly
+
+from pandas.core.arrays.arrow.array import ArrowExtensionArray
+from pandas.core.arrays.arrow.dtype import ArrowDtype
+
+T = TypeVar("T", bound="FloatingArrowArray")
+
+
+class FloatingArrowDtype(ArrowDtype):
+    _default_pa_dtype: pa.null()
+    _dtype_checker: Callable[[Any], bool]  # pa.types.is_<type>
+
+    @property
+    def _is_numeric(self) -> bool:
+        return False
+
+    @property
+    def _is_float(self) -> bool:
+        return True
+
+    @classmethod
+    def _str_to_dtype_mapping(cls):
+        raise AbstractMethodError(cls)
+
+
+class FloatingArrowArray(ArrowExtensionArray):
+    """
+    Base class for Floating dtypes.
+    """
+
+    _dtype_cls: type[FloatingArrowDtype]
+
+    def __init__(self, values: pa.ChunkedArray) -> None:
+        checker = self._dtype_cls._dtype_checker
+        if not (isinstance(values, pa.ChunkedArray) and checker(values.type)):
+            descr = (
+                "floating"
+            )
+            raise TypeError(f"values should be {descr} arrow array.")
+        super().__init__(values)
+
+    @cache_readonly
+    def dtype(self) -> FloatingArrowDtype:
+        mapping = self._dtype_cls._str_to_dtype_mapping()
+        return mapping[str(self._data.type)]
+
+    @classmethod
+    def _from_sequence(cls, scalars, *, dtype=None, copy: bool = False):
+        if dtype is None:
+            dtype = cls._dtype_cls._default_pa_dtype
+        return cls(pa.chunked_array([scalars], type=dtype.type))
+
+    @classmethod
+    def _from_sequence_of_strings(cls, strings, *, dtype=None, copy: bool = False):
+        from pandas.core.tools.numeric import to_numeric
+
+        scalars = to_numeric(strings, errors="raise")
+        return cls._from_sequence(scalars, dtype=dtype, copy=copy)
\ No newline at end of file

From f8e5551312334ba67244c135fcae647e5a3376e2 Mon Sep 17 00:00:00 2001
From: Gaurav Sheni <gvsheni@gmail.com>
Date: Wed, 11 May 2022 22:09:57 -0400
Subject: [PATCH 05/19] more dtype checking

---
 pandas/core/arrays/arrow/array.py | 28 +++++++++++++++++++++++++++-
 pandas/core/arrays/arrow/dtype.py | 31 ++++++++++++++++++++++++++++++-
 2 files changed, 57 insertions(+), 2 deletions(-)

diff --git a/pandas/core/arrays/arrow/array.py b/pandas/core/arrays/arrow/array.py
index fdd505e259dd9..b805e82660f3d 100644
--- a/pandas/core/arrays/arrow/array.py
+++ b/pandas/core/arrays/arrow/array.py
@@ -39,6 +39,7 @@
     import pyarrow.compute as pc
 
     from pandas.core.arrays.arrow._arrow_utils import fallback_performancewarning
+    from pandas.core.arrays.arrow.dtype import ArrowDtype
 
 if TYPE_CHECKING:
     from pandas import Series
@@ -53,8 +54,11 @@ class ArrowExtensionArray(ExtensionArray):
 
     _data: pa.ChunkedArray
 
-    def __init__(self, values: pa.ChunkedArray) -> None:
+    def __init__(self, values: pa.ChunkedArray, pa_dtype: pa.DataType) -> None:
         self._data = values
+        self.storage = storage
+        self._dtype = ArrowDtype(storage="pyarrow", pa_dtype=pa_dtype)
+
 
     def __arrow_array__(self, type=None):
         """Convert myself to a pyarrow Array or ChunkedArray."""
@@ -468,3 +472,25 @@ def _replace_with_indices(
             return pc.if_else(mask, None, chunk)
 
         return pc.replace_with_mask(chunk, mask, value)
+
+    @cache_readonly
+    def dtype(self) -> NumericArrowDtype:
+        return self._dtype
+
+    @classmethod
+    def _from_sequence_of_strings(cls, strings, *, dtype=None, copy: bool = False):
+        if self.dtype._is_numeric:
+            from pandas.core.tools.numeric import to_numeric
+
+            scalars = to_numeric(strings, errors="raise")
+        elif self.dtype._is_temporal:
+            from pandas.core.tools.datetimes import to_datetime
+
+            scalars = to_datetime(strings, error="raise")
+        return cls._from_sequence(scalars, dtype=dtype, copy=copy)
+
+    def mean(self, skipna=True):
+        if self.dtype._is_numeric:
+            return pa.compute.mean(self._data, skip_nulls=skipna)
+        else:
+            raise TypeError(f"Cannot compute mean from '{string}'")
diff --git a/pandas/core/arrays/arrow/dtype.py b/pandas/core/arrays/arrow/dtype.py
index c0ecb0856f27f..ca75f33efc725 100644
--- a/pandas/core/arrays/arrow/dtype.py
+++ b/pandas/core/arrays/arrow/dtype.py
@@ -23,9 +23,38 @@ class ArrowDtype(StorageExtensionDtype):
 
     na_value = pa.NA
 
-    def __init__(self, storage="pyarrow") -> None:
+    def __init__(self, pa_dtype, storage="pyarrow") -> None:
+        self.pa_dtype = pa_dtype
+        self.storage = storage
         super().__init__(storage)
 
+    def _is_numeric(self):
+        return pa.types.is_integer(self.pa_dtype) or pa.types.is_float(self.pa_dtype)
+
+    def _is_integer(self):
+        return pa.types.is_integer(self.pa_dtype)
+
+    def _is_boolean(self):
+        return pa.types.is_boolean(self.pa_dtype)
+
+    def _is_floating(self):
+        return pa.types.is_floating(self.pa_dtype)
+
+    def _is_temporal(self):
+        return pa.types.is_temporal(self.pa_dtype)
+
+    def _is_floating(self):
+        return pa.types.is_floating(self.pa_dtype)
+
+    def _is_date(self):
+        return pa.types.is_date(self.pa_dtype)
+
+    def _is_time(self):
+        return pa.types.is_time(self.pa_dtype)
+
+    def _is_string(self):
+        return pa.types.is_string(self.pa_dtype)
+
     @cache_readonly
     def numpy_dtype(self) -> np.dtype:
         """Return an instance of the related numpy dtype"""

From f35674420fb820d55df2ff48002a702dad5a8a53 Mon Sep 17 00:00:00 2001
From: Gaurav Sheni <gvsheni@gmail.com>
Date: Wed, 11 May 2022 22:25:23 -0400
Subject: [PATCH 06/19] change imports:

---
 pandas/core/arrays/arrow/floating.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/pandas/core/arrays/arrow/floating.py b/pandas/core/arrays/arrow/floating.py
index aa1e82eeaa79b..ac2d60a4e0316 100644
--- a/pandas/core/arrays/arrow/floating.py
+++ b/pandas/core/arrays/arrow/floating.py
@@ -4,21 +4,20 @@
 
 from pandas.core.dtypes.base import register_extension_dtype
 
-from pandas.core.arrays.arrow.numeric import (
+from pandas.core.arrays.arrow.dtype import (
+    FloatingArrowArray,
     FloatingArrowArray,
-    FloatingArrowDtype,
 )
 
 
-class FloatingArrowDtype(NumericArrowDtype):
+class FloatingArrowDtype(FloatingArrowArray):
     """
     An ExtensionDtype to hold a single size & kind of integer Arrow dtype.
     These specific implementations are subclasses of the non-public
     FloatingArrowDtype. 
     """
 
-    _default_pa_dtype = pa.float64()
-    _dtype_checker = pa.types.is_floating
+    self.pa_dtype = pa.float64()
 
     @classmethod
     def construct_array_type(cls) -> type[FloatingArrowArray]:

From d414a9e46e58aab501ffa94f42ffa0d56ebfb264 Mon Sep 17 00:00:00 2001
From: Gaurav Sheni <gvsheni@gmail.com>
Date: Wed, 11 May 2022 22:28:40 -0400
Subject: [PATCH 07/19] add float 16 arrow dtype

---
 pandas/__init__.py | 1 +
 pandas/core/api.py | 1 +
 2 files changed, 2 insertions(+)

diff --git a/pandas/__init__.py b/pandas/__init__.py
index 7f1d3732ed008..f31d021119598 100644
--- a/pandas/__init__.py
+++ b/pandas/__init__.py
@@ -57,6 +57,7 @@
     UInt64Dtype,
     Float32Dtype,
     Float64Dtype,
+    Float16ArrowDtype,
     Float32ArrowDtype,
     Float64ArrowDtype,
     CategoricalDtype,
diff --git a/pandas/core/api.py b/pandas/core/api.py
index cbea384b7ad8e..4f7f726bace85 100644
--- a/pandas/core/api.py
+++ b/pandas/core/api.py
@@ -33,6 +33,7 @@
     Float64Dtype,
 )
 from pandas.core.arrays.arrow.floating import (
+    Float16ArrowDtype,
     Float32ArrowDtype,
     Float64ArrowDtype,
 )

From ad867311a5771b878a1484f3ef33fe56547c2eec Mon Sep 17 00:00:00 2001
From: Gaurav Sheni <gvsheni@gmail.com>
Date: Wed, 11 May 2022 22:36:48 -0400
Subject: [PATCH 08/19] fix return

---
 pandas/core/arrays/arrow/array.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pandas/core/arrays/arrow/array.py b/pandas/core/arrays/arrow/array.py
index b805e82660f3d..4ba82dc8a9853 100644
--- a/pandas/core/arrays/arrow/array.py
+++ b/pandas/core/arrays/arrow/array.py
@@ -474,7 +474,7 @@ def _replace_with_indices(
         return pc.replace_with_mask(chunk, mask, value)
 
     @cache_readonly
-    def dtype(self) -> NumericArrowDtype:
+    def dtype(self) -> ArrowDtype:
         return self._dtype
 
     @classmethod

From 39c01ac3eda61336fe38c57efb5ca21daeb300ec Mon Sep 17 00:00:00 2001
From: Gaurav Sheni <gvsheni@gmail.com>
Date: Wed, 11 May 2022 22:42:05 -0400
Subject: [PATCH 09/19] add more methods

---
 pandas/core/arrays/arrow/array.py | 24 ++++++++++++++++++++++++
 1 file changed, 24 insertions(+)

diff --git a/pandas/core/arrays/arrow/array.py b/pandas/core/arrays/arrow/array.py
index 4ba82dc8a9853..0844225a3618b 100644
--- a/pandas/core/arrays/arrow/array.py
+++ b/pandas/core/arrays/arrow/array.py
@@ -494,3 +494,27 @@ def mean(self, skipna=True):
             return pa.compute.mean(self._data, skip_nulls=skipna)
         else:
             raise TypeError(f"Cannot compute mean from '{string}'")
+    
+    def max(self, skipna=True):
+        if self.dtype._is_numeric:
+            return pa.compute.max(self._data, skip_nulls=skipna)
+        else:
+            raise TypeError(f"Cannot compute max from '{string}'")
+
+    def min(self, skipna=True):
+        if self.dtype._is_numeric:
+            return pa.compute.min(self._data, skip_nulls=skipna)
+        else:
+            raise TypeError(f"Cannot compute min from '{string}'")
+
+    def mode(self, skipna=True):
+        if self.dtype._is_numeric:
+            return pa.compute.mode(self._data, skip_nulls=skipna)
+        else:
+            raise TypeError(f"Cannot compute mode from '{string}'")
+
+    def quantile(self, q=0.5, interpolation='linear'):
+        if self.dtype._is_numeric:
+            return pa.compute.quantile(self._data, q=q, interpolation=interpolation)
+        else:
+            raise TypeError(f"Cannot compute quantile from '{string}'")

From 604f05acbde781181bf94abbceff009a11c447bf Mon Sep 17 00:00:00 2001
From: Gaurav Sheni <gvsheni@gmail.com>
Date: Wed, 11 May 2022 23:35:30 -0400
Subject: [PATCH 10/19] update docstrings

---
 pandas/core/arrays/arrow/floating.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/pandas/core/arrays/arrow/floating.py b/pandas/core/arrays/arrow/floating.py
index ac2d60a4e0316..1d7dcbc0731d0 100644
--- a/pandas/core/arrays/arrow/floating.py
+++ b/pandas/core/arrays/arrow/floating.py
@@ -12,7 +12,7 @@
 
 class FloatingArrowDtype(FloatingArrowArray):
     """
-    An ExtensionDtype to hold a single size & kind of integer Arrow dtype.
+    An ExtensionDtype to hold a single size & kind of floating Arrow dtype.
     These specific implementations are subclasses of the non-public
     FloatingArrowDtype. 
     """
@@ -36,13 +36,13 @@ def _str_to_dtype_mapping(cls):
 
 class FloatingArrowArray(NumericArrowArray):
     """
-    Array of pyarrow integer values.
-    To construct an IntegerArray from generic array-like ipaut, use
-    :func:`pandas.array` with one of the integer dtypes (see examples).
+    Array of pyarrow floating values.
+    To construct an FloatingArray from generic array-like ipaut, use
+    :func:`pandas.array` with one of the floating dtypes (see examples).
     Parameters
     ----------
     values : pa.ChunkedArray
-        A 1-d integer-dtype array.
+        A 1-d floating-dtype array.
     Attributes
     ----------
     None

From e09dfb8b0f1a7287fa4aec1c2f85fb686b351206 Mon Sep 17 00:00:00 2001
From: Gaurav Sheni <gvsheni@gmail.com>
Date: Sat, 14 May 2022 22:23:34 -0400
Subject: [PATCH 11/19] remove floating.py

---
 pandas/core/arrays/arrow/floating.py | 98 ----------------------------
 1 file changed, 98 deletions(-)
 delete mode 100644 pandas/core/arrays/arrow/floating.py

diff --git a/pandas/core/arrays/arrow/floating.py b/pandas/core/arrays/arrow/floating.py
deleted file mode 100644
index 1d7dcbc0731d0..0000000000000
--- a/pandas/core/arrays/arrow/floating.py
+++ /dev/null
@@ -1,98 +0,0 @@
-from __future__ import annotations
-
-import pyarrow as pa
-
-from pandas.core.dtypes.base import register_extension_dtype
-
-from pandas.core.arrays.arrow.dtype import (
-    FloatingArrowArray,
-    FloatingArrowArray,
-)
-
-
-class FloatingArrowDtype(FloatingArrowArray):
-    """
-    An ExtensionDtype to hold a single size & kind of floating Arrow dtype.
-    These specific implementations are subclasses of the non-public
-    FloatingArrowDtype. 
-    """
-
-    self.pa_dtype = pa.float64()
-
-    @classmethod
-    def construct_array_type(cls) -> type[FloatingArrowArray]:
-        """
-        Return the array type associated with this dtype.
-        Returns
-        -------
-        type
-        """
-        return FloatingArrowArray
-
-    @classmethod
-    def _str_to_dtype_mapping(cls):
-        return INT_STR_TO_DTYPE
-
-
-class FloatingArrowArray(NumericArrowArray):
-    """
-    Array of pyarrow floating values.
-    To construct an FloatingArray from generic array-like ipaut, use
-    :func:`pandas.array` with one of the floating dtypes (see examples).
-    Parameters
-    ----------
-    values : pa.ChunkedArray
-        A 1-d floating-dtype array.
-    Attributes
-    ----------
-    None
-    Methods
-    -------
-    None
-    Returns
-    -------
-    FloatingArrowArray
-    """
-
-    _dtype_cls = FloatingArrowDtype
-
-
-_dtype_docstring = """
-An ExtensionDtype for {dtype} integer pyarrow data.
-Attributes
-----------
-None
-Methods
--------
-None
-"""
-
-# create the Dtype
-
-
-@register_extension_dtype
-class Float16ArrowDtype(FloatingArrowDtype):
-    type = pa.float16()
-    name = "float16"
-    __doc__ = _dtype_docstring.format(dtype="float16")
-
-
-@register_extension_dtype
-class Float32ArrowDtype(FloatingArrowDtype):
-    type = pa.float32()
-    name = "float32"
-    __doc__ = _dtype_docstring.format(dtype="float32")
-
-
-@register_extension_dtype
-class Float64ArrowDtype(FloatingArrowDtype):
-    type = pa.float64()
-    name = "float64"
-    __doc__ = _dtype_docstring.format(dtype="float64")
-
-
-INT_STR_TO_DTYPE: dict[str, FloatingArrowDtype] = {
-    "float16": Float16ArrowDtype(),
-    "float32": Float32ArrowDtype(),
-    "float64": Float64ArrowDtype(),
-}
\ No newline at end of file

From 8fcf3a2745a4d0e66475e1d86301888690214915 Mon Sep 17 00:00:00 2001
From: Gaurav Sheni <gvsheni@gmail.com>
Date: Sat, 14 May 2022 22:24:14 -0400
Subject: [PATCH 12/19] fix attr

---
 pandas/core/arrays/arrow/numeric.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pandas/core/arrays/arrow/numeric.py b/pandas/core/arrays/arrow/numeric.py
index dc3d8c4131724..66d85d8099112 100644
--- a/pandas/core/arrays/arrow/numeric.py
+++ b/pandas/core/arrays/arrow/numeric.py
@@ -23,7 +23,7 @@ class FloatingArrowDtype(ArrowDtype):
 
     @property
     def _is_numeric(self) -> bool:
-        return False
+        return True
 
     @property
     def _is_float(self) -> bool:

From 6047ed07370b20a372294f9f1238a76e2e4c8c9f Mon Sep 17 00:00:00 2001
From: Gaurav Sheni <gvsheni@gmail.com>
Date: Sat, 14 May 2022 22:29:27 -0400
Subject: [PATCH 13/19] lint

---
 pandas/core/arrays/arrow/array.py   | 5 ++---
 pandas/core/arrays/arrow/numeric.py | 6 ++----
 2 files changed, 4 insertions(+), 7 deletions(-)

diff --git a/pandas/core/arrays/arrow/array.py b/pandas/core/arrays/arrow/array.py
index 0844225a3618b..b680b5af091bd 100644
--- a/pandas/core/arrays/arrow/array.py
+++ b/pandas/core/arrays/arrow/array.py
@@ -59,7 +59,6 @@ def __init__(self, values: pa.ChunkedArray, pa_dtype: pa.DataType) -> None:
         self.storage = storage
         self._dtype = ArrowDtype(storage="pyarrow", pa_dtype=pa_dtype)
 
-
     def __arrow_array__(self, type=None):
         """Convert myself to a pyarrow Array or ChunkedArray."""
         return self._data
@@ -494,7 +493,7 @@ def mean(self, skipna=True):
             return pa.compute.mean(self._data, skip_nulls=skipna)
         else:
             raise TypeError(f"Cannot compute mean from '{string}'")
-    
+
     def max(self, skipna=True):
         if self.dtype._is_numeric:
             return pa.compute.max(self._data, skip_nulls=skipna)
@@ -513,7 +512,7 @@ def mode(self, skipna=True):
         else:
             raise TypeError(f"Cannot compute mode from '{string}'")
 
-    def quantile(self, q=0.5, interpolation='linear'):
+    def quantile(self, q=0.5, interpolation="linear"):
         if self.dtype._is_numeric:
             return pa.compute.quantile(self._data, q=q, interpolation=interpolation)
         else:
diff --git a/pandas/core/arrays/arrow/numeric.py b/pandas/core/arrays/arrow/numeric.py
index 66d85d8099112..c02b6cefec6b3 100644
--- a/pandas/core/arrays/arrow/numeric.py
+++ b/pandas/core/arrays/arrow/numeric.py
@@ -44,9 +44,7 @@ class FloatingArrowArray(ArrowExtensionArray):
     def __init__(self, values: pa.ChunkedArray) -> None:
         checker = self._dtype_cls._dtype_checker
         if not (isinstance(values, pa.ChunkedArray) and checker(values.type)):
-            descr = (
-                "floating"
-            )
+            descr = "floating"
             raise TypeError(f"values should be {descr} arrow array.")
         super().__init__(values)
 
@@ -66,4 +64,4 @@ def _from_sequence_of_strings(cls, strings, *, dtype=None, copy: bool = False):
         from pandas.core.tools.numeric import to_numeric
 
         scalars = to_numeric(strings, errors="raise")
-        return cls._from_sequence(scalars, dtype=dtype, copy=copy)
\ No newline at end of file
+        return cls._from_sequence(scalars, dtype=dtype, copy=copy)

From 3bd3a9f28b31b37b446ceed944afad45f19d891f Mon Sep 17 00:00:00 2001
From: Gaurav Sheni <gvsheni@gmail.com>
Date: Sat, 14 May 2022 23:21:24 -0400
Subject: [PATCH 14/19] lint

---
 doc/source/user_guide/style.ipynb | 634 +++++++++++++++++-------------
 pandas/__init__.py                |   7 +-
 pandas/core/api.py                |  10 +-
 pandas/core/arrays/arrow/array.py |  30 +-
 pandas/core/arrays/arrow/dtype.py |   4 +-
 5 files changed, 395 insertions(+), 290 deletions(-)

diff --git a/doc/source/user_guide/style.ipynb b/doc/source/user_guide/style.ipynb
index 85c552a7d596f..1b54ec9e13b7c 100644
--- a/doc/source/user_guide/style.ipynb
+++ b/doc/source/user_guide/style.ipynb
@@ -37,6 +37,7 @@
    "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"
    ]
@@ -51,9 +52,16 @@
     "import numpy as np\n",
     "import matplotlib as mpl\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 = 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"
    ]
   },
@@ -75,63 +83,66 @@
    "outputs": [],
    "source": [
     "# Hidden cell to just create the below example: code is covered throughout the guide.\n",
-    "s = df.style\\\n",
-    "      .hide_columns([('Random', 'Tumour'), ('Random', 'Non-Tumour')])\\\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_columns([(\"Random\", \"Tumour\"), (\"Random\", \"Non-Tumour\")])\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",
+    ")"
    ]
   },
   {
@@ -167,10 +178,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df.style.format(precision=0, na_rep='MISSING', thousands=\" \",\n",
-    "                formatter={('Decision Tree', 'Tumour'): \"{:.2f}\",\n",
-    "                           ('Regression', 'Non-Tumour'): lambda x: \"$ {:,.1f}\".format(x*-1e6)\n",
-    "                          })"
+    "df.style.format(\n",
+    "    precision=0,\n",
+    "    na_rep=\"MISSING\",\n",
+    "    thousands=\" \",\n",
+    "    formatter={\n",
+    "        (\"Decision Tree\", \"Tumour\"): \"{:.2f}\",\n",
+    "        (\"Regression\", \"Non-Tumour\"): lambda x: \"$ {:,.1f}\".format(x * -1e6),\n",
+    "    },\n",
+    ")"
    ]
   },
   {
@@ -186,17 +202,21 @@
    "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",
@@ -204,6 +224,7 @@
     "    styler.background_gradient(axis=None, vmin=1, vmax=5, cmap=\"YlGnBu\")\n",
     "    return styler\n",
     "\n",
+    "\n",
     "weather_df"
    ]
   },
@@ -241,7 +262,9 @@
    "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"
    ]
   },
@@ -253,8 +276,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -311,16 +334,16 @@
    "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])"
    ]
@@ -333,8 +356,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -350,11 +373,14 @@
    "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",
+    ")"
    ]
   },
   {
@@ -365,8 +391,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -384,10 +410,16 @@
    "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",
+    ")"
    ]
   },
   {
@@ -398,8 +430,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -424,7 +456,7 @@
    "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])"
    ]
   },
   {
@@ -447,14 +479,18 @@
    "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)"
    ]
   },
@@ -466,8 +502,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -496,7 +532,7 @@
    "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"
    ]
   },
@@ -513,10 +549,13 @@
    "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"
    ]
   },
@@ -528,8 +567,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -545,9 +584,11 @@
    "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)"
    ]
   },
   {
@@ -558,8 +599,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -575,8 +616,9 @@
    "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",
+    ")"
    ]
   },
   {
@@ -587,8 +629,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -629,8 +671,10 @@
    "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",
+    ")"
    ]
   },
   {
@@ -650,11 +694,12 @@
    "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",
+    ")"
    ]
   },
   {
@@ -665,8 +710,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -684,12 +729,22 @@
    "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",
+    ")"
    ]
   },
   {
@@ -700,8 +755,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -717,14 +772,18 @@
    "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)"
    ]
   },
@@ -736,8 +795,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -763,9 +822,11 @@
    "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"
    ]
   },
@@ -782,9 +843,10 @@
    "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_)"
    ]
   },
   {
@@ -801,9 +863,10 @@
    "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_)"
    ]
   },
   {
@@ -819,9 +882,10 @@
    "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_)"
    ]
   },
   {
@@ -839,9 +903,10 @@
    "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_)"
    ]
   },
   {
@@ -897,7 +962,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4 = pd.DataFrame([[1,2],[3,4]])\n",
+    "df4 = pd.DataFrame([[1, 2], [3, 4]])\n",
     "s4 = df4.style"
    ]
   },
@@ -919,6 +984,7 @@
    "outputs": [],
    "source": [
     "from pandas.io.formats.style import Styler\n",
+    "\n",
     "s4 = Styler(df4, uuid_len=0, cell_ids=False)"
    ]
   },
@@ -969,7 +1035,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4.style.set_table_styles([{'selector': 'td.col1', 'props': props}])"
+    "df4.style.set_table_styles([{\"selector\": \"td.col1\", \"props\": props}])"
    ]
   },
   {
@@ -998,9 +1064,11 @@
    "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",
+    ")"
    ]
   },
   {
@@ -1021,14 +1089,18 @@
    "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)"
    ]
   },
   {
@@ -1068,10 +1140,14 @@
     "    \"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())"
    ]
   },
@@ -1129,9 +1205,9 @@
    "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\")"
    ]
   },
   {
@@ -1147,7 +1223,9 @@
    "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",
+    ")"
    ]
   },
   {
@@ -1165,7 +1243,9 @@
    "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",
+    ")"
    ]
   },
   {
@@ -1182,7 +1262,7 @@
    "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\")"
    ]
   },
   {
@@ -1206,6 +1286,7 @@
    "outputs": [],
    "source": [
     "import seaborn as sns\n",
+    "\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
     "\n",
     "df2.style.background_gradient(cmap=cm)"
@@ -1245,9 +1326,9 @@
    "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",
+    ")"
    ]
   },
   {
@@ -1270,7 +1351,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df2.style.bar(subset=['A', 'B'], color='#d65f5f')"
+    "df2.style.bar(subset=[\"A\", \"B\"], color=\"#d65f5f\")"
    ]
   },
   {
@@ -1288,10 +1369,15 @@
    "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)"
    ]
   },
   {
@@ -1314,10 +1400,10 @@
     "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",
@@ -1333,19 +1419,22 @@
     "\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_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_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>\"\"\""
    ]
@@ -1379,11 +1468,12 @@
    "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"
    ]
   },
@@ -1442,11 +1532,14 @@
    "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",
     "    )"
    ]
   },
@@ -1464,16 +1557,15 @@
    "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",
+    "    ]"
    ]
   },
   {
@@ -1483,14 +1575,12 @@
    "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())"
    ]
   },
   {
@@ -1527,8 +1617,8 @@
    "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])"
    ]
   },
   {
@@ -1546,7 +1636,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4 = pd.DataFrame([['<div></div>', '\"&other\"', '<span></span>']])\n",
+    "df4 = pd.DataFrame([[\"<div></div>\", '\"&other\"', \"<span></span>\"]])\n",
     "df4.style"
    ]
   },
@@ -1565,7 +1655,9 @@
    "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",
+    ")"
    ]
   },
   {
@@ -1606,10 +1698,9 @@
    "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",
+    ")"
    ]
   },
   {
@@ -1678,7 +1769,11 @@
    "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",
+    ")"
    ]
   },
   {
@@ -1696,9 +1791,8 @@
    "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;\")"
    ]
   },
   {
@@ -1707,8 +1801,7 @@
    "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;\")"
    ]
   },
   {
@@ -1733,9 +1826,9 @@
    "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;\")"
    ]
   },
   {
@@ -1751,11 +1844,12 @@
    "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\"]]))"
    ]
   },
   {
@@ -1771,12 +1865,13 @@
    "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\"]]))"
    ]
   },
   {
@@ -1794,12 +1889,15 @@
    "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",
+    ")"
    ]
   },
   {
@@ -1873,10 +1971,12 @@
    "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\")"
    ]
@@ -2019,7 +2119,7 @@
     "# from IPython.display import HTML\n",
     "# with open(\"themes/nature_with_gtoc/static/nature.css_t\") as f:\n",
     "#     css = f.read()\n",
-    "    \n",
+    "\n",
     "# HTML('<style>{}</style>'.format(css))"
    ]
   }
diff --git a/pandas/__init__.py b/pandas/__init__.py
index f31d021119598..7d266566528d9 100644
--- a/pandas/__init__.py
+++ b/pandas/__init__.py
@@ -55,11 +55,11 @@
     UInt16Dtype,
     UInt32Dtype,
     UInt64Dtype,
-    Float32Dtype,
-    Float64Dtype,
     Float16ArrowDtype,
     Float32ArrowDtype,
     Float64ArrowDtype,
+    Float32Dtype,
+    Float64Dtype,
     CategoricalDtype,
     PeriodDtype,
     IntervalDtype,
@@ -320,6 +320,9 @@ def __getattr__(name):
     "ExcelFile",
     "ExcelWriter",
     "Flags",
+    "Float16ArrowDtype",
+    "Float32ArrowDtype",
+    "Float64ArrowDtype",
     "Float32Dtype",
     "Float64Dtype",
     "Grouper",
diff --git a/pandas/core/api.py b/pandas/core/api.py
index 4f7f726bace85..3c49cbd5a16ad 100644
--- a/pandas/core/api.py
+++ b/pandas/core/api.py
@@ -27,16 +27,16 @@
     value_counts,
 )
 from pandas.core.arrays import Categorical
-from pandas.core.arrays.boolean import BooleanDtype
-from pandas.core.arrays.floating import (
-    Float32Dtype,
-    Float64Dtype,
-)
 from pandas.core.arrays.arrow.floating import (
     Float16ArrowDtype,
     Float32ArrowDtype,
     Float64ArrowDtype,
 )
+from pandas.core.arrays.boolean import BooleanDtype
+from pandas.core.arrays.floating import (
+    Float32Dtype,
+    Float64Dtype,
+)
 from pandas.core.arrays.integer import (
     Int8Dtype,
     Int16Dtype,
diff --git a/pandas/core/arrays/arrow/array.py b/pandas/core/arrays/arrow/array.py
index b680b5af091bd..47b4cda39962a 100644
--- a/pandas/core/arrays/arrow/array.py
+++ b/pandas/core/arrays/arrow/array.py
@@ -53,11 +53,11 @@ class ArrowExtensionArray(ExtensionArray):
     """
 
     _data: pa.ChunkedArray
+    _dtype: pa.DataType
 
     def __init__(self, values: pa.ChunkedArray, pa_dtype: pa.DataType) -> None:
         self._data = values
-        self.storage = storage
-        self._dtype = ArrowDtype(storage="pyarrow", pa_dtype=pa_dtype)
+        self._dtype = ArrowDtype(pa_dtype=pa_dtype, storage="pyarrow")
 
     def __arrow_array__(self, type=None):
         """Convert myself to a pyarrow Array or ChunkedArray."""
@@ -472,12 +472,14 @@ def _replace_with_indices(
 
         return pc.replace_with_mask(chunk, mask, value)
 
-    @cache_readonly
+    @property
     def dtype(self) -> ArrowDtype:
         return self._dtype
 
     @classmethod
-    def _from_sequence_of_strings(cls, strings, *, dtype=None, copy: bool = False):
+    def _from_sequence_of_strings(
+        self, cls, strings, *, dtype=None, copy: bool = False
+    ):
         if self.dtype._is_numeric:
             from pandas.core.tools.numeric import to_numeric
 
@@ -488,32 +490,32 @@ def _from_sequence_of_strings(cls, strings, *, dtype=None, copy: bool = False):
             scalars = to_datetime(strings, error="raise")
         return cls._from_sequence(scalars, dtype=dtype, copy=copy)
 
-    def mean(self, skipna=True):
+    def mean(self, skipna: bool = True):
         if self.dtype._is_numeric:
             return pa.compute.mean(self._data, skip_nulls=skipna)
         else:
-            raise TypeError(f"Cannot compute mean from '{string}'")
+            raise TypeError("Cannot compute mean")
 
-    def max(self, skipna=True):
+    def max(self, skipna: bool = True):
         if self.dtype._is_numeric:
             return pa.compute.max(self._data, skip_nulls=skipna)
         else:
-            raise TypeError(f"Cannot compute max from '{string}'")
+            raise TypeError("Cannot compute max")
 
-    def min(self, skipna=True):
+    def min(self, skipna: bool = True):
         if self.dtype._is_numeric:
             return pa.compute.min(self._data, skip_nulls=skipna)
         else:
-            raise TypeError(f"Cannot compute min from '{string}'")
+            raise TypeError("Cannot compute min")
 
-    def mode(self, skipna=True):
+    def mode(self, skipna: bool = True):
         if self.dtype._is_numeric:
             return pa.compute.mode(self._data, skip_nulls=skipna)
         else:
-            raise TypeError(f"Cannot compute mode from '{string}'")
+            raise TypeError("Cannot compute mode")
 
-    def quantile(self, q=0.5, interpolation="linear"):
+    def quantile(self, q: float = 0.5, interpolation: str = "linear"):
         if self.dtype._is_numeric:
             return pa.compute.quantile(self._data, q=q, interpolation=interpolation)
         else:
-            raise TypeError(f"Cannot compute quantile from '{string}'")
+            raise TypeError("Cannot compute quantile")
diff --git a/pandas/core/arrays/arrow/dtype.py b/pandas/core/arrays/arrow/dtype.py
index ca75f33efc725..4aebb5a6f3e97 100644
--- a/pandas/core/arrays/arrow/dtype.py
+++ b/pandas/core/arrays/arrow/dtype.py
@@ -43,8 +43,8 @@ def _is_floating(self):
     def _is_temporal(self):
         return pa.types.is_temporal(self.pa_dtype)
 
-    def _is_floating(self):
-        return pa.types.is_floating(self.pa_dtype)
+    def _is_timestamp(self):
+        return pa.types.is_timestamp(self.pa_dtype)
 
     def _is_date(self):
         return pa.types.is_date(self.pa_dtype)

From 36adf1300e53c5de92d430f8643f6b56afc5eab3 Mon Sep 17 00:00:00 2001
From: Gaurav Sheni <gvsheni@gmail.com>
Date: Sat, 14 May 2022 23:32:29 -0400
Subject: [PATCH 15/19] add checker dtype

---
 pandas/core/arrays/arrow/floating.py | 36 ++++++++++++++++++++++++++++
 pandas/core/arrays/arrow/numeric.py  |  2 +-
 2 files changed, 37 insertions(+), 1 deletion(-)
 create mode 100644 pandas/core/arrays/arrow/floating.py

diff --git a/pandas/core/arrays/arrow/floating.py b/pandas/core/arrays/arrow/floating.py
new file mode 100644
index 0000000000000..364de46c5e623
--- /dev/null
+++ b/pandas/core/arrays/arrow/floating.py
@@ -0,0 +1,36 @@
+from __future__ import annotations
+
+import pyarrow as pa
+
+from pandas.core.arrays.arrow.numeric import FloatingArrowDtype
+
+
+@register_extension_dtype
+class Float16ArrowDtype(FloatingArrowDtype):
+    name = "float16"
+     type = pa.float16()
+    __doc__ = _dtype_docstring.format(dtype="float16")
+    _dtype_checker = pa.is_float16()
+
+
+@register_extension_dtype
+class Float32ArrowDtype(FloatingArrowDtype):
+    name = "float32"
+    type = pa.float32()
+    __doc__ = _dtype_docstring.format(dtype="float32")
+    _dtype_checker = pa.is_float32()
+
+
+@register_extension_dtype
+class Float64ArrowDtype(FloatingArrowDtype):
+    name = "float64"
+    type = pa.float64()
+    __doc__ = _dtype_docstring.format(dtype="float64")
+    _dtype_checker = pa.is_float64()
+
+
+INT_STR_TO_DTYPE: dict[str, FloatingArrowDtype] = {
+    "float16": Float16ArrowDtype(),
+    "float32": Float32ArrowDtype(),
+    "float64": Float64ArrowDtype(),
+}
diff --git a/pandas/core/arrays/arrow/numeric.py b/pandas/core/arrays/arrow/numeric.py
index c02b6cefec6b3..698f3bf6fbfa5 100644
--- a/pandas/core/arrays/arrow/numeric.py
+++ b/pandas/core/arrays/arrow/numeric.py
@@ -64,4 +64,4 @@ def _from_sequence_of_strings(cls, strings, *, dtype=None, copy: bool = False):
         from pandas.core.tools.numeric import to_numeric
 
         scalars = to_numeric(strings, errors="raise")
-        return cls._from_sequence(scalars, dtype=dtype, copy=copy)
+        return cls._from_sequence(scalars, dtype=dtype, copy=copy)
\ No newline at end of file

From bb09938258642d33cf7df9f401582a1a599d1f95 Mon Sep 17 00:00:00 2001
From: Gaurav Sheni <gvsheni@gmail.com>
Date: Sat, 14 May 2022 23:34:16 -0400
Subject: [PATCH 16/19] restore style from main

---
 doc/source/user_guide/style.ipynb | 634 +++++++++++++-----------------
 1 file changed, 267 insertions(+), 367 deletions(-)

diff --git a/doc/source/user_guide/style.ipynb b/doc/source/user_guide/style.ipynb
index 33b27709fc8da..0b2341bef413e 100644
--- a/doc/source/user_guide/style.ipynb
+++ b/doc/source/user_guide/style.ipynb
@@ -37,7 +37,6 @@
    "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"
    ]
@@ -52,16 +51,9 @@
     "import numpy as np\n",
     "import matplotlib as mpl\n",
     "\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 = 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"
    ]
   },
@@ -83,66 +75,63 @@
    "outputs": [],
    "source": [
     "# Hidden cell to just create the below example: code is covered throughout the guide.\n",
-    "s = (\n",
-    "    df.style.hide_columns([(\"Random\", \"Tumour\"), (\"Random\", \"Non-Tumour\")])\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_columns([('Random', 'Tumour'), ('Random', 'Non-Tumour')])\\\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"
    ]
   },
   {
@@ -178,15 +167,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df.style.format(\n",
-    "    precision=0,\n",
-    "    na_rep=\"MISSING\",\n",
-    "    thousands=\" \",\n",
-    "    formatter={\n",
-    "        (\"Decision Tree\", \"Tumour\"): \"{:.2f}\",\n",
-    "        (\"Regression\", \"Non-Tumour\"): lambda x: \"$ {:,.1f}\".format(x * -1e6),\n",
-    "    },\n",
-    ")"
+    "df.style.format(precision=0, na_rep='MISSING', thousands=\" \",\n",
+    "                formatter={('Decision Tree', 'Tumour'): \"{:.2f}\",\n",
+    "                           ('Regression', 'Non-Tumour'): lambda x: \"$ {:,.1f}\".format(x*-1e6)\n",
+    "                          })"
    ]
   },
   {
@@ -202,21 +186,17 @@
    "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",
@@ -224,7 +204,6 @@
     "    styler.background_gradient(axis=None, vmin=1, vmax=5, cmap=\"YlGnBu\")\n",
     "    return styler\n",
     "\n",
-    "\n",
     "weather_df"
    ]
   },
@@ -262,9 +241,7 @@
    "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"
    ]
   },
@@ -276,8 +253,8 @@
    },
    "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')"
    ]
   },
   {
@@ -334,16 +311,16 @@
    "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])"
    ]
@@ -356,8 +333,8 @@
    },
    "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')"
    ]
   },
   {
@@ -373,14 +350,11 @@
    "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)"
    ]
   },
   {
@@ -391,8 +365,8 @@
    },
    "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')"
    ]
   },
   {
@@ -410,16 +384,10 @@
    "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)"
    ]
   },
   {
@@ -430,8 +398,8 @@
    },
    "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')"
    ]
   },
   {
@@ -456,7 +424,7 @@
    "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])"
    ]
   },
   {
@@ -479,18 +447,14 @@
    "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)"
    ]
   },
@@ -502,8 +466,8 @@
    },
    "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')"
    ]
   },
   {
@@ -532,7 +496,7 @@
    "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"
    ]
   },
@@ -549,13 +513,10 @@
    "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"
    ]
   },
@@ -567,8 +528,8 @@
    },
    "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')"
    ]
   },
   {
@@ -584,11 +545,9 @@
    "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)"
    ]
   },
   {
@@ -599,8 +558,8 @@
    },
    "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')"
    ]
   },
   {
@@ -616,9 +575,8 @@
    "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)"
    ]
   },
   {
@@ -629,8 +587,8 @@
    },
    "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')"
    ]
   },
   {
@@ -671,10 +629,8 @@
    "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)"
    ]
   },
   {
@@ -694,12 +650,11 @@
    "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)"
    ]
   },
   {
@@ -710,8 +665,8 @@
    },
    "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')"
    ]
   },
   {
@@ -729,22 +684,12 @@
    "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;')"
    ]
   },
   {
@@ -755,8 +700,8 @@
    },
    "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')"
    ]
   },
   {
@@ -772,18 +717,14 @@
    "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)"
    ]
   },
@@ -795,8 +736,8 @@
    },
    "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')"
    ]
   },
   {
@@ -822,11 +763,9 @@
    "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"
    ]
   },
@@ -843,10 +782,9 @@
    "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_)"
    ]
   },
   {
@@ -863,10 +801,9 @@
    "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_)"
    ]
   },
   {
@@ -882,10 +819,9 @@
    "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_)"
    ]
   },
   {
@@ -903,10 +839,9 @@
    "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_)"
    ]
   },
   {
@@ -962,7 +897,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4 = pd.DataFrame([[1, 2], [3, 4]])\n",
+    "df4 = pd.DataFrame([[1,2],[3,4]])\n",
     "s4 = df4.style"
    ]
   },
@@ -984,7 +919,6 @@
    "outputs": [],
    "source": [
     "from pandas.io.formats.style import Styler\n",
-    "\n",
     "s4 = Styler(df4, uuid_len=0, cell_ids=False)"
    ]
   },
@@ -1035,7 +969,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4.style.set_table_styles([{\"selector\": \"td.col1\", \"props\": props}])"
+    "df4.style.set_table_styles([{'selector': 'td.col1', 'props': props}])"
    ]
   },
   {
@@ -1064,11 +998,9 @@
    "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)"
    ]
   },
   {
@@ -1089,18 +1021,14 @@
    "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)"
    ]
   },
   {
@@ -1140,14 +1068,10 @@
     "    \"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())"
    ]
   },
@@ -1205,9 +1129,9 @@
    "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')"
    ]
   },
   {
@@ -1223,9 +1147,7 @@
    "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;')"
    ]
   },
   {
@@ -1243,9 +1165,7 @@
    "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;')"
    ]
   },
   {
@@ -1262,7 +1182,7 @@
    "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')"
    ]
   },
   {
@@ -1286,7 +1206,6 @@
    "outputs": [],
    "source": [
     "import seaborn as sns\n",
-    "\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
     "\n",
     "df2.style.background_gradient(cmap=cm)"
@@ -1326,9 +1245,9 @@
    "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'})"
    ]
   },
   {
@@ -1351,7 +1270,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df2.style.bar(subset=[\"A\", \"B\"], color=\"#d65f5f\")"
+    "df2.style.bar(subset=['A', 'B'], color='#d65f5f')"
    ]
   },
   {
@@ -1369,15 +1288,10 @@
    "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)"
    ]
   },
   {
@@ -1400,10 +1314,10 @@
     "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",
@@ -1419,22 +1333,19 @@
     "\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_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_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>\"\"\""
    ]
@@ -1468,12 +1379,11 @@
    "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"
    ]
   },
@@ -1532,14 +1442,11 @@
    "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",
     "    )"
    ]
   },
@@ -1557,15 +1464,16 @@
    "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",
+    "]"
    ]
   },
   {
@@ -1575,12 +1483,14 @@
    "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())"
    ]
   },
   {
@@ -1617,8 +1527,8 @@
    "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])"
    ]
   },
   {
@@ -1636,7 +1546,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4 = pd.DataFrame([[\"<div></div>\", '\"&other\"', \"<span></span>\"]])\n",
+    "df4 = pd.DataFrame([['<div></div>', '\"&other\"', '<span></span>']])\n",
     "df4.style"
    ]
   },
@@ -1655,9 +1565,7 @@
    "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\")"
    ]
   },
   {
@@ -1698,9 +1606,10 @@
    "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')"
    ]
   },
   {
@@ -1769,11 +1678,7 @@
    "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())"
    ]
   },
   {
@@ -1791,8 +1696,9 @@
    "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;')"
    ]
   },
   {
@@ -1801,7 +1707,8 @@
    "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;')"
    ]
   },
   {
@@ -1826,9 +1733,9 @@
    "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;')"
    ]
   },
   {
@@ -1844,12 +1751,11 @@
    "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']]))"
    ]
   },
   {
@@ -1865,13 +1771,12 @@
    "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']]))"
    ]
   },
   {
@@ -1889,15 +1794,12 @@
    "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']]))"
    ]
   },
   {
@@ -1971,12 +1873,10 @@
    "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\")"
    ]
@@ -2119,7 +2019,7 @@
     "# 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 a73eec4bf7127aaf1b489932d22631dd824751f5 Mon Sep 17 00:00:00 2001
From: Gaurav Sheni <gvsheni@gmail.com>
Date: Sat, 14 May 2022 23:35:34 -0400
Subject: [PATCH 17/19] lint

"
---
 doc/source/user_guide/style.ipynb    | 634 ++++++++++++++++-----------
 pandas/core/arrays/arrow/floating.py |   2 +-
 pandas/core/arrays/arrow/numeric.py  |   2 +-
 3 files changed, 369 insertions(+), 269 deletions(-)

diff --git a/doc/source/user_guide/style.ipynb b/doc/source/user_guide/style.ipynb
index 0b2341bef413e..33b27709fc8da 100644
--- a/doc/source/user_guide/style.ipynb
+++ b/doc/source/user_guide/style.ipynb
@@ -37,6 +37,7 @@
    "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"
    ]
@@ -51,9 +52,16 @@
     "import numpy as np\n",
     "import matplotlib as mpl\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 = 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"
    ]
   },
@@ -75,63 +83,66 @@
    "outputs": [],
    "source": [
     "# Hidden cell to just create the below example: code is covered throughout the guide.\n",
-    "s = df.style\\\n",
-    "      .hide_columns([('Random', 'Tumour'), ('Random', 'Non-Tumour')])\\\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_columns([(\"Random\", \"Tumour\"), (\"Random\", \"Non-Tumour\")])\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",
+    ")"
    ]
   },
   {
@@ -167,10 +178,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df.style.format(precision=0, na_rep='MISSING', thousands=\" \",\n",
-    "                formatter={('Decision Tree', 'Tumour'): \"{:.2f}\",\n",
-    "                           ('Regression', 'Non-Tumour'): lambda x: \"$ {:,.1f}\".format(x*-1e6)\n",
-    "                          })"
+    "df.style.format(\n",
+    "    precision=0,\n",
+    "    na_rep=\"MISSING\",\n",
+    "    thousands=\" \",\n",
+    "    formatter={\n",
+    "        (\"Decision Tree\", \"Tumour\"): \"{:.2f}\",\n",
+    "        (\"Regression\", \"Non-Tumour\"): lambda x: \"$ {:,.1f}\".format(x * -1e6),\n",
+    "    },\n",
+    ")"
    ]
   },
   {
@@ -186,17 +202,21 @@
    "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",
@@ -204,6 +224,7 @@
     "    styler.background_gradient(axis=None, vmin=1, vmax=5, cmap=\"YlGnBu\")\n",
     "    return styler\n",
     "\n",
+    "\n",
     "weather_df"
    ]
   },
@@ -241,7 +262,9 @@
    "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"
    ]
   },
@@ -253,8 +276,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -311,16 +334,16 @@
    "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])"
    ]
@@ -333,8 +356,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -350,11 +373,14 @@
    "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",
+    ")"
    ]
   },
   {
@@ -365,8 +391,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -384,10 +410,16 @@
    "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",
+    ")"
    ]
   },
   {
@@ -398,8 +430,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -424,7 +456,7 @@
    "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])"
    ]
   },
   {
@@ -447,14 +479,18 @@
    "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)"
    ]
   },
@@ -466,8 +502,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -496,7 +532,7 @@
    "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"
    ]
   },
@@ -513,10 +549,13 @@
    "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"
    ]
   },
@@ -528,8 +567,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -545,9 +584,11 @@
    "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)"
    ]
   },
   {
@@ -558,8 +599,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -575,8 +616,9 @@
    "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",
+    ")"
    ]
   },
   {
@@ -587,8 +629,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -629,8 +671,10 @@
    "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",
+    ")"
    ]
   },
   {
@@ -650,11 +694,12 @@
    "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",
+    ")"
    ]
   },
   {
@@ -665,8 +710,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -684,12 +729,22 @@
    "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",
+    ")"
    ]
   },
   {
@@ -700,8 +755,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -717,14 +772,18 @@
    "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)"
    ]
   },
@@ -736,8 +795,8 @@
    },
    "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\")"
    ]
   },
   {
@@ -763,9 +822,11 @@
    "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"
    ]
   },
@@ -782,9 +843,10 @@
    "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_)"
    ]
   },
   {
@@ -801,9 +863,10 @@
    "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_)"
    ]
   },
   {
@@ -819,9 +882,10 @@
    "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_)"
    ]
   },
   {
@@ -839,9 +903,10 @@
    "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_)"
    ]
   },
   {
@@ -897,7 +962,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4 = pd.DataFrame([[1,2],[3,4]])\n",
+    "df4 = pd.DataFrame([[1, 2], [3, 4]])\n",
     "s4 = df4.style"
    ]
   },
@@ -919,6 +984,7 @@
    "outputs": [],
    "source": [
     "from pandas.io.formats.style import Styler\n",
+    "\n",
     "s4 = Styler(df4, uuid_len=0, cell_ids=False)"
    ]
   },
@@ -969,7 +1035,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4.style.set_table_styles([{'selector': 'td.col1', 'props': props}])"
+    "df4.style.set_table_styles([{\"selector\": \"td.col1\", \"props\": props}])"
    ]
   },
   {
@@ -998,9 +1064,11 @@
    "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",
+    ")"
    ]
   },
   {
@@ -1021,14 +1089,18 @@
    "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)"
    ]
   },
   {
@@ -1068,10 +1140,14 @@
     "    \"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())"
    ]
   },
@@ -1129,9 +1205,9 @@
    "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\")"
    ]
   },
   {
@@ -1147,7 +1223,9 @@
    "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",
+    ")"
    ]
   },
   {
@@ -1165,7 +1243,9 @@
    "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",
+    ")"
    ]
   },
   {
@@ -1182,7 +1262,7 @@
    "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\")"
    ]
   },
   {
@@ -1206,6 +1286,7 @@
    "outputs": [],
    "source": [
     "import seaborn as sns\n",
+    "\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
     "\n",
     "df2.style.background_gradient(cmap=cm)"
@@ -1245,9 +1326,9 @@
    "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",
+    ")"
    ]
   },
   {
@@ -1270,7 +1351,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df2.style.bar(subset=['A', 'B'], color='#d65f5f')"
+    "df2.style.bar(subset=[\"A\", \"B\"], color=\"#d65f5f\")"
    ]
   },
   {
@@ -1288,10 +1369,15 @@
    "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)"
    ]
   },
   {
@@ -1314,10 +1400,10 @@
     "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",
@@ -1333,19 +1419,22 @@
     "\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_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_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>\"\"\""
    ]
@@ -1379,11 +1468,12 @@
    "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"
    ]
   },
@@ -1442,11 +1532,14 @@
    "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",
     "    )"
    ]
   },
@@ -1464,16 +1557,15 @@
    "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",
+    "    ]"
    ]
   },
   {
@@ -1483,14 +1575,12 @@
    "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())"
    ]
   },
   {
@@ -1527,8 +1617,8 @@
    "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])"
    ]
   },
   {
@@ -1546,7 +1636,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4 = pd.DataFrame([['<div></div>', '\"&other\"', '<span></span>']])\n",
+    "df4 = pd.DataFrame([[\"<div></div>\", '\"&other\"', \"<span></span>\"]])\n",
     "df4.style"
    ]
   },
@@ -1565,7 +1655,9 @@
    "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",
+    ")"
    ]
   },
   {
@@ -1606,10 +1698,9 @@
    "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",
+    ")"
    ]
   },
   {
@@ -1678,7 +1769,11 @@
    "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",
+    ")"
    ]
   },
   {
@@ -1696,9 +1791,8 @@
    "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;\")"
    ]
   },
   {
@@ -1707,8 +1801,7 @@
    "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;\")"
    ]
   },
   {
@@ -1733,9 +1826,9 @@
    "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;\")"
    ]
   },
   {
@@ -1751,11 +1844,12 @@
    "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\"]]))"
    ]
   },
   {
@@ -1771,12 +1865,13 @@
    "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\"]]))"
    ]
   },
   {
@@ -1794,12 +1889,15 @@
    "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",
+    ")"
    ]
   },
   {
@@ -1873,10 +1971,12 @@
    "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\")"
    ]
@@ -2019,7 +2119,7 @@
     "# from IPython.display import HTML\n",
     "# with open(\"themes/nature_with_gtoc/static/nature.css_t\") as f:\n",
     "#     css = f.read()\n",
-    "    \n",
+    "\n",
     "# HTML('<style>{}</style>'.format(css))"
    ]
   }
diff --git a/pandas/core/arrays/arrow/floating.py b/pandas/core/arrays/arrow/floating.py
index 364de46c5e623..1d29537eec993 100644
--- a/pandas/core/arrays/arrow/floating.py
+++ b/pandas/core/arrays/arrow/floating.py
@@ -8,7 +8,7 @@
 @register_extension_dtype
 class Float16ArrowDtype(FloatingArrowDtype):
     name = "float16"
-     type = pa.float16()
+    type = pa.float16()
     __doc__ = _dtype_docstring.format(dtype="float16")
     _dtype_checker = pa.is_float16()
 
diff --git a/pandas/core/arrays/arrow/numeric.py b/pandas/core/arrays/arrow/numeric.py
index 698f3bf6fbfa5..c02b6cefec6b3 100644
--- a/pandas/core/arrays/arrow/numeric.py
+++ b/pandas/core/arrays/arrow/numeric.py
@@ -64,4 +64,4 @@ def _from_sequence_of_strings(cls, strings, *, dtype=None, copy: bool = False):
         from pandas.core.tools.numeric import to_numeric
 
         scalars = to_numeric(strings, errors="raise")
-        return cls._from_sequence(scalars, dtype=dtype, copy=copy)
\ No newline at end of file
+        return cls._from_sequence(scalars, dtype=dtype, copy=copy)

From 51ea909af3b9a8024fba9afa0b4259041287fc7d Mon Sep 17 00:00:00 2001
From: Gaurav Sheni <gvsheni@gmail.com>
Date: Sat, 14 May 2022 23:43:10 -0400
Subject: [PATCH 18/19] lint

"
---
 pandas/core/arrays/arrow/floating.py | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/pandas/core/arrays/arrow/floating.py b/pandas/core/arrays/arrow/floating.py
index 1d29537eec993..504822fae5dee 100644
--- a/pandas/core/arrays/arrow/floating.py
+++ b/pandas/core/arrays/arrow/floating.py
@@ -2,8 +2,24 @@
 
 import pyarrow as pa
 
+from pandas.core.dtypes.dtypes import register_extension_dtype
+
 from pandas.core.arrays.arrow.numeric import FloatingArrowDtype
 
+_dtype_docstring = """
+An ExtensionDtype for {dtype} data.
+
+This dtype uses ``pa.null`` as missing value indicator.
+
+Attributes
+----------
+None
+
+Methods
+-------
+None
+"""
+
 
 @register_extension_dtype
 class Float16ArrowDtype(FloatingArrowDtype):

From 4d3d006b9766ff15d0318f352ba58940d44a5728 Mon Sep 17 00:00:00 2001
From: Gaurav Sheni <gvsheni@gmail.com>
Date: Sat, 14 May 2022 23:47:46 -0400
Subject: [PATCH 19/19] undo ipynb

---
 doc/source/user_guide/style.ipynb | 634 +++++++++++++-----------------
 1 file changed, 267 insertions(+), 367 deletions(-)

diff --git a/doc/source/user_guide/style.ipynb b/doc/source/user_guide/style.ipynb
index 33b27709fc8da..0b2341bef413e 100644
--- a/doc/source/user_guide/style.ipynb
+++ b/doc/source/user_guide/style.ipynb
@@ -37,7 +37,6 @@
    "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"
    ]
@@ -52,16 +51,9 @@
     "import numpy as np\n",
     "import matplotlib as mpl\n",
     "\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 = 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"
    ]
   },
@@ -83,66 +75,63 @@
    "outputs": [],
    "source": [
     "# Hidden cell to just create the below example: code is covered throughout the guide.\n",
-    "s = (\n",
-    "    df.style.hide_columns([(\"Random\", \"Tumour\"), (\"Random\", \"Non-Tumour\")])\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_columns([('Random', 'Tumour'), ('Random', 'Non-Tumour')])\\\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"
    ]
   },
   {
@@ -178,15 +167,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df.style.format(\n",
-    "    precision=0,\n",
-    "    na_rep=\"MISSING\",\n",
-    "    thousands=\" \",\n",
-    "    formatter={\n",
-    "        (\"Decision Tree\", \"Tumour\"): \"{:.2f}\",\n",
-    "        (\"Regression\", \"Non-Tumour\"): lambda x: \"$ {:,.1f}\".format(x * -1e6),\n",
-    "    },\n",
-    ")"
+    "df.style.format(precision=0, na_rep='MISSING', thousands=\" \",\n",
+    "                formatter={('Decision Tree', 'Tumour'): \"{:.2f}\",\n",
+    "                           ('Regression', 'Non-Tumour'): lambda x: \"$ {:,.1f}\".format(x*-1e6)\n",
+    "                          })"
    ]
   },
   {
@@ -202,21 +186,17 @@
    "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",
@@ -224,7 +204,6 @@
     "    styler.background_gradient(axis=None, vmin=1, vmax=5, cmap=\"YlGnBu\")\n",
     "    return styler\n",
     "\n",
-    "\n",
     "weather_df"
    ]
   },
@@ -262,9 +241,7 @@
    "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"
    ]
   },
@@ -276,8 +253,8 @@
    },
    "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')"
    ]
   },
   {
@@ -334,16 +311,16 @@
    "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])"
    ]
@@ -356,8 +333,8 @@
    },
    "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')"
    ]
   },
   {
@@ -373,14 +350,11 @@
    "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)"
    ]
   },
   {
@@ -391,8 +365,8 @@
    },
    "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')"
    ]
   },
   {
@@ -410,16 +384,10 @@
    "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)"
    ]
   },
   {
@@ -430,8 +398,8 @@
    },
    "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')"
    ]
   },
   {
@@ -456,7 +424,7 @@
    "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])"
    ]
   },
   {
@@ -479,18 +447,14 @@
    "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)"
    ]
   },
@@ -502,8 +466,8 @@
    },
    "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')"
    ]
   },
   {
@@ -532,7 +496,7 @@
    "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"
    ]
   },
@@ -549,13 +513,10 @@
    "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"
    ]
   },
@@ -567,8 +528,8 @@
    },
    "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')"
    ]
   },
   {
@@ -584,11 +545,9 @@
    "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)"
    ]
   },
   {
@@ -599,8 +558,8 @@
    },
    "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')"
    ]
   },
   {
@@ -616,9 +575,8 @@
    "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)"
    ]
   },
   {
@@ -629,8 +587,8 @@
    },
    "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')"
    ]
   },
   {
@@ -671,10 +629,8 @@
    "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)"
    ]
   },
   {
@@ -694,12 +650,11 @@
    "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)"
    ]
   },
   {
@@ -710,8 +665,8 @@
    },
    "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')"
    ]
   },
   {
@@ -729,22 +684,12 @@
    "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;')"
    ]
   },
   {
@@ -755,8 +700,8 @@
    },
    "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')"
    ]
   },
   {
@@ -772,18 +717,14 @@
    "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)"
    ]
   },
@@ -795,8 +736,8 @@
    },
    "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')"
    ]
   },
   {
@@ -822,11 +763,9 @@
    "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"
    ]
   },
@@ -843,10 +782,9 @@
    "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_)"
    ]
   },
   {
@@ -863,10 +801,9 @@
    "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_)"
    ]
   },
   {
@@ -882,10 +819,9 @@
    "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_)"
    ]
   },
   {
@@ -903,10 +839,9 @@
    "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_)"
    ]
   },
   {
@@ -962,7 +897,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4 = pd.DataFrame([[1, 2], [3, 4]])\n",
+    "df4 = pd.DataFrame([[1,2],[3,4]])\n",
     "s4 = df4.style"
    ]
   },
@@ -984,7 +919,6 @@
    "outputs": [],
    "source": [
     "from pandas.io.formats.style import Styler\n",
-    "\n",
     "s4 = Styler(df4, uuid_len=0, cell_ids=False)"
    ]
   },
@@ -1035,7 +969,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4.style.set_table_styles([{\"selector\": \"td.col1\", \"props\": props}])"
+    "df4.style.set_table_styles([{'selector': 'td.col1', 'props': props}])"
    ]
   },
   {
@@ -1064,11 +998,9 @@
    "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)"
    ]
   },
   {
@@ -1089,18 +1021,14 @@
    "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)"
    ]
   },
   {
@@ -1140,14 +1068,10 @@
     "    \"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())"
    ]
   },
@@ -1205,9 +1129,9 @@
    "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')"
    ]
   },
   {
@@ -1223,9 +1147,7 @@
    "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;')"
    ]
   },
   {
@@ -1243,9 +1165,7 @@
    "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;')"
    ]
   },
   {
@@ -1262,7 +1182,7 @@
    "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')"
    ]
   },
   {
@@ -1286,7 +1206,6 @@
    "outputs": [],
    "source": [
     "import seaborn as sns\n",
-    "\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
     "\n",
     "df2.style.background_gradient(cmap=cm)"
@@ -1326,9 +1245,9 @@
    "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'})"
    ]
   },
   {
@@ -1351,7 +1270,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df2.style.bar(subset=[\"A\", \"B\"], color=\"#d65f5f\")"
+    "df2.style.bar(subset=['A', 'B'], color='#d65f5f')"
    ]
   },
   {
@@ -1369,15 +1288,10 @@
    "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)"
    ]
   },
   {
@@ -1400,10 +1314,10 @@
     "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",
@@ -1419,22 +1333,19 @@
     "\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_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_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>\"\"\""
    ]
@@ -1468,12 +1379,11 @@
    "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"
    ]
   },
@@ -1532,14 +1442,11 @@
    "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",
     "    )"
    ]
   },
@@ -1557,15 +1464,16 @@
    "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",
+    "]"
    ]
   },
   {
@@ -1575,12 +1483,14 @@
    "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())"
    ]
   },
   {
@@ -1617,8 +1527,8 @@
    "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])"
    ]
   },
   {
@@ -1636,7 +1546,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df4 = pd.DataFrame([[\"<div></div>\", '\"&other\"', \"<span></span>\"]])\n",
+    "df4 = pd.DataFrame([['<div></div>', '\"&other\"', '<span></span>']])\n",
     "df4.style"
    ]
   },
@@ -1655,9 +1565,7 @@
    "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\")"
    ]
   },
   {
@@ -1698,9 +1606,10 @@
    "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')"
    ]
   },
   {
@@ -1769,11 +1678,7 @@
    "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())"
    ]
   },
   {
@@ -1791,8 +1696,9 @@
    "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;')"
    ]
   },
   {
@@ -1801,7 +1707,8 @@
    "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;')"
    ]
   },
   {
@@ -1826,9 +1733,9 @@
    "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;')"
    ]
   },
   {
@@ -1844,12 +1751,11 @@
    "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']]))"
    ]
   },
   {
@@ -1865,13 +1771,12 @@
    "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']]))"
    ]
   },
   {
@@ -1889,15 +1794,12 @@
    "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']]))"
    ]
   },
   {
@@ -1971,12 +1873,10 @@
    "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\")"
    ]
@@ -2119,7 +2019,7 @@
     "# 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))"
    ]
   }