DOC: update style.ipynb user guide for recent enhancements (pandas-dev#41013)

Author: attack68

doc/source/user_guide/style.ipynb

@@ -1006,7 +1006,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We expect certain styling functions to be common enough that we've included a few \"built-in\" to the `Styler`, so you don't have to write them yourself."
+    "Some styling functions are common enough that we've \"built them in\" to the `Styler`, so you don't have to write them and apply them yourself. The current list of such functions is:\n",
+    "\n",
+    " - [.highlight_null][nullfunc]: for use with identifying missing data. \n",
+    " - [.highlight_min][minfunc] and [.highlight_max][maxfunc]: for use with identifying extremeties in data.\n",
+    " - [.highlight_between][betweenfunc] and [.highlight_quantile][quantilefunc]: for use with identifying classes within data.\n",
+    " - [.background_gradient][bgfunc]: a flexible method for highlighting cells based or their, or other, values on a numeric scale.\n",
+    " -  [.bar][barfunc]: to display mini-charts within cell backgrounds.\n",
+    " \n",
+    "The individual documentation on each function often gives more examples of their arguments.\n",
+    "\n",
+    "[nullfunc]: ../reference/api/pandas.io.formats.style.Styler.highlight_null.rst\n",
+    "[minfunc]: ../reference/api/pandas.io.formats.style.Styler.highlight_min.rst\n",
+    "[maxfunc]: ../reference/api/pandas.io.formats.style.Styler.highlight_max.rst\n",
+    "[betweenfunc]: ../reference/api/pandas.io.formats.style.Styler.highlight_between.rst\n",
+    "[quantilefunc]: ../reference/api/pandas.io.formats.style.Styler.highlight_quantile.rst\n",
+    "[bgfunc]: ../reference/api/pandas.io.formats.style.Styler.background_gradient.rst\n",
+    "[barfunc]: ../reference/api/pandas.io.formats.style.Styler.bar.rst"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Highlight Null"
    ]
   },
   {
@@ -1017,14 +1040,14 @@
    "source": [
     "df2.iloc[0,2] = np.nan\n",
     "df2.iloc[4,3] = np.nan\n",
-    "df2.loc[:4].style.highlight_null(null_color='red')"
+    "df2.loc[:4].style.highlight_null(null_color='yellow')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can create \"heatmaps\" with the `background_gradient` method. These require matplotlib, and we'll use [Seaborn](https://stanford.edu/~mwaskom/software/seaborn/) to get a nice colormap."
+    "### Highlight Min or Max"
    ]
   },
   {
@@ -1033,17 +1056,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import seaborn as sns\n",
-    "cm = sns.light_palette(\"green\", as_cmap=True)\n",
-    "\n",
-    "df2.style.background_gradient(cmap=cm)"
+    "df2.loc[:4].style.highlight_max(axis=1, props='color:white; font-weight:bold; background-color:darkblue;')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`Styler.background_gradient` takes the keyword arguments `low` and `high`. Roughly speaking these extend the range of your data by `low` and `high` percent so that when we convert the colors, the colormap's entire range isn't used. This is useful so that you can actually read the text still."
+    "### Highlight Between\n",
+    "This method accepts ranges as float, or NumPy arrays or Series provided the indexes match."
    ]
   },
   {
@@ -1052,8 +1073,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Uses the full color range\n",
-    "df2.loc[:4].style.background_gradient(cmap='viridis')"
+    "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;')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Highlight Quantile\n",
+    "Useful for detecting the highest or lowest percentile values"
    ]
   },
   {
@@ -1062,17 +1091,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Compress the color range\n",
-    "df2.loc[:4].style\\\n",
-    "   .background_gradient(cmap='viridis', low=.5, high=0)\\\n",
-    "   .highlight_null('red')"
+    "df2.loc[:4].style.highlight_quantile(q_left=0.85, axis=None, color='yellow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Background Gradient"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "There's also `.highlight_min` and `.highlight_max`, which is almost identical to the user defined version we created above, and also a `.highlight_null` method. "
+    "You can create \"heatmaps\" with the `background_gradient` method. These require matplotlib, and we'll use [Seaborn](https://stanford.edu/~mwaskom/software/seaborn/) to get a nice colormap."
    ]
   },
   {
@@ -1081,7 +1114,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df2.loc[:4].style.highlight_max(axis=0)"
+    "import seaborn as sns\n",
+    "cm = sns.light_palette(\"green\", as_cmap=True)\n",
+    "\n",
+    "df2.style.background_gradient(cmap=cm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[.background_gradient][bgfunc] has a number of keyword arguments to customise the gradients and colors. See its documentation.\n",
+    "\n",
+    "[bgfunc]: ../reference/api/pandas.io.formats.style.Styler.background_gradient.rst"
    ]
   },
   {