diff --git a/README.md b/README.md
index 128fa5d..39fd21c 100644
--- a/README.md
+++ b/README.md
@@ -16,8 +16,8 @@ Here I have implemented few graphs from the book using Python and matplotlib lib
 ![](images/Figure_0-5.png)                   |![](images/Figure_3-34.png)
 [Figure 4.9](horizontal-bar/figure-4-9.ipynb)|[Figure 5.13](vertical-bar/figure-5-13.ipynb)
 ![](images/Figure_4-9.png)                   |![](images/Figure_5-13.png)
-[Figure 6.4](vertical-bar/figure-6-4.ipynb)  |
-![](images/Figure_6-4.png)                   |
+[Figure 6.4](vertical-bar/figure-6-4.ipynb)  |[Figure 9.20](horizontal-bar/figure-9-20.ipynb)
+![](images/Figure_6-4.png)                   |![](images/Figure_9-20.png)
 
 ## Slopegraphs
 [Figure 9.32](slopegraph/figure-9-32.ipynb)|
diff --git a/horizontal-bar/figure-9-20.ipynb b/horizontal-bar/figure-9-20.ipynb
new file mode 100644
index 0000000..27dfcc0
--- /dev/null
+++ b/horizontal-bar/figure-9-20.ipynb
@@ -0,0 +1,542 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "25b6096f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "from matplotlib import transforms, patches, pyplot as plt\n",
+    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
+    "import numpy as np\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bc2df025",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# inline matplotlib plots\n",
+    "get_ipython().run_line_magic('matplotlib', 'inline')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "05fb3781",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define colors\n",
+    "GRAY7, GRAY9 = '#929497', '#BFBEBE'\n",
+    "BLUE_GREEN = '#31859D'\n",
+    "BLUE_GREEN_LIGHT ='#92CDDD'\n",
+    "BLUE_DARK = '#1E497D'\n",
+    "BLUE_LIGHT = '#95B3D7'\n",
+    "ORANGE_LIGHT = '#FBC08F'\n",
+    "RED3 = '#DE3A2F'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b3c3c0c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def hex_to_rgb(hex_value):\n",
+    "  h = hex_value.lstrip('#')\n",
+    "  return tuple(int(h[i:i + 2], 16) / 255.0 for i in (0, 2, 4))\n",
+    "\n",
+    "def print_colors(colors):\n",
+    "    rgb_colors = list(map(hex_to_rgb, colors))\n",
+    "    sns.palplot(rgb_colors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7bf98f1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoQAAABlCAYAAADUMPJBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAD3klEQVR4nO3bMW4jZRjH4c92UMYkjnvLKVIgIXEEkNJwBSoqCnqUKiLVaiXKNJwBTsEJkKhpg4x8AJzgWJDMFmip8GbnU9jR7P952vFYr17b8k8ez6ht27YAABBr3PcAAAD0SxACAIQThAAA4QQhAEA4QQgAEE4QAgCEE4QAAOEOak98fHws6/W6zGazMhqNnnMmAACeQdu2ZbPZlMViUcbj/b8DVgfher0up6entacDAPCOrFarslwu9x6vDsLZbFZKKeXFy+9K0zS1TxPnk48/6nuEQfr259/6HmFwvvz8s75HGKTvX/7Y9wiD883XX/Q9wiB9dfxT3yMMzu8vfuh7hMG5fXgon/7y67/dtk91EL6+TNw0TZlOp7VPE+fo6KjvEQZp0niPdTU9fvOHn/82/uCw7xEGZ/rhcd8jDNLJkR9TupodTPoeYbCe+nufm0oAAMIJQgCAcIIQACCcIAQACCcIAQDCCUIAgHCCEAAgnCAEAAgnCAEAwglCAIBwghAAIJwgBAAIJwgBAMIJQgCAcIIQACCcIAQACCcIAQDCCUIAgHCCEAAgnCAEAAgnCAEAwglCAIBwghAAIJwgBAAIJwgBAMIJQgCAcIIQACCcIAQACCcIAQDCCUIAgHCCEAAgnCAEAAgnCAEAwglCAIBwghAAIJwgBAAIJwgBAMIJQgCAcIIQACCcIAQACCcIAQDCCUIAgHCCEAAgnCAEAAgnCAEAwglCAIBwghAAIJwgBAAIJwgBAMIJQgCAcIIQACCcIAQACCcIAQDCCUIAgHCCEAAgnCAEAAgnCAEAwglCAIBwghAAIJwgBAAIJwgBAMIJQgCAcIIQACCcIAQACCcIAQDCCUIAgHCCEAAgnCAEAAgnCAEAwglCAIBwghAAIJwgBAAIJwgBAMId1J7Ytm0ppZT7+/tnGybB3d1d3yMM0sP9tu8RBmd7u+l7hEF6/GvX9wiDs/3ztu8RBumPse/PrjZ/P/Q9wuDcPvyzs9fdts+ofeoRe9zc3JSzs7OaUwEAeIdWq1VZLpd7j1cH4Xa7LVdXV+Xy8rI0TVM9YJLdbleur6/LxcVFOTw87HucwbC37uysjr11Z2d17K07O6vTtm3ZbDZlsViU8Xj/PwWrLxmPx+Myn8/LfD73wryl3W5XmqYpJycndtaBvXVnZ3XsrTs7q2Nv3dlZvfl8/uRj3FQCABBOEAIAhKsOwslkUs7Pz8tkMnnOed5rdlbH3rqzszr21p2d1bG37uzs/1V9UwkAAO8Hl4wBAMIJQgCAcIIQACCcIAQACCcIAQDCCUIAgHCCEAAgnCAEAAj3CnGmrRWs+EaAAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 800x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print_colors([\n",
+    "        GRAY7,\n",
+    "        GRAY9,\n",
+    "        BLUE_GREEN,\n",
+    "        BLUE_GREEN_LIGHT,\n",
+    "        BLUE_DARK,\n",
+    "        BLUE_LIGHT,\n",
+    "        ORANGE_LIGHT,\n",
+    "        RED3\n",
+    "        ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c9f8090f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# configure plot font family to Arial\n",
+    "plt.rcParams['font.family'] = 'Arial'\n",
+    "# configure mathtext bold and italic font family to Arial\n",
+    "matplotlib.rcParams['mathtext.fontset'] = 'custom'\n",
+    "matplotlib.rcParams['mathtext.bf'] = 'Arial:bold'\n",
+    "matplotlib.rcParams['mathtext.it'] = 'Arial:italic'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6f5673b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Returns the x coordinates of a text element on a given axis of a given\n",
+    "# figure.\n",
+    "# Used to position elements on the canvas\n",
+    "# Returns object with attributes:\n",
+    "#   x0 coordinate of the text element\n",
+    "#   x1 coordinate of the text element\n",
+    "#   y0 coordinate of the text element\n",
+    "#   y1 coordinate of the text element\n",
+    "def get_text_coordinates(text_element, ax, fig):\n",
+    "        x0 = text_element.get_window_extent(fig.canvas.get_renderer()).x0\n",
+    "        x1 = text_element.get_window_extent(fig.canvas.get_renderer()).x1\n",
+    "        y0 = text_element.get_window_extent(fig.canvas.get_renderer()).y0\n",
+    "        y1 = text_element.get_window_extent(fig.canvas.get_renderer()).y1\n",
+    "        return {\n",
+    "                 'x0': round(ax.transData.inverted().transform_point((x0, 0))[0], 2),\n",
+    "                 'x1': round(ax.transData.inverted().transform_point((x1, 0))[0], 2),\n",
+    "                 'y0': round(ax.transData.inverted().transform_point((0, y0))[1], 2),\n",
+    "                 'y1': round(ax.transData.inverted().transform_point((0, y1))[1], 2)\n",
+    "               }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "af835f79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A to O\n",
+    "features = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K' ,'L', 'M', 'N', 'O']\n",
+    "feature_labels = [f'Feature {x}' for x in features]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "02422d06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_values = [\n",
+    "        [0, 1, 1, 11, 40, 47],   # A\n",
+    "        [0, 2, 2, 13, 36, 47],   # B\n",
+    "        [2, 2, 5, 24, 34, 33],   # C\n",
+    "        [8, 1, 4, 21, 37, 29],   # D\n",
+    "        [6, 1, 6, 23, 36, 28],   # E\n",
+    "        [14, 1, 5, 20, 35, 25],  # F\n",
+    "        [19, 2, 5, 15, 26, 33],  # G\n",
+    "        [13, 1, 6, 23, 32, 25],  # H\n",
+    "        [22, 2, 5, 17, 27, 27],  # I\n",
+    "        [2, 8, 14, 24, 27, 25],  # J\n",
+    "        [29, 1, 4, 17, 28, 21],  # K\n",
+    "        [29, 1, 4, 23, 27, 16],  # L\n",
+    "        [33, 3, 8, 25, 18, 13],  # M\n",
+    "        [26, 9, 14, 24, 17, 10], # N\n",
+    "        [51, 1, 6, 15, 16, 11]   # O\n",
+    "        ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "520d8d7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['#92CDDD', '#FBC08F', '#FBC08F', '#BFBEBE', '#1E497D', '#1E497D'],\n",
+       " ['#92CDDD', '#FBC08F', '#FBC08F', '#BFBEBE', '#1E497D', '#1E497D'],\n",
+       " ['#92CDDD', '#FBC08F', '#FBC08F', '#BFBEBE', '#95B3D7', '#95B3D7'],\n",
+       " ['#92CDDD', '#FBC08F', '#FBC08F', '#BFBEBE', '#95B3D7', '#95B3D7'],\n",
+       " ['#92CDDD', '#FBC08F', '#FBC08F', '#BFBEBE', '#95B3D7', '#95B3D7'],\n",
+       " ['#92CDDD', '#FBC08F', '#FBC08F', '#BFBEBE', '#95B3D7', '#95B3D7'],\n",
+       " ['#92CDDD', '#FBC08F', '#FBC08F', '#BFBEBE', '#95B3D7', '#95B3D7'],\n",
+       " ['#92CDDD', '#FBC08F', '#FBC08F', '#BFBEBE', '#95B3D7', '#95B3D7'],\n",
+       " ['#92CDDD', '#FBC08F', '#FBC08F', '#BFBEBE', '#95B3D7', '#95B3D7'],\n",
+       " ['#92CDDD', '#DE3A2F', '#DE3A2F', '#BFBEBE', '#95B3D7', '#95B3D7'],\n",
+       " ['#92CDDD', '#FBC08F', '#FBC08F', '#BFBEBE', '#95B3D7', '#95B3D7'],\n",
+       " ['#92CDDD', '#FBC08F', '#FBC08F', '#BFBEBE', '#95B3D7', '#95B3D7'],\n",
+       " ['#92CDDD', '#FBC08F', '#FBC08F', '#BFBEBE', '#95B3D7', '#95B3D7'],\n",
+       " ['#92CDDD', '#DE3A2F', '#DE3A2F', '#BFBEBE', '#95B3D7', '#95B3D7'],\n",
+       " ['#31859D', '#FBC08F', '#FBC08F', '#BFBEBE', '#95B3D7', '#95B3D7']]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "colors = [\n",
+    "        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_DARK]*2),   # A\n",
+    "        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_DARK]*2),   # B\n",
+    "        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2),  # C\n",
+    "        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2),  # D\n",
+    "        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2),  # E\n",
+    "        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2),  # F\n",
+    "        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2),  # G\n",
+    "        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2),  # H\n",
+    "        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2),  # I\n",
+    "        ([BLUE_GREEN_LIGHT] + [RED3]*2    + [GRAY9] + [BLUE_LIGHT]*2),       # J\n",
+    "        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2),  # K\n",
+    "        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2),  # L\n",
+    "        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2),  # M\n",
+    "        ([BLUE_GREEN_LIGHT] + [RED3]*2    + [GRAY9] + [BLUE_LIGHT]*2),       # N\n",
+    "        ([BLUE_GREEN] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2)         # 0\n",
+    "        ]\n",
+    "\n",
+    "colors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8714d2e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Feature A',\n",
+       " 'Feature B',\n",
+       " 'Feature C',\n",
+       " 'Feature D',\n",
+       " 'Feature E',\n",
+       " 'Feature F',\n",
+       " 'Feature G',\n",
+       " 'Feature H',\n",
+       " 'Feature I',\n",
+       " 'Feature J',\n",
+       " 'Feature K',\n",
+       " 'Feature L',\n",
+       " 'Feature M',\n",
+       " 'Feature N',\n",
+       " 'Feature O']"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "feature_labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1a720616",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_values_reversed_transposed = np.array(X_values[::-1]).T.tolist()\n",
+    "colors_reversed_transposed = np.array(colors[::-1]).T.tolist()\n",
+    "feature_labels_reversed = feature_labels[::-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "0099e616",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 2750x770 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# create a horizontal stacked bar chart\n",
+    "fig, ax = plt.subplots(figsize=(25, 7),  # width, height in inches\n",
+    "                          dpi=110)       # resolution of the figure\n",
+    "\n",
+    "# tune the subplot layout by setting sides of the figure\n",
+    "fig.subplots_adjust(left=0.28, right=0.53, top=0.61, bottom=0.107)\n",
+    "\n",
+    "widths = np.cumsum(X_values, axis=1)[::-1].T.tolist()\n",
+    "\n",
+    "for i, (colname, color) in enumerate(zip(X_values_reversed_transposed, colors_reversed_transposed)):\n",
+    "    bars = ax.barh(feature_labels_reversed,\n",
+    "             colname,\n",
+    "             color=color,\n",
+    "             edgecolor='white',\n",
+    "             height=0.65,\n",
+    "             left=np.sum(X_values_reversed_transposed[:i], axis=0))\n",
+    "\n",
+    "\n",
+    "    for j, b in enumerate(bars):\n",
+    "            # 3% is too small to display the label\n",
+    "            if b.get_width() <= 3:\n",
+    "                continue\n",
+    "\n",
+    "            # We could annotate all values, but only need to draw attention\n",
+    "            # to these specific ones\n",
+    "            if color[j] not in [BLUE_DARK, RED3, BLUE_GREEN]:\n",
+    "                continue\n",
+    "\n",
+    "            width = widths[i][j]\n",
+    "\n",
+    "            plt.text(width - 0.7, b.get_y() + b.get_height() / 2,\n",
+    "                     f'{b.get_width():.0f}%',\n",
+    "                     ha='right',\n",
+    "                     va='center',\n",
+    "                     fontsize=6,\n",
+    "                     color='white')\n",
+    "\n",
+    "# set y ticks and labels\n",
+    "ax.set_yticks(range(len(feature_labels_reversed)))\n",
+    "ax.set_yticklabels(feature_labels_reversed, fontsize=10, fontweight='bold')\n",
+    "\n",
+    "# set x axis limits\n",
+    "ax.set_xlim(0, 100)\n",
+    "\n",
+    "# set x ticks and labels\n",
+    "ax.set_xticks([0, 25, 50, 75, 100])\n",
+    "\n",
+    "ax.tick_params(top=False, bottom=False, left=False, labelbottom=False, labeltop=False)\n",
+    "ax.tick_params(color=GRAY7)\n",
+    "ax.spines['top'].set_visible(False)\n",
+    "ax.spines['left'].set_visible(False)\n",
+    "ax.spines['right'].set_visible(False)\n",
+    "ax.spines['bottom'].set_visible(False)\n",
+    "\n",
+    "# Legend\n",
+    "legends_end_x1 = 100\n",
+    "\n",
+    "legend_texts = [\n",
+    "    '- Have not used',\n",
+    "    '- Not satisfied at all',\n",
+    "    '- Not very satisfied',\n",
+    "    '- Somewhat satisfied',\n",
+    "    '- Very satisfied',\n",
+    "    '- Completely satisfied',\n",
+    "]\n",
+    "\n",
+    "legend_fontsize = 8\n",
+    "\n",
+    "# Drawing the elements to get their size with respect to coordinates to\n",
+    "# calculate the starting position given the end position and the desired spacing\n",
+    "legends_length = 0\n",
+    "for text in legend_texts:\n",
+    "    element = ax.text(0, 0, text, fontsize=legend_fontsize, fontweight='bold')\n",
+    "    legends_length += get_text_coordinates(element, ax=ax, fig=fig)['x1']\n",
+    "    element.set_visible(False)\n",
+    "\n",
+    "spacing = 3\n",
+    "\n",
+    "legends_start_x0 = legends_end_x1 - legends_length - spacing * (len(legend_texts) - 1)\n",
+    "\n",
+    "legends_y = len(feature_labels_reversed) + 0.05\n",
+    "\n",
+    "legend_colors = [BLUE_GREEN] + [RED3] * 2 + [GRAY9] + [BLUE_DARK] * 2\n",
+    "\n",
+    "x = legends_start_x0\n",
+    "\n",
+    "for text, color in zip(legend_texts, legend_colors):\n",
+    "    previous_element = ax.text(x, legends_y, text, fontsize=legend_fontsize, color=color, fontweight='bold')\n",
+    "    x = get_text_coordinates(previous_element, ax=ax, fig=fig)['x1'] + spacing\n",
+    "\n",
+    "title_x0 = legends_start_x0 - 5\n",
+    "\n",
+    "# Calculate spacing dynamically\n",
+    "side_box_text_1 = 'Features A and B\\ncontinue to top user\\nsatisfaction'\n",
+    "side_box_text_2 = 'Users are least\\nsatisfied with\\nFeatures J and N;\\nwhat improvements\\n' \\\n",
+    "                        'can we make here\\nfor a better user\\nexperience?'\n",
+    "\n",
+    "# In the following text, I added two spaces to match the size of the above boxes.\n",
+    "# Should be fixed by using a monospace font\n",
+    "side_box_text_3 = 'Feature O is least\\nused. What steps\\n' \\\n",
+    "                        'can we proactively  \\n' \\\n",
+    "                        'take with existing\\n' \\\n",
+    "                        'users to increase\\n' \\\n",
+    "                        'utilization?'\n",
+    "\n",
+    "linespacing = 1.3\n",
+    "fontsize = 10\n",
+    "\n",
+    "\n",
+    "side_boxes_height = 0\n",
+    "__el = ax.text(0, 0, side_box_text_1, fontsize=fontsize, linespacing=linespacing)\n",
+    "side_boxes_height += get_text_coordinates(__el, ax=ax, fig=fig)['y1']\n",
+    "__el.set_visible(False)\n",
+    "\n",
+    "__el = ax.text(0, 0, side_box_text_2, fontsize=fontsize, linespacing=linespacing)\n",
+    "side_boxes_height += get_text_coordinates(__el, ax=ax, fig=fig)['y1']\n",
+    "__el.set_visible(False)\n",
+    "\n",
+    "__el = ax.text(0, 0, side_box_text_3, fontsize=fontsize, linespacing=linespacing)\n",
+    "side_boxes_height += get_text_coordinates(__el, ax=ax, fig=fig)['y1']\n",
+    "__el.set_visible(False)\n",
+    "\n",
+    "\n",
+    "side_boxes_start_y0 = 0.07 # to handle the padding from the background color\n",
+    "side_boxes_end_y1 = len(feature_labels_reversed) - 1\n",
+    "\n",
+    "spacing = (side_boxes_end_y1 - side_boxes_start_y0 - side_boxes_height) / (3 - 1) # n_elements - 1\n",
+    "\n",
+    "side_box_x = 102.5\n",
+    "\n",
+    "y1 = get_text_coordinates(previous_element, ax=ax, fig=fig)['y1'] + spacing\n",
+    "\n",
+    "previous_element = ax.text(side_box_x, side_boxes_start_y0, side_box_text_3,\n",
+    "        fontsize=fontsize, linespacing=linespacing, backgroundcolor=BLUE_GREEN,\n",
+    "        color='white')\n",
+    "\n",
+    "y1 = get_text_coordinates(previous_element, ax=ax, fig=fig)['y1'] + spacing\n",
+    "\n",
+    "previous_element = ax.text(side_box_x, y1, side_box_text_2,\n",
+    "        fontsize=fontsize, linespacing=linespacing, backgroundcolor=RED3,\n",
+    "        color='white')\n",
+    "\n",
+    "y1 = get_text_coordinates(previous_element, ax=ax, fig=fig)['y1'] + spacing\n",
+    "\n",
+    "ax.text(side_box_x, y1, side_box_text_1,\n",
+    "        fontsize=fontsize, linespacing=linespacing, backgroundcolor=BLUE_DARK,\n",
+    "        color='white')\n",
+    "\n",
+    "\n",
+    "rightmost_x = get_text_coordinates(previous_element, ax=ax, fig=fig)['x1']\n",
+    "\n",
+    "\n",
+    "title = ax.text(title_x0, len(feature_labels_reversed) + 3.15,\n",
+    "        'User satisfaction varies greatly by feature',\n",
+    "        fontsize=15, backgroundcolor='none', color='white',\n",
+    "        ha='left')\n",
+    "\n",
+    "title_coordinates =  get_text_coordinates(title, ax=ax, fig=fig)\n",
+    "\n",
+    "# I cannot use background color with the title, because it does not extend up to\n",
+    "# the rightmost x coordinate.\n",
+    "# I could not do this with a bbox config.\n",
+    "# I could not use a rectangle directly because it is not being drawn out of the\n",
+    "# ax x/y limits.\n",
+    "# I create another axis, ax2, and I create a rectangle there.\n",
+    "title_x1 = title_coordinates['x1']\n",
+    "title_y0 = title_coordinates['y0']\n",
+    "title_y1 = title_coordinates['y1']\n",
+    "\n",
+    "height = title_y1 - title_y0\n",
+    "width = rightmost_x - title_x0\n",
+    "\n",
+    "padding = 0.65 # space around the text\n",
+    "\n",
+    "padding_coord = (height / 2 * padding)\n",
+    "\n",
+    "ax2_x_min = title_x0 - padding_coord\n",
+    "ax2_y_min = title_y0 - padding_coord\n",
+    "ax2_y_max = title_y1 + padding_coord\n",
+    "\n",
+    "ax2_height = ax2_y_max - ax2_y_min\n",
+    "\n",
+    "tweak_coords = 1.45 # extend the box a bit to align with background color of side texts\n",
+    "\n",
+    "ax2_width = width + 2 * padding_coord + tweak_coords\n",
+    "ax2 = ax.inset_axes([ax2_x_min, ax2_y_min, ax2_width, ax2_height], transform=ax.transData, zorder=1)\n",
+    "ax2.xaxis.set_visible(False)\n",
+    "ax2.yaxis.set_visible(False)\n",
+    "\n",
+    "ax2_x_max = ax2.get_xlim()[1]\n",
+    "\n",
+    "ax2.spines['top'].set_visible(False)\n",
+    "ax2.spines['left'].set_visible(False)\n",
+    "ax2.spines['right'].set_visible(False)\n",
+    "ax2.spines['bottom'].set_visible(False)\n",
+    "\n",
+    "rect = patches.Rectangle((0, 0), ax2_x_max, ax2_y_max, linewidth=1, edgecolor=GRAY7, facecolor=GRAY7)\n",
+    "ax2.add_patch(rect)\n",
+    "\n",
+    "# subtitle\n",
+    "ax.text(title_x0, len(feature_labels_reversed) + 1.50,\n",
+    "        'Product X User Satisfaction: '\n",
+    "        '$\\\\bf{Features}$',\n",
+    "        fontsize=10,\n",
+    "        color='#4B4B4B')\n",
+    "\n",
+    "# footnote\n",
+    "ax.text(title_x0, -2,\n",
+    "        'Reponses based on survey question \"How satisfied have you been with each'\n",
+    "        ' of these features?\"\\n'\n",
+    "        'Need more details here to help put this data into context: How many'\n",
+    "        ' people completed survey? What proportion of users does this represent?\\n'\n",
+    "        'Do those who completed survey look like the overall population'\n",
+    "        ' demographic wise? When was the survey conducted?',\n",
+    "        fontsize=6, linespacing=1.3)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2e379fa",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,py:percent"
+  },
+  "kernelspec": {
+   "display_name": "myenv",
+   "language": "python",
+   "name": "myenv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/horizontal-bar/figure-9-20.py b/horizontal-bar/figure-9-20.py
new file mode 100644
index 0000000..871f93f
--- /dev/null
+++ b/horizontal-bar/figure-9-20.py
@@ -0,0 +1,343 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+import matplotlib
+from matplotlib import transforms, patches, pyplot as plt
+from mpl_toolkits.axes_grid1.inset_locator import inset_axes
+import numpy as np
+import seaborn as sns
+
+
+# inline matplotlib plots
+get_ipython().run_line_magic('matplotlib', 'inline')
+
+
+# define colors
+GRAY7, GRAY9 = '#929497', '#BFBEBE'
+BLUE_GREEN = '#31859D'
+BLUE_GREEN_LIGHT ='#92CDDD'
+BLUE_DARK = '#1E497D'
+BLUE_LIGHT = '#95B3D7'
+ORANGE_LIGHT = '#FBC08F'
+RED3 = '#DE3A2F'
+
+
+def hex_to_rgb(hex_value):
+  h = hex_value.lstrip('#')
+  return tuple(int(h[i:i + 2], 16) / 255.0 for i in (0, 2, 4))
+
+def print_colors(colors):
+    rgb_colors = list(map(hex_to_rgb, colors))
+    sns.palplot(rgb_colors)
+
+
+print_colors([
+        GRAY7,
+        GRAY9,
+        BLUE_GREEN,
+        BLUE_GREEN_LIGHT,
+        BLUE_DARK,
+        BLUE_LIGHT,
+        ORANGE_LIGHT,
+        RED3
+        ])
+
+
+# configure plot font family to Arial
+plt.rcParams['font.family'] = 'Arial'
+# configure mathtext bold and italic font family to Arial
+matplotlib.rcParams['mathtext.fontset'] = 'custom'
+matplotlib.rcParams['mathtext.bf'] = 'Arial:bold'
+matplotlib.rcParams['mathtext.it'] = 'Arial:italic'
+
+
+# Returns the x coordinates of a text element on a given axis of a given
+# figure.
+# Used to position elements on the canvas
+# Returns object with attributes:
+#   x0 coordinate of the text element
+#   x1 coordinate of the text element
+#   y0 coordinate of the text element
+#   y1 coordinate of the text element
+def get_text_coordinates(text_element, ax, fig):
+        x0 = text_element.get_window_extent(fig.canvas.get_renderer()).x0
+        x1 = text_element.get_window_extent(fig.canvas.get_renderer()).x1
+        y0 = text_element.get_window_extent(fig.canvas.get_renderer()).y0
+        y1 = text_element.get_window_extent(fig.canvas.get_renderer()).y1
+        return {
+                 'x0': round(ax.transData.inverted().transform_point((x0, 0))[0], 2),
+                 'x1': round(ax.transData.inverted().transform_point((x1, 0))[0], 2),
+                 'y0': round(ax.transData.inverted().transform_point((0, y0))[1], 2),
+                 'y1': round(ax.transData.inverted().transform_point((0, y1))[1], 2)
+               }
+
+
+# A to O
+features = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K' ,'L', 'M', 'N', 'O']
+feature_labels = [f'Feature {x}' for x in features]
+
+
+X_values = [
+        [0, 1, 1, 11, 40, 47],   # A
+        [0, 2, 2, 13, 36, 47],   # B
+        [2, 2, 5, 24, 34, 33],   # C
+        [8, 1, 4, 21, 37, 29],   # D
+        [6, 1, 6, 23, 36, 28],   # E
+        [14, 1, 5, 20, 35, 25],  # F
+        [19, 2, 5, 15, 26, 33],  # G
+        [13, 1, 6, 23, 32, 25],  # H
+        [22, 2, 5, 17, 27, 27],  # I
+        [2, 8, 14, 24, 27, 25],  # J
+        [29, 1, 4, 17, 28, 21],  # K
+        [29, 1, 4, 23, 27, 16],  # L
+        [33, 3, 8, 25, 18, 13],  # M
+        [26, 9, 14, 24, 17, 10], # N
+        [51, 1, 6, 15, 16, 11]   # O
+        ]
+
+
+colors = [
+        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_DARK]*2),   # A
+        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_DARK]*2),   # B
+        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2),  # C
+        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2),  # D
+        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2),  # E
+        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2),  # F
+        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2),  # G
+        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2),  # H
+        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2),  # I
+        ([BLUE_GREEN_LIGHT] + [RED3]*2    + [GRAY9] + [BLUE_LIGHT]*2),       # J
+        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2),  # K
+        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2),  # L
+        ([BLUE_GREEN_LIGHT] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2),  # M
+        ([BLUE_GREEN_LIGHT] + [RED3]*2    + [GRAY9] + [BLUE_LIGHT]*2),       # N
+        ([BLUE_GREEN] + [ORANGE_LIGHT]*2 + [GRAY9] + [BLUE_LIGHT]*2)         # 0
+        ]
+
+colors
+
+
+feature_labels
+
+
+X_values_reversed_transposed = np.array(X_values[::-1]).T.tolist()
+colors_reversed_transposed = np.array(colors[::-1]).T.tolist()
+feature_labels_reversed = feature_labels[::-1]
+
+
+# create a horizontal stacked bar chart
+fig, ax = plt.subplots(figsize=(25, 7),  # width, height in inches
+                          dpi=110)       # resolution of the figure
+
+# tune the subplot layout by setting sides of the figure
+fig.subplots_adjust(left=0.28, right=0.53, top=0.61, bottom=0.107)
+
+widths = np.cumsum(X_values, axis=1)[::-1].T.tolist()
+
+for i, (colname, color) in enumerate(zip(X_values_reversed_transposed, colors_reversed_transposed)):
+    bars = ax.barh(feature_labels_reversed,
+             colname,
+             color=color,
+             edgecolor='white',
+             height=0.65,
+             left=np.sum(X_values_reversed_transposed[:i], axis=0))
+
+
+    for j, b in enumerate(bars):
+            # 3% is too small to display the label
+            if b.get_width() <= 3:
+                continue
+
+            # We could annotate all values, but only need to draw attention
+            # to these specific ones
+            if color[j] not in [BLUE_DARK, RED3, BLUE_GREEN]:
+                continue
+
+            width = widths[i][j]
+
+            plt.text(width - 0.7, b.get_y() + b.get_height() / 2,
+                     f'{b.get_width():.0f}%',
+                     ha='right',
+                     va='center',
+                     fontsize=6,
+                     color='white')
+
+# set y ticks and labels
+ax.set_yticks(range(len(feature_labels_reversed)))
+ax.set_yticklabels(feature_labels_reversed, fontsize=10, fontweight='bold')
+
+# set x axis limits
+ax.set_xlim(0, 100)
+
+# set x ticks and labels
+ax.set_xticks([0, 25, 50, 75, 100])
+
+ax.tick_params(top=False, bottom=False, left=False, labelbottom=False, labeltop=False)
+ax.tick_params(color=GRAY7)
+ax.spines['top'].set_visible(False)
+ax.spines['left'].set_visible(False)
+ax.spines['right'].set_visible(False)
+ax.spines['bottom'].set_visible(False)
+
+# Legend
+legends_end_x1 = 100
+
+legend_texts = [
+    '- Have not used',
+    '- Not satisfied at all',
+    '- Not very satisfied',
+    '- Somewhat satisfied',
+    '- Very satisfied',
+    '- Completely satisfied',
+]
+
+legend_fontsize = 8
+
+# Drawing the elements to get their size with respect to coordinates to
+# calculate the starting position given the end position and the desired spacing
+legends_length = 0
+for text in legend_texts:
+    element = ax.text(0, 0, text, fontsize=legend_fontsize, fontweight='bold')
+    legends_length += get_text_coordinates(element, ax=ax, fig=fig)['x1']
+    element.set_visible(False)
+
+spacing = 3
+
+legends_start_x0 = legends_end_x1 - legends_length - spacing * (len(legend_texts) - 1)
+
+legends_y = len(feature_labels_reversed) + 0.05
+
+legend_colors = [BLUE_GREEN] + [RED3] * 2 + [GRAY9] + [BLUE_DARK] * 2
+
+x = legends_start_x0
+
+for text, color in zip(legend_texts, legend_colors):
+    previous_element = ax.text(x, legends_y, text, fontsize=legend_fontsize, color=color, fontweight='bold')
+    x = get_text_coordinates(previous_element, ax=ax, fig=fig)['x1'] + spacing
+
+title_x0 = legends_start_x0 - 5
+
+# Calculate spacing dynamically
+side_box_text_1 = 'Features A and B\ncontinue to top user\nsatisfaction'
+side_box_text_2 = 'Users are least\nsatisfied with\nFeatures J and N;\nwhat improvements\n'                         'can we make here\nfor a better user\nexperience?'
+
+# In the following text, I added two spaces to match the size of the above boxes.
+# Should be fixed by using a monospace font
+side_box_text_3 = 'Feature O is least\nused. What steps\n'                         'can we proactively  \n'                         'take with existing\n'                         'users to increase\n'                         'utilization?'
+
+linespacing = 1.3
+fontsize = 10
+
+
+side_boxes_height = 0
+__el = ax.text(0, 0, side_box_text_1, fontsize=fontsize, linespacing=linespacing)
+side_boxes_height += get_text_coordinates(__el, ax=ax, fig=fig)['y1']
+__el.set_visible(False)
+
+__el = ax.text(0, 0, side_box_text_2, fontsize=fontsize, linespacing=linespacing)
+side_boxes_height += get_text_coordinates(__el, ax=ax, fig=fig)['y1']
+__el.set_visible(False)
+
+__el = ax.text(0, 0, side_box_text_3, fontsize=fontsize, linespacing=linespacing)
+side_boxes_height += get_text_coordinates(__el, ax=ax, fig=fig)['y1']
+__el.set_visible(False)
+
+
+side_boxes_start_y0 = 0.07 # to handle the padding from the background color
+side_boxes_end_y1 = len(feature_labels_reversed) - 1
+
+spacing = (side_boxes_end_y1 - side_boxes_start_y0 - side_boxes_height) / (3 - 1) # n_elements - 1
+
+side_box_x = 102.5
+
+y1 = get_text_coordinates(previous_element, ax=ax, fig=fig)['y1'] + spacing
+
+previous_element = ax.text(side_box_x, side_boxes_start_y0, side_box_text_3,
+        fontsize=fontsize, linespacing=linespacing, backgroundcolor=BLUE_GREEN,
+        color='white')
+
+y1 = get_text_coordinates(previous_element, ax=ax, fig=fig)['y1'] + spacing
+
+previous_element = ax.text(side_box_x, y1, side_box_text_2,
+        fontsize=fontsize, linespacing=linespacing, backgroundcolor=RED3,
+        color='white')
+
+y1 = get_text_coordinates(previous_element, ax=ax, fig=fig)['y1'] + spacing
+
+ax.text(side_box_x, y1, side_box_text_1,
+        fontsize=fontsize, linespacing=linespacing, backgroundcolor=BLUE_DARK,
+        color='white')
+
+
+rightmost_x = get_text_coordinates(previous_element, ax=ax, fig=fig)['x1']
+
+
+title = ax.text(title_x0, len(feature_labels_reversed) + 3.15,
+        'User satisfaction varies greatly by feature',
+        fontsize=15, backgroundcolor='none', color='white',
+        ha='left')
+
+title_coordinates =  get_text_coordinates(title, ax=ax, fig=fig)
+
+# I cannot use background color with the title, because it does not extend up to
+# the rightmost x coordinate.
+# I could not do this with a bbox config.
+# I could not use a rectangle directly because it is not being drawn out of the
+# ax x/y limits.
+# I create another axis, ax2, and I create a rectangle there.
+title_x1 = title_coordinates['x1']
+title_y0 = title_coordinates['y0']
+title_y1 = title_coordinates['y1']
+
+height = title_y1 - title_y0
+width = rightmost_x - title_x0
+
+padding = 0.65 # space around the text
+
+padding_coord = (height / 2 * padding)
+
+ax2_x_min = title_x0 - padding_coord
+ax2_y_min = title_y0 - padding_coord
+ax2_y_max = title_y1 + padding_coord
+
+ax2_height = ax2_y_max - ax2_y_min
+
+tweak_coords = 1.45 # extend the box a bit to align with background color of side texts
+
+ax2_width = width + 2 * padding_coord + tweak_coords
+ax2 = ax.inset_axes([ax2_x_min, ax2_y_min, ax2_width, ax2_height], transform=ax.transData, zorder=1)
+ax2.xaxis.set_visible(False)
+ax2.yaxis.set_visible(False)
+
+ax2_x_max = ax2.get_xlim()[1]
+
+ax2.spines['top'].set_visible(False)
+ax2.spines['left'].set_visible(False)
+ax2.spines['right'].set_visible(False)
+ax2.spines['bottom'].set_visible(False)
+
+rect = patches.Rectangle((0, 0), ax2_x_max, ax2_y_max, linewidth=1, edgecolor=GRAY7, facecolor=GRAY7)
+ax2.add_patch(rect)
+
+# subtitle
+ax.text(title_x0, len(feature_labels_reversed) + 1.50,
+        'Product X User Satisfaction: '
+        '$\\bf{Features}$',
+        fontsize=10,
+        color='#4B4B4B')
+
+# footnote
+ax.text(title_x0, -2,
+        'Reponses based on survey question "How satisfied have you been with each'
+        ' of these features?"\n'
+        'Need more details here to help put this data into context: How many'
+        ' people completed survey? What proportion of users does this represent?\n'
+        'Do those who completed survey look like the overall population'
+        ' demographic wise? When was the survey conducted?',
+        fontsize=6, linespacing=1.3)
+
+plt.show()
+
+
+
+
diff --git a/images/Figure_9-20.png b/images/Figure_9-20.png
new file mode 100644
index 0000000..00c9333
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