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   "source": [
    "### <center>San Jose State University<br>Department of Applied Data Science<br><br>**DATA 200<br>Computational Programming for Data Analytics**<br><br>Spring 2024<br>Instructor: Ron Mak</center>"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "# `seaborn` Tree maps and the `squarify` module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import squarify\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A ***tree map*** displays hiearchical data as a set of nested rectangles. Each rectangle represents a group, and its area is proportional to its value. Colors can represent the hierarchy. Use the `squarify` module with color palettes from `seaborn`."
   ]
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   "source": [
    "colors = sns.light_palette('brown', 4)\n",
    "\n",
    "squarify.plot(sizes=[50, 25, 10, 15],\n",
    "              label=['Group A', 'Group B', 'Group C', 'Group D'],\n",
    "              color=colors)\n",
    "\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Water usage\n",
    "#### Use a tree map to visualize water usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "usage = pd.read_csv(\"water_usage.csv\")\n",
    "usage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Show the percentages for each tile and add a title."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(dpi=200)\n",
    "\n",
    "labels = usage['Usage'] + ' (' + usage['Percentage'].astype('str') + '%)'\n",
    "\n",
    "squarify.plot(sizes=usage['Percentage'], \n",
    "              label=labels, \n",
    "              color=sns.light_palette('green', usage.shape[0]))\n",
    "\n",
    "plt.axis('off')\n",
    "plt.title('Water usage')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adapted from ***Data Visualization with Python***, by Mario Döbler and Tim Großmann, Packt 2019, ISBN 978-1-78995-646-7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional material (c) 2024 by Ronald Mak"
   ]
  }
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