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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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   "source": [
    "# Stacked Area Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## Stacked area chart parameters\n",
    "#### To create a stacked area plot, call `plt.stackplot(x, y1, y2, y3, ...)`, where\n",
    "- #### *x* is the sequence of x values\n",
    "- #### *y1, y2, y3, ...* are sequences of y values\n",
    "Example:"
   ]
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   "source": [
    "plt.stackplot([1, 2, 3, 4],\n",
    "              [2, 4, 5, 8], [1, 5, 4, 2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example: Smartphone Sales Units\n",
    "#### You want to invest in one of the biggest five smartphone manufacturers. Looking at the quarterly sales units as part of a whole may be a good indicator to invest in one of the companies. Therefore, compare smartphone sales units using a stacked area chart."
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "sales = pd.read_csv('smartphone_sales.csv')\n",
    "sales"
   ]
  },
  {
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   "source": [
    "# Create the figure.\n",
    "plt.figure(figsize=(10, 6), dpi=300)\n",
    "\n",
    "# Create the stacked area chart.\n",
    "labels = sales.columns[1:]\n",
    "plt.stackplot('Quarter', 'Apple', 'Samsung', 'Huawei', 'Xiaomi', 'OPPO', \n",
    "              data=sales, labels=labels)\n",
    "\n",
    "# Add the labels.\n",
    "plt.xlabel('Quarters')\n",
    "plt.ylabel('Sales units in thousands')\n",
    "\n",
    "plt.title('Smartphone sales units')\n",
    "plt.legend()\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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