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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>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "423dfbdd-b5ec-4359-a470-546cd68da52c",
   "metadata": {},
   "source": [
    "# GridSpec\n",
    "#### A `matplotlib.gridspec.GridSpec(nrows, ncols)` object specifies the grid coordinates of a subplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b67a1674-597c-4937-be58-ffc8041630c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas import Series\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b530d87-f38c-41a0-877b-148b5775be2e",
   "metadata": {},
   "source": [
    "# Example: Stock Prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "310d55bf-1e2f-4a36-912e-a81698d2d5bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "google    = pd.read_csv('GOOGL_data.csv')\n",
    "facebook  = pd.read_csv('FB_data.csv')\n",
    "apple     = pd.read_csv('AAPL_data.csv')\n",
    "amazon    = pd.read_csv('AMZN_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cf20487-f17a-4a38-929e-0b6ffebaf019",
   "metadata": {},
   "outputs": [],
   "source": [
    "stocks = ['Google', 'Facebook', 'Apple', 'Amazon']\n",
    "\n",
    "closing_prices = [google.close, facebook.close, \n",
    "                  apple.close,  amazon.close]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "800d4fe8-d14a-43f1-a3e4-cf69b4897b60",
   "metadata": {},
   "source": [
    "#### Create a 3 by 4 `GridSpec` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d23488f0-c213-4ceb-8bda-639aefacb311",
   "metadata": {},
   "outputs": [],
   "source": [
    "gs = matplotlib.gridspec.GridSpec(3, 4)\n",
    "gs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cf1e2ca-5fe1-40b4-a016-e0be8b8bef18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The Apple subplot uses the leftmost \n",
    "# 3 by 3 block of grid cells.\n",
    "ax1 = plt.subplot(gs[:3, :3])\n",
    "ax1.plot(apple.close)\n",
    "ax1.set_title('Apple')\n",
    "\n",
    "# The Google subplot uses the\n",
    "# upper right grid cell.\n",
    "ax2 = plt.subplot(gs[0, 3])\n",
    "ax2.plot(google.close)\n",
    "ax2.set_title('Google')\n",
    "\n",
    "# The Facebook subplot uses the\n",
    "# middle grid cell on the right.\n",
    "ax3 = plt.subplot(gs[1, 3])\n",
    "ax3.plot(facebook.close)\n",
    "ax3.set_title('Facebook')\n",
    "\n",
    "# The Amazon subplot uses the\n",
    "# lower right grid cell\n",
    "ax4 = plt.subplot(gs[2, 3])\n",
    "ax4.plot(amazon.close)\n",
    "ax4.set_title('Amazon')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c3419bb-6d17-4091-892a-9741486d10f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "119a1669-a1d5-41fc-b9b3-7dc3536f1dda",
   "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,
   "id": "e0318687-f07b-4f33-8f00-cf30b925d7a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional material (c) 2024 by Ronald Mak"
   ]
  }
 ],
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