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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": [
    "# Line Graphs: Visualize Stock Trends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### You are interested in investing in stocks, and so you downloaded the stock prices for the “big five”: Amazon, Google, Apple, Facebook, and Microsoft. Create a line plot to show stock trends."
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## Figure title\n",
    "#### Set the title for the figure. For example:\n",
    "``` Python\n",
    "plt.title('Graph Title')\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load datasets\n",
    "#### Use `pandas` to read the CSV data files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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')\n",
    "microsoft = pd.read_csv('MSFT_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "google"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use `matplotlib` to create a line chart visualizing the closing prices for the past five years (whole data sequence) for all five companies. Add labels, titles, and a legend to make the visualization self-explanatory. Use `plt.grid()` to add a grid to your plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create figure\n",
    "plt.figure(figsize=(16, 8), dpi=300)\n",
    "\n",
    "# Plot data\n",
    "plt.plot('date', 'close', data=google,    label='Google')\n",
    "plt.plot('date', 'close', data=facebook,  label='Facebook')\n",
    "plt.plot('date', 'close', data=apple,     label='Apple')\n",
    "plt.plot('date', 'close', data=amazon,    label='Amazon')\n",
    "plt.plot('date', 'close', data=microsoft, label='Microsoft')\n",
    "\n",
    "# Specify the ticks for x- and y-axis.\n",
    "plt.xticks(np.arange(0, 1260, 40), rotation=70)\n",
    "plt.yticks(np.arange(0, 1450, 100))\n",
    "\n",
    "# Add the title and label for y-axis.\n",
    "plt.title('Stock trend', fontsize=16)\n",
    "plt.ylabel('Closing price in $', fontsize=14)\n",
    "\n",
    "# Add grid.\n",
    "plt.grid()\n",
    "\n",
    "# Add legend.\n",
    "plt.legend()\n",
    "\n",
    "# Show plot\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) 2023 by Ronald Mak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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