4-Stacked Bars.ipynb

{
 "cells": [
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RECREATE GRAPHS USING MATPLOTLIB!\n",
    "# Stacked Bars\n",
    "Now, we are going to look at the chart called `stacked-bars.png`. This graph displays the breakdown of students who got As, Bs, Cs, Ds, and Fs in each unit.\n",
    "\n",
    "<n><div style=\"width: 600px\">![stacked](attachment:image.png)</div>\n",
    "\n",
    "The data you will need to recreate this chart is in the lists `As`, `Bs`, `Cs`, `Ds`, `Fs`, and `unit_topics`.\n",
    "\n",
    "Save your recreated chart to a file called `my_stacked_bar.png`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Instructions**\n",
    "\n",
    "**1.**\n",
    "The Bs bars will go on top of the As bars, but at what heights will the Cs, Ds, and Fs bars start?\n",
    "\n",
    "The bottom of the bars representing the Cs will be at the height of the As plus the Bs. We can do this in NumPy (a scientific computing package for Python) with the np.add function. c_bottom, the starting heights for the Cs, will be:\n",
    "```py\n",
    "c_bottom = np.add(As, Bs)\n",
    "```\n",
    "Underneath the definition of c_bottom, define d_bottom (where the Cs end), and f_bottom (where the Ds end).\n",
    "\n",
    "\n",
    "**2.**\n",
    "Create a figure of width 10 and height 8.\n",
    "\n",
    "\n",
    "**3.**\n",
    "Plot the As, Bs, Cs, Ds, and Fs. Give each one the appropriate bottom list that will stack them on top of each other.\n",
    "\n",
    "\n",
    "**4.**\n",
    "Create a set of axes and save them to ax.\n",
    "\n",
    "\n",
    "***Hint***\n",
    "Use plt.subplot() to create a set of axes.\n",
    "\n",
    "\n",
    "**5.**\n",
    "Set the x-ticks to be range(len(unit_topics)).\n",
    "\n",
    "\n",
    "**6.**\n",
    "Set the x-tick labels to be the unit_topics.\n",
    "\n",
    "\n",
    "**7.**\n",
    "Give the plot the title you see in the final graph, and the same x-axis label and y-axis label.\n",
    "\n",
    "\n",
    "**8.**\n",
    "Save your figure to a file called my_stacked_bar.png."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "unit_topics = ['Limits', 'Derivatives', 'Integrals', 'Diff Eq', 'Applications']\n",
    "As = [6, 3, 4, 3, 5]\n",
    "Bs = [8, 12, 8, 9, 10]\n",
    "Cs = [13, 12, 15, 13, 14]\n",
    "Ds = [2, 3, 3, 2, 1]\n",
    "Fs = [1, 0, 0, 3, 0]\n",
    "\n",
    "x = range(5)\n",
    "\n",
    "c_bottom = np.add(As, Bs)\n",
    "#create d_bottom and f_bottom here\n",
    "d_bottom = np.add(c_bottom, Cs)\n",
    "f_bottom = np.add(d_bottom, Ds)\n",
    "\n",
    "#create your plot here\n",
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "plt.bar(x, As)\n",
    "plt.bar(x, Bs, bottom=As)\n",
    "plt.bar(x, Cs, bottom=c_bottom)\n",
    "plt.bar(x, Ds, bottom=d_bottom)\n",
    "plt.bar(x, Fs, bottom=f_bottom)\n",
    "\n",
    "ax = plt.subplot()\n",
    "ax.set_xticks(range(len(unit_topics)))\n",
    "ax.set_xticklabels(unit_topics)\n",
    "\n",
    "plt.title('Grade Distribution')\n",
    "plt.xlabel('Unit')\n",
    "plt.ylabel('Number of Students')\n",
    "\n",
    "plt.savefig('my_stacked_bar.png')\n",
    "\n",
    "plt.show()\n",
    "plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "myenv",
   "language": "python",
   "name": "python3"
  },
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