EX03b-jupyter-notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--- ![tu-dresden-blue.png](attachment:tu-dresden-blue.png) --->\n",
    "<img src=\"tu-dresden-blue.png\" alt=\"drawing\" width=\"200\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Professur für Angewandte Umweltsystemanalyse an der TU Dresden <br/>\n",
    "Prof. Dr.-Ing. habil. Olaf Kolditz <br/>\n",
    "***Hydroinformatik I (HyBHW-1-01)*** <br/>\n",
    "*[Lehre-Webseite](https://www.ufz.de/index.php?de=40416)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Exercise 2 - Figures*** <br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import FuncFormatter\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "year = np.arange(11)\n",
    "publications = [9,1,7,4,8,7,6,7,3,2,3]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.set_title('Hydroinformatik I 2019 - Notenspiegel')\n",
    "ax.set_ylabel('Anzahl von Noten')\n",
    "\n",
    "plt.bar(year, publications)\n",
    "plt.xticks(year, ('1.0','1.3','1.7','2.0','2.3','2.7','3.0','3.3', '3.7', '4.0', '5.0'))\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import modules\n",
    "#from matplotlib.ticker import FuncFormatter\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "#data\n",
    "year = np.arange(11)\n",
    "publications = [0,6,11,9,7,2,5,2,3,0,1]\n",
    "#define plot\n",
    "fig, ax = plt.subplots()\n",
    "#axis\n",
    "ax.set_title('Hydroinformatik I 2020 - Notenspiegel')\n",
    "ax.set_ylabel('Anzahl von Noten')\n",
    "#plot\n",
    "plt.bar(year, publications)\n",
    "plt.xticks(year, ('1.0','1.3','1.7','2.0','2.3','2.7','3.0','3.3', '3.7', '4.0', '5.0'))\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  }
 ],
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  "kernelspec": {
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