EX01-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 II (HyBHW-2-01)*** <br/>\n",
    "*[Lehre-Webseite](https://www.ufz.de/index.php?de=40425)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Exercise 2 - Figures*** <br/>"
   ]
  },
  {
   "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": [
    "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": 4,
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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