{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove_cell"
    ]
   },
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "    <div style=\"width: 500px; float: left;\"> \n",
    "        <h1>Ingenieurinformatik – Übung</h1>\n",
    "        Lehrstuhl <b>Computational Civil Engineering</b><br>\n",
    "        Kontakt: <a href = \"mailto: cce-inginf@uni-wuppertal.de\">Email senden</a> | Individuelle Kontakte siehe Webseite des Lehrstuhls<br>\n",
    "        Links: \n",
    "        <a href=\"cce.uni-wuppertal.de/inginf\">Vorlesungsskript</a> | \n",
    "        <a href=\"cce.uni-wuppertal.de/\">Webseite des Lehrstuhls</a>\n",
    "    </div>\n",
    "    <div style=\"float:right;\"> \n",
    "        <img src=\"logo_cce_combined.png\" style=\"width:150px;\"/>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Einwohnerzahl Europa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bei dieser Aufgabe werden Sie sowohl Schleifen als auch Verzweigungen nutzen, um Daten aus einer Datei auszulesen und in sinvolle Datentypen abzuspeichern. In der Bonusaufgabe werden Sie einen Filter auf die Daten anwenden und eine Berechnung mit ihnen durchführen.<br>\n",
    "\n",
    "Die in dieser Aufgabe verwendete Datei finden Sie {download}`hier <./einwohner_europa_2019.csv>`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aufgabenteil A – Auslesen der Datei"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Schreiben Sie ein Programm, dass den Inhalt der Datei `einwohner_europa_2019.csv` ausließt und ausgibt."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "loesung",
     "hide-cell"
    ]
   },
   "source": [
    "### Lösungsvorschlag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "loesung",
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GEO,Value\n",
      "\n",
      "Belgien,11467923\n",
      "\n",
      "Bulgarien,7000039\n",
      "\n",
      "Tschechien,10528984\n",
      "\n",
      "Daenemark,5799763\n",
      "\n",
      "Deutschland einschliesslich ehemalige DDR,82940663\n",
      "\n",
      "Estland,1324820\n",
      "\n",
      "Irland,4904240\n",
      "\n",
      "Griechenland,10722287\n",
      "\n",
      "Spanien,46934632\n",
      "\n",
      "Frankreich,67028048\n",
      "\n",
      "Kroatien,4076246\n",
      "\n",
      "Italien,61068437\n",
      "\n",
      "Zypern,875898\n",
      "\n",
      "Lettland,1919968\n",
      "\n",
      "Litauen,2794184\n",
      "\n",
      "Luxemburg,612179\n",
      "\n",
      "Uganda,-1\n",
      "\n",
      "Ungarn,9772756\n",
      "\n",
      "Malta,493559\n",
      "\n",
      "Niederlande,17423013\n",
      "\n",
      "Oesterreich,8842000\n",
      "\n",
      "Polen,37972812\n",
      "\n",
      "Portugal,10276617\n",
      "\n",
      "Rumaenien,19405156\n",
      "\n",
      "Slowenien,2080908\n",
      "\n",
      "Slowakei,5450421\n",
      "\n",
      "Finnland,5512119\n",
      "\n",
      "Schweden,10243000\n",
      "\n",
      "Vereinigtes Koenigreich,66647112\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Auslesen und Ausgeben einer Datei\n",
    "\n",
    "file = open('einwohner_europa_2019.csv', 'r')\n",
    "\n",
    "for line in file:\n",
    "    print(line)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aufgabenteil B – Speichern in Listen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verändern Sie obiges Programm, sodass die ausgelesenen Daten nicht mehr ausgegeben, sondern in zwei Listen gespeichert werden. Die Erste Zeile soll hierbei ignoriert werden. Die Liste mit den Einwohnern soll Zahlen anstelle von Strings enthalten."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lösungshinweis\n",
    "\n",
    "Benutzen Sie eine Bool-Variable um zu überprüfen, ob Sie gerade die erste Zeile der Datei auslesen. Alternativ können Sie mit der `pop`-Methode von Listen Elemente an einer bestimmten Position löschen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "loesung",
     "hide-cell"
    ]
   },
   "source": [
    "### Lösungsvorschlag I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "loesung",
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Belgien', 'Bulgarien', 'Tschechien', 'Daenemark', 'Deutschland einschliesslich ehemalige DDR', 'Estland', 'Irland', 'Griechenland', 'Spanien', 'Frankreich', 'Kroatien', 'Italien', 'Zypern', 'Lettland', 'Litauen', 'Luxemburg', 'Uganda', 'Ungarn', 'Malta', 'Niederlande', 'Oesterreich', 'Polen', 'Portugal', 'Rumaenien', 'Slowenien', 'Slowakei', 'Finnland', 'Schweden', 'Vereinigtes Koenigreich']\n",
      "[11467923, 7000039, 10528984, 5799763, 82940663, 1324820, 4904240, 10722287, 46934632, 67028048, 4076246, 61068437, 875898, 1919968, 2794184, 612179, -1, 9772756, 493559, 17423013, 8842000, 37972812, 10276617, 19405156, 2080908, 5450421, 5512119, 10243000, 66647112]\n"
     ]
    }
   ],
   "source": [
    "# Auslesen einer Datei und Speichern in Listen\n",
    "\n",
    "laender = []\n",
    "bevoelkerungen = []\n",
    "erste_zeile = True\n",
    "file = open('einwohner_europa_2019.csv', 'r')\n",
    "\n",
    "# Speichern der Datei in zwei Listen\n",
    "for line in file:\n",
    "    \n",
    "    # Überprüfung, ob das Programm gerade die erste Zeile der Datei ausließt\n",
    "    if not erste_zeile:\n",
    "\n",
    "        # Aufteilen der Zeile in zwei Variablen\n",
    "        [land, einwohner] = line.split(',')\n",
    "\n",
    "        # Konvertierung in einen Integer\n",
    "        einwohner = int(einwohner)\n",
    "\n",
    "        # Erweiterung der Listen um die beiden Elemente der aktuell ausgelesenen Zeile\n",
    "        laender.append(land)\n",
    "        bevoelkerungen.append(int(einwohner))\n",
    "    \n",
    "    else:\n",
    "        erste_zeile = False\n",
    "        \n",
    "print(laender)\n",
    "print(bevoelkerungen)\n",
    "\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "loesung",
     "hide-cell"
    ]
   },
   "source": [
    "### Lösungsvorschlag II"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "loesung",
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Belgien', 'Bulgarien', 'Tschechien', 'Daenemark', 'Deutschland einschliesslich ehemalige DDR', 'Estland', 'Irland', 'Griechenland', 'Spanien', 'Frankreich', 'Kroatien', 'Italien', 'Zypern', 'Lettland', 'Litauen', 'Luxemburg', 'Uganda', 'Ungarn', 'Malta', 'Niederlande', 'Oesterreich', 'Polen', 'Portugal', 'Rumaenien', 'Slowenien', 'Slowakei', 'Finnland', 'Schweden', 'Vereinigtes Koenigreich']\n",
      "[11467923, 7000039, 10528984, 5799763, 82940663, 1324820, 4904240, 10722287, 46934632, 67028048, 4076246, 61068437, 875898, 1919968, 2794184, 612179, -1, 9772756, 493559, 17423013, 8842000, 37972812, 10276617, 19405156, 2080908, 5450421, 5512119, 10243000, 66647112]\n"
     ]
    }
   ],
   "source": [
    "# Auslesen einer Datei und Speichern in Listen\n",
    "\n",
    "laender = []\n",
    "einwohner = []\n",
    "file = open('einwohner_europa_2019.csv', 'r')\n",
    "\n",
    "# Speichern der Datei in zwei Listen\n",
    "for line in file:\n",
    "    laender.append(line.split(',')[0])\n",
    "    einwohner.append(line.split(',')[1])\n",
    "    \n",
    "# Entfernen des ersten Elements beider Listen\n",
    "laender.pop(0)\n",
    "einwohner.pop(0)\n",
    "\n",
    "# Konvertieren der Elemente in Einwohner in Integers\n",
    "for i in range(0,len(einwohner)):\n",
    "    einwohner[i] = int(einwohner[i])\n",
    "    \n",
    "print(laender)\n",
    "print(einwohner)\n",
    "\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aufgabenteil C – Filtern und Verwenden – Optional\n",
    "\n",
    "Erweitern Sie Ihr Programm, sodass Länder, die eine negative Einwohnerzahl haben, aus beiden Listen gelöscht werden. Addieren Sie danach alle Einwohner, um die gesammte Einwohnerzahl aller EU-Staaten zu erhalten und geben sie das Ergebnis in Millionen aus."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lösungshinweis\n",
    "\n",
    "Falls Sie beim Löschen auf einen `out-of-range IndexError` treffen, überlegen Sie sich, warum dies passiert und ob Sie vielleicht mit einer anderen Schleifenart das Problem umgehen können."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "loesung",
     "hide-cell"
    ]
   },
   "source": [
    "### Lösungsvorschlag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "loesung",
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Die gesammte EU-Bevölkerung in 2019 waren ungefähr 514.12 Millionen Personen.\n"
     ]
    }
   ],
   "source": [
    "# Auswerten von Daten\n",
    "\n",
    "laender = []\n",
    "einwohner = []\n",
    "file = open('einwohner_europa_2019.csv', 'r')\n",
    "\n",
    "# Speichern der Datei in zwei Listen\n",
    "for line in file:\n",
    "    laender.append(line.split(',')[0])\n",
    "    einwohner.append(line.split(',')[1])\n",
    "    \n",
    "# Entfernen des ersten Elements beider Listen\n",
    "laender.pop(0)\n",
    "einwohner.pop(0)\n",
    "\n",
    "# Konvertieren der Elemente in Einwohner in Integers\n",
    "for i in range(0,len(einwohner)):\n",
    "    einwohner[i] = int(einwohner[i])\n",
    "    \n",
    "# Entfernen von Ausreißern\n",
    "i = 0\n",
    "while i < len(einwohner):\n",
    "    if einwohner[i] < 0:\n",
    "        einwohner.pop(i)\n",
    "        laender.pop(i)\n",
    "    i += 1\n",
    "    \n",
    "# Berechnung der gesammten EU-Bevölkerung\n",
    "gesammtBev = 0\n",
    "for i in einwohner:\n",
    "    gesammtBev += i\n",
    "    \n",
    "print(f'Die gesammte EU-Bevölkerung in 2019 waren ungefähr {gesammtBev/10**6:.2f} Millionen Personen.')\n",
    "\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aufgabenteil D\n",
    "\n",
    "Plotten Sie nun die Bevölkerungszahlen (y-Achse) für die entsprechenden Länder (x-Achse). Fügen Sie Achsen-Beschriftungen hinzu. Berechnen Sie den Mittelwert aller Bevölkerungszahlen und Ploten Sie diesen als gestrichelte rote Linie.\n",
    "Hinweis:\n",
    "* Um die Ländernamen besser lesen zu können, soll die Beschriftung um 90 grad gedreht werden.\n",
    "* Wenn Sie plt.bar() anstelle von plt.plot() verwenden, erhalten Sie ein Balkendiagramm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "loesung",
     "hide-cell"
    ]
   },
   "source": [
    "### Lösungsvorschlag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "loesung",
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Belgien', 'Bulgarien', 'Tschechien', 'Daenemark', 'Deutschland einschliesslich ehemalige DDR', 'Estland', 'Irland', 'Griechenland', 'Spanien', 'Frankreich', 'Kroatien', 'Italien', 'Zypern', 'Lettland', 'Litauen', 'Luxemburg', 'Ungarn', 'Malta', 'Niederlande', 'Oesterreich', 'Polen', 'Portugal', 'Rumaenien', 'Slowenien', 'Slowakei', 'Finnland', 'Schweden', 'Vereinigtes Koenigreich']\n",
      "[11467923, 7000039, 10528984, 5799763, 82940663, 1324820, 4904240, 10722287, 46934632, 67028048, 4076246, 61068437, 875898, 1919968, 2794184, 612179, 9772756, 493559, 17423013, 8842000, 37972812, 10276617, 19405156, 2080908, 5450421, 5512119, 10243000, 66647112]\n"
     ]
    },
    {
     "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 matplotlib.pyplot as plt\n",
    "\n",
    "print(laender)\n",
    "print(einwohner)\n",
    "\n",
    "plt.bar(laender,einwohner)\n",
    "plt.xlabel(\"Laender\")\n",
    "plt.ylabel(\"Bevoelkerungszahlen\")\n",
    "plt.axhline(gesammtBev/len(einwohner),color=\"red\",ls=\"--\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}