{ "cells": [ { "cell_type": "markdown", "metadata": { "tags": [ "remove_cell" ] }, "source": [ "
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Ingenieurinformatik – Übung

\n", " Lehrstuhl Computational Civil Engineering
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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# NumPy Arrays" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In dieser Übung sollen die Unterschiede zwischen Listen und NumPy Arrays untersucht werden. Daraus ergibt sich das Verständnis in welchen Situation welches Datenkonstrukt sinnvoll eingesetzt werden kann. Darauf folgend soll das Zugreifen auf ein Array geübt werden. Im letzten Teil wird der Umgang mit Arrays weiter vertieft." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Aufgabenteil A - Verständnis, Operatoren\n", "1. Erstellen Sie ein NumPy Array mit den Zahlen 0 - 9 nach folgenden Methoden:\n", " - konvertieren Sie eine Liste in ein Array\n", " - konvertieren Sie eine Range in ein Array\n", " - Nutzen Sie die `arange` Funktion vom NumPy-Modul und vergleichen Sie diese mit Range\n", "2. Vergleichen Sie folgende Operatoren zwischen Listen und Arrays und ermitteln Sie die Unterschiede:\n", " - addieren/subtrahieren Sie zwei Listen bzw. Arrays\n", " - addieren Sie eine Zahl\n", " - multiplizieren/dividieren Sie mit eine Zahl\n", "3. Beantworten Sie folgende Fragen und begründen Sie Ihre Antwort. Überprüfen Sie diese gegebenenfalls.\n", " - Ist es möglich die Liste `[1, 2.3, True, \"Hallo\"]` in ein Array zu konvertieren?\n", " - Ist es möglich zwischen Arrays unterschiedlicher Größe die Operatoren `+`, `-`, `*` und `/` anzuwenden?\n", " - Ist es möglich ein Array zu vergrößern?" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "loesung" ] }, "source": [ "### Lösung" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "tags": [ "hide-cell", "loesung" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 1 2 3 4 5 6 7 8 9]\n", "[0 1 2 3 4 5 6 7 8 9]\n", "[0 1 2 3 4 5 6 7 8 9]\n" ] } ], "source": [ "import numpy as np\n", "\n", "array = np.array([0,1,2,3,4,5,6,7,8,9])\n", "print(array)\n", "array = np.array(range(10))\n", "print(array)\n", "array = np.arange(10) # Hat die selben Parameter wie range(...)\n", "print(array)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "tags": [ "hide-cell", "loesung" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Addition von Listen bzw. Arrays:\n", "[0, 1, 2, 3, 0, 1, 2, 3]\n", "[0 2 4 6]\n", "[0 0 0 0]\n", "Addition/Subtraktion mit einer Zahl\n", "[1 2 3 4]\n", "[-1 0 1 2]\n", "Multiplikation/Division mit einer Zahl\n", "[0, 1, 2, 3, 0, 1, 2, 3]\n", "[0 2 4 6]\n", "[0. 0.5 1. 1.5]\n" ] } ], "source": [ "import numpy as np\n", "\n", "print(\"Addition von Listen bzw. Arrays:\")\n", "print([0,1,2,3]+[0,1,2,3])\n", "# print([0,1,2,3]-[0,1,2,3]) Bei Listen nicht möglich\n", "print(np.arange(4)+np.arange(4))\n", "print(np.arange(4)-np.arange(4))\n", "\n", "print(\"Addition/Subtraktion mit einer Zahl\")\n", "# print([0,1,2,3]+1) Bei Listen nicht möglich\n", "# print([0,1,2,3]-1) Bei Listen nicht möglich\n", "print(np.arange(4)+1)\n", "print(np.arange(4)-1)\n", "\n", "print(\"Multiplikation/Division mit einer Zahl\")\n", "print([0,1,2,3]*2)\n", "# print([0,1,2,3]/1) Bei Listen nicht möglich\n", "print(np.arange(4)*2)\n", "print(np.arange(4)/2)" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "loesung" ] }, "source": [ ":::{toggle}\n", "- Bedingt, ein Array kann nur einen Typ haben, daher werden alle Elemente in `str` umgewandelt.\n", " _Zusatz: Der genaue Typ kann mit dem parameter `dtype` festgelegt werden._\n", "- Nein, Arrays müssen für die angegeben Operatoren immer die gleiche Länge haben, da sonst nicht für jeden Index der Operator angewandt werden kann.\n", "- Nein, ein Array hat immer eine fixe Größe. Es ist jedoch möglich ein neues Array zu erstellen, mit dem hinzugefügten Element. \n", "\n", ":::" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Aufgabenteil B - Verständnis, mehrdimensionale Arrays\n", "So wie Listen, können auch Arrays mehrdimensional sein. Solch ein Array kann wie folgt definiert werden." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "array = np.array([\n", " [ 1, 2, 3, 4],\n", " [ 5, 6, 7, 8],\n", " [ 9,10,11,12],\n", " [13,14,15,16],\n", "])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Ermitteln Sie aus dem Array:\n", " - die Größe (size), also die gesamte Anzahl an Elementen,\n", " - die Form (shape), also wie viele Einträge pro Axe,\n", " - und die Dimension, also wie viele Axen vorhanden sind.\n", "2. Ähnlich wie bei Listen mit `[index]` für eine einzelne Stelle, `[start:ende]` für einen Teil und `[:]` für die gesamte Liste, kann auch ein Array modifiziert werden. Dabei wird durch ein `,` auf die nächste Axe zugegriffen. \n", " Greifen Sie auf das Array zu, sodass ...\n", " - 1 zurückgegeben wird.\n", " - die 2. Reihe zurück gegeben wird.\n", " - die 3. Spalte zurück gegeben wird.\n", " - die 4 mittleren Werte zurückgegeben werden." ] }, { "cell_type": "markdown", "metadata": { "tags": [ "loesung" ] }, "source": [ "### Lösung" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "tags": [ "hide-cell", "loesung" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Größe: 16\n", "Form: (4, 4)\n", "Dimension: 2\n" ] } ], "source": [ "print(\"Größe:\", array.size)\n", "print(\"Form:\", array.shape)\n", "print(\"Dimension:\", len(array.shape))\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "tags": [ "hide-cell", "loesung" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "[5 6 7 8]\n", "[ 3 7 11 15]\n", "[[ 6 7]\n", " [10 11]]\n" ] } ], "source": [ "print(array[0,0])\n", "print(array[1])\n", "print(array[:,2])\n", "print(array[1:3,1:3])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Aufgabenteil C - Laden von Daten aus Dateien\n", "Mit der Funktion `loadtxt` vom Modul NumPy ist es möglich Daten einfach aus einer Textdatei zu laden. Lesen Sie sich die [Dokumentation](https://numpy.org/doc/stable/reference/generated/numpy.loadtxt.html#numpy.loadtxt) zu der Funktion durch und laden Sie die Daten der Datei, die Sie {download}`hier <./hochhaeuser.csv>` downloaden können. \n", "\n", "Die Datei enthält Daten bezüglich der gebauten Hochhäuser in Berlin, mit ihrer Gebäudehöhe, Vollgeschossanzahl und Baubeginn. Laden Sie die Daten und plotten Sie ..\n", "1. die Gebäudehöhe in Abhängigkeit vom Baubeginn.\n", "2. das Verhältnis aus Vollgeschosszahl und Gebäudehöhe in Abhängigkeit vom Baubeginn." ] }, { "cell_type": "markdown", "metadata": { "tags": [ "loesung" ] }, "source": [ "### Lösung" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "tags": [ "hide-cell", "loesung" ] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "daten = np.loadtxt(\"hochhaeuser.csv\", skiprows=1, delimiter=\";\")\n", "hoehe = daten[:,0]\n", "geschoss = daten[:,1]\n", "jahr = daten[:,2]\n", "\n", "plt.scatter(jahr, hoehe)\n", "plt.title(\"Bauhöhe Hochhäuser Berlin\")\n", "plt.xlabel(\"Baubeginn\")\n", "plt.ylabel(\"Bauhöhe [m]\")\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "tags": [ "hide-cell", "loesung" ] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(jahr, geschoss/hoehe)\n", "plt.title(\"Geschosseffizienz Hochhäuser Berlin\")\n", "plt.xlabel(\"Baubeginn\")\n", "plt.ylabel(\"Geschosseffizienz [Geschoss/m]\")\n", "plt.show()" ] } ], "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 }