01_Erzeugung.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# Array Erzeugung und deren Eigenschaften"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Array aus iterable erzeugen"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_list = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]\n",
      "data = np.array(my_list)\n",
      "\n",
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "array([[0, 1, 2],\n",
        "       [3, 4, 5],\n",
        "       [6, 7, 8]])"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Sicherstellen, dass immer ein ndarray vorliegt:\n",
      "\n",
      "my_big_list = range(100000)\n",
      "big_data = np.array(my_big_list)\n",
      "\n",
      "print(\"ndarray aus liste erzeugen\")\n",
      "%timeit -n 10 np.asarray(my_big_list)\n",
      "\n",
      "print(\"\\nndarray aus ndarray erzeugen\")\n",
      "%timeit -n 10 np.asarray(big_data)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "ndarray aus liste erzeugen\n",
        "10 loops, best of 3: 45.9 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "ndarray aus ndarray erzeugen\n",
        "10 loops, best of 3: 2.6 \u00b5s per loop\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "## Erzeugung (leerer) Arrays\n",
      "#### `shape` gibt Anzahl der Elemente je Dimension an"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.empty(shape=(2, 3))  # 2 zeilen, 3 spalten"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "array([[  6.90403180e-310,   6.90403180e-310,   2.55790433e-316],\n",
        "       [  1.73445105e-316,   2.55887151e-316,   1.74678688e-316]])"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.zeros(shape=(2, 3))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "array([[ 0.,  0.,  0.],\n",
        "       [ 0.,  0.,  0.]])"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.ones(shape=(2, 3)) * 42  # numpy magic!!! Mehr dazu im Kapitel \"broadcasting\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "array([[ 42.,  42.,  42.],\n",
        "       [ 42.,  42.,  42.]])"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "## Erzeugung gef\u00fcllter Arrays\n",
      "\n",
      "Beispiel: Range von 0 bis 2$\\pi$ in 10 Schritten\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# klassisch \u00fcber arange (entspricht range; kann jedoch auch andere Datentypen)\n",
      "np.arange(0, 2 * np.pi + 2 * np.pi / 9., 2 * np.pi / 9.)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "array([ 0.        ,  0.6981317 ,  1.3962634 ,  2.0943951 ,  2.7925268 ,\n",
        "        3.4906585 ,  4.1887902 ,  4.88692191,  5.58505361,  6.28318531])"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# einfacher:\n",
      "np.linspace(0, 2 * np.pi, num=10, endpoint=True)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "array([ 0.        ,  0.6981317 ,  1.3962634 ,  2.0943951 ,  2.7925268 ,\n",
        "        3.4906585 ,  4.1887902 ,  4.88692191,  5.58505361,  6.28318531])"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "Beispiel: 2 x 5 array mit Zufallszahlen aus dem Interval [0, 1]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.random.rand(2, 5)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "array([[ 0.41418303,  0.28224619,  0.01413351,  0.20686712,  0.09541362],\n",
        "       [ 0.33518403,  0.10826765,  0.41953215,  0.474691  ,  0.23817055]])"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "Beispiel: 3 x 3 array mit \"1\" auf der Hauptdiagonale"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.identity(3)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "array([[ 1.,  0.,  0.],\n",
        "       [ 0.,  1.,  0.],\n",
        "       [ 0.,  0.,  1.]])"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Wichtige Attribute von ndarrays"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = np.identity(5)\n",
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "array([[ 1.,  0.,  0.,  0.,  0.],\n",
        "       [ 0.,  1.,  0.,  0.,  0.],\n",
        "       [ 0.,  0.,  1.,  0.,  0.],\n",
        "       [ 0.,  0.,  0.,  1.,  0.],\n",
        "       [ 0.,  0.,  0.,  0.,  1.]])"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Dimension des arrays\n",
      "data.ndim"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "2"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# shape: Anzahl der Elemente in jeder Dimension\n",
      "data.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "(5, 5)"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Anzahl der Elemente\n",
      "data.size"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "25"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Datentyp der Elemente\n",
      "data.dtype"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "dtype('float64')"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Anzahl der bits zum n\u00e4chsten Eintrag\n",
      "data.strides"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "(40, 8)"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "## Flags von ndarrays\n",
      "### Geben Informationen \u00fcber Speicherbelegung an"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.flags"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "  C_CONTIGUOUS : True\n",
        "  F_CONTIGUOUS : False\n",
        "  OWNDATA : True\n",
        "  WRITEABLE : True\n",
        "  ALIGNED : True\n",
        "  UPDATEIFCOPY : False"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "die letzten drei flags k\u00f6nnen vom Nutzer gesetzt werden."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "---\n",
      "\n",
      "# \u00dcbung:\n",
      "\n",
      "* erzeuge ein array arr1 der Form [100, 80, ... , 0] und ein array arr2 mit 100 Elementen von 0.07 bis 42 exclusiv Endpunkt\n",
      "    - welcher Datentyp liegt jeweils vor?\n",
      "    - wie kann der Datentyp explizit gesetzt werden?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* erzeuge ein leeres / mit \"0\" / mit \"1\" / mit \"47.11\" gef\u00fclltes array mit 10 x 20 x 30 Eintr\u00e4gen\n",
      "    - welche Methode ist schneller und warum?\n",
      "    - was gibt len(a) zur\u00fcck?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* erzeuge ein 5 x 5 array mit \"1\" auf der Nebendiagonale (np.eye)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* was bewirkt array.astype?\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---\n",
      "<p align=\"center\">Tutorial: Einf\u00fchrung in NumPy von Henning Dickten und Christian Geier, PyCon.DE 2013</p>\n",
      "<p align=\"center\"><a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/3.0/deed.de\"><img alt=\"Creative Commons Lizenzvertrag\" style=\"border-width:0\" src=\"http://i.creativecommons.org/l/by-nc-sa/3.0/88x31.png\"/></a></p>\n",
      "\n",
      "<p align=\"center\">Dieses Werk bzw. Inhalt ist lizenziert unter einer <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/3.0/deed.de\">Creative Commons Namensnennung - Nicht-kommerziell - Weitergabe unter gleichen Bedingungen 3.0 Unported Lizenz</a>.</p>\n",
      "\n"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}