wie_visualisiert_man_daten.ipynb

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 "cells": [
  {
   "source": [
    "# Wie visualisiert man Daten?"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Daten Laden"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature = np.array([[4.0, 37.92655435, 23.90101111],\n",
    "                    [4.0, 35.88942857, 22.73639281],\n",
    "                    [4.0, 29.49674574, 21.42168559],\n",
    "                    [4.0, 32.48016326, 21.7340484 ],\n",
    "                    [4.0, 38.00676226, 24.37202837],\n",
    "                    [4.0, 30.73073988, 22.69832608],\n",
    "                    [4.0, 35.93672343, 21.07445241],\n",
    "                    [4.0, 38.65212459, 20.57099727],\n",
    "                    [4.0, 35.52041768, 21.74519457],\n",
    "                    [4.0, 37.69535497, 20.33073640],\n",
    "                    [4.0, 33.00699292, 22.57063861],\n",
    "                    [4.0, 33.73140934, 23.81730782],\n",
    "                    [4.0, 43.85053380, 20.05153803],\n",
    "                    [4.0, 32.95555986, 24.12153986],\n",
    "                    [4.0, 36.38192916, 19.20280266],\n",
    "                    [4.0, 36.54270168, 20.45388966],\n",
    "                    [4.0, 33.08246118, 22.20524015],\n",
    "                    [4.0, 31.76866280, 21.01201139],\n",
    "                    [4.0, 42.24260825, 20.44394610],\n",
    "                    [4.0, 29.04450264, 22.46633771],\n",
    "                    [4.0, 30.04284328, 21.54561621],\n",
    "                    [4.0, 18.95626707, 19.66737753],\n",
    "                    [4.0, 18.60176718, 17.74023009],\n",
    "                    [4.0, 12.85314993, 18.42746953],\n",
    "                    [4.0, 28.62450072, 17.94781944],\n",
    "                    [4.0, 21.00655655, 19.33438286],\n",
    "                    [4.0, 17.33580556, 18.81696459],\n",
    "                    [4.0, 31.17129195, 17.23625014],\n",
    "                    [4.0, 19.36176482, 20.67772798],\n",
    "                    [4.0, 27.26581705, 16.71312863],\n",
    "                    [4.0, 21.19107828, 19.00673617],\n",
    "                    [4.0, 19.08131597, 15.24401994],\n",
    "                    [4.0, 26.69761925, 17.05937466],\n",
    "                    [2.0, 4.44136559 , 3.52432493 ],\n",
    "                    [2.0, 10.26395607, 1.07729281 ],\n",
    "                    [2.0, 7.39058439 , 3.44234423 ],\n",
    "                    [2.0, 4.23565118 , 4.28840232 ],\n",
    "                    [2.0, 3.87875761 , 5.12407692 ],\n",
    "                    [2.0, 15.12959925, 6.26045879 ],\n",
    "                    [0.0, 5.93041263 , 1.70841905 ],\n",
    "                    [0.0, 4.25054779 , 5.01371294 ],\n",
    "                    [0.0, 2.15139117 , 4.16668657 ],\n",
    "                    [0.0, 2.38283228 , 3.83347914 ]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = np.concatenate((np.ones(21), np.zeros(22)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(43, 3)\n(43,)\n"
     ]
    }
   ],
   "source": [
    "print(feature.shape)\n",
    "print(label.shape)"
   ]
  },
  {
   "source": [
    "## Matplotlib"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot([0, 1],[0, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.bar([1, 2, 3], [1, 2, 3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {}
    }
   ],
   "source": [
    "plt.pie([23, 17, 50])\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "### Unsere Daten"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.scatter(feature[:,0], feature[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## Plotten mit Label"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.title('Trainingsdaten')\n",
    "plt.xlabel('Anzahl der Beine')\n",
    "plt.ylabel('Größe in cm')\n",
    "plt.scatter(feature[:,0], feature[:,1], c=label)\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## Größe vs. Breite"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.title('Trainingsdaten')\n",
    "plt.xlabel('Größe in cm')\n",
    "plt.ylabel('Breite in cm')\n",
    "plt.scatter(feature[:,1], feature[:,2], c=label)\n",
    "plt.show()"
   ]
  }
 ]
}