Histograms-checkpoint.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:8a576b2ecf09b2c9510a3d3dad6f64857857f8bca13391921444ec7084678c7a"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy.random import normal\n",
      "gaussian_numbers = normal(size=1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "from numpy.random import normal\n",
      "gaussian_numbers = normal(size=1000)\n",
      "plt.hist(gaussian_numbers)\n",
      "plt.title(\"Gaussian Histogram\")\n",
      "plt.xlabel(\"Value\")\n",
      "plt.ylabel(\"Frequency\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xc2acdd8>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "plt.hist(gaussian_numbers, bins=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "(array([   5.,    9.,   13.,   29.,   41.,   76.,   94.,  131.,  115.,\n",
        "         124.,  120.,   80.,   61.,   41.,   34.,   17.,    6.,    3.,\n",
        "           0.,    1.]),\n",
        " array([-2.92156788, -2.60050688, -2.27944587, -1.95838486, -1.63732386,\n",
        "        -1.31626285, -0.99520184, -0.67414083, -0.35307983, -0.03201882,\n",
        "         0.28904219,  0.6101032 ,  0.9311642 ,  1.25222521,  1.57328622,\n",
        "         1.89434723,  2.21540823,  2.53646924,  2.85753025,  3.17859126,\n",
        "         3.49965226]),\n",
        " <a list of 20 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xbde5eb8>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.hist(gaussian_numbers, bins=20, normed=True, cumulative=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "(array([ 0.005,  0.014,  0.027,  0.056,  0.097,  0.173,  0.267,  0.398,\n",
        "         0.513,  0.637,  0.757,  0.837,  0.898,  0.939,  0.973,  0.99 ,\n",
        "         0.996,  0.999,  0.999,  1.   ]),\n",
        " array([-2.92156788, -2.60050688, -2.27944587, -1.95838486, -1.63732386,\n",
        "        -1.31626285, -0.99520184, -0.67414083, -0.35307983, -0.03201882,\n",
        "         0.28904219,  0.6101032 ,  0.9311642 ,  1.25222521,  1.57328622,\n",
        "         1.89434723,  2.21540823,  2.53646924,  2.85753025,  3.17859126,\n",
        "         3.49965226]),\n",
        " <a list of 20 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xbb6c128>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "from numpy.random import normal, uniform\n",
      "gaussian_numbers = normal(size=1000)\n",
      "uniform_numbers = uniform(low=-3, high=3, size=1000)\n",
      "plt.hist(gaussian_numbers, bins=20, histtype='stepfilled', normed=True, color='b', label='Gaussian')\n",
      "plt.hist(uniform_numbers, bins=20, histtype='stepfilled', normed=True, color='r', alpha=0.5, label='Uniform')\n",
      "plt.title(\"Gaussian/Uniform Histogram\")\n",
      "plt.xlabel(\"Value\")\n",
      "plt.ylabel(\"Probability\")\n",
      "plt.legend()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qjiIa/5IuqRy4HXgr3bULcG5EPN5Evc7Aa8AngEUkTVFn5nYqS9oFeAw4OyKe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\nLKhH3HWD/wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xbb9e780>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}