jepatti/gist:5321560

## gistfile1.txt
{
 "metadata": {
  "name": "DataPhilly"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#Welcome to Monetate for the March DataPhilly meetup",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import numpy as np\nimport math\nimport time\nfrom scipy import stats ",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 460
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#Introducing the normal distribution",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "a = np.random.randn(1000000)\na",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 492,
       "text": "array([-1.63750585,  0.74482092, -0.04815637, ..., -1.5315872 ,\n       -0.45943841, -0.25171966])"
      }
     ],
     "prompt_number": 492
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "_ = hist(a, bins=100)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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OTnp6erh+/ToAkUgEr9cL3HyEf/XqVXp6eli6dCkej4dIJBLbZiQSwePxTLRJ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\nAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 493
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "a.mean()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 477,
       "text": "0.022361050204139612"
      }
     ],
     "prompt_number": 477
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "np.std(a)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 478,
       "text": "1.0021686012238014"
      }
     ],
     "prompt_number": 478
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#standard deviation = sqrt(variance)\n#variance = mean of square minus square of mean\n\n#mean of squares\nmean_of_squares = a.dot(a)/len(a)\n\n#square of mean\nsquare_of_mean = (a.mean()**2)\n\nvariance = mean_of_squares - square_of_mean\nprint 'variance', variance\n\nprint 'standard deviation', math.sqrt(variance)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "variance 1.00434190528\nstandard deviation 1.00216860122\n"
      }
     ],
     "prompt_number": 479
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "len(a)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 484,
       "text": "1000000"
      }
     ],
     "prompt_number": 484
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "start = time.time()\na.dot(a)\nprint time.time()-start",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "0.00362396240234\n"
      }
     ],
     "prompt_number": 485
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "start = time.time()\nsum(i*i for i in a)\nprint time.time()-start",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "1.50220799446\n"
      }
     ],
     "prompt_number": 486
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 487,
       "text": "414.52085526329444"
      }
     ],
     "prompt_number": 487
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "a[:10]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 488,
       "text": "array([ 1.54337579, -0.22298286,  1.09256106,  0.83276554,  0.04672008,\n       -0.92547545,  1.47941986,  0.03762187,  0.18366413, -0.79001251])"
      }
     ],
     "prompt_number": 488
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "sigma = 5\n\na*=sigma\nprint a[:10]\nprint 'mean', a.mean()\nprint 'std dev', np.std(a)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "[-8.18752926  3.72410459 -0.24078185 -4.66832525  0.54021312  1.75742153\n -5.98583905  0.86952862 -1.30490708 -0.79470159]\nmean 0.00556193052795\nstd dev 4.99427902019\n"
      }
     ],
     "prompt_number": 494
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "_ = hist(a, bins=100)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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B4LougUAAuHEP4OrVq3R1dbFw4UIsy8J1Xa9N13WxLCvRLonIhBs4PdVo72+G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\no1AQERGPQkFERDz/H/aRkQ7Qcu/NAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 495
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "mu = 100\n\na+= mu\nprint 'mean', a.mean()\nprint 'std dev', np.std(a)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "mean 100.005561931\nstd dev 4.99427902019\n"
      }
     ],
     "prompt_number": 496
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "_ = hist(a, bins=100)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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GLHYuye2Rr3L9l+fq6uoR73PYU0wzZswAIDs7m6VLl3LixAk6Ojq4cuUKAJFI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\nIiKOFBAiIuLo/wHA0vr9AbP4OQAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 497
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def gen_norms(n, mu, sigma):\n    return np.random.randn(n) * sigma + mu",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 498
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "_ = hist(gen_norms(10000, 200, 5), bins=10)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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FTkSuQmCC\n"
      }
     ],
     "prompt_number": 500
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "_ = hist(stats.norm.rvs(size=1000))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 501
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "_ = hist(stats.norm.rvs(loc=10, size=1000))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 502
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "_ = hist(stats.norm.rvs(scale=10, size=1000))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 503
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "_ = hist(stats.norm.rvs(loc=100, scale=10, size=1000))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 504
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#Introducing confidence interval",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# http://en.wikipedia.org/wiki/Confidence_interval\ndef confidence_interval(vals):\n    n = len(vals)\n    mean = vals.mean()\n    sigma = np.std(vals)\n    ssd = sigma/math.sqrt(n)\n    z_score = 1.96 # p=-0.05\n    return mean - z_score*ssd, mean+z_score*ssd\n\n\nconfidence_interval(stats.norm.rvs(loc=100, scale=10, size=100))\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 544,
       "text": "(97.502924102225123, 101.50639835800867)"
      }
     ],
     "prompt_number": 544
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "mean = 100\nwrong = 0\nfor i in range(100):\n    a, b = confidence_interval(stats.norm.rvs(loc=mean, scale=10, size=100))\n    if not (a<mean<b):\n        wrong += 1\nprint wrong",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "6\n"
      }
     ],
     "prompt_number": 545
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def confidence_wrongness():\n    mean = 100\n    wrong = 0\n    for i in range(100):\n        a, b = confidence_interval(stats.norm.rvs(loc=mean, scale=10, size=100))\n        if not (a<mean<b):\n            wrong += 1\n    return wrong",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 546
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "hist([confidence_wrongness() for i in range(100)])",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 547,
       "text": "(array([ 8, 12,  9, 18, 19, 24,  5,  4,  0,  1]),\n array([  1. ,   2.2,   3.4,   4.6,   5.8,   7. ,   8.2,   9.4,  10.6,\n        11.8,  13. ]),\n <a list of 10 Patch objects>)"
      },
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 547
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#hooray! math works.",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 435
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#Introducing one-way ANOVA",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "a = stats.norm.rvs(loc=5, scale=10, size=100)\nb = stats.norm.rvs(loc=10, scale=20, size=100)\n\n#The one-way ANOVA tests the null hypothesis that two or more groups have the same population mean.\n# http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.f_oneway.html\nstats.f_oneway(a,b)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 530,
       "text": "(2.4906409666614393, 0.11612135274608126)"
      }
     ],
     "prompt_number": 530
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# from http://stackoverflow.com/questions/6871201/plot-two-histograms-at-the-same-time-with-matplotlib\nbins = np.linspace(-20, 30, 50)\n\npyplot.hist(a, bins, alpha=0.5)\npyplot.hist(b, bins, alpha=0.5)\npyplot.show()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 531
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print a.mean(), np.std(a)\nprint b.mean(), np.std(b)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "4.64916410873 10.9514365695\n8.30930618331 20.3117320034\n"
      }
     ],
     "prompt_number": 532
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#Introducting Linear Regression",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "_ = hist(stats.uniform.rvs(scale=10, size=1000))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 394
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#y = norm + x\ny = stats.norm.rvs(loc=5, scale=1, size=1000)\nx = stats.uniform.rvs(scale=10, size=1000)\nprint list(zip(x[:3],y[:3]))\ny+=x\nprint list(zip(x[:3],y[:3]))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "[(5.9080163574090774, 6.5572216419803731), (7.7466873643872871, 5.4299635923047918), (4.1182370314828578, 5.9690268176702057)]\n[(5.9080163574090774, 12.465237999389451), (7.7466873643872871, 13.176650956692079), (4.1182370314828578, 10.087263849153064)]\n"
      }
     ],
     "prompt_number": 539
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "scatter(x,y)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 540,
       "text": "<matplotlib.collections.PathCollection at 0x144fa30c>"
      },
      {
       "output_type": "display_data",
       "png": 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txeazS8asc4GMSSco6GXNKyin8yaZFfwZgtMVFXWm6tZtKJ+vf7ExNgviFBPT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\nYII=\n"
      }
     ],
     "prompt_number": 540
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "slope, intercept, r_value, p_value, stderr = stats.linregress(x,y)\n\nprint 'slope', slope\nprint 'intercept', intercept\nprint 'r_value', r_value\nprint 'p_value', p_value\nprint 'stderr', stderr\n\n#slope : float\n#    slope of the regression line\n#intercept : float\n#    intercept of the regression line\n#r-value : float\n#    correlation coefficient\n#p-value : float\n#    two-sided p-value for a hypothesis test whose null hypothesis is that the slope is zero.\n#stderr : float\n#    Standard error of the estimate",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "slope 0.992016829072\nintercept 5.05417778483\nr_value 0.939887787031\np_value 0.0\nstderr 0.0114089959349\n"
      }
     ],
     "prompt_number": 541
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#from http://stackoverflow.com/questions/2369492/generate-a-heatmap-in-matplotlib-using-a-scatter-data-set\n#plot the a 2d histogram\na = hist2d(x,y, bins=100) # a = supresses output\n\n#plot the computed fit line\nfit_xs = [0,10]\nfit_ys = [fit_x*slope + intercept for fit_x in fit_xs]\nplot(fit_xs,fit_ys, 'k',linewidth=2)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 542,
       "text": "[<matplotlib.lines.Line2D at 0x1496932c>]"
      },
      {
       "output_type": "display_data",
       "png": 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OJXYdCLk/gKHfhYze9M/6vbsxiIjEJMDFGYD22Sop19jYyL59+3jggQc4ePAg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\nSUIDtohIkvj/MP3kBb9BF3UAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 542
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fit_xs = np.linspace(0, 10, 51)\n\nfit_ys = fit_xs * slope + intercept\n_ = hist2d(x,y, bins=100)\n\nplot(fit_xs,fit_ys, 'k',linewidth=2)\nprint fit_xs[0],fit_ys[0]\nprint fit_xs[1],fit_ys[1]\nprint fit_xs[2],fit_ys[2]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "0.0 5.05417778483\n0.2 5.25258115064\n0.4 5.45098451646\n"
      },
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 543
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# http://engineering.monetate.com/2012/11/02/red-states-pinterest-blue-states-macs/",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}