{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this post, I'll try to replicate the reasoning in Chris Fonnesbeck's talk \"statistical thinking for data science\". "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with the code. We want to build a sample of random values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def make_sample(size):\n",
    "    \"Generates a random sample.\"\n",
    "    return np.random.normal(size=size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.46661601,  0.41063078,  0.30758075,  1.60073702, -0.86336121,\n",
       "       -0.23355055, -1.32449623,  0.11956636,  2.62381423, -1.26601074])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.66381504, -0.28121257,  0.2762167 , -0.68387184, -2.0854523 ,\n",
       "        0.54882485, -1.21564105, -2.59720332, -0.61130765, -0.78036254,\n",
       "       -1.07960788, -1.03531623, -0.45372958, -0.14735239, -0.904162  ,\n",
       "       -0.34943136, -0.55128214,  0.82399238,  1.194479  ,  0.12733662])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_sample(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What he then does, is that he builds in a bias. What sort of bias?\n",
    "\n",
    "- some values are missing\n",
    "- the negative values are left out\n",
    "\n",
    "Let's tackle these two separately. First, missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def missing(true_sample, prob):\n",
    "    \"Removes missing values following a binomial law.\"\n",
    "    missing_indices = np.random.binomial(1, prob, size=true_sample.size).astype(np.bool)\n",
    "    return true_sample[~missing_indices]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What this function does is as follows: it first draws a vector of binomial values that it casts to booleans, then it censors the initial sample based on the values drawn. Let's check the output of a the binomial sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False,  True, False, False, False, False, False,\n",
       "       False, False, False,  True, False, False, False, False, False,\n",
       "        True, False], dtype=bool)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.binomial(1, 0.1, size=20).astype(np.bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True,  True, False,  True,  True, False,  True,  True,\n",
       "       False,  True,  True,  True,  True, False,  True, False, False,\n",
       "       False,  True], dtype=bool)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.binomial(1, 0.5, size=20).astype(np.bool)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the logical ~ (not) operator yields a restricted output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original sample: [-0.2340218  -0.72057151 -0.04013565 -1.30307987 -0.6110323   1.61743412\n",
      " -0.84212281  0.15583878 -1.79058383  1.58297096]\n",
      "original sample with missing values: [-0.84212281 -1.79058383  1.58297096]\n"
     ]
    }
   ],
   "source": [
    "sample = make_sample(10)\n",
    "missing_sample = sample[~np.random.binomial(1, 0.5, size=10).astype(np.bool)]\n",
    "print(\"original sample: {}\".format(sample))\n",
    "print(\"original sample with missing values: {}\".format(missing_sample))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the output from this procedure using the seaborn package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116e58828>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XJMnYew7H2e8kdNnOW2/9nWnTpnbrngypMm/exzzzzBNEohE8Y3viHJyf0ePbSl3kTeiF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\n8y0CxqSkonbyeDz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OO1deeR233XYXpaW9jS4to6xWK1dccS0XXngJeiRB02e7ie7O7q6E\n8cYojfN3kdgTYcKEH3DbbXdldCecbOT15nHLLXfg9eaxIBRgVyxmdEnfE9d1Pgw0E9Q0Lr7454we\nnV29Zb6rW4c3JJfVT5jwAx55+EnGjz8eLVxPcOtsItVfoWvZt6+iriWIVH9FcOtHaOF6xo8/gUce\nnsaECT8wfCWpUZK7mp/NL395C1aLQvOXVYS3NBldVptiNSGaPtuFFopzwQU/5Yorru32w1vtVVxc\nwi9/eQuSLPNRMBmS2WRB0E9tIs6JJ56UNc3cDqbbh3ervLx8rr32l9x88+0U9SgiWreW4JYPiQdr\njC5tr3iwhuCWD4nWraWoRxG33HIH1157U86ftbUaOXIMt99+Nx63h8CKWoJr67OmwyRApMJP88JK\nZE3i2mt/ydlnn5ezf3AP5PDDVS688GeENI1PAs1Z8/5tjEZYF43Qt29/Lr30F0aX0y45E96thg8f\nwYMPPs5pp52OHvMT2vYJ4arl6JpxzeV1LU64ajmhbZ+gx/ycdtrpPPjg4xx99DGG1ZStDjtsMHfd\ndT/FxSWE1u8huKouKwIgvKUJ/6JqbDY7t9xyh6GzE7LdpElncswxI9kZj7EiCzY3bkok+DTox25L\n9iy3Ws2xyC3nwhuSN8J+9rPLufPOeykt7UWsfgPBLbNJhGo79XqKtx+Kt3OzFRKhWoJbZhOr30Bp\naS/uvPNefvazy3E4jOvVku1KS3vzf/93H+XlfQlvbsK/tAZdMy7AQxv2EFhRi8fj4Y7b72bo0GGG\n1WIGkiRx5ZXXUZBfwOJQkMq4cePfCV3n40AzUV3n0suuoFcv89xTysnwbnX44Sr33/8okyefhR7z\nE9z6SXIsXO/YWJyjdASO0o71ptB1rWVsO3m2PXnyWdx//6McfrixnQnNoqCgkDvvvIdBgw4nusNP\n86Iq9ERmA1zXdYJr6wl+XU9hYQ+mTLmv2867TzWvN49rr/slSBIfB5oN2xdzUShAdSLOccdN4IQT\nJhpSQ2fldHhDcnuuiy66lNtvv5uiopax8K2foEXTt1+mFk3+oYjWraWoqIjbb7+biy661HQ9SYzm\ndnu49dYpHHHEUcR2B2n+shI9kZkQ0HWd4Nf1hNbvoaSkJ1Om/JbevY1pjGZWRxwxlPPOuwC/QePf\nW6IRVkXC9OpVxmWXXZnRY6dCzod3K1U9kvvvf7RlRkodwa2ziTXtTPlxYk07CG6djRauY/z4E7j/\n/kdR1SNTfpxc4XA4uPnm2xg+fASxqhBNn1eix9Mb4LquE1xVR/ibRnr1KuPOO39LcXFJWo/ZXZ1z\nzo846qij2R6PsTycufHvxkSCuUF/y96q/2vKYUoR3vtwuVxce+0vueqq61FkiXDFAiLVqzo8jNKW\n5DDJSsIVC7FaJK666nquvfYmU7VuzVY2m42bbrqFkSPHEK8JJwM8lp4A13WdwIpawpubKC/vy513\n3kNhYWFajpULZFnm2mtvorCwB0vCwYzsxhPXdWa39Cy5/PKr6dOnb9qPmQ4ivNtwwgkTueeeB+nZ\ns5Ro3TpCOxegJzp/U0VPxAjtXEC0bj09e/binnseMt34WrazWq3ccMOvGTPmWOK1YZo+353yANd1\nncDyWiJbm+nXrz933HE3eXmZaSXbnXm9edxww6+RZJmPg/60z/9eGAxQl0hw0kmnctxxE9J6rHQS\n4X0Affr05d57H+Koo44m4d9FcPtctHi4w6+jxcMEt88l4d/FUUcdzb33Pkh5efq2TMpliqJw3XW/\n5NhjjydeF6Fp4W60FAV4MrhriGxrpn//gdx22114PN6UvLYAgwcP4Sc/uZiQpjE3jePfm6IR1kaT\njd0uvviytBwjU0R4H4TL5ebmm29n4sST0cINhLZ9ghZr/7icFgslvybcwMSJJ3PzzbfjSmFTHuH7\nLBYLV199QzLA6yM0p2AMvHWoJLLNz4ABA/nNb6Z02z7cRpo06UyGDTuGHfEYayIdP1E6lICm8Wkw\ngNVq5brrfmWa+dwHIsL7ECwWC5dffjVnnHEOWrSZ0PZ56PHIIb9Oj0cIbZ+HFm3mjDPO4fLLr8Zi\nyfwGt7moNcDHjRtPvC5M0xedn4Wi6zrBr+r2DpXceqsI7nSRZZkrr7wWt8vNF+EgexKpa1+h6zqf\nBpuJ6Bo//emllJWVp+y1jSLCux0kSeInP7mYyZPPRIs2Edz534P2RdG1BMGd/0WLNjF58ln85CcX\ni2XSGZYM8Bv33sT0L6nu1KV4aP0ewpuaKC/vI4I7AwoKCrns51cS13XmBVM3fOKLRtgWizF06DBO\nPvm0lLym0UR4t5MkSVx44SWMG3ccWqiWSPWKA35upGoFWqiWceOO48ILfyaC2yCKonD99b/iiCOG\nEt0VJLCqrkNfH97aRGhdA0VFxdx66xS8XtFjJhPGjh3P6NHjqIzH+SoFwyd+LcHCUAC73c4vfnFN\nt/l97FR4q6qap6rqv1VVna+q6kJVVXNiaxBZlrniimspL+9LrGEjcf+u731OvHkXsT0bKS/vyxVX\nXJsTfbezmdVq5Ve/+n/06dOPyOYmwpvb140wVhsisLIOl9vNrbdOoaBATAfMFEmSuOyyK/B4PCwK\nB2lIdL7vkK7rzA/4ieo6P/3ppd1qPn5nk+X/AXN8Pt9JwBXAjJRVlOXsdjvXXXcTsiwTrly2X0Or\nZIOpZciyzHXX/RK73W5gpUIrp9PFr399Kx6vl8CqOmL1Bz+b08IJ/IuqkZG46cabTdXvorvIy8vn\n5z+/moSuMzfgJ9HJ4ZM10TA74jGGDRvOD35wSoqrNFZnw/sp4IWWj62A8a3BMqhPn35MnnwmeixA\nrGHT3udjDRvRYwEmTz7LtBP/u6vi4hJuuP7XSEBgSc0BZ6Douo5/eQ1aJMH//M/FHHnkUZktVNhr\nzJhxHHfcBKoTcZaFgx3++oZEnC9CQVwuF1dccV23GS5pdcjwVlX1SlVVV6uq+lXr/wOH+3y+iKqq\nvYBXgDvTXmmWOeus87DbHUTrfei6hq5rROt92O0Ozjor/bvICx135JFHcfrpZ5EIxAiub2jzc6IV\nAWKVQY488igmTz4zwxUK33XJJb+gqKiI5eFQh7oPJnSdTwJ+4rrOL35xTbdcBXvILT58Pt9fgL98\n93lVVY8GXgdu9fl8Cw71OoWFLhSl+0yVKynxctpppzJr1iwS/t0A6PEQp51+NgMHisvsbHXVVb9g\n6bJF1G2swzFg/xuQekIj+HU9iqJw882/prRUrJ40npfbbruNKVOmMDfg5yd5BVjbcQa9PBykJhHn\nlFNO4cwzu8fsku/q1P5MqqoOBd4ELvT5fKvb8zUNDR2/7Ml2I0Ycy6xZs4jWrdv73MiRx1JT02xg\nVcKh/Pj8i3jxxemEvmnc7/nIdj9aMM6k08/GavWK9zFL9OzZj9NPP4sPP5zF4lCAE1wHn65ZF4+z\nLByiR2EPfvzjn5n+fSwpaXslb2fHvB8B7MDvVFWdp6rqu50tzMwGDjyMwsIiEqFaEqFaCguLRD9n\nExg79liKioqJbm+GfW6EhTc1IcuyGC7JQuef/xN69ixldSRMbfzAs090XeezoB8duPwX13Trxm+d\nOvP2+Xw/SnUhZiTLMlOm3MvOnduB5I1MMTUw+1ksFiZOPJl3330LEjqSIhFvipJoijJy5BgKC3sY\nXaLwHVarjUsvvYKnnnqUL0MBzvG2PaS1ORalMhFnzJhx3X4bQZE0XVRcXMKIEaMZMWJ0t5pD2t2N\nGjUWYO/uO7HKYMvzYwyrSTi4YcOGM3ToMHbEY+xu4+alrussCQeRZZkLLrjIgAozS4S3kJPKyspx\nu93QsvdlvCHZr0ZsjJHdfvjD8wH4uo2NGyriMRoSCcaNG09paa9Ml5ZxIryFnCRJEv36DQAd0HUS\nTVGcLhdFRcVGlyYcxJAhR1BW1ofNsSiR7/T99kWTf4BPOWWyEaVlnAhvIWcVFSWHuXQdtFCCkuKS\nbreQo7uRJIljjz0eDdi6z647CV1nayxKUVExgwYdblyBGSTCW8hZXm/LFCwd9LgmGk+ZxIgRowDY\nuc+4d1U8TlTXGTFiVM78ARbhLeSsvZvOtkwXtNvNtwltLiov74PX62XXPuHdegPziCNyp52BCG8h\nZ8my5TuPxa+DGciyzKBBh+PXtNb7zVS17DE7aNBgAyvLLPHTKuQ8pVB0fzSb/v0HAqCRTO/aRIKC\ngoKcat0rwlvIWVrLbkj2/t6Wx+ndtVxInb59+wGQIDnqFdA0+vbtb2xRGSbCW8hZ8ZZl1pJF3u+x\nkP3Ky5Mtl92STC9F2e+5XCHCW8hZe8PbKrU8bn/LUcFYJSU9URQFRZIob9kFvnfvMoOryiwR3kLO\nSiT2P/NOpHC3ciG9ZFmmtLQXe7QEe1reRxHegpAjEomWMW4p+T8R3ubSs2cvYrpOZcsVVC4sid+X\nCG8hh7XMM8uRRR3dTc+epQBUJeI4HA48nrb7XndXIryFHNYS2p3c3FYw1r59aIpzsLWBCG8hZ1la\nxrqTzamSfb4F8+jR49u+67nYg12Et5CzrC2zFLRoouWx1chyhA7ad0FOLi3OaSXCW8hZrb1N9EjL\nYh27WGlpJnl5+W1+nCtEeAs5y+lM7m+YCMZbHruNLEfooL1dISEnO0KK8BZyltudDGvNH9vvsWAO\nNtu3V0q5+N6J8BZyVuvUskRzMrzz8nLv7M3M9p1d4nQ6DazEGCK8hZzVOk4ab0ruyJJr84S7E4dD\nhLcg5Iy9N7ladpDPzy8wsBqhK3LxZrMIbyFneb3e/S69RXibV+u0z1wiwlvIWbIs491nnDsXp5t1\nF1arYnQJGSfCW8hped5vA3vfqWeCuUhS7kVZ7v0XC8I+WmeY2Gw2FCX3zt66ixxrawKI8BZy3Lcz\nTHLwt78bycXeYiK8hZzWurgjF8/cuoPW+d25eNWUe//FgrAP0YzK3G655Q42btxAcXGJ0aVknAhv\nIad9e7ktTr3NaPDgIQwePMToMgwhhk0EAdi7q44gmIQIbyGntW7I4HZ7DK5EEDpGhLeQ044/fiK9\nevXm4osvM7oUQegQSc/QHJuammZxXSoIgtBBJSXeNm/IiDNvQRAEExLhLQiCYEJdmiqoquoRwJdA\nT5/PF01NSYIgCMKhdPrMW1VVL/AkEE5dOYIgCEJ7dGXY5EVgChBMUS2CIAhCOx1y2ERV1SuBW9h/\nFcN24O8+n2+1qqrtWppWWOhCUSydq1IQBEHYT6emCqqqugHYSXJN8Xhgkc/nO+lgXyOmCgqCIHTc\ngaYKduqGpc/n29tMQFXVLcCkTtYlCIIgdEIqpgrqiK4+giAIGSVWWAqCIGQxscJSEAShGxHhLQiC\nYEIivAVBEExIhLcgCIIJifAWBEEwIRHegiAIJiTCWxAEwYREeAuCIJiQCG9BEAQTEuEtCIJgQiK8\nBUEQTEiEtyAIggmJ8BYEQTAhEd6CIAgmJMJbEATBhER4C4IgmJAIb0EQBBMS4S0IgmBCGdsGTRAE\nQUgdceYtCIJgQiK8BUEQTEiEtyAIggmJ8BYEQTAhEd6CIAgmJMJbEATBhBSjCzAzVVUl4A/AMUAY\nuNrn8202tiqhI1RVPRZ41OfznWx0LUL7qaqqAH8BBgA24GGfz/cfQ4vKMHHm3TU/Auw+n+94YArw\nlMH1CB2gquptwB8Bu9G1CB12KVDr8/kmAmcB0w2uJ+NEeHfNBOBDAJ/PtwgYY2w5QgdtBM43ugih\nU94E7mn5WAJiBtZiCBHeXZMHNO7zOK6qqviemoTP53sXiBtdh9BxPp8v6PP5AqqqeoG3gLuMrinT\nRNB0TRPg3eex7PP5NKOKEYRcoqpqX2Au8JLP53vD6HoyTYR31ywkOd6GqqrjgdXGliN0kmR0AULH\nqKpaCswGbvf5fC8ZXY8RxGyTrnkXmKSq6sKWx1cYWYzQaaI7m/lMAQqAe1RVvZfke3imz+eLGFtW\n5oiugoIgCCYkhk0EQRBMSIS3IAiCCYnwFgRBMCER3oIgCCYkwlsQBMGERHgLgiCYkAhvQRAEExLh\nLQiCYEL/H5uH0MRYbHM9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1112ea5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data=[missing(make_sample(100), 0.5),\n",
    "                  missing(make_sample(500), 0.5),\n",
    "                    missing(make_sample(5000), 0.5)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that missing values, dont really have an effect even with lots of samples. What about censoring samples? Or, as called in the video, bias?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def censored(sample):\n",
    "    \"Returns censored sample.\"\n",
    "    negative_values = sample < 0\n",
    "    return sample[~negative_values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111459048>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O8dCvHySZsvBPuQPNl/1hZU6j+YrxTbqD3tNbeOjXD/LgL35BVVWV3WWNyMGD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\nIQlvIYRwIQlvIYRwof8fCusOeDpv7nsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116b5cc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data=[censored(make_sample(100)),\n",
    "                  censored(make_sample(500)),\n",
    "                    censored(make_sample(50000))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}