notebooks/16 replication and sharding/accidents-analysis.ipynb

   1 {
   2  "metadata": {
   3   "name": "",
   4   "signature": "sha256:b22ffb64838b8bafe45dd711b92d6d1fb1687bf9640ef576e0243b9ea7729cd1"
   5  },
   6  "nbformat": 3,
   7  "nbformat_minor": 0,
   8  "worksheets": [
   9   {
  10    "cells": [
  11     {
  12      "cell_type": "markdown",
  13      "metadata": {},
  14      "source": [
  15       "# Analysis of the accidents data set\n",
  16       "\n",
  17       "This is a notebook to get you familiar with the contents of the `accidents` dataset."
  18      ]
  19     },
  20     {
  21      "cell_type": "code",
  22      "collapsed": false,
  23      "input": [
  24       "# Import the required libraries and open the connection to Mongo\n",
  25       "\n",
  26       "import collections\n",
  27       "import datetime\n",
  28       "from itertools import chain, repeat\n",
  29       "import matplotlib as mpl\n",
  30       "import matplotlib.pyplot as plt\n",
  31       "from mpl_toolkits.basemap import Basemap\n",
  32       "%matplotlib inline\n",
  33       "\n",
  34       "import numpy as np\n",
  35       "import pandas as pd\n",
  36       "import scipy.stats\n",
  37       "\n",
  38       "import pymongo\n",
  39       "from bson.objectid import ObjectId\n",
  40       "# client = pymongo.MongoClient('mongodb://localhost:27117/')\n",
  41       "# client = pymongo.MongoClient('mongodb://ogedei:27017')\n",
  42       "client = pymongo.MongoClient('mongodb://localhost:27017')"
  43      ],
  44      "language": "python",
  45      "metadata": {},
  46      "outputs": [],
  47      "prompt_number": 3
  48     },
  49     {
  50      "cell_type": "code",
  51      "collapsed": false,
  52      "input": [
  53       "db = client.accidents\n",
  54       "accidents = db.accidents\n",
  55       "labels = db.labels\n",
  56       "roads = db.roads"
  57      ],
  58      "language": "python",
  59      "metadata": {},
  60      "outputs": [],
  61      "prompt_number": 4
  62     },
  63     {
  64      "cell_type": "code",
  65      "collapsed": false,
  66      "input": [
  67       "# Load the expanded names of keys and human-readable codes into memory\n",
  68       "\n",
  69       "expanded_name = collections.defaultdict(str)\n",
  70       "for e in labels.find({'expanded': {\"$exists\": True}}):\n",
  71       "    expanded_name[e['label']] = e['expanded']\n",
  72       "    \n",
  73       "label_of = collections.defaultdict(str)\n",
  74       "for l in labels.find({'codes': {\"$exists\": True}}):\n",
  75       "    for c in l['codes']:\n",
  76       "        try:\n",
  77       "            label_of[(l['label'], int(c))] = l['codes'][c]\n",
  78       "        except ValueError: \n",
  79       "            label_of[(l['label'], c)] = l['codes'][c]"
  80      ],
  81      "language": "python",
  82      "metadata": {},
  83      "outputs": [],
  84      "prompt_number": 5
  85     },
  86     {
  87      "cell_type": "code",
  88      "collapsed": false,
  89      "input": [
  90       "ordered_severity_labels = [label_of[k] for k in \n",
  91       "                  sorted([(l, c) for l, c in label_of.keys() if l == 'Accident_Severity'], \n",
  92       "                         key = lambda ln: ln[1])]"
  93      ],
  94      "language": "python",
  95      "metadata": {},
  96      "outputs": [],
  97      "prompt_number": 6
  98     },
  99     {
 100      "cell_type": "markdown",
 101      "metadata": {},
 102      "source": [
 103       "Let's compare accident severities across speed limits"
 104      ]
 105     },
 106     {
 107      "cell_type": "code",
 108      "collapsed": false,
 109      "input": [
 110       "severity_by_speed_dict = collections.Counter((a['Speed_limit'], a['Accident_Severity']) \n",
 111       "                                               for a in accidents.find({}, ['Speed_limit', 'Accident_Severity']))\n",
 112       "severity_by_speed_dict"
 113      ],
 114      "language": "python",
 115      "metadata": {},
 116      "outputs": [
 117       {
 118        "metadata": {},
 119        "output_type": "pyout",
 120        "prompt_number": 7,
 121        "text": [
 122         "Counter({(30, 3): 754813, (60, 3): 175248, (30, 2): 106734, (40, 3): 94668, (70, 3): 86159, (60, 2): 40952, (50, 3): 34889, (40, 2): 14633, (70, 2): 11861, (20, 3): 9953, (60, 1): 7076, (30, 1): 5959, (50, 2): 5940, (70, 1): 2411, (40, 1): 1680, (20, 2): 1619, (50, 1): 914, (20, 1): 73, (15, 3): 15, (10, 3): 12, (10, 1): 3, (10, 2): 2, (15, 2): 1})"
 123        ]
 124       }
 125      ],
 126      "prompt_number": 7
 127     },
 128     {
 129      "cell_type": "markdown",
 130      "metadata": {},
 131      "source": [
 132       "Capture the severities and speeds and put them in the right order"
 133      ]
 134     },
 135     {
 136      "cell_type": "code",
 137      "collapsed": false,
 138      "input": [
 139       "speeds = sorted(set(k[0] for k in severity_by_speed_dict.keys()))\n",
 140       "severities = {label_of[('Accident_Severity', c)]: c \n",
 141       "              for c in sorted(code for t, code in label_of.keys() if t == 'Accident_Severity')}"
 142      ],
 143      "language": "python",
 144      "metadata": {},
 145      "outputs": [],
 146      "prompt_number": 8
 147     },
 148     {
 149      "cell_type": "code",
 150      "collapsed": false,
 151      "input": [
 152       "speeds"
 153      ],
 154      "language": "python",
 155      "metadata": {},
 156      "outputs": [
 157       {
 158        "metadata": {},
 159        "output_type": "pyout",
 160        "prompt_number": 9,
 161        "text": [
 162         "[10, 15, 20, 30, 40, 50, 60, 70]"
 163        ]
 164       }
 165      ],
 166      "prompt_number": 9
 167     },
 168     {
 169      "cell_type": "code",
 170      "collapsed": false,
 171      "input": [
 172       "severity_by_speed = pd.DataFrame([{sv: severity_by_speed_dict[(sp, severities[sv])] \n",
 173       "                                   for sv in severities} \n",
 174       "                                  for sp in speeds])\n",
 175       "severity_by_speed.index = speeds\n",
 176       "severity_by_speed.index.name = 'Speed'\n",
 177       "severity_by_speed"
 178      ],
 179      "language": "python",
 180      "metadata": {},
 181      "outputs": [
 182       {
 183        "html": [
 184         "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
 185         "<table border=\"1\" class=\"dataframe\">\n",
 186         "  <thead>\n",
 187         "    <tr style=\"text-align: right;\">\n",
 188         "      <th></th>\n",
 189         "      <th>Fatal</th>\n",
 190         "      <th>Serious</th>\n",
 191         "      <th>Slight</th>\n",
 192         "    </tr>\n",
 193         "    <tr>\n",
 194         "      <th>Speed</th>\n",
 195         "      <th></th>\n",
 196         "      <th></th>\n",
 197         "      <th></th>\n",
 198         "    </tr>\n",
 199         "  </thead>\n",
 200         "  <tbody>\n",
 201         "    <tr>\n",
 202         "      <th>10</th>\n",
 203         "      <td>    3</td>\n",
 204         "      <td>      2</td>\n",
 205         "      <td>     12</td>\n",
 206         "    </tr>\n",
 207         "    <tr>\n",
 208         "      <th>15</th>\n",
 209         "      <td>    0</td>\n",
 210         "      <td>      1</td>\n",
 211         "      <td>     15</td>\n",
 212         "    </tr>\n",
 213         "    <tr>\n",
 214         "      <th>20</th>\n",
 215         "      <td>   73</td>\n",
 216         "      <td>   1619</td>\n",
 217         "      <td>   9953</td>\n",
 218         "    </tr>\n",
 219         "    <tr>\n",
 220         "      <th>30</th>\n",
 221         "      <td> 5959</td>\n",
 222         "      <td> 106734</td>\n",
 223         "      <td> 754813</td>\n",
 224         "    </tr>\n",
 225         "    <tr>\n",
 226         "      <th>40</th>\n",
 227         "      <td> 1680</td>\n",
 228         "      <td>  14633</td>\n",
 229         "      <td>  94668</td>\n",
 230         "    </tr>\n",
 231         "    <tr>\n",
 232         "      <th>50</th>\n",
 233         "      <td>  914</td>\n",
 234         "      <td>   5940</td>\n",
 235         "      <td>  34889</td>\n",
 236         "    </tr>\n",
 237         "    <tr>\n",
 238         "      <th>60</th>\n",
 239         "      <td> 7076</td>\n",
 240         "      <td>  40952</td>\n",
 241         "      <td> 175248</td>\n",
 242         "    </tr>\n",
 243         "    <tr>\n",
 244         "      <th>70</th>\n",
 245         "      <td> 2411</td>\n",
 246         "      <td>  11861</td>\n",
 247         "      <td>  86159</td>\n",
 248         "    </tr>\n",
 249         "  </tbody>\n",
 250         "</table>\n",
 251         "</div>"
 252        ],
 253        "metadata": {},
 254        "output_type": "pyout",
 255        "prompt_number": 10,
 256        "text": [
 257         "       Fatal  Serious  Slight\n",
 258         "Speed                        \n",
 259         "10         3        2      12\n",
 260         "15         0        1      15\n",
 261         "20        73     1619    9953\n",
 262         "30      5959   106734  754813\n",
 263         "40      1680    14633   94668\n",
 264         "50       914     5940   34889\n",
 265         "60      7076    40952  175248\n",
 266         "70      2411    11861   86159"
 267        ]
 268       }
 269      ],
 270      "prompt_number": 10
 271     },
 272     {
 273      "cell_type": "markdown",
 274      "metadata": {},
 275      "source": [
 276       "Plot this data to see what we've got."
 277      ]
 278     },
 279     {
 280      "cell_type": "code",
 281      "collapsed": false,
 282      "input": [
 283       "severity_by_speed.plot(kind='bar')"
 284      ],
 285      "language": "python",
 286      "metadata": {},
 287      "outputs": [
 288       {
 289        "metadata": {},
 290        "output_type": "pyout",
 291        "prompt_number": 47,
 292        "text": [
 293         "<matplotlib.axes.AxesSubplot at 0x7ff7b4d34fd0>"
 294        ]
 295       },
 296       {
 297        "metadata": {},
 298        "output_type": "display_data",
 299        "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAERCAYAAAB/4wAeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X90VPWd//Hn8ENATJgElAgSUi0itLipClQrbfAH0i0q\n3bPlh1WTrsftSq3QbbfIsd8VjkdFtq7VbS11VQKe1YI/urT+4Ie1Ufhu5dcai40YQCkhFBASkoh+\nUXS+f3w+k9yZzI8bMjP3k+T1OOeeufcz9868ZgLzmft537kXRERERERERERERERERERERERERERE\nArUA+DOwHXgS6AcUAuuBWmAdEI5bfyewA5jiab/QPsZO4EFPez9gpW1/HRjpua/cPkctcGOmXpCI\niJy8EuBdzIc3mA/wcmAJ8GPbNh9YbOfHAtVAX7vtLiBk79sMTLDzLwJT7fwc4GE7PxP4tZ0vBHZj\nOp2wZ15ERAJUCLwDFAB9gN8BV2L2BobadYrsMpi9hfme7dcAXwbOBN72tM8ClnrWmWjn+wDv2/nZ\nwC892yy124mISBb1SnN/A3A/sBfYDxzFDCENBQ7adQ7S1kkMA/Z5tt8HDE/QXm/bsbd1dv4E0AQM\nTvFYIiKSRek6hnOAeZhhoWHAacD1cetE7CQiIt1AnzT3XwT8D3DELj8HXAwcwAwhHcAMEx2y99cD\nIzzbn4X5pl9v5+Pbo9sUY/ZI+gCD7PPVA2WebUYAr8QHPOeccyK7d+9O8zJERCTOm0BpojvS7THs\nwNQIBmCKyFcANZhaQ7ldpxz4bzv/W0wd4BTgc8AoTNH5ANCMqSWEgBuA1Z5too/198Dv7fw6zFFN\nYUyN40pgbXzA3bt3E4lEsj7deeedOXme7pqvK2R0PZ8y9ox8ucoI/E2yD/50ewxvAiuArcBnwP8C\njwB5wCrgJmAPMMOuX2PbazD1gjm0DTPNASoxncyLmKIzwGPAE5jDVY/QVmBuAO4CttjlRZgaRyD2\n7NkT1FP74no+cD+j6/lAGTPB9XwQfMZ0HQOYQ1OXxLU1YPYeErnHTvG2AeMStB+nrWOJt8xOIiKS\nI72DDpABCxcuXJj1JwmHw5SUlGT9eU6W6/nA/Yyu5wNlzATX80FuMi5atAjMSEw7oUSNXUzEjpeJ\niIhPoVAIkvQB6YrPYlVVVQUdISXX84H7GV3PB907Y2FhIaFQSFOGp8LCwg7/LfzUGEREsq6xsRHt\n/Wee3TPo2DZZyJFrGkoS6QZCoZA6hixI9r5qKElERHxTx+CT62O7rucD9zO6ng+UUXJDHYOISIAW\nLlzIDTfcEHSMGKoxiIgTEo2F5+cX0tLSmLXnzMsroLm5IeU6JSUlHDp0iN69e7fmrK2tpaioKOH6\nZWVl3HDDDdx0002+MixatIhdu3bxxBNPdCy8T6oxiGRYYX6+/8MC8/ODjtvtmE4hkrXJT6cTCoV4\n/vnnaWlpoaWlhebm5qSdQnT9jnDxi606Bp9cHzd1PR+4nzFRvsaWFt8fM40tLYFkdE1XyNgZR48e\nZdq0aZxxxhkUFhZy9dVXU19fD8Add9zBhg0buPXWW8nLy+O2224DYO7cuRQXFzNo0CAuuugiNm7c\nGORLSEsdg4hIGt5v9Z9++ik33XQTe/fuZe/evQwYMIBbb70VgLvvvptJkybxi1/8gpaWFh566CEA\nJkyYwJtvvkljYyPXXXcd3/rWt/j4448DeS1+qMYgkkIoFPJ9FaoQbg4LdBWJxsLNsEw239P0v50o\nKSnhyJEj9Oljfg88efJknnvuudb7q6urueyyy2hoaGi9//rrr09ZYygsLOTVV19l3LhxLFy4kN27\ndztVY9Avn0VEUgiFQqxevZrLLrsMgI8++ojvfve7rF27lsZGU6P44IMPiEQirfWF+DrDT3/6Ux5/\n/HH2799PKBSiubmZw4cP5/aFdICGknxyfdzU9XzgfkbX84EyuuCnP/0ptbW1bN68maamJl599VXv\nxW/adQobNmzg3/7t33j66ac5evQojY2NDBo0yOm9S+0xiIh0wAcffMCAAQMYNGgQDQ0N0dNXtxo6\ndCjeyw23tLTQp08fhgwZwscff8zixYtpbm7OdewO0R6DT2VlZUFHSMn1fOB+RtfzgTK6YN68eXz0\n0UcMGTKESy65hK9//esxewlz587lmWeeobCwkHnz5jF16lSmTp3KueeeS0lJCQMGDKC4uLh1/ejh\nzi5xK83JUfFZskbF59xx9QduXZ1+4JZFro+bup4P3M/oej7oeRmbmxsycuH7ZFN37xROlp+OYTTw\nhmdqAm4DCoH1QC2wDgh7tlkA7AR2AFM87RcC2+19D3ra+wErbfvrwEjPfeX2OWqBG/29LBEROVkd\nHUrqBdQDE4DvA4eBJcB8oAC4HRgLPAmMB4YDLwOjMAcjbwZutbcvAg8Ba4A5wBft7Uzgm8AsTOez\nBdOhAGyz80c9mTSUJFmjoaTc0fUYsiMXQ0lXALuAOuAaYLltXw5Mt/PXAk8BnwB77PoTgTOBPEyn\nALDCs433sZ4FLrfzV2H2Ro7aaT0wtYOZRUSkAzraMczCfOgDDAUO2vmDdhlgGLDPs80+zJ5DfHu9\nbcfe1tn5E5jhqsEpHivnXB/bdT0fuJ/R9XygjJIbHekYTgGuBp5OcF/0PGIiItLFdeQHbl/HjPG/\nb5cPAkXAAcww0SHbXg+M8Gx3Fuabfr2dj2+PblMM7LeZBgFHbHuZZ5sRwCvxwSoqKigpKQEgHA5T\nWlraeix19NuLloNfLisrcyqPn3wAVbT9I6yyt0mXs5w32ubC+5Vq2Zu1o69PsqOqqorKykqA1s/L\nZDpSfP418BJttYAlmA/v+zBF5zCxxecJtBWfP4/Zo9iEOaJpM/ACscXnccAtmOGq6bQVn7cCF9is\n2+y8is+SEyo+546Kz9mRzeLzQEzh+TlP22LgSsxhpJfZZYAaYJW9fQnzoR9NNQd4FHNY6i5MpwDw\nGKamsBOYh+lgABqAuzBHJm0GFhHbKeSM6+OmrucD9zO6ng+UsTvIy8tjz549QcdIye9Q0jFgSFxb\nA6azSOQeO8XbhtkziHccmJHksZbZSUR6mPxwPi1N2bsAUt6gPJqPpj9v0caNG/nxj39MTU0NvXv3\nZsyYMfzsZz/joosu6vBztuTggk6dpVNiiKSgoaTcSXo9hoVZfNKF6f9mzc3NFBcX86tf/YoZM2Zw\n/PhxNmzYQFFREePGJfqem9iJEydar+mQSzolhohIhtXW1hIKhZg5cyahUIj+/ftz5ZVXtnYKjz/+\nOGPHjqWwsJCpU6eyd+/e1m179erFww8/zKhRoxg9enRr27vvvgtAU1MTN954I2eccQYlJSXcfffd\nrR/iCxcu5IYbbmh9rD179tCrVy8+++wzACorKznnnHPIz8/n7LPP5sknn8zYa1bH4JPr46au5wP3\nM7qeD5QxCKNHj6Z3795UVFSwZs2a1ovzAKxevZp7772X3/zmNxw+fJhJkyYxe/bsmO1Xr17Nli1b\nqKmpaffY3//+92lpaeG9997j1VdfZcWKFSxbZkbOU51x9dixY8ydO5c1a9bQ3NzMH//4R0pLSzP0\nitUxiIiklJeXx8aNGwmFQtx8882cccYZXHvttRw6dIilS5eyYMECRo8eTa9evViwYAHV1dXU1dW1\nbr9gwQLC4TD9+vWLedxPP/2UlStXcu+99zJw4EBGjhzJD3/4w9ZLfKYb4urVqxfbt2/no48+YujQ\noYwdOzZjr1kdg0+uH2ftej5wP6Pr+UAZg3LeeeexbNky6urqeOutt9i/fz/z5s1j7969zJ07l4KC\nAgoKChg8eDAA9fX1rduOGDEi4WMePnyYTz75hJEj284ZWlxcHLNtMgMHDmTlypUsXbqUYcOGMW3a\nNN55551Ovso26hhERDpg9OjRlJeX89Zbb1FcXMwjjzxCY2Nj63Ts2DG+/OUvt66fbEhoyJAh9O3b\nN+bQ1b1793LWWeZ3wAMHDuTDDz9sve/AgQMx20+ZMoV169Zx4MABzjvvPG6++eaMvUZ1DD65Pm7q\nej5wP6Pr+UAZg/DOO+/w7//+763f5Ovq6njqqae4+OKL+e53v8s999zTWj9oamri6acTnTWovd69\nezNjxgzuuOMOPvjgA/7yl7/wwAMPcP311wPwpS99iddee426ujqampq49957W7c9dOgQq1ev5tix\nY/Tt25eBAwfSu3fvjL1mdQwiIink5eWxadMmJk6cyGmnncbFF1/M+eefz/3338/06dOZP38+s2bN\nYtCgQYwbN461a9e2bptob8Hb9h//8R8MHDiQs88+m0mTJvHtb3+b73znOwBcccUVzJw5k/PPP5/x\n48dz9dVXt2772Wef8cADDzB8+HAGDx7Mhg0b+OUvf5mx16zfMYikoN8x5E7CS3s68gO3ruxkfseg\njkEkBXUMuaNzJWWHfuCWRa6Pm7qeD9zP6Ho+UEbJDXUMIiISQ0NJIiloKCl3NJSUHRpKEhGRTlPH\n4JPr46au5wP3M7qeD5RRckMdg4iIxFCNQSQF1RhyRzWG7FCNQUQkhyorK5k0aVLrckcu2+m9LoNr\n1DH45Pq4qev5wP2MrueDnpexMD+fUCiUtakwP99Xjo0bN3LJJZcQDocZPHgwl156KVu3bm23XktL\nCyUlJZ1+3fEdTq757RjCwDPA20ANMBEoBNYDtcA6u07UAmAnsAOY4mm/ENhu73vQ094PWGnbXwdG\neu4rt89RC9zoM6+IdAONLS1EIGtTo4/rLzc3NzNt2jTmzp1LY2Mj9fX13Hnnne2ur9ATLQf+wc73\nAQYBS4Af27b5wGI7PxaoBvoCJcAu2saxNgMT7PyLwFQ7Pwd42M7PBH5t5wuB3ZhOJ+yZ94qIZAsQ\nific9G+xcxK9fx15/09m8vM327JlSyQcDie8b9myZZFLL720dTkUCkV2794diUQikcOHD0emTZsW\nyc/Pj4wfPz5yxx13tFt36dKlkVGjRkXC4XDke9/7XiQSiURqamoi/fv3j/Tu3Tty2mmnRQoKCjr0\nPsZL9hohefnMzx7DIGAS8LhdPgE0AddgOgzs7XQ7fy3wFPAJsAfTMUwEzgTyMJ0DwArPNt7Heha4\n3M5fhdkbOWqn9bR1JiIiWZfq0p6pfO973yMvL4+DBw+yfPlyVqxY0e5sqy+88AJbt27lT3/6E6tW\nrWLt2rWMGTOGpUuXcvHFF9PS0kJDQ0M2XlZKfjqGzwHvA8uA/wX+ExgIDAUO2nUO2mWAYcA+z/b7\ngOEJ2uttO/Y2ei28aMczOMVj5ZzrY7uu5wP3M7qeD5QxCKku7ZnMp59+ynPPPceiRYvo378/Y8aM\noby8vN3RQbfffjv5+fmMGDGCyZMnU11dDQR/dJufjqEPcAFmqOcC4Bhwe9w6KXdLRES6smSX9kx2\ndbb333+fEydOxFzWM3plNq+ioqLW+VNPPZVjx45lPvxJ6ONjnX122mKXn8EUlw8ARfb2TCDafdYD\n3oucnmW3r7fz8e3RbYqB/bTVMI7Y9jLPNiOAV+IDVlRUtB4JEA6HKS0tbb3ubPTbi5aDXy4rK3Mq\nj598AFW0/SOssrdJl7OcN9rmwvuVatmbtaOvz3XRS3s+8sgjXHXVVQnXOf300+nTpw91dXWMGjUK\nMFd+8ytZh9MZVVVVVFZWAmTkyCmA14Bz7fxCTOF5CaboDGYPIr74fApmGGo3bcXnTZh6Q4j2xefo\n5YdmEVt8fhdTcC7wzHt1qjAjkgoqPudMovevI+9/torPO3bsiNx///2Rffv2RSKRSGTv3r2RSy65\nJPKP//iPkcrKyqTF55kzZ0auu+66yIcffhh5++23I8XFxZFJkyYlXDcSiUTKy8sjP/nJTyKRSCTy\n0ksvRUpKSiIff/zxyb2Z8e9hkvZkH/h+D1f9PvBfwJvA+cDdtiO4EnMY6WW0dQw1wCp7+5L90I8G\nmAM8ijksdRewxrY/hqkp7ATm0TZU1QDchdlb2QwswhShc871cVPX84H7GV3PB8oYhFSX9oTYb/fe\n+Z///Oc0NTVRVFREeXk5s2fP5pRTTkm4bnQ52nb55ZfzhS98gaKiIs4444xsvryE/AwlgekQxido\nvyLJ+vfYKd42YFyC9uPAjCSPtcxOItLDFOTlEfLxW4POPH46w4YNY+XKlQnvKy8vp7y8vHX5008/\nbZ0fMmQIzz//fOvy/PnzY2oO3nUBli1r+5jr27dvzLa5pnMliaSgcyXlTnc7V9I777zD8ePHGTdu\nHFu2bOEb3/gGjz32GNdcc01Oc5zMuZL87jGIiEgHtLS0MHv2bPbv38/QoUP50Y9+lPNO4WTpXEk+\nuT5u6no+cD+j6/lAGbuSiy66iJ07d3Ls2DHeffdd5s+fn34jR6hjEBGRGKoxiKSgGkPudLcagyt0\nPQYREek0dQw+uT5u6no+cD+j6/lAGSU3dFSSiDihoKAgK6eC6OkKCgo6vE13+CuoxiBZoxqDdFeq\nMYiIiG/qGHxyfdzU9XzgfkbX84EyZoLr+SD4jOoYREQkhmoMIimoxiDdlWoMIiLimzoGn4Ie80vH\n9XzgfkbX84EyZoLr+SD4jOoYREQkhmoMIimoxiDdlWoMIiLimzoGn4Ie80vH9XzgfkbX84EyZoLr\n+SD4jH47hj3An4A3gM22rRBYD9QC64CwZ/0FwE5gBzDF034hsN3e96CnvR+w0ra/Doz03Fdun6MW\nuNFnXhEROUl+awzvYT7UGzxtS4DD9nY+UADcDowFngTGA8OBl4FRQATTqdxqb18EHgLWAHOAL9rb\nmcA3gVmYzmeLfW6AbXb+qCeHagySNaoxSHeVqRpD/ANcAyy388uB6Xb+WuAp4BPMnsYuYCJwJpBH\n2x7HCs823sd6Frjczl+F2Rs5aqf1wNQOZBYRkQ7y2zFEMN/8twI327ahwEE7f9AuAwwD9nm23YfZ\nc4hvr7ft2Ns6O38CaAIGp3isnAt6zC8d1/OB+xldzwfKmAmu54PgM/q9HsNXgL8Cp2O+te+Iuz9i\nJxER6eL8dgx/tbfvA78BJmD2EoqAA5hhokN2nXpghGfbszDf9OvtfHx7dJtiYL/NNAg4YtvLPNuM\nAF6JD1dRUUFJSQkA4XCY0tJSysrMZtGeV8vBL5eVlTmVx08+gCra/hFW2duky1nOG21z4f1KtezN\n6kKerpYvG8tVVVVUVlYCtH5eJuOn+Hwq0BtoAQZixvwXAVdgPrzvwxSdw8QWnyfQVnz+PGaPYhNw\nG6bO8AKxxedxwC2YovN02orPW4ELbNZtdl7FZ8kJFZ+lu+ps8XkosAGoxnywP4/pHBYDV2IOI73M\nLgPUAKvs7UuYD/3o/5Y5wKOYw1J3YToFgMcwNYWdwDxMBwPmKKi7MEcmbcZ0SN5OIWfiv2m4xvV8\n4H5G1/OBMmaC6/kg+Ix+hpLeA0oTtDdg9hoSucdO8bZh9gziHQdmJHmsZXYSEZEc0LmSRFLQUJJ0\nVzpXkoiI+KaOwaegx/zScT0fuJ/R9XygjJngej4IPqM6BhERiaEag0gKqjFId6Uag4iI+KaOwaeg\nx/zScT0fuJ/R9XygjJngej4IPqM6BhERiaEag0gKqjFId6Uag4iI+KaOwaegx/zScT0fuJ/R9Xyg\njJngej4IPqM6BhERiaEag0gKqjFId6Uag4iI+KaOwaegx/zScT0fuJ/R9XygjJngej4IPqM6BhER\niaEag0gKqjFId6Uag4iI+KaOwaegx/zScT0fuJ/R9XygjJngej4IPqPfjqE38AbwO7tcCKwHaoF1\nQNiz7gJgJ7ADmOJpvxDYbu970NPeD1hp218HRnruK7fPUQvc6DOriIh0gt8awz9jPtjzgGuAJcBh\nezsfKABuB8YCTwLjgeHAy8AoIAJsBm61ty8CDwFrgDnAF+3tTOCbwCxM57PFPi/ANjt/NC6bagyS\nNaoxSHfV2RrDWcDfAo96HuQaYLmdXw5Mt/PXAk8BnwB7gF3AROBMTKey2a63wrON97GeBS6381dh\n9kaO2mk9MNVHXhER6QQ/HcMDwL8An3nahgIH7fxBuwwwDNjnWW8fZs8hvr3etmNv6+z8CaAJGJzi\nsQIR9JhfOq7nA/czup4PlDETXM8HwWdM1zFMAw5h6gvJhp0idhIRkW6gT5r7L8EM9fwt0B/IB57A\n7CUUAQcww0SH7Pr1wAjP9mdhvunX2/n49ug2xcB+m2cQcMS2l3m2GQG8kihkRUUFJSUlAITDYUpL\nSykrM5tGe14tB79cVlbmVB4/+QCqaPuHWGVvky5nOW+0zYX3K9WyN6sLebpavmwsV1VVUVlZCdD6\neZlMR37g9jXgR8DVmKLzEeA+TNE5TGzxeQJtxefPY/YoNgG3YeoMLxBbfB4H3IIpOk+nrfi8FbjA\n5txm51V8lpxR8Vm6q0z+wC36r34xcCXmMNLL7DJADbDK3r6E+dCPbjMHU8DeiSlKr7Htj2FqCjuB\neZgOBqABuAtzZNJmYBHtO4Wcif+m4RrX84H7GV3PB8qYCa7ng+AzphtK8nrVTmA+tK9Ist49doq3\nDbNnEO84MCPJYy2zk4iI5IjOlSSSgoaSpLvSuZJERMQ3dQw+BT3ml47r+cD9jK7nA2XMBNfzQfAZ\n1TGIiEgM1RhEUlCNQbor1RhERMQ3dQw+BT3ml47r+cD9jK7nA2XMBNfzQfAZ1TGIiEgM1RhEUlCN\nQbor1RhERMQ3dQw+BT3ml47r+cD9jK7nA2XMBNfzQfAZ1TGIiEgM1RhEUlCNQbor1RhERMQ3dQw+\nBT3ml47r+cD9jK7nA2XMBNfzQfAZ1TGIiEgM1RhEUlCNQbor1RhERMQ3dQw+BT3ml47r+cD9jK7n\nA2XMBNfzQfAZ03UM/YFNQDVQA9xr2wuB9UAtsA4Ie7ZZAOwEdgBTPO0XAtvtfQ962vsBK23768BI\nz33l9jlqgRt9viYREekEPzWGU4EPgT7ARuBHwDXAYWAJMB8oAG4HxgJPAuOB4cDLwCggAmwGbrW3\nLwIPAWuAOcAX7e1M4JvALEznswXToQBss/NH4/KpxiBZoxqDdFedrTF8aG9PAXoDjZiOYbltXw5M\nt/PXAk8BnwB7gF3AROBMIA/TKQCs8Gzjfaxngcvt/FWYvZGjdloPTPWRV0REOsFPx9ALM5R0EPgD\n8GdgqF3G3g6188OAfZ5t92H2HOLb62079rbOzp8AmoDBKR4rEEGP+aXjej5wP6Pr+UAZM8H1fBB8\nxj4+1vkMKAUGAWuByXH3R+wkIiLdgJ+OIaoJeAEzzn8QKAIOYIaJDtl16oERnm3OwnzTr7fz8e3R\nbYqB/TbPIOCIbS/zbDMCeCVRsIqKCkpKSgAIh8OUlpZSVmY2jfa8Wg5+uayszKk8fvIBVNH2D7HK\n3iZdznLeaJsL71eqZW9WF/J0tXzZWK6qqqKyshKg9fMymXTF5yGY4Z2jwADMHsMizPj/EeA+TNE5\nTGzxeQJtxefPY/YoNgG3YeoMLxBbfB4H3IIpOk+nrfi8FbjA5txm51V8lpxR8Vm6q84Un8/EfEuv\nxnyw/w74PbAYuBJzGOlldhnMIa2r7O1LmA/96P+UOcCjmMNSd2E6BYDHMDWFncA8TAcD0ADchTky\naTOmQ4rvFHIm/puGa1zPB+5ndD0fKGMmuJ4Pgs+YbihpO+ZberwG4Iok29xjp3jbMHsG8Y4DM5I8\n1jI7iYhIjuhcSSIpaChJuiudK0lERHxTx+BT0GN+6bieD9zP6Ho+UMZMcD0fBJ9RHYOIiMRQjUEk\nBdUYpLtSjUFERHxTx+BT0GN+6bieD9zP6Ho+UMZMcD0fBJ9RHYOIiMRQjUEkBdUYpLtSjUFERHxT\nx+BT0GN+6bieD9zP6Ho+UMZMcD0fBJ9RHYOIiMRQjUEkBdUYpLtSjUFERHxTx+BT0GN+6bieD9zP\n6Ho+UMZMcD0fBJ9RHYOIiMRQjUEkBdUYpLtSjUFERHxTx+BT0GN+6bieD9zP6Ho+UMZMcD0fBJ/R\nT8cwAvgD8GfgLeA2214IrAdqgXVA2LPNAmAnsAOY4mm/EHMd6Z3Ag572fsBK2/46MNJzX7l9jlrg\nRh95RUSkE/zUGIrsVA2cBmwDpgPfAQ4DS4D5QAFwOzAWeBIYDwwHXgZGARFgM3CrvX0ReAhYA8wB\nvmhvZwLfBGZhOp8tmA4F+9wXAkc9+VRjkKxRjUG6q87WGA5gOgWAD4C3MR/41wDLbftyTGcBcC3w\nFPAJsAfYBUwEzgTyMJ0CwArPNt7Heha43M5fhdkbOWqn9cBUH5lFROQkdbTGUAJ8CdgEDAUO2vaD\ndhlgGLDPs80+TEcS315v27G3dXb+BNAEDE7xWDkX9JhfOq7nA/czup4PlDETXM8HwWfs04F1T8N8\nm58LtMTdF7FTICoqKigpKQEgHA5TWlpKWVkZ0PYGd3Y5KlOP19PyddVlgCqgzDNPquUs56murs7q\n42diubq62qk8XS2fVyYfv6qqisrKSoDWz8tk/P6OoS/wPPAS8DPbtgPz/+EAZpjoD8B5mDoDwGJ7\nuwa4E/iLXWeMbZ8NfBW4xa6zEFN47gP8FTgdU2coA/7JbvMr4BVMoTpKNQbJGtUYpLvqbI0hBDwG\n1NDWKQD8FnPEEPb2vz3ts4BTgM9hCs+bMR1IM6beEAJuAFYneKy/B35v59dhjmoKY4rbVwJrfWQW\nEZGT5Kdj+ApwPTAZeMNOUzF7BFdiDiO9jLY9hBpglb19CXOkUfRr1BzgUcxhqbswewpgOp7Btn0e\nbXsdDcBdmCOTNgOLiD0iKWfid/Fc43o+cD+j6/lAGTPB9XwQfEY/NYaNJO9ArkjSfo+d4m0DxiVo\nPw7MSPJYy+wkIiI5oHMliaSgGoN0VzpXkoiI+KaOwaegx/zScT0fuJ/R9XygjJmQKF9hfj6hUCjt\nVJifH1jGXOrI7xhERLqlxpYWX0OGoZb4n3B1T6oxiKSgGkPP4Pfv3J3+xqoxiIiIb+oYfAp6zC8d\n1/OB+xldzwfKmAmu54PgM6pjEBGRGKoxiKSgGkPPoBpDLO0xiIhIDHUMPgU95peO6/nA/Yyu5wNl\nzATX80HgHl9iAAAKEUlEQVTwGdUxiIhIDNUYRFJQjaFnUI0hlvYYREQkhjoGn4Ie80vH9XzgfkbX\n84EyZoLr+SD4jOoYREQkhmoMIimoxtAzqMYQS3sMIiISw0/H8DhwENjuaSsE1mOu97wOCHvuW4C5\ndvMOYIqn/UL7GDuBBz3t/YCVtv11YKTnvnL7HLXAjT6yZk3QY37puJ4P3M/oej5QxkxwPR8En9FP\nx7AMmBrXdjumYzgX+L1dBhgLzLS3U4GHadtV+SVwEzDKTtHHvAk4YtseAO6z7YXAvwIT7HQnsR2Q\niIhkgd8aQwnwO2CcXd4BfA2zJ1EEVAHnYfYWPqPtw30NsBD4C/AKMMa2zwLKgH+y69wJbMJcOOiv\nwOnAbOCrwC12m6X2eX4dl001Bska1Rh6BtUYYp1sjWEoplPA3g6188OAfZ719gHDE7TX23bsbZ2d\nPwE0AYNTPJZIp+WH/V3KUaQnykTxOWKnbi3oMb90XM8HbmVsaWox+7LeqZz2bY5x6T1MxvWMrueD\n4DOe7DWfo0NIB4AzgUO2vR4Y4VnvLMw3/Xo7H98e3aYY2G/zDMLUHOoxw01RIzDDUe1UVFRQUlIC\nQDgcprS0lLIys2n0De7sclSmHq+n5XNtudV79vZzSZYx45dlnnlSLWc5f3V1dVYfPxPL1dXVTuXx\nky8qulSWZDm6TVf8/1xVVUVlZSVA6+dlMidbY1iC+fC+D1N4DtvbscCTmGLxcOBl4POYPYpNwG3A\nZuAF4CFMfWGOfdxbMLWH6fa2ENgKXGBzbrPzR+OyqcYgHRYKhfztESz0vzvcncafexrVGGL52WN4\nClNoHoKpBfwrsBhYhTmiaA8ww65bY9trMPWCObT9v5oDVAIDgBcxnQLAY8ATmMNVj2A6BYAG4C5g\ni11eRPtOQaTHK8zPp7GlJe16BXl5NDQ35yCRdHV+agyzMYXgUzDDOcswH9pXYA5XnULsB/Y9mL2E\n84C1nvZtmD2Dz2P2HKKOYzqWUcCXMR1N1DLaDm9d7u8lZUe7IQjHuJ4PukDG99KvErRE72FjS0tr\noS/V5KfzyFZGl7ieD4LPqF8+i4h0AYX5/o6kK8zP7/RzdYfj8VRjkA7rTjWGnjg+nmld4T3MdEad\nK0lERHxTx+BT0GN+6bieD7pAxi5aY3CN6xldzwfBZ1THICIiMVRjkB5JNQbx6grvoWoMIiISGHUM\nPgU95peO6/mgC2RUjSEjXM/oej4IPqM6BhERiaEag/RIqjGIV1d4D1VjEBGRwKhj8CnoMb90XM8H\nXSCjagwZ4XpG1/NB8BnVMYiISAzVGKRHUo2hZ8gP55ur9fng+nuYyxrDyV7BTUTEea2XcE3Hzzo9\niIaSfAp6zC8d1/NBF8ioGkNGJMqYy1NGp6W/c1raYxCRrIteTCidUI4uJiSpqcYgPZJqDLkVVMZM\n/52703uo3zGIiIhvXaFjmArsAHYC84MKEfSYXzqu54MukFFjzxnhfEb9ndNyvWPoDfwc0zmMBWYD\nY4IIUl1dHcTT+uZ6PugCGQ8EHSBWfrh9wXby5Mnt2lyjv3PnBf0eul58ngDsAvbY5V8D1wJv5zrI\n0aNHc/2UHeJ6PkicsTA/n0YfBceCvDwampuzEavN/8vuw3dUwkMt/wBMjmuLXydgzv9bdOzvnOy3\nFj/4wQ8CSGO43jEMB+o8y/uAiQFlkSzQ0SpyMvLzC2lpaQw6Rka4+AXA9aEkZw432rNnT9ARUnI9\nH+QmY35+oa/j5RNy/Isu4FTGRENdoVCIRYsWZX24y3QKER9TAg69h0kFnNG9AcpYX8b0k1Pt8gLg\nM+A+zzrVwN/kNpaISJf3JlAadIiT0QfYDZQAp2A6gUCKzyIi4o6vA+9gitALAs4iIiIiIiIiIiIi\nIiKSTBhYjDkNRyPQYOcX2/uCNtUzHwYeA7YDTwJDA0nUnuvvoev5QBkzwfV84GhG13/HEIRVmD9Q\nGVBop8mYI4tXBRer1b2e+fuBvwJXA1uAXwWSqD3X30PX84EyZoLr+aBrZBSg9iTvy5U3PPNvEvtb\nlDdznCUZ199D1/OBMmaC6/nA0YzaY2jvL8CPiR2WKcKc2XVvIIlinQ78M/BDYFDcfa78YNH199D1\nfKCMmeB6PnA0ozqG9mYCQ4BXMbt4jUAVMBiYEVysVo8CecBpwDJMRwFwJuYHgC5w/T10PR8oYyYk\nyvcH3MkH7r+H4sN3gg6Qxj8EHcBjDHAFphPzmppg3SBcCnzBzk8GfgRcHlwcX54IOkAakzB7slOC\nDmJNpG2veiBwF/ACsIT2e9tBuQ0YEXQI6Zy69KsEypV8t2F+rf7fmF3l6Z773ki4RW7dC7yOKdgv\nsfP/B3gN+JcAc3n9DvitvY1OxzztLtjsmb8Zs8d6J/B/ceMsBTW0nUH6P4GfYb4QLASeCyhTvCbM\nASQbgDm0jQCIY7anmD4OMFeU6/kA3sIMdYE5z9U2YJ5ddqFjiH5gnAq00PbtcQDwp6BCxXkD+C/M\n3szXMEet/NXOfy24WDG8f8uttH2oDcT8Gwia97ot/xt3nysHaryBGdKfAjwOvA+sAcppv7ctAToI\nfAnzgRY/7Q8mUgzX8wH8OW75NGAt8ABu1EGqk8wnWg5Kb8xBBi9j/t7g3kUp/4Q5vHIw7Tt8F97H\nZ2gbXl0GjLfz52L2Fl0Q/76dgrkY2a+Bw7mPI8k8jhkrTeSpXAZJwvV8YAp88afz7QuswJw2PWib\nMHsLEHsARpj23yyDdhbwNPAL3BkqjNqD6azeA97FHAAB5puuCx1DGFiOybYJ+AST9TXcOVV/qj3o\ngTlLIZIDIzCH3MULYcZ4g9Y/SfsQYFwug3TANOCeoEP4dCrwuaBDeAzCfFG5iMT/LoM0OugAIiIi\nIiIiIiIiIiIiIpJbd2COzX8TcyTJhCw+VxVwYRYfXySlPulXEenxLga+gfk9wSeYY/f7ZfH5InYS\nCYROoieSXhHmx0af2OUGzK+Q9wD3YX7otQk4x95/OubHVZvtdIltH4j5HcomzO8lrrHtAzA/aKrB\nnKphAO6cKVdERBIYiBk+egfzQ7Ov2vb3aDsn0A2Y8xiBuZreV+x8MeYDH8zvEL5t58P28U7F/ML5\nUds+DtMBXZDpFyEiIpnVC3OOooWYvYUKTMdQYu/vS9spDA5hOpLoVIfpXLZizmkVbd8DnAf8BnMu\npKhtqGOQAKnGIOLPZ5hz5r+K+XCvSLBOtC4QwpzyOdFJDf8O2JmgXUNH4gzVGETSOxcY5Vn+Eubb\nPpgLrURv/8fOr8Ocejwqel6etXHt0ZPjvQZcZ+e/CJzf6cQiIpJVF2CuMfBnzOGqz2DOKPoesNi2\nbQLOtusPxhST37TbPGzb+wNLMcXqt2i7rkJ/zAkQa4BngT+ioSQRkS7pPcyhqyLdioaSRE6efmsg\nIiIiIiIiIiIiIiIiIiIiIiIiIiIi0jH/HxteaUVLjBN9AAAAAElFTkSuQmCC\n",
 300        "text": [
 301         "<matplotlib.figure.Figure at 0x7ff7b4d10dd8>"
 302        ]
 303       }
 304      ],
 305      "prompt_number": 47
 306     },
 307     {
 308      "cell_type": "markdown",
 309      "metadata": {},
 310      "source": [
 311       "It's difficult to see the detail because there are so many accidents at 30 mph. How many?"
 312      ]
 313     },
 314     {
 315      "cell_type": "code",
 316      "collapsed": false,
 317      "input": [
 318       "# What proportion of the accidents occur at each speed?\n",
 319       "severity_by_speed.sum(axis='columns') / severity_by_speed.sum(axis='columns').sum()"
 320      ],
 321      "language": "python",
 322      "metadata": {},
 323      "outputs": [
 324       {
 325        "metadata": {},
 326        "output_type": "pyout",
 327        "prompt_number": 48,
 328        "text": [
 329         "Speed\n",
 330         "10       0.000013\n",
 331         "15       0.000012\n",
 332         "20       0.008590\n",
 333         "30       0.639935\n",
 334         "40       0.081868\n",
 335         "50       0.030793\n",
 336         "60       0.164705\n",
 337         "70       0.074085\n",
 338         "dtype: float64"
 339        ]
 340       }
 341      ],
 342      "prompt_number": 48
 343     },
 344     {
 345      "cell_type": "code",
 346      "collapsed": false,
 347      "input": [
 348       "# scale the accident numbers so the largest value is 1.\n",
 349       "severity_by_speed.sum(axis='columns') / severity_by_speed.sum(axis='columns').max()"
 350      ],
 351      "language": "python",
 352      "metadata": {},
 353      "outputs": [
 354       {
 355        "metadata": {},
 356        "output_type": "pyout",
 357        "prompt_number": 49,
 358        "text": [
 359         "Speed\n",
 360         "10       0.000020\n",
 361         "15       0.000018\n",
 362         "20       0.013424\n",
 363         "30       1.000000\n",
 364         "40       0.127931\n",
 365         "50       0.048118\n",
 366         "60       0.257377\n",
 367         "70       0.115770\n",
 368         "dtype: float64"
 369        ]
 370       }
 371      ],
 372      "prompt_number": 49
 373     },
 374     {
 375      "cell_type": "code",
 376      "collapsed": false,
 377      "input": [
 378       "severity_by_speed.sum(axis='columns').plot(kind='bar')"
 379      ],
 380      "language": "python",
 381      "metadata": {},
 382      "outputs": [
 383       {
 384        "metadata": {},
 385        "output_type": "pyout",
 386        "prompt_number": 50,
 387        "text": [
 388         "<matplotlib.axes.AxesSubplot at 0x7ff7b4c4b630>"
 389        ]
 390       },
 391       {
 392        "metadata": {},
 393        "output_type": "display_data",
 394        "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAERCAYAAAB/4wAeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nP0yHA3PEKadiAAAAAElFTkSuQmCC\n",
 395        "text": [
 396         "<matplotlib.figure.Figure at 0x7ff7b4d9a550>"
 397        ]
 398       }
 399      ],
 400      "prompt_number": 50
 401     },
 402     {
 403      "cell_type": "markdown",
 404      "metadata": {},
 405      "source": [
 406       "As with the `small_accidents` data from 2012, the full data set covering 2005-2012 still shows that accidents at 30mph are four times more common than at any other speed, and account for about two thirds of all accidents. \n",
 407       "\n",
 408       "Let's look at the distribtion of severities of accidents."
 409      ]
 410     },
 411     {
 412      "cell_type": "code",
 413      "collapsed": false,
 414      "input": [
 415       "severity_by_speed.sum()"
 416      ],
 417      "language": "python",
 418      "metadata": {},
 419      "outputs": [
 420       {
 421        "metadata": {},
 422        "output_type": "pyout",
 423        "prompt_number": 51,
 424        "text": [
 425         "Fatal        18116\n",
 426         "Serious     181742\n",
 427         "Slight     1155757\n",
 428         "dtype: int64"
 429        ]
 430       }
 431      ],
 432      "prompt_number": 51
 433     },
 434     {
 435      "cell_type": "code",
 436      "collapsed": false,
 437      "input": [
 438       "severity_by_speed.sum() / severity_by_speed.sum().sum()"
 439      ],
 440      "language": "python",
 441      "metadata": {},
 442      "outputs": [
 443       {
 444        "metadata": {},
 445        "output_type": "pyout",
 446        "prompt_number": 52,
 447        "text": [
 448         "Fatal      0.013364\n",
 449         "Serious    0.134066\n",
 450         "Slight     0.852570\n",
 451         "dtype: float64"
 452        ]
 453       }
 454      ],
 455      "prompt_number": 52
 456     },
 457     {
 458      "cell_type": "code",
 459      "collapsed": false,
 460      "input": [
 461       "severity_by_speed.sum().plot(kind='bar')"
 462      ],
 463      "language": "python",
 464      "metadata": {},
 465      "outputs": [
 466       {
 467        "metadata": {},
 468        "output_type": "pyout",
 469        "prompt_number": 53,
 470        "text": [
 471         "<matplotlib.axes.AxesSubplot at 0x7ff7b4bc42b0>"
 472        ]
 473       },
 474       {
 475        "metadata": {},
 476        "output_type": "display_data",
 477        "png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEaCAYAAADg2nttAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiRJkiRJkiRJkiQpG/8PyXeQ2ZCJdI0AAAAASUVORK5CYII=\n",
 478        "text": [
 479         "<matplotlib.figure.Figure at 0x7ff7b4be3470>"
 480        ]
 481       }
 482      ],
 483      "prompt_number": 53
 484     },
 485     {
 486      "cell_type": "markdown",
 487      "metadata": {},
 488      "source": [
 489       "85% of all accidents are \"slight\"; only 1.3% are fatal. "
 490      ]
 491     },
 492     {
 493      "cell_type": "markdown",
 494      "metadata": {},
 495      "source": [
 496       "It's clear that there are very few accidents at 20mph and below. Let's remove them so that the proportional statistics below aren't thrown off by these small numbers. "
 497      ]
 498     },
 499     {
 500      "cell_type": "code",
 501      "collapsed": false,
 502      "input": [
 503       "severity_by_speed_reduced = severity_by_speed.loc[30:70]\n",
 504       "severity_by_speed_reduced"
 505      ],
 506      "language": "python",
 507      "metadata": {},
 508      "outputs": [
 509       {
 510        "html": [
 511         "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
 512         "<table border=\"1\" class=\"dataframe\">\n",
 513         "  <thead>\n",
 514         "    <tr style=\"text-align: right;\">\n",
 515         "      <th></th>\n",
 516         "      <th>Fatal</th>\n",
 517         "      <th>Serious</th>\n",
 518         "      <th>Slight</th>\n",
 519         "    </tr>\n",
 520         "    <tr>\n",
 521         "      <th>Speed</th>\n",
 522         "      <th></th>\n",
 523         "      <th></th>\n",
 524         "      <th></th>\n",
 525         "    </tr>\n",
 526         "  </thead>\n",
 527         "  <tbody>\n",
 528         "    <tr>\n",
 529         "      <th>30</th>\n",
 530         "      <td> 5959</td>\n",
 531         "      <td> 106734</td>\n",
 532         "      <td> 754813</td>\n",
 533         "    </tr>\n",
 534         "    <tr>\n",
 535         "      <th>40</th>\n",
 536         "      <td> 1680</td>\n",
 537         "      <td>  14633</td>\n",
 538         "      <td>  94668</td>\n",
 539         "    </tr>\n",
 540         "    <tr>\n",
 541         "      <th>50</th>\n",
 542         "      <td>  914</td>\n",
 543         "      <td>   5940</td>\n",
 544         "      <td>  34889</td>\n",
 545         "    </tr>\n",
 546         "    <tr>\n",
 547         "      <th>60</th>\n",
 548         "      <td> 7076</td>\n",
 549         "      <td>  40952</td>\n",
 550         "      <td> 175248</td>\n",
 551         "    </tr>\n",
 552         "    <tr>\n",
 553         "      <th>70</th>\n",
 554         "      <td> 2411</td>\n",
 555         "      <td>  11861</td>\n",
 556         "      <td>  86159</td>\n",
 557         "    </tr>\n",
 558         "  </tbody>\n",
 559         "</table>\n",
 560         "<p>5 rows \u00d7 3 columns</p>\n",
 561         "</div>"
 562        ],
 563        "metadata": {},
 564        "output_type": "pyout",
 565        "prompt_number": 54,
 566        "text": [
 567         "       Fatal  Serious  Slight\n",
 568         "Speed                        \n",
 569         "30      5959   106734  754813\n",
 570         "40      1680    14633   94668\n",
 571         "50       914     5940   34889\n",
 572         "60      7076    40952  175248\n",
 573         "70      2411    11861   86159\n",
 574         "\n",
 575         "[5 rows x 3 columns]"
 576        ]
 577       }
 578      ],
 579      "prompt_number": 54
 580     },
 581     {
 582      "cell_type": "markdown",
 583      "metadata": {},
 584      "source": [
 585       "Let's normalise each row so that we can see the proportions of accidents at each severity at eash speed.\n",
 586       "\n",
 587       "(And push the legend out beyond the right hand side of the plot so that it doesn't obscure the bars.)"
 588      ]
 589     },
 590     {
 591      "cell_type": "code",
 592      "collapsed": false,
 593      "input": [
 594       "severity_by_speed_norm_row = severity_by_speed_reduced.div(severity_by_speed_reduced.sum(axis='columns'), axis='index')\n",
 595       "ax = severity_by_speed_norm_row.plot(kind='bar', legend=False)\n",
 596       "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))"
 597      ],
 598      "language": "python",
 599      "metadata": {},
 600      "outputs": [
 601       {
 602        "metadata": {},
 603        "output_type": "pyout",
 604        "prompt_number": 55,
 605        "text": [
 606         "<matplotlib.legend.Legend at 0x7ff7b49cbbe0>"
 607        ]
 608       },
 609       {
 610        "metadata": {},
 611        "output_type": "display_data",
 612        "png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAERCAYAAAD2e5d3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiIiIiIiIiIiIiIiIiIiIiIiIv3x/wEizcuXPiginQAAAABJRU5ErkJggg==\n",
 613        "text": [
 614         "<matplotlib.figure.Figure at 0x7ff7b4b78e48>"
 615        ]
 616       }
 617      ],
 618      "prompt_number": 55
 619     },
 620     {
 621      "cell_type": "markdown",
 622      "metadata": {},
 623      "source": [
 624       "That looks reasonably consistent, though the proportions of fatal accidents are so low that its difficult to see any differences between the different speed categories.\n",
 625       "\n",
 626       "Is there a difference of accident severity with speed? We'll normalise the data down each column, so we can see the proportion of fatal accidents that occur at each speed (and the same for all the severities).\n",
 627       "\n",
 628       "We'll plot the data grouped by severity."
 629      ]
 630     },
 631     {
 632      "cell_type": "markdown",
 633      "metadata": {},
 634      "source": [
 635       "# Insert statistical analysis here\n",
 636       "\n",
 637       "Need to talk to statisticians to find a sensible way of applying something like chi-squared analysis to see if the different numbers in each bucket are statistically significant."
 638      ]
 639     },
 640     {
 641      "cell_type": "code",
 642      "collapsed": false,
 643      "input": [
 644       "severity_by_speed_norm_col = severity_by_speed_reduced / severity_by_speed_reduced.sum()\n",
 645       "ax = severity_by_speed_norm_col.T.plot(kind='bar')\n",
 646       "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))"
 647      ],
 648      "language": "python",
 649      "metadata": {},
 650      "outputs": [
 651       {
 652        "metadata": {},
 653        "output_type": "pyout",
 654        "prompt_number": 56,
 655        "text": [
 656         "<matplotlib.legend.Legend at 0x7ff7b4c17358>"
 657        ]
 658       },
 659       {
 660        "metadata": {},
 661        "output_type": "display_data",
 662        "png": "iVBORw0KGgoAAAANSUhEUgAAAa4AAAEaCAYAAABJrrP5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nY+KLRrrymXIAAAAASUVORK5CYII=\n",
 663        "text": [
 664         "<matplotlib.figure.Figure at 0x7ff7b392f470>"
 665        ]
 666       }
 667      ],
 668      "prompt_number": 56
 669     },
 670     {
 671      "cell_type": "markdown",
 672      "metadata": {},
 673      "source": [
 674       "This shows something interesting: there are many more fatal accidents at 60mph, though the data seems to be swamped by the number of accidents at 30mph.\n",
 675       "\n",
 676       "Let's normalise first by rows, giving the proportion of accident severities at each speed (effectively meaning we have the same number of accidents at each speed). Then we'll normalise by columns, so we can see how the proportions change with speed."
 677      ]
 678     },
 679     {
 680      "cell_type": "code",
 681      "collapsed": false,
 682      "input": [
 683       "severity_by_speed_norm_row = severity_by_speed_reduced.div(severity_by_speed_reduced.sum(axis='columns'), axis='index')\n",
 684       "severity_by_speed_norm_row_col = severity_by_speed_norm_row / severity_by_speed_norm_row.sum()\n",
 685       "ax = severity_by_speed_norm_row_col.T.plot(kind='bar')\n",
 686       "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))"
 687      ],
 688      "language": "python",
 689      "metadata": {},
 690      "outputs": [
 691       {
 692        "metadata": {},
 693        "output_type": "pyout",
 694        "prompt_number": 57,
 695        "text": [
 696         "<matplotlib.legend.Legend at 0x7ff7b492ab00>"
 697        ]
 698       },
 699       {
 700        "metadata": {},
 701        "output_type": "display_data",
 702        "png": "iVBORw0KGgoAAAANSUhEUgAAAbUAAAEaCAYAAACW4MnmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nB/3dHEyuqWoRAAAAAElFTkSuQmCC\n",
 703        "text": [
 704         "<matplotlib.figure.Figure at 0x7ff7b4a47e10>"
 705        ]
 706       }
 707      ],
 708      "prompt_number": 57
 709     },
 710     {
 711      "cell_type": "markdown",
 712      "metadata": {},
 713      "source": [
 714       "This shows that the chances of a fatality vary a lot with speed, serious injury varies a little with speed, but speed has little influence of the chance of a slight injury. This seemingly counter-intutive results is explained when you recall that fatal accidents are only 1.3% of the total and 85% of accidents are \"slight\". Enormous relative changes in fatal accident rates have virtually no effect on the rate of slight accidents.\n",
 715       "\n",
 716       "Could this have something to do with the types of person involved in the accidents? We'd expect there to be more pedestrians involved in accidents in 30mph zones (mainly urban) than at 60 and 70mph (rural or trunk roads)."
 717      ]
 718     },
 719     {
 720      "cell_type": "markdown",
 721      "metadata": {},
 722      "source": [
 723       "\"Casualty Class\" shows the broad category of person involved. (\"Casualty Type\" has more detail.)"
 724      ]
 725     },
 726     {
 727      "cell_type": "code",
 728      "collapsed": false,
 729      "input": [
 730       "{label_of[('Casualty_Class', c)]: c for t, c in label_of.keys() if t == 'Casualty_Class'}"
 731      ],
 732      "language": "python",
 733      "metadata": {},
 734      "outputs": [
 735       {
 736        "metadata": {},
 737        "output_type": "pyout",
 738        "prompt_number": 58,
 739        "text": [
 740         "{1: 'Driver or rider', 2: 'Passenger', 3: 'Pedestrian'}"
 741        ]
 742       }
 743      ],
 744      "prompt_number": 58
 745     },
 746     {
 747      "cell_type": "code",
 748      "collapsed": false,
 749      "input": [
 750       "severity_by_speed_and_class_dict = collections.Counter(\n",
 751       "        (a['Speed_limit'], c['Casualty_Class'], c['Casualty_Severity']) \n",
 752       "                        for a in accidents.find() for c in a['Casualties'])\n",
 753       "severity_by_speed_and_class_dict"
 754      ],
 755      "language": "python",
 756      "metadata": {},
 757      "outputs": [
 758       {
 759        "metadata": {},
 760        "output_type": "pyout",
 761        "prompt_number": 59,
 762        "text": [
 763         "Counter({(30, 1, 3): 590630, (30, 2, 3): 233891, (60, 1, 3): 193571, (30, 3, 3): 161080, (40, 1, 3): 96548, (70, 1, 3): 94738, (60, 2, 3): 82941, (30, 1, 2): 58733, (70, 2, 3): 44869, (30, 3, 2): 40944, (40, 2, 3): 39101, (50, 1, 3): 38319, (60, 1, 2): 37463, (50, 2, 3): 15552, (30, 2, 2): 13967, (60, 2, 2): 12069, (40, 1, 2): 11104, (70, 1, 2): 10032, (60, 1, 1): 5691, (20, 1, 3): 5672, (40, 3, 3): 5610, (50, 1, 2): 5237, (70, 2, 2): 4130, (60, 3, 3): 3940, (20, 3, 3): 3861, (40, 2, 2): 2964, (30, 3, 1): 2771, (30, 1, 1): 2642, (40, 3, 2): 2600, (20, 2, 3): 2395, (70, 1, 1): 1673, (60, 3, 2): 1665, (60, 2, 1): 1557, (50, 2, 2): 1506, (40, 1, 1): 1035, (20, 3, 2): 878, (30, 2, 1): 774, (50, 3, 3): 764, (50, 1, 1): 675, (20, 1, 2): 641, (70, 2, 1): 618, (60, 3, 1): 554, (70, 3, 3): 530, (40, 3, 1): 431, (70, 3, 2): 429, (50, 3, 2): 424, (70, 3, 1): 405, (40, 2, 1): 344, (50, 2, 1): 190, (20, 2, 2): 156, (50, 3, 1): 146, (20, 1, 1): 34, (20, 3, 1): 32, (10, 3, 3): 11, (20, 2, 1): 9, (15, 1, 3): 7, (15, 3, 3): 6, (15, 2, 3): 4, (10, 3, 1): 3, (10, 1, 2): 2, (10, 2, 3): 2, (10, 1, 3): 1, (10, 2, 2): 1, (15, 1, 2): 1})"
 764        ]
 765       }
 766      ],
 767      "prompt_number": 59
 768     },
 769     {
 770      "cell_type": "markdown",
 771      "metadata": {},
 772      "source": [
 773       "Now we can build a data frame, with a hierarchical index, to store these values."
 774      ]
 775     },
 776     {
 777      "cell_type": "code",
 778      "collapsed": false,
 779      "input": [
 780       "casualty_classes = {label_of[('Casualty_Class', c)]: c \n",
 781       "                    for c in sorted(code for t, code in label_of.keys() if t == 'Casualty_Class')}\n",
 782       "casualty_classes"
 783      ],
 784      "language": "python",
 785      "metadata": {},
 786      "outputs": [
 787       {
 788        "metadata": {},
 789        "output_type": "pyout",
 790        "prompt_number": 60,
 791        "text": [
 792         "{'Driver or rider': 1, 'Pedestrian': 3, 'Passenger': 2}"
 793        ]
 794       }
 795      ],
 796      "prompt_number": 60
 797     },
 798     {
 799      "cell_type": "code",
 800      "collapsed": false,
 801      "input": [
 802       "list(casualty_classes)"
 803      ],
 804      "language": "python",
 805      "metadata": {},
 806      "outputs": [
 807       {
 808        "metadata": {},
 809        "output_type": "pyout",
 810        "prompt_number": 68,
 811        "text": [
 812         "['Driver or rider', 'Pedestrian', 'Passenger']"
 813        ]
 814       }
 815      ],
 816      "prompt_number": 68
 817     },
 818     {
 819      "cell_type": "code",
 820      "collapsed": false,
 821      "input": [
 822       "speed_and_class_index = pd.MultiIndex.from_product((speeds, list(casualty_classes)), names=['Speed limit', 'Casualty class'])\n",
 823       "speed_and_class_index"
 824      ],
 825      "language": "python",
 826      "metadata": {},
 827      "outputs": [
 828       {
 829        "metadata": {},
 830        "output_type": "pyout",
 831        "prompt_number": 69,
 832        "text": [
 833         "MultiIndex(levels=[[10, 15, 20, 30, 40, 50, 60, 70], ['Driver or rider', 'Passenger', 'Pedestrian']],\n",
 834         "           labels=[[0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7], [0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1]],\n",
 835         "           names=['Speed limit', 'Casualty class'])"
 836        ]
 837       }
 838      ],
 839      "prompt_number": 69
 840     },
 841     {
 842      "cell_type": "code",
 843      "collapsed": false,
 844      "input": [
 845       "severity_by_speed_and_class_full = pd.DataFrame(\n",
 846       "    [{c_sev: severity_by_speed_and_class_dict[(speed, \n",
 847       "                                               casualty_classes[c_class], \n",
 848       "                                               severities[c_sev])]\n",
 849       "            for c_sev in severities} \n",
 850       "         for speed, c_class in speed_and_class_index],\n",
 851       "    index=speed_and_class_index)\n",
 852       "severity_by_speed_and_class = severity_by_speed_and_class_full.loc[30:70]\n",
 853       "severity_by_speed_and_class"
 854      ],
 855      "language": "python",
 856      "metadata": {},
 857      "outputs": [
 858       {
 859        "html": [
 860         "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
 861         "<table border=\"1\" class=\"dataframe\">\n",
 862         "  <thead>\n",
 863         "    <tr style=\"text-align: right;\">\n",
 864         "      <th></th>\n",
 865         "      <th></th>\n",
 866         "      <th>Fatal</th>\n",
 867         "      <th>Serious</th>\n",
 868         "      <th>Slight</th>\n",
 869         "    </tr>\n",
 870         "    <tr>\n",
 871         "      <th>Speed limit</th>\n",
 872         "      <th>Casualty class</th>\n",
 873         "      <th></th>\n",
 874         "      <th></th>\n",
 875         "      <th></th>\n",
 876         "    </tr>\n",
 877         "  </thead>\n",
 878         "  <tbody>\n",
 879         "    <tr>\n",
 880         "      <th rowspan=\"3\" valign=\"top\">30</th>\n",
 881         "      <th>Driver or rider</th>\n",
 882         "      <td> 2642</td>\n",
 883         "      <td> 58733</td>\n",
 884         "      <td> 590630</td>\n",
 885         "    </tr>\n",
 886         "    <tr>\n",
 887         "      <th>Pedestrian</th>\n",
 888         "      <td> 2771</td>\n",
 889         "      <td> 40944</td>\n",
 890         "      <td> 161080</td>\n",
 891         "    </tr>\n",
 892         "    <tr>\n",
 893         "      <th>Passenger</th>\n",
 894         "      <td>  774</td>\n",
 895         "      <td> 13967</td>\n",
 896         "      <td> 233891</td>\n",
 897         "    </tr>\n",
 898         "    <tr>\n",
 899         "      <th rowspan=\"3\" valign=\"top\">40</th>\n",
 900         "      <th>Driver or rider</th>\n",
 901         "      <td> 1035</td>\n",
 902         "      <td> 11104</td>\n",
 903         "      <td>  96548</td>\n",
 904         "    </tr>\n",
 905         "    <tr>\n",
 906         "      <th>Pedestrian</th>\n",
 907         "      <td>  431</td>\n",
 908         "      <td>  2600</td>\n",
 909         "      <td>   5610</td>\n",
 910         "    </tr>\n",
 911         "    <tr>\n",
 912         "      <th>Passenger</th>\n",
 913         "      <td>  344</td>\n",
 914         "      <td>  2964</td>\n",
 915         "      <td>  39101</td>\n",
 916         "    </tr>\n",
 917         "    <tr>\n",
 918         "      <th rowspan=\"3\" valign=\"top\">50</th>\n",
 919         "      <th>Driver or rider</th>\n",
 920         "      <td>  675</td>\n",
 921         "      <td>  5237</td>\n",
 922         "      <td>  38319</td>\n",
 923         "    </tr>\n",
 924         "    <tr>\n",
 925         "      <th>Pedestrian</th>\n",
 926         "      <td>  146</td>\n",
 927         "      <td>   424</td>\n",
 928         "      <td>    764</td>\n",
 929         "    </tr>\n",
 930         "    <tr>\n",
 931         "      <th>Passenger</th>\n",
 932         "      <td>  190</td>\n",
 933         "      <td>  1506</td>\n",
 934         "      <td>  15552</td>\n",
 935         "    </tr>\n",
 936         "    <tr>\n",
 937         "      <th rowspan=\"3\" valign=\"top\">60</th>\n",
 938         "      <th>Driver or rider</th>\n",
 939         "      <td> 5691</td>\n",
 940         "      <td> 37463</td>\n",
 941         "      <td> 193571</td>\n",
 942         "    </tr>\n",
 943         "    <tr>\n",
 944         "      <th>Pedestrian</th>\n",
 945         "      <td>  554</td>\n",
 946         "      <td>  1665</td>\n",
 947         "      <td>   3940</td>\n",
 948         "    </tr>\n",
 949         "    <tr>\n",
 950         "      <th>Passenger</th>\n",
 951         "      <td> 1557</td>\n",
 952         "      <td> 12069</td>\n",
 953         "      <td>  82941</td>\n",
 954         "    </tr>\n",
 955         "    <tr>\n",
 956         "      <th rowspan=\"3\" valign=\"top\">70</th>\n",
 957         "      <th>Driver or rider</th>\n",
 958         "      <td> 1673</td>\n",
 959         "      <td> 10032</td>\n",
 960         "      <td>  94738</td>\n",
 961         "    </tr>\n",
 962         "    <tr>\n",
 963         "      <th>Pedestrian</th>\n",
 964         "      <td>  405</td>\n",
 965         "      <td>   429</td>\n",
 966         "      <td>    530</td>\n",
 967         "    </tr>\n",
 968         "    <tr>\n",
 969         "      <th>Passenger</th>\n",
 970         "      <td>  618</td>\n",
 971         "      <td>  4130</td>\n",
 972         "      <td>  44869</td>\n",
 973         "    </tr>\n",
 974         "  </tbody>\n",
 975         "</table>\n",
 976         "<p>15 rows \u00d7 3 columns</p>\n",
 977         "</div>"
 978        ],
 979        "metadata": {},
 980        "output_type": "pyout",
 981        "prompt_number": 71,
 982        "text": [
 983         "                             Fatal  Serious  Slight\n",
 984         "Speed limit Casualty class                         \n",
 985         "30          Driver or rider   2642    58733  590630\n",
 986         "            Pedestrian        2771    40944  161080\n",
 987         "            Passenger          774    13967  233891\n",
 988         "40          Driver or rider   1035    11104   96548\n",
 989         "            Pedestrian         431     2600    5610\n",
 990         "            Passenger          344     2964   39101\n",
 991         "50          Driver or rider    675     5237   38319\n",
 992         "            Pedestrian         146      424     764\n",
 993         "            Passenger          190     1506   15552\n",
 994         "60          Driver or rider   5691    37463  193571\n",
 995         "            Pedestrian         554     1665    3940\n",
 996         "            Passenger         1557    12069   82941\n",
 997         "70          Driver or rider   1673    10032   94738\n",
 998         "            Pedestrian         405      429     530\n",
 999         "            Passenger          618     4130   44869\n",
1000         "\n",
1001         "[15 rows x 3 columns]"
1002        ]
1003       }
1004      ],
1005      "prompt_number": 71
1006     },
1007     {
1008      "cell_type": "code",
1009      "collapsed": false,
1010      "input": [
1011       "severity_by_speed_and_class\n",
1012       "ax = severity_by_speed_and_class.plot(kind='bar')\n",
1013       "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))"
1014      ],
1015      "language": "python",
1016      "metadata": {},
1017      "outputs": [
1018       {
1019        "metadata": {},
1020        "output_type": "pyout",
1021        "prompt_number": 72,
1022        "text": [
1023         "<matplotlib.legend.Legend at 0x7ff7b46e9400>"
1024        ]
1025       },
1026       {
1027        "metadata": {},
1028        "output_type": "display_data",
1029        "png": "iVBORw0KGgoAAAANSUhEUgAAAeIAAAFmCAYAAACxyJltAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERGJl/8PK8Qz+uGzIaoAAAAASUVORK5CYII=\n",
1030        "text": [
1031         "<matplotlib.figure.Figure at 0x7ff7b4a1d198>"
1032        ]
1033       }
1034      ],
1035      "prompt_number": 72
1036     },
1037     {
1038      "cell_type": "code",
1039      "collapsed": false,
1040      "input": [
1041       "pedestrian_severities = severity_by_speed_and_class.xs('Pedestrian', level='Casualty class')\n",
1042       "pedestrian_severities"
1043      ],
1044      "language": "python",
1045      "metadata": {},
1046      "outputs": [
1047       {
1048        "html": [
1049         "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
1050         "<table border=\"1\" class=\"dataframe\">\n",
1051         "  <thead>\n",
1052         "    <tr style=\"text-align: right;\">\n",
1053         "      <th></th>\n",
1054         "      <th>Fatal</th>\n",
1055         "      <th>Serious</th>\n",
1056         "      <th>Slight</th>\n",
1057         "    </tr>\n",
1058         "    <tr>\n",
1059         "      <th>Speed limit</th>\n",
1060         "      <th></th>\n",
1061         "      <th></th>\n",
1062         "      <th></th>\n",
1063         "    </tr>\n",
1064         "  </thead>\n",
1065         "  <tbody>\n",
1066         "    <tr>\n",
1067         "      <th>30</th>\n",
1068         "      <td> 2771</td>\n",
1069         "      <td> 40944</td>\n",
1070         "      <td> 161080</td>\n",
1071         "    </tr>\n",
1072         "    <tr>\n",
1073         "      <th>40</th>\n",
1074         "      <td>  431</td>\n",
1075         "      <td>  2600</td>\n",
1076         "      <td>   5610</td>\n",
1077         "    </tr>\n",
1078         "    <tr>\n",
1079         "      <th>50</th>\n",
1080         "      <td>  146</td>\n",
1081         "      <td>   424</td>\n",
1082         "      <td>    764</td>\n",
1083         "    </tr>\n",
1084         "    <tr>\n",
1085         "      <th>60</th>\n",
1086         "      <td>  554</td>\n",
1087         "      <td>  1665</td>\n",
1088         "      <td>   3940</td>\n",
1089         "    </tr>\n",
1090         "    <tr>\n",
1091         "      <th>70</th>\n",
1092         "      <td>  405</td>\n",
1093         "      <td>   429</td>\n",
1094         "      <td>    530</td>\n",
1095         "    </tr>\n",
1096         "  </tbody>\n",
1097         "</table>\n",
1098         "<p>5 rows \u00d7 3 columns</p>\n",
1099         "</div>"
1100        ],
1101        "metadata": {},
1102        "output_type": "pyout",
1103        "prompt_number": 73,
1104        "text": [
1105         "             Fatal  Serious  Slight\n",
1106         "Speed limit                        \n",
1107         "30            2771    40944  161080\n",
1108         "40             431     2600    5610\n",
1109         "50             146      424     764\n",
1110         "60             554     1665    3940\n",
1111         "70             405      429     530\n",
1112         "\n",
1113         "[5 rows x 3 columns]"
1114        ]
1115       }
1116      ],
1117      "prompt_number": 73
1118     },
1119     {
1120      "cell_type": "code",
1121      "collapsed": false,
1122      "input": [
1123       "occupant_severities = (severity_by_speed_and_class.xs('Driver or rider', level='Casualty class') + \n",
1124       "    severity_by_speed_and_class.xs('Passenger', level='Casualty class') )\n",
1125       "occupant_severities"
1126      ],
1127      "language": "python",
1128      "metadata": {},
1129      "outputs": [
1130       {
1131        "html": [
1132         "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
1133         "<table border=\"1\" class=\"dataframe\">\n",
1134         "  <thead>\n",
1135         "    <tr style=\"text-align: right;\">\n",
1136         "      <th></th>\n",
1137         "      <th>Fatal</th>\n",
1138         "      <th>Serious</th>\n",
1139         "      <th>Slight</th>\n",
1140         "    </tr>\n",
1141         "    <tr>\n",
1142         "      <th>Speed limit</th>\n",
1143         "      <th></th>\n",
1144         "      <th></th>\n",
1145         "      <th></th>\n",
1146         "    </tr>\n",
1147         "  </thead>\n",
1148         "  <tbody>\n",
1149         "    <tr>\n",
1150         "      <th>30</th>\n",
1151         "      <td> 3416</td>\n",
1152         "      <td> 72700</td>\n",
1153         "      <td> 824521</td>\n",
1154         "    </tr>\n",
1155         "    <tr>\n",
1156         "      <th>40</th>\n",
1157         "      <td> 1379</td>\n",
1158         "      <td> 14068</td>\n",
1159         "      <td> 135649</td>\n",
1160         "    </tr>\n",
1161         "    <tr>\n",
1162         "      <th>50</th>\n",
1163         "      <td>  865</td>\n",
1164         "      <td>  6743</td>\n",
1165         "      <td>  53871</td>\n",
1166         "    </tr>\n",
1167         "    <tr>\n",
1168         "      <th>60</th>\n",
1169         "      <td> 7248</td>\n",
1170         "      <td> 49532</td>\n",
1171         "      <td> 276512</td>\n",
1172         "    </tr>\n",
1173         "    <tr>\n",
1174         "      <th>70</th>\n",
1175         "      <td> 2291</td>\n",
1176         "      <td> 14162</td>\n",
1177         "      <td> 139607</td>\n",
1178         "    </tr>\n",
1179         "  </tbody>\n",
1180         "</table>\n",
1181         "<p>5 rows \u00d7 3 columns</p>\n",
1182         "</div>"
1183        ],
1184        "metadata": {},
1185        "output_type": "pyout",
1186        "prompt_number": 76,
1187        "text": [
1188         "             Fatal  Serious  Slight\n",
1189         "Speed limit                        \n",
1190         "30            3416    72700  824521\n",
1191         "40            1379    14068  135649\n",
1192         "50             865     6743   53871\n",
1193         "60            7248    49532  276512\n",
1194         "70            2291    14162  139607\n",
1195         "\n",
1196         "[5 rows x 3 columns]"
1197        ]
1198       }
1199      ],
1200      "prompt_number": 76
1201     },
1202     {
1203      "cell_type": "code",
1204      "collapsed": false,
1205      "input": [
1206       "pedestrian_severities_norm_col = pedestrian_severities / pedestrian_severities.sum()\n",
1207       "ax = pedestrian_severities_norm_col.T.plot(kind='bar')\n",
1208       "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))"
1209      ],
1210      "language": "python",
1211      "metadata": {},
1212      "outputs": [
1213       {
1214        "metadata": {},
1215        "output_type": "pyout",
1216        "prompt_number": 77,
1217        "text": [
1218         "<matplotlib.legend.Legend at 0x7ff7b48c3898>"
1219        ]
1220       },
1221       {
1222        "metadata": {},
1223        "output_type": "display_data",
1224        "png": "iVBORw0KGgoAAAANSUhEUgAAAa4AAAEaCAYAAABJrrP5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHehJREFUeJzt3X90nXWd4PH3tWltm+SalFZEfnVt6BYCNorUuqKmrcdB\nOYVTI8Myg1h3FBm36K57zirbsk32yMxwdmZlR6oDM2VaGMas2pyVOYuixgaLVhGGRlOBQrAUqEfp\n2ObXmrNNyf7x3Py4uTfNven98X1y369zntN8732e535vPs393Ofz/T7PA5IkSZIkSZIkSZIkSZIk\nSZIkzUn3Ab8BfnGadf4aeA7oBt5Wik5JkjSd9xAlo+kS14eAh1M/vxP4SSk6JUnS6Sxn+sT1N8D1\nk9rPAGcXu0OSpMr0ugLs41zgpUntl4HzCrBfSZIyFCJxASSmtEcLtF9JktJUFWAfrwDnT2qfl3os\nzYoVK0Z7e3sL8HKSVFG6gaZydyIkhTjiegi4KfXzWuAE0SzENL29vYyOjlbssn379rL3wcXYxG0x\nNqMAqwvwOT2n5HLE9TXgfcBSorGs7cD81HP3EM0o/BDwPDAEfLzw3Yy/w4cPl7sLmoaxCZexUTa5\nJK4bclhny5l2RJKkXBRqcoZmsHnz5nJ3QdMwNuEyNspm6mzAYhpN1WslSTlKJBJQ2s/q4HnEVSJd\nXV3l7oKmYWzCZWyUjYlLkhQrlgolKWCWCjN5xCVJihUTV4lYqw+XsQmXsVE2Ji5JUqw4xiVJAXOM\nK5NHXJKkWDFxlYi1+nAZG0gml5BIJGa1JJNLitYvY6NsCnFbE0kxNzBwnNneRm9gwCqWSssxLkmp\ncZTZ/n0m8G+7eBzjymSpUJIUKyauErFWHy5jEy5jo2xMXJKkWHGMS5JjXAFzjCuTR1ySpFgxcZWI\ntfpwGZtwGRtlY+KSJMWKY1ySHOMKmGNcmTziUsmEelkhSfFi4ioRa/WTLyuU/xJtWxzGJlzGRtmY\nuCRJseIYl0rGcZRwGZtwOcaVySMuSVKsmLhKxFp9uIxNuIyNsjFxSZJixTEulYzjKOEyNuFyjCuT\nR1ySpFgxcZWItfpwGZtwGRtlY+KSJMWKY1wqGcdRwmVswuUYVyaPuCRJsWLiKhFr9eEyNuEyNsrG\nxCVJipVc6qZXAXcB84C/A+6c8vxS4B+ANwFVwF8Cu7LsxzGuCuc4SriMTbgc48o00y9jHvAs8H7g\nFeBnwA3A05PWaQVeD9xGlMSeBc4GRqbsy8RV4fxwDJexCZeJK9NMpcI1wPPAYeAk0A5cO2WdXwPJ\n1M9J4F/ITFoVz1p9uIxNuIyNsqma4flzgZcmtV8G3jllnb8FfgAcBWqBPyxY7yRJmmKmxJXL8f9/\nAQ4AzcAK4HvAamBg6oqbN29m+fLlANTV1dHU1ERzczMw8c1qrrbHHgulP+VqTxhrN+fYLt7vr7m5\nOZjfTzn/f0a/8+ZJP5Nzu9z9n0vtrq4udu3aBTD+eal0M9VN1xKNYV2Vat8GvEb6BI2HgTuAH6Xa\nncDngSem7MsxrgrnOEq4jE24HOPKNNMY1xPARcByYAFwPfDQlHWeIZq8AdGkjH8NvFC4Ls4NmUcc\nCoWxCZexUTYzlQpHgC3AI0QzDHcSzSj8VOr5e4A/A/4e6CZKhP8Z+F0xOitJktcqVMlYjgqXsQmX\npcJMXjlDkhQrJq4SsVYfLmMTLmOjbExckqRYcYxLJeM4SriMTbgc48rkEZckKVZMXCVirT5cxiZc\nxkbZmLgkSbHiGJdKxnGUcBmbcDnGlckjLklSrJi4SsRafbiMTbiMjbIxcUmSYsUxLpWM4yjhMjbh\ncowrk0dckqRYMXGViLX6cBmbcBmb6VVVVfUTHSbP2SX1HjPf+2x/aZKk8hkZGamd6yXaRCJRm/Xx\nEvbBMa4K5zhKuIxNuE4zxjXnP1One++WCiVJsWLiKhFr9eEyNuEyNsrGxCVJihUTV4k0NzeXuwua\nhrEJl7HJTzK5hEQiUbQlmVySUz9uvPFGzjnnHJLJJG95y1u44447xp/r7Oxk1apVVFdXs379eo4c\nOZL3+3RyhkrGCQDhMjbhymdyxpnFMafe5BTrgwcPsmLFChYuXMizzz7L+973Pnbv3s3ll1/OihUr\nuO+++9i4cSPbtm1j37597N+/P/urOTmjvKzVh8vYhMvYxFNjYyMLFy4cb8+fP59ly5bR0dHBZZdd\nRktLCwsWLKC1tZXu7m4OHTqU1/5NXJKkgvv0pz9NdXU1jY2NbN26lbe//e0cPHiQ1atXj6+zePFi\nGhoa6OnpyWvfJq4SsVYfLmMTLmMTX1/5ylcYHBzk+9//Ptu2bePxxx9naGiIZDKZtl4ymWRwcDCv\nfZu4JElFkUgkaG5u5rrrruNrX/saNTU19PenX8Wpr6+P2tqsF8iYlomrRKzVh8vYhMvYzA0nT54c\nLxt2d3ePPz40NERvby+NjY157c/EJUkqmFdffZX29naGhoY4deoUjzzyCN/4xje49tpr2bRpEz09\nPXR0dDA8PExbWxtNTU2sXLkyr9dwOrxKxinX4TI24YrbdPhjx47xkY98hO7ubkZHR1m5ciXbtm3j\nmmuuAaLzuLZs2cKLL77I2rVr2bVrFxdccEH2V5vmvZu4VDJ+OIbL2IQrn8SVTC5hYOB40fpSW1tP\nf//virb/qTyPq8ys1YfL2ITL2OSnv/93jI6OFm0pZdI6nTmXuM7kkie5Xs5EklQ+c65UaMkjXMYm\nXMYmXN6Py1KhJCnmTFwlYq0+XMYmXMZG2Zi4JEmxkssY11XAXcA84O+AO7Os0wx8CZgPHEu1p3KM\nq8IZm3AZm3A5xpX/eVzzgGeB9wOvAD8DbgCenrROHfAj4A+Al4GlRMlrKhNXhTM24TI24TJx5T85\nYw3wPHAYOAm0A9dOWeePgD1ESQuyJ62KZ60+XMYmXMZG2cyUuM4FXprUfjn12GQXAUuAvcATwEcL\n1jtJUs6SdclZn8ea07mudcmZOzHJc889x8KFC/noRyfSQmdnJ6tWraK6upr169dz5MiRvN/nTKXC\nFqIxrk+m2jcC7wRunbTO3cDbgQ3AYmA/cDXw3JR9WSqscMYmXMYmXHlfq7C1iJ1pJa9Yf+ADH2B4\neJjly5dz//33c+zYMRoaGti5cycbN25k27Zt7Nu3j/3792fdfrr3XjXD674CnD+pfT4TJcExLxGV\nB3+fWn4IrCYzcbF582aWL18OQF1dHU1NTeM3ihsrCZxpe8JYuznPNgXtj+309oSxdnOO7Wgf5e7/\nXG1Husj/7yWM/s+ldldXF7t27QIY/7yMo/b2durr67nkkkt4/vnnAejo6ODSSy+lpaUFgNbWVpYu\nXcqhQ4fyukL8TEdcVUSTMzYAR4HHyZycsYroqOsPgNcDPwWuB345ZV8VfcQ1+UO3UhmbcBmbcMXx\niKu/v58rrriCvXv3cu+999Lb28sDDzzAZz/7WUZGRtixY8f4um9961tpbW3lwx/+cMZ+ZnvENQJs\nAR4hmmG4kyhpfSr1/D3AM8B3gJ8DrwF/S2bSkiRViNtvv51PfOITvPnNbx4fH4PoxpHLli1LWzeZ\nTDI4OJjX/mdKXADfTi2T3TOl/ZepRdOo9G+NITM24TI28XPgwAE6Ozt56qmnAMavLA9QU1NDf39/\n2vp9fX3U1tbm9Rq5JC5JknLy6KOPcvjw4fGbQw4ODnLq1Cl++ctfcsstt7B79+7xdYeGhujt7aWx\nsTGv1/CSTyWSOTlBoTA24TI28XPzzTfzwgsv0N3dzYEDB7jlllu4+uqr+e53v8umTZvo6emho6OD\n4eFh2traaGpqymtiBnjEJUkqoEWLFrFo0aLxdk1NDYsWLeKss84CYM+ePWzZsoUbb7yRtWvX0t7e\nnvdreD+u9K09H6WIjE24jE248plVmKxLMtA3ULS+1L6hlv4T/TOvWCCzvVZhIZm4KpyxCZexCZfX\nKvRGkmVjrT5cxiZcxkbZmLgkSbFiqTB9a0seRWRswmVswmWp0FKhJCnmTFwlYq0+XMYmXMZG2Zi4\nJEmx4hhX+tbW6ovI2ITL2ITLMS7HuCRJMWfiKhFr9eEyNuEyNvlZkkyO30akGMuSZDKnfjQ3N7No\n0SJqa2upra3l4osvHn+us7OTVatWUV1dzfr16zly5Eje79PEJUlzxPGBAUahaMvxgdwuJ5VIJNix\nYwcDAwMMDAzw9NPRvYePHTtGS0sLd9xxB8ePH+cd73gH119/fd7v08RVIt5XKFzGJlzGJr6yjb91\ndHRw6aWX0tLSwoIFC2htbaW7u5tDhw7ltW8TlySp4G677TaWLVvGlVdeyaOPPgrAwYMHWb169fg6\nixcvpqGhgZ6enrz2beIqEWv14TI24TI28XTnnXfyq1/9iqNHj3LzzTezceNGXnjhBYaGhkhOGSdL\nJpMMDg7mtX8TlySpoNasWUN1dTXz58/npptu4t3vfjcPP/wwNTU19Pen3xalr6+P2travPZv4ioR\na/XhMjbhMjZzS2NjI93d3ePtoaEhent7aWxszGs/Ji5JUsH09fXxyCOPMDw8zMjICA8++CD79u3j\nqquuYtOmTfT09NDR0cHw8DBtbW00NTWxcuXKvF7DxFUi1urDZWzCZWzi5+TJk9x+++288Y1vZNmy\nZezYsYNvfetbNDQ0sHTpUvbs2cPWrVtZsmQJTzzxBO3t7Xm/RlUR+i1JKoP62loSOZ5rNdv9z2Tp\n0qU8/vjj0z6/YcOG8fO6ZstrFaZv7TXXisjYhMvYhMtrFXqtQklSzJm4SsRafbiMTbiMjbIxcUmS\nYsUxrvStrdUXkbEJl7EJl2NcjnFJkmLOxFUi1urDZWzCZWyUjYlLkhQrjnGlb22tvoiMTbiMTbgc\n43KMS5LmrGR9PYlEomhLsr4+5760t7dz8cUXU1NTQ0NDA4899hgAnZ2drFq1iurqatavX8+RI0fy\nfp8ecaVvXbRvjl1dXRV/pWtjEy5jE658jrgSiQTs3Vu8zqxbl1Osv/e97/HJT36Sr3/966xZs4Zf\n//rXjI6OsmDBAlasWMF9993Hxo0b2bZtG/v27WP//v1Z9zPde/dahZKkgtq+fTvbt29nzZo1AJxz\nzjkA3HvvvVx22WW0tLQA0NraytKlSzl06FBeV4i3VFgilf6tMWTGJlzGJn5OnTrFk08+yW9/+1su\nuugizj//fG699VaGh4c5ePAgq1evHl938eLFNDQ00NPTk9dr5JK4rgKeAZ4DPn+a9a4ARoAP59UD\nSdKc8Zvf/IaTJ0+yZ88eHnvsMQ4cOMBTTz3FF7/4RYaGhkgmk2nrJ5NJBgcH83qNmRLXPOBuouR1\nCXADcPE0690JfIfSjpvFhuejhMvYhMvYxM+iRYsAuPXWWzn77LM566yz+NznPsfDDz9MTU0N/f39\naev39fVRm8PtUiabKXGtAZ4HDgMngXbg2izr3Qp8E3g1r1eXJM0p9fX1nHfeeVmfa2xspLu7e7w9\nNDREb28vjY2Neb3GTInrXOClSe2XU49NXeda4Kup9tw+sWCWrNWHy9iEy9jE08c//nG+/OUv8+qr\nr3L8+HG+9KUvsXHjRjZt2kRPTw8dHR0MDw/T1tZGU1NTXhMzYObElUsSugv4QmrdBJYKJami3X77\n7VxxxRWsXLmSSy65hMsvv5ytW7eydOlS9uzZw9atW1myZAlPPPEE7e3tee9/punwrwDnT2qfT3TU\nNdnlRCVEgKXAB4nKig9N3dnmzZtZvnw5AHV1dTQ1NY1/oxqrZZ9pe8JYuznPNgXtz1j7rrvuKsr7\njVt7wli7Ocd2+jk9hezf5L6V+/dTrnaki/z/Xorbv7HHyv37KWW7q6uLXbt2AYx/Xuaqtq6OgXXr\n8tom3/3noqqqih07drBjx46M5zZs2MDTTz99Rv2Y6eioCngW2AAcBR4nmqAx3av+PfBPQEeW5zwB\nucLLHsYmXMYmXF7yKfO951LW+yBROXAesBP4c+BTqefumbKuiUvTMjbhMjbhMnHNLnEViomrwhmb\ncBmbcJm4vMhu2WSO8SgUxiZcxkbZmLgkSbFiqTB9a0seRWRswmVswmWp0FKhJCnmTFwlYq0+XMYm\nXMZG2Zi4JEmxYuIqkUo/iTJkxiZcxiY/9cl6EolE0Zb6ZP2MfaipqaG2tnZ8qaqq4jOf+cz4852d\nnaxatYrq6mrWr1/PkSNH8n6fTs5I39pB5iIyNuEyNuHKZ3JGIpFgL3uL1pd1rMsr1kNDQ7zpTW/i\n29/+NldeeSXHjh2joaGBnTt3snHjRrZt28a+ffvYv39/1u2dnFFm1urDZWzCZWzi7Zvf/CZnn302\nV155JQAdHR1ceumltLS0sGDBAlpbW+nu7ubQoUN57dfEJUkqit27d3PTTTeNtw8ePMjq1avH24sX\nL6ahoYGenp689mviKhFr9eEyNuEyNvH14osv8sMf/pCPfexj448NDQ2RTCbT1ksmkwwODua1bxOX\nJKngHnjgAd7znvdw4YUXjj9WU1NDf39/2np9fX3U1tbmtW8TV4lYqw+XsQmXsYmv+++/P+1oC6Cx\nsZHu7u7x9tDQEL29vTQ2Nua1bxOXJKmgfvzjH3P06FGuu+66tMc3bdpET08PHR0dDA8P09bWRlNT\nEytXrsxr/06HT9/aab1FZGzCZWzCFcfp8Lfccgu///3v2b17d8ZznZ2dbNmyhRdffJG1a9eya9cu\nLrjggqz78X5cuW3tH2ARGZtwGZtw5ZO46pP1nBg4UbS+1NXWcbz/eNH2P5XncZWZtfpwGZtwGZv8\nHO8/zujoaNGWUiat0zFxSZJixVJh+taWPIrI2ITL2ITL+3FZKpQkxZyJq0Ss1YfL2ITL2CgbE5ck\nKVYc40rf2lp9ERmbcBmbcDnGlfneq0rfFUnSmaqqqhpIJBL5XeQvZqqqqgZGRkYyHrdUWCLW6sNl\nbMJlbKY3MjKSJDoambNL6j1mMHFJkmLFMa70ra3VF5GxCZexCddpxrgqlkdckqRYMXGViLX6cBmb\ncBkbZWPikiTFimNc6Vtbqy8iYxMuYxMux7gyecQlSYoVE1eJWKsPl7EJl7FRNiYuSVKsOMaVvrW1\n+iIyNuEyNuFyjCuTR1ySpFjJNXFdBTwDPAd8Psvzfwx0Az8HfgS8tSC9m0Os1YfL2ITL2CibXK4O\nPw+4G3g/8ArwM+Ah4OlJ67wAvBfoI0py9wJrC9pTSZLIrW76LmA7UUIC+ELq37+YZv164BfAeVMe\nd4yrwhmbcBmbcDnGlSmXUuG5wEuT2i+nHpvOnwAPn0mnJEmaTi6lwny+Sq0D/h3w7mxPbt68meXL\nlwNQV1dHU1MTzc3NwEQt+0zbE8bazXm2KWh/xtp33XVXUd5v3NoTxtrNObajfRSjf5P7Vu7fT7na\nkS7y/3spbv/GHiv376eU7a6uLnbt2gUw/nmpdLkcfq4FWpkoFd4GvAbcOWW9twIdqfWez7Kfii4V\nTv7QrVTGJlzGJlyWCjPl8suoAp4FNgBHgceBG0ifnHEB8APgRuAn0+ynohOXjE3IjE24TFyZcikV\njgBbgEeIZhjuJEpan0o9fw/wX4kmZXw19dhJYE1BeypJEl45Y+rWljyKyNiEy9iEyyOuTF45Q5IU\nKx5xpW9trb6IjE24jE24POLK5BGXJClWTFwlknkek0JhbMJlbJSNiUuSFCuOcaVvba2+iIxNuIxN\nuBzjyuQRlyQpVkxcJWKtPlzGJlzGRtmYuCRJseIYV/rW1uqLyNiEy9iEyzGuTB5xSZJixcRVIjPV\n6pP19SQSiVkvyfr60ryROchxlHAZG2WTy9XhVQIDJ07A3r2z337dugL2RpLC5RhX+tZlq9UnEokz\nSlysWxf8OENcY1MJjE24HOPKZKlQkhQrJq4SsVYfLmMTLmOjbExckqRYcYwrfWvHuIoorrGpBMYm\nXI5xZXJW4WSvG/9PMiu1b6il/0R/ATskSZrKxDXZa0Dr7DcfaB2Y9rmuri6am5tnv/MZzGPerJNu\nXW0dx/uPF7hH8VHs2Gj2jI2yMXHNEac4xV5mV2pcN+A5YJLiw8kZJeK3xnAZm3AZG2Vj4pIkxYqJ\nq0Q8HyVcxiZcxkbZmLgkSbFi4ioRa/XhMjbhMjbKxsQlSYoVE1eJWKs/Q6mTw2d9v7K65LS7Njbh\nMjbKxvO4FA9FPDlcUrx4xFUi1urDZWzCZWyUjYlLkhQrJq4SsVYfLmMTLmOjbBzjKqAqzuzq8gpT\nsr6egRMnZr19bV0d/ccr9yLGUqGZuApohDO5o5HKZaZxlIETJ87oXmkD6+b4RYyLeDsgx7iUjYlL\n0plxxqdKLJcxrquAZ4DngM9Ps85fp57vBt5WmK7NLV3l7oCm5ThKuGaKTbK+/szO76uvL80bUUHN\ndMQ1D7gbeD/wCvAz4CHg6UnrfAhoAC4C3gl8FVhb8J7G3AGgudydUFYHDhywJBWomWJjGbcyzXTE\ntQZ4HjgMnATagWunrHMNsDv180+BOuDswnVxbpj90L6K7cQZTLxQcRkbZTNT4joXeGlS++XUYzOt\nc96Zd00qnLEZn9mWtra205aTztQ85s26lFWftJQlTTVTqTDXSXJT/7pnO7luzjpc7g5UuNPN+NwM\n7DrNtmeauk5xir3Mrpy1bmDul7JmOo2kra2taK899qViNupq6zje72kOIVoLfGdS+zYyJ2j8DfBv\nJ7WfIXup8ADRZ4eLi4uLS+7LAZSXKqAXWA4sIPoFXjxlnQ8BD6d+Xgv8pFSdkyQpmw8CzxJN0rgt\n9dinUsuYu1PPdwNvL2nvJEmSJEmSJClIg8DANEv2C7Kp1BqAhamf1wGfITr/UOV3XY6PSVJF6Saa\neNQAHAL+OxMTjFReT+X4mCqYF9ktvjcy8e0e4Ei5OqJxrxGd2vVh4MupxQ/H8vog0Qzlc4mufTp2\nclUt0VV7pHEmruK5Bvgr4M3Ab4ELia7x2FjOTgmA/wf8EXATsDH12PzydUfAUeBJokvKPclE4uoH\n/mO5OiVVmp8DS5n4Jr8OuK983dEkjURHWTek2m8BvlC+7mgSv0BIZfRk6t9uoqvsQ5TMJE3vSuB7\nRLdJ+lVqeaGsPVJwLBUWz3Gi+vw+4EGicuFgWXukMb/K8tgo0ZGXymsn8B+AfwZOlbkvCpR3jC+e\namCY6Ar8fwwkiRLYv5SzUwKiEu6YhcBHgLOA28vTHU3yU6L7+kkqgztzfExh+Odyd6DCXZ5a/oLo\n9IR3EV0+bmyRxnnEVTxPAW+b8tgvgMvK0Belu5yoNAjREfE7gD8FVpetR+piIibZzP37uyhnjnEV\n3p8CnwZWECWqMbXAj8rSI031V0x8SI4Q3S7tD8vWGwE0l7sDig+PuArvDUA9Ucnj80z8jgdwfEua\nyX8i88irj2iWrvelEmDiKgWvnBGeOmA78N5Uuwv4b0QfkCqvfyQq3f4T0efT1USViwuBb+I4sVRU\n1xCdizJENP36NeBgWXukMR1AG9H09xVAa+oxld8+oGZSuwb4IbCY6MozkorIK2eEqzvHx1R6zxDd\nbX3M64luZAteT1IpTs4onpPAMaJZa/OAvcD/LGuPNOb3wHuIvt1DdLWG/1u+7miSB4nO5frfRKXC\njUTlw2rgl2XslwLiGFfxfB/YBPw50ZHXb4lq9/+mnJ0SAE3A/UQTaSC6ysnH8KgrFFcA7yaapPEj\n4InydkehMXEV3gVEEzC8ckb4kql/vcFn+SWJ4rAk1R77bBqbYfi7kvdIwTJxFd7kE4/3AC1l7IvS\nfRR4gMwp14lU+3+Uo1MC4P8QzSA8TPYTkf9VSXujoDnGVVxetDUsi1P/1px2LZXD1al/l5ezE4oH\nE5cqyT1EE2UG8OgqNDNdj9BrSWqcpcLCO8XEDLVFRDPYxowyMa6i8vkZ0QQAhaMLr1WoHJm4VIm+\nRHSn3f9FdIL4GL/Vl88a4CXg16n2x4jGh18kOkHcSU0aZ+JSJeoi+7d7v9WXz1PABqLZg+8l+lKx\nhWii0yqie6ZJkhSMyefQ7SA6ysr2nMTryt0BqQzeRHSL+O+k2pcAf1K+7oho0sz81M/vJ7rSzBgn\nkUmqeN8Brie6niREH5g95euOgK3Aj4GHiMqGY1+qL8L72EnS+CWEJl+01Xs9ld+7iC6TVj3psZXM\nPFVeFcZDcFWiQeCsSe21eC+uEOzP8tihkvdCkgJ0OVFZqi/17yFgdVl7JElSFmuAc1I/zwf+PfAD\nollsS6bbSJKkcnmKiQT1XqKTXVuALxLdFl6SpKB4rpA0B3gelyqJ5wpJc4B/rKokXwMeBY4RXQh5\nX+rxi4AT5eqUJEmn47lCkiRJkiRJkiRJkiRJkiRJ0pzw/wFhrM9sS3/DJAAAAABJRU5ErkJggg==\n",
1225        "text": [
1226         "<matplotlib.figure.Figure at 0x7ff7b3ab3898>"
1227        ]
1228       }
1229      ],
1230      "prompt_number": 77
1231     },
1232     {
1233      "cell_type": "code",
1234      "collapsed": false,
1235      "input": [
1236       "pedestrian_severities_norm_row = pedestrian_severities.div(pedestrian_severities.sum(axis=1), axis=0)\n",
1237       "ax = pedestrian_severities_norm_row.plot(kind='bar', legend=False)\n",
1238       "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))"
1239      ],
1240      "language": "python",
1241      "metadata": {},
1242      "outputs": [
1243       {
1244        "metadata": {},
1245        "output_type": "pyout",
1246        "prompt_number": 78,
1247        "text": [
1248         "<matplotlib.legend.Legend at 0x7ff7b4b7b080>"
1249        ]
1250       },
1251       {
1252        "metadata": {},
1253        "output_type": "display_data",
1254        "png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAERCAYAAAD2e5d3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERERERERERERERERERERERHJl/8P1tw653WKbT0AAAAASUVORK5CYII=\n",
1255        "text": [
1256         "<matplotlib.figure.Figure at 0x7ff7b3eeca20>"
1257        ]
1258       }
1259      ],
1260      "prompt_number": 78
1261     },
1262     {
1263      "cell_type": "code",
1264      "collapsed": false,
1265      "input": [
1266       "pedestrian_severities_norm_row = pedestrian_severities.div(pedestrian_severities.sum(axis=1), axis=0)\n",
1267       "pedestrian_severities_norm_row_col = pedestrian_severities_norm_row / pedestrian_severities_norm_row.sum()\n",
1268       "ax = pedestrian_severities_norm_row_col.T.plot(kind='bar')\n",
1269       "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))"
1270      ],
1271      "language": "python",
1272      "metadata": {},
1273      "outputs": [
1274       {
1275        "metadata": {},
1276        "output_type": "pyout",
1277        "prompt_number": 79,
1278        "text": [
1279         "<matplotlib.legend.Legend at 0x7ff7b44d0668>"
1280        ]
1281       },
1282       {
1283        "metadata": {},
1284        "output_type": "display_data",
1285        "png": "iVBORw0KGgoAAAANSUhEUgAAAa4AAAEaCAYAAABJrrP5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
1286        "text": [
1287         "<matplotlib.figure.Figure at 0x7ff7b44d0a90>"
1288        ]
1289       }
1290      ],
1291      "prompt_number": 79
1292     },
1293     {
1294      "cell_type": "code",
1295      "collapsed": false,
1296      "input": [
1297       "pedestrian_severities_norm_row"
1298      ],
1299      "language": "python",
1300      "metadata": {},
1301      "outputs": [
1302       {
1303        "html": [
1304         "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
1305         "<table border=\"1\" class=\"dataframe\">\n",
1306         "  <thead>\n",
1307         "    <tr style=\"text-align: right;\">\n",
1308         "      <th></th>\n",
1309         "      <th>Fatal</th>\n",
1310         "      <th>Serious</th>\n",
1311         "      <th>Slight</th>\n",
1312         "    </tr>\n",
1313         "    <tr>\n",
1314         "      <th>Speed limit</th>\n",
1315         "      <th></th>\n",
1316         "      <th></th>\n",
1317         "      <th></th>\n",
1318         "    </tr>\n",
1319         "  </thead>\n",
1320         "  <tbody>\n",
1321         "    <tr>\n",
1322         "      <th>30</th>\n",
1323         "      <td> 0.013531</td>\n",
1324         "      <td> 0.199927</td>\n",
1325         "      <td> 0.786543</td>\n",
1326         "    </tr>\n",
1327         "    <tr>\n",
1328         "      <th>40</th>\n",
1329         "      <td> 0.049878</td>\n",
1330         "      <td> 0.300891</td>\n",
1331         "      <td> 0.649230</td>\n",
1332         "    </tr>\n",
1333         "    <tr>\n",
1334         "      <th>50</th>\n",
1335         "      <td> 0.109445</td>\n",
1336         "      <td> 0.317841</td>\n",
1337         "      <td> 0.572714</td>\n",
1338         "    </tr>\n",
1339         "    <tr>\n",
1340         "      <th>60</th>\n",
1341         "      <td> 0.089950</td>\n",
1342         "      <td> 0.270336</td>\n",
1343         "      <td> 0.639714</td>\n",
1344         "    </tr>\n",
1345         "    <tr>\n",
1346         "      <th>70</th>\n",
1347         "      <td> 0.296921</td>\n",
1348         "      <td> 0.314516</td>\n",
1349         "      <td> 0.388563</td>\n",
1350         "    </tr>\n",
1351         "  </tbody>\n",
1352         "</table>\n",
1353         "<p>5 rows \u00d7 3 columns</p>\n",
1354         "</div>"
1355        ],
1356        "metadata": {},
1357        "output_type": "pyout",
1358        "prompt_number": 80,
1359        "text": [
1360         "                Fatal   Serious    Slight\n",
1361         "Speed limit                              \n",
1362         "30           0.013531  0.199927  0.786543\n",
1363         "40           0.049878  0.300891  0.649230\n",
1364         "50           0.109445  0.317841  0.572714\n",
1365         "60           0.089950  0.270336  0.639714\n",
1366         "70           0.296921  0.314516  0.388563\n",
1367         "\n",
1368         "[5 rows x 3 columns]"
1369        ]
1370       }
1371      ],
1372      "prompt_number": 80
1373     },
1374     {
1375      "cell_type": "code",
1376      "collapsed": false,
1377      "input": [
1378       "occupant_severities_norm_col = occupant_severities / occupant_severities.sum()\n",
1379       "ax = occupant_severities_norm_col.T.plot(kind='bar')\n",
1380       "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))"
1381      ],
1382      "language": "python",
1383      "metadata": {},
1384      "outputs": [
1385       {
1386        "metadata": {},
1387        "output_type": "pyout",
1388        "prompt_number": 81,
1389        "text": [
1390         "<matplotlib.legend.Legend at 0x7ff7b4a127b8>"
1391        ]
1392       },
1393       {
1394        "metadata": {},
1395        "output_type": "display_data",
1396        "png": "iVBORw0KGgoAAAANSUhEUgAAAa4AAAEaCAYAAABJrrP5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2AAAAABJRU5ErkJggg==\n",
1397        "text": [
1398         "<matplotlib.figure.Figure at 0x7ff7b44bdeb8>"
1399        ]
1400       }
1401      ],
1402      "prompt_number": 81
1403     },
1404     {
1405      "cell_type": "code",
1406      "collapsed": false,
1407      "input": [
1408       "occupant_severities_norm_row = occupant_severities.div(occupant_severities.sum(axis=1), axis=0)\n",
1409       "occupant_severities_norm_row_col = occupant_severities_norm_row / occupant_severities_norm_row.sum()\n",
1410       "ax = occupant_severities_norm_row_col.T.plot(kind='bar')\n",
1411       "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))"
1412      ],
1413      "language": "python",
1414      "metadata": {},
1415      "outputs": [
1416       {
1417        "metadata": {},
1418        "output_type": "pyout",
1419        "prompt_number": 82,
1420        "text": [
1421         "<matplotlib.legend.Legend at 0x7ff7b3e69908>"
1422        ]
1423       },
1424       {
1425        "metadata": {},
1426        "output_type": "display_data",
1427        "png": "iVBORw0KGgoAAAANSUhEUgAAAbUAAAEaCAYAAACW4MnmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4F8gAAAAAElFTkSuQmCC\n",
1428        "text": [
1429         "<matplotlib.figure.Figure at 0x7ff7b3c56be0>"
1430        ]
1431       }
1432      ],
1433      "prompt_number": 82
1434     },
1435     {
1436      "cell_type": "code",
1437      "collapsed": false,
1438      "input": [
1439       "occupant_severities_norm_row = occupant_severities.div(occupant_severities.sum(axis=1), axis=0)\n",
1440       "ax = occupant_severities_norm_row.plot(kind='bar', legend=False)\n",
1441       "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))"
1442      ],
1443      "language": "python",
1444      "metadata": {},
1445      "outputs": [
1446       {
1447        "metadata": {},
1448        "output_type": "pyout",
1449        "prompt_number": 83,
1450        "text": [
1451         "<matplotlib.legend.Legend at 0x7ff7b42b9390>"
1452        ]
1453       },
1454       {
1455        "metadata": {},
1456        "output_type": "display_data",
1457        "png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAERCAYAAAD2e5d3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAH6ZJREFUeJzt3X10VfW95/H3IdEqmJAABaYK5tpbUTtS7LQojrSxPozt\nwmK9q1qqHeidYVhd1cK0d66iXQO2vdJOfZpbZ/nQIlQ7o1bHLvp0oe0qUei1KDMFpQoqSo1wKVIi\nRMpYImf+2DvxEJKcXzjJ2Sfn936tdRZ777P3zu+TQ/LN3t+z9wFJkiRJkiRJkiRJkiRJkiRJkiSp\nR/cBfwSe7WOdfwReBDYCZ5VjUJIkVZrpJEWwt4L5CeDn6fTZwG/LMShJkipRE70XzLuBKwvmNwPj\nBntAkiSV07AB2MeJQGvB/GvASQOwX0mSKsZAFEyAXLf5/ADtV5KkilA7APvYDkwomD8pXXaY9773\nvfmtW7cOwJeTpKhsBKZkPQgNzBHmj4F/n06fA7xB8q7aw2zdupV8Pl+xj0WLFmU+BvOb3fzm7/4A\nPjAAv6c1AEKOMB8EPgqMIelVLgKOSZ+7h+Qdsp8AXgL2A58f+GEOvm3btmU9hEzFnD/m7GD+2PMr\nXEjBnBWwzjWlDkSSpEo2UG/6GfLmzJmT9RAyFXP+mLOD+WPPr3Dd3906mPLp+XhJUqBcLgfl/V2t\nXniEmWppacl6CJmKOX/M2cH8sedXOAumJEkBPCUrSRXMU7KVwyNMSZICWDBTsfcxYs4fc3Ywf+z5\nFc6CKUlSAHuYklTB7GFWDo8wJUkKYMFMxd7HiDl/zNnB/LHnVzgLpiRJAexhSlIFs4dZOTzClCQp\ngAUzFXsfI+b8MWcH88eeX+EsmIFG1deTy+WO+jGqvj7rCJKkEtjDDJTL5Shl9DlgKOeXlA17mJXD\nI0xJkgJYMFOx9zFizh9zdjB/7PkVzoKpIKX0cO3fSqoG9jADxd7DLCX/UM+uuI2qr6etvf2ot2+s\nq2PPvn1Hvb09zMphwQxkwbRgKk5Z/+xbMCuHp2RTsfcxYs4fc3Ywf+z5Fc6CKUlSAE/JBsr6tEzW\nYj4lm3UPS9nK+mffU7KVw4IZKOsfmqzFXDBjf+1jl/Xrb8GsHJ6STcXex4g5f8zZwfyx51c4C6Yk\nSQE8JRso69MyWfOUbAnbM7Tzxy7r199TspXDI0xJkgJYMFOx9zFizh9zdjB/7PkVzoIpSVIAe5iB\nsu5jZM0eZgnbM7Tzxy7r198eZuXwCFOSpAAWzFTsfYyY88ecHYrnL+Wj3YbCx7vF/vorXG3WA5BU\n2dra20s7JVnCbQWlShJyXvwS4A6gBvge8K1uz48BfgCMJynAtwDLe9iPPcxI88ecHcxvfnuY1aLY\ni1ADbAEuBLYDTwOzgOcL1lkMvAtYSFI8twDjgI5u+7JgRpo/5uxgfvNbMKtFsR7mVOAlYBtwEHgI\nmNltnX8BOpsU9cCfOLJYVrzY+xgx5485O5g/9vwKV6yHeSLQWjD/GnB2t3W+C/wa2AHUAVcM2Ogk\nSaoQxQ7z/4akhzk3nb+apGBeW7DOV0lOxS4A3gv8EvgA0L3T7ynZSPPHnB3Mb35PyVaLYkeY24EJ\nBfMTSI4yC50L/EM6vRV4BZgErO++szlz5tDU1ARAQ0MDU6ZMobm5GXjntEilzgO0AM0F0/RjvnOf\nlZKn3PmzHn/J88XyFZvPevzmL22+WL5i8/34ei0tLSxfvhyg6/elhoZakiLYBBwLbABO77bObcCi\ndHocSUEd1cO+8pVs9erVfT4P5PMlPGLOH3N285u/1PxQ0gGuBlCxI8wO4BpgFck7ZpeSvEN2Xvr8\nPcDNwDJgI8mbiP4e2DMYg5UkKSveSzZQ1n2MrNnDLGF7zG9+e5jVwFvjSZIUwIKZiv1arJjzx5wd\nzB97foWzYEqSFMAeZqCs+xhZs4dZwvaY3/z2MKuBR5iSJAWwYKZi72PEnD/m7GD+2PMrnAVTkqQA\n9jADZd3HyJo9zBK2x/zmt4dZDTzClCQpgAUzFXsfI+b8MWcH88eeX+EsmJIkBbCHGSjrPkbW7GGW\nsD3mN789zGrgEaYkSQEsmKnY+xgx5485O5g/9vwKZ8GUJCmAPcxAWfcxsmYPs4TtMb/57WFWA48w\nJUkKYMFMxd7HiDl/zNnB/LHnVzgLpiRJAexhBsq6j5E1e5glbI/5zW8Psxp4hClJUgALZir2PkbM\n+WPODuaPPb/CWTAlSQpgDzNQ1n2MrNnDLGF7zG9+e5jVwCNMSZICWDBTsfcxYs4fc3Ywf+z5Fc6C\nKUlSAHuYgbLuY2TNHmYJ22N+89vDrAYeYUqSFMCCmYq9jxFz/pizg/ljz69wFkxJkgLYwwyUdR8j\na/YwS9ge85vfHmY1qM16AJKk/qutrd3X0dFRl/U4qk1tbW17R0dHfU/PeUo2FXsfI+b8MWcH8w/V\n/B0dHXX5fB4fA/vo648QC6YkSQFCzotfAtwB1ADfA77VwzrNwO3AMcDudL47e5iR5o85O5jf/IPW\nwxzSv1MrVV8942IFswbYAlwIbAeeBmYBzxes0wD8Bvh3wGvAGJKi2d2QfnGz/qHJmgWzhO0xv/kt\nmENFXwWz2CnZqcBLwDbgIPAQMLPbOp8F/jdJsYSei2XFG6p9jIESc/6Ys4P5Y88/1CxevJjPfe5z\nmXztYgXzRKC1YP61dFmh9wGjgNXAeiCbJJKkzDU1NTF8+HDq6uqoq6ujvr6enTt39rp+c3MzS5cu\nDd5/egSYiWKXlYQc7x8DfBC4ABgOPAn8FnixtKGVV3Nzc9ZDyFTM+WPODuavpvz19aNob28btP3X\n1TWyb9+ePtfJ5XL89Kc/5WMf+1jQPvtbALM8DV3sCHM7MKFgfgLvnHrt1Ar8AjgA/Al4AvhATzub\nM2cOixcvZvHixdxxxx2HnQppaWmp6HmAlm7T/Znv3Gel5Cl3/qzHX/J8sXzF5rMev/mHTP6Wlhbm\nzJnT9fuyP5JimR+0x9EU4zfeeIMZM2YwduxYRo0axaWXXsr27dsBuPHGG1mzZg3XXHMNdXV1fOlL\nXwJg/vz5TJw4kZEjR/KhD32ItWvX9vvrZqEW2Ao0AccCG4DTu61zGvArkjcIDQeeBc7oYV/5SrZ6\n9eo+nwfy+RIeMeePObv5zV9qfno/09fLuiUNt8ijeJampqb8r371q6753bt35x977LH8gQMH8u3t\n7flPf/rT+csuu6zr+ebm5vzSpUsP28cPfvCD/J49e/Jvv/12/tZbb82PHz8+/9Zbb+Xz+Xx+0aJF\n+auvvvpov51F9fH9LnqE2QFcA6wCngMeJnmH7Lz0AbAZWAk8A6wDvpuuK0mKTD6f57LLLqOxsZHG\nxkbmzp3Lpz71KY477jhOOOEEbrjhBh5//PEjtil01VVX0djYyLBhw/jyl7/MW2+9xZYtW8oZo0ch\nt8b7p/RR6J5u87ekjyGrmvoYRyPm/DFnB/PHnn+g5XI5VqxY0dXDPHDgAPPmzWPVqlW0tSWndN98\n803y+XxX/7J7H/OWW27hvvvuY8eOHeRyOfbt28fu3dlfgOGdfiRJg+aWW27hhRde4KmnnmLv3r08\n/vjjXbehgyOL5Zo1a/j2t7/NI488whtvvEFbWxsjR46siGt5LZipwgZ8jGLOH3N2MH/s+Qfbm2++\nyfHHH8/IkSPZs2cPN91002HPjxs3jq1bt3bNt7e3U1tby5gxY/jLX/7C1772Nfbt21fuYffIgilJ\nGjQLFizgwIEDjBkzhnPPPZePf/zjhx1Vzp8/n0cffZRRo0axYMECLrnkEi655BJOPfVUmpqaOP74\n45k4cWLX+rlcLrNrMf08zEBZ3x4ra94ar4TtMb/5j34P/bk1XiVchznU9XVrPD8PU5KqRLUXs6x5\nSjYVex8j5vwxZwfzx55f4SyYkiQFsIcZKOs+RtbsYZawPeY3f3l6mCpdKR/vJUmSsGB2ib2PEXP+\nmLOD+WPPr3AWTEmSAtjDDJR1HyNr9jBL2B7zm98e5lBhD1OSNCTV1dWxbdu2rIcBWDC7xN7HiDl/\nzNnB/LHnHwxr167l3HPPpaGhgdGjR3Peeeexfv36o9pXe3s7TU1NAzvAo+SdfiSpStQ31NO+t33Q\n9l83so59b/R9I/R9+/YxY8YM7rnnHq644greeust1qxZw7ve9a5+fa2Ojg5qayurRNnDDJR1HyNr\n9jBL2B7zm788PcxcLgeLj/pLFbe4eJb169dz0UUXdX32ZXf33Xcft9xyCzt37mTq1Knce++9XTdX\nHzZsGHfeeSe33347hw4dYuvWrQwbNoyXXnqJU045hb1793LttdeycuVKhg8fzty5c7nhhhvI5XIs\nXryYrVu38sADDwCwbds2TjnlFDo6Ohg2bBjLly/n61//Oq+//jpjxozhG9/4Bp/97GePGJ89TElS\nWUyaNImamhrmzJnDypUrDyucK1asYMmSJfzoRz9i9+7dTJ8+nVmzZh22/YoVK3j66ad57rnnjtj3\ntddeS3t7O6+88gqPP/44999/P8uWLQOO/FzNQvv372f+/PmsXLmSffv28eSTTzJlypR+Z7NgpmLv\nY8ScP+bsYP7Y8w+0uro61q5dSy6XY+7cuYwdO5aZM2eya9cu7r77bhYuXMikSZMYNmwYCxcuZMOG\nDbS2tnZtv3DhQhoaGo44hfv222/z8MMPs2TJEkaMGMHJJ5/MV77yla4jymJHvsOGDePZZ5/lwIED\njBs3jjPOOKPf2SyYkqQBddppp7Fs2TJaW1vZtGkTO3bsYMGCBbz66qvMnz+fxsZGGhsbGT16NADb\nt2/v2nbChAk97nP37t0cPHiQk08+uWvZxIkTD9u2NyNGjODhhx/m7rvv5j3veQ8zZsxgy5Yt/c5l\nwUw1NzdnPYRMxZw/5uxg/tjzD7ZJkyYxe/ZsNm3axMSJE7n33ntpa2vreuzfv59zzjmna/3eTq2O\nGTOGY4455rBLTF599VVOOukkICmKf/7zn7ue27lz52HbX3zxxfziF79g586dnHbaacydO7ffWSyY\nkqQBs2XLFm677bauI7/W1lYefPBBpk2bxrx587j55pu7+pN79+7lkUceCdpvTU0NV1xxBTfeeCNv\nvvkmf/jDH7j99tu5+uqrATjrrLN44oknaG1tZe/evSxZsqRr2127drFixQr279/PMcccw4gRI6ip\nqel3NgtmKvY+Rsz5Y84O5o89/0Crq6tj3bp1nH322ZxwwglMmzaNyZMnc+utt3LZZZdx3XXX8ZnP\nfIaRI0dy5plnsmrVqq5tezq6LFz2ne98hxEjRnDKKacwffp0rrrqKj7/+c8DcOGFF3LllVcyefJk\nPvzhD3PppZd2bXvo0CFuv/12TjzxREaPHs2aNWu46667+p3Ny0pSLS0tfZ6ayfqt5YNtMPPHnB3M\nb/7yXVZSCddhDnV9XVZiwQyU9Q9N1qq5YBbja2/+oVIwVTqvw5QkqUQWzFTsfYyY88ecHcwfe36F\ns2BKkhTAHmagrPsYWbOHWcL2mN/89jCHCnuYkiSVyIKZir2PEXP+mLOD+WPPr3AWTEmSAtjDDJR1\nHyNr9jBL2B7zm98eJsDy5ctZunQpa9asAZK7Aj377LM0NTUV3bbwczEHkz1MSVLZrF27lnPPPZeG\nhgZGjx7Neeedx/r1649Yr729PahYFrN8+XKmT59e8n6KsWCmYu9jxJw/5uxg/mrKP6q+nlwuN2iP\nUfX1Rcewb98+ZsyYwfz582lra2P79u0sWrToiM+3HIosmJJUJdra28nDoD3a2ovfp/aFF14gl8tx\n5ZVXksvlOO6447jooos488wzj1h32LBhvPzyywD86U9/4tJLL2XkyJFMnTqVr371q0ccNf7yl7/k\n1FNPpbGxkWuuuQaA559/ni984Qs8+eST1NXVMWrUqP58y/rFgpmK/TPxYs4fc3Ywf+z5B9qkSZOo\nqalhzpw5rFy5kra2tqDtvvjFL1JXV8cf//hHvv/973P//fcf8eklP/vZz1i/fj3PPPMMP/zhD1m1\nahWnn346d999N9OmTaO9vZ09e/YMRiwgrGBeAmwGXgSu62O9DwMdwOUDMC5J0hBUV1fH2rVryeVy\nzJ07l7FjxzJz5kx27drV6zZvv/02jz32GDfddBPHHXccp59+OrNnzz7izVLXX3899fX1TJgwgfPP\nP58NGzYA5XtTWbGCWQPcSVI0zwBmAaf3st63gJWU9523A6aa+hhHI+b8MWcH88eefzCcdtppLFu2\njNbWVjZt2sSOHTtYsGBBj593CfD666/T0dHBhAkTupaddNJJR6w3fvz4runhw4ezf//+gR98H4oV\nzKnAS8A24CDwEDCzh/WuBR4FXh/IwUmShrZJkyYxe/ZsNm3a1Os67373u6mtraW1tbVrWeF0Mb0V\n4oFWrGCeCBSO+rV0Wfd1ZgKdH189tC4MSsXex4g5f8zZwfyx5x9oW7Zs4bbbbmP79u1AUvgefPBB\npk2b1us2NTU1XH755SxevJgDBw6wefNmHnjggT4LYT6f7zoVO27cOF577TUOHjw4sGG6KVYwQ4rf\nHcD16bo5hugpWUlS6erq6li3bh1nn302J5xwAtOmTWPy5MnceuutwOFHg4XTd955J3v37mX8+PHM\nnj2bWbNmceyxx/a4bud857ILLriA97///YwfP56xY8cOWrZixe0cYDFJDxNgIXCIpF/Z6eWC/YwB\n/gzMBX7cbV/52bNnd12k2tDQwJQpU7r+uuvsI2Q1f8cdd/Q5nlwux2qgOQ3Tkv4bOp8DVq9eXTF5\ny5m/M3sl5S2cL+xh9fR8Z/bQvN3nzW/+/uRvaWlh+fLlADQ1NXHTTTdB4J1+RtXXB136cbQa6+rY\ns2/foO2/0HXXXceuXbtYtmxZWb5ep77u9FNMLbAVaAKOBTbQ85t+Oi2j93fJ5ivZ6tWr+3weyOdL\neMScP+bs5jd/qfnp/UxfSfutNJs3b85v3Lgxf+jQofy6devyY8aMya9YsaLs4+jj+x1URT9Octq1\nBlgKLAHmpc/d023dZcBPgMd6eXEDvlxlyvp+klnzXrIlbI/5zX/0e6ime8n2Zf369cyaNYsdO3Yw\nbtw45s2bx3XX9XUl4+Do6wjTm68HyvqHJmsWzBK2x/zmt2AOFd58PUDs12LFnD/m7GD+2PMrnAVT\nkqQAnpINlPVpmax5SraE7TG/+T0lO1R4SlaSpBJZMFOx9zFizh9zdjD/UM1fW1vbPpiffRnro7a2\nttcLWWvL+QJLkgZGR0dH8U9zVr91dHT0+pw9zEBZ9zGyZg+zhO0xv/kHpYepMvOUrCRJASyYqaHa\nxxgoMeePOTuYP/b8CmfBlCQpgD3MQFn3MbJmD7OE7TG/+e1hVgOPMCVJCmDBTMXex4g5f8zZwfyx\n51c4C6YkSQHsYQbKuo+RNXuYJWyP+c1vD7MaeIQpSVIAC2Yq9j5GzPljzg7mjz2/wlkwJUkKYA8z\nUNZ9jKzZwyxhe8xvfnuY1cAjTEmSAlgwU7H3MWLOH3N2MH/s+RXOgilJUgB7mIGy7mNkzR5mCdtj\nfvPbw6wGHmFKkhTAgpmKvY8Rc/6Ys4P5Y8+vcBZMSZIC2MMMlHUfI2v2MEvYHvOb3x5mNfAIU5Kk\nABbMVOx9jJjzx5wdzB97foWzYEqSFMAeZqCs+xhZs4dZwvaY3/z2MKuBR5iSJAWwYKZi72PEnD/m\n7GD+2PMrnAVTkqQA9jADZd3HyJo9zBK2x/zmt4dZDTzClCQpQGjBvATYDLwIXNfD81cBG4FngN8A\nkwdkdGUUex8j5vwxZwfzx55f4WoD1qkB7gQuBLYDTwM/Bp4vWOdl4CPAXpLiei9wzoCOVJKkDIWc\nF58GLCIphADXp/9+s5f1G4FngZO6LbeHGWn+mLOD+c1vD7NahJySPRFoLZh/LV3Wm/8A/LyUQUmS\nVGlCCmZ//jQ6H/hbeu5zVrTY+xgx5485O5g/9vwKF9LD3A5MKJifQHKU2d1k4Lskp27betrRnDlz\naGpqAqChoYEpU6bQ3NwMvPOfNqv5DRs29Pk8QAvQXDBNP+Y791kpecudP+t8Jc8Xy1dsPuvxm7+0\n+WL5is334+u1tLSwfPlygK7fl6oMIefFa4EtwAXADuApYBaHv+lnIvBr4Grgt73sxx5mpPljzg7m\nN789zGoRcoTZAVwDrCJ5x+xSkmI5L33+HuC/krzZ56502UFg6oCOVJKkDIVeh/lPwCTgr4El6bJ7\n0gfAfwRGA2eljyFXLGPvY8ScP+bsYP7Y8yucd/qRJCmA95INlHUfI2v2MEvYHvOb3x5mNfAIU5Kk\nABbMVOx9jJjzx5wdzB97foWzYEqSFMAeZqCs+xhZs4dZwvaY3/z2MKuBR5iSJAWwYKZi72PEnL/a\ns9c31JPL5Y76Ue2q/fXXwAm504+kIax9bzss7mOFV4C/6uP5vraVImIPM1DWfYys2cMsYXuyzZ/L\n5Uoreov795FFR3x9fP3tYVYHT8lKkhTAgpmKvY8Rc/6YswPJKdmIRf/6K5gFU5KkAPYwA2Xdx8ia\nPcwStscepq+/Pcxq4BGmJEkBLJip2PsYMeePOTtgDzP211/BvA5TVa++oT65FlGSSmAPM1DWfYys\nDeUeZuw9vNjzlyrrn317mJXDU7KSJAWwYKaqvY/h/UT7EHkPL/b81f6zr4FjDzMS3k9UsbKHrYFi\nwUw1NzdnPYRs9VUsq13M2aHq8xf9Y7GYUrZVVYmmYPpXpiSpFNEUTE9JFlEsfzWLOTuYP/b8Cuab\nfiRJCmDB7BT7X5gx5485O5g/9vwKZsGUJCmABbNT5NeiRZ0/5uxg/tjzK5gFU5KkABbMTrH3MWLO\nH3N2MH/s+RXMgilJUgALZqfY+xgx5485O5g/9vwKZsGUJCmABbNT7H2MmPPHnB3MH3t+BbNgSpIU\nIKRgXgJsBl4ErutlnX9Mn98InDUwQyuz2PsYMeev8Oz19aMG97NMKzz/oIs9v4IVK5g1wJ0kRfMM\nYBZwerd1PgH8NfA+4D8Bdw3wGMtjZ9YDyFjM+Ss8e3t7G5Av4VFEhecfdLHnV7BiBXMq8BKwDTgI\nPATM7LbOJ4Hvp9PrgAZg3MANsUz+X9YDyFgF5x/0I6wKzl4WFZ7f11+VoljBPBFoLZh/LV1WbJ2T\nSh+aCg36L40KNuhHWKpovv6qFMU+DzP0f1v338gD/r+0vn5U+oMzSN4YvF0PhHd+aRytIkWzwvMP\nqpizg/ljz68Bcw6wsmB+IUe+8edu4DMF85vp+ZTsBkr7M9GHDx8+YnxsQENCLbAVaAKOJXnhenrT\nz8/T6XOA35ZrcJIkVZKPA1tI3vyzMF02L310ujN9fiPwwbKOTpIkSZIkSZIkSZKGtgbgmyTv5m0D\n9qTT30yfq3Yx5485O5g/9vwqUYw3X/8hyQ9LMzAqfZxPcjXWD7MbVtnEnD/m7GD+2PNL/fbCUT5X\nLWLOH3N2MH/s+VWiGI8w/wD8PYffXGE8yQ0ZXs1kROUVc/6Ys4P5Y8+vEsVYMK8ExgCPk5yeaQNa\ngNHAFdkNq2xizh9zdug5/2rizt9CPPmlo3Ie8P50+nzg74ALshtO5h7IegAZmQ58Bbg464GUydnA\nyHR6BPB14GfAfytYXs2+BEzIehAauob2x1gcnSUkRbKG5K/rj5D80rgI+Anw7eyGVhY/Ibk/ZeFr\n/zHg1+nyT2YxqDJ5iuQj6wDmAl8EfkRSMH9K8n+jmj0HTAY6gO8C+4FHgQvT5ZdnN7Sy2Av8meSu\nZA8CjwCvZzoiqcI9R3KP3OFAO+/8ZX088ExWgyqj3wH/k+SPho+SvGPwX9Lpj2Y3rLL4XcH0euDd\n6fQIYFP5h1N2zxdM/99uz20s50Ay8juSNtTFwH0kxXIlMBuoy3BcUsXa0Mt0T/PVqAb4MvAr4Kx0\n2SvZDaesniG5lGA0hxdPiOO1fxT423R6GfDhdPpU4OlMRlRe3V/zY4GZwEPA7vIPR6p860iOLuHw\nNz01cORf3dXsJJJTUv+Dwz8AvJptI/nj4BXgZeBfpcvriKNgNgDfJ8m+DjhI8r14AvhAhuMql+4F\ns9CIso1CGkKO62X5GODMcg6kQswAbs56EBkbDvxV1oMoo5HAFOBDJJdVxGJS1gOQJEmSJEmSJEmS\nJEmqdjeSXBu5keTdjVP7Xr1kLcC/6WX5B9PpnwH1/djnpST3KQW4DDj9KMcmSVKPpgH/DByTzo/i\nnUtABstq3imMIcv7aznwNwOwH0kZifHm66p840kuJD+Yzu8huRsRJNdSfovkJgTrgPemy99NcmH+\nU+nj3HT5CJK7uqwjuc6289Z/x5NcsP4c8Fg6X+xWkdtIincTyQcPLwO2kNw56WLgNyQfE9V5Q4A5\nwHdI/gC4lOS2i78DTinydSRJCjKCpLBsIbmxwkcKnnsFWJhOf47k3rgA/wv4t+n0RJJCCMk1plel\n0w3pPoeT3O3oe+nyM0mKc7EjzFd4p2AeJLmBf47kNntL03U+SXJ/WninYEJSXKv9Xq1SVavNegBS\nD/aT9BOnk9zz9mHgepK71EBy42xIjhBvT6cv5PAeYR1J4b2Y5Oju79Ll7yIpqNOB/54ue5b+30f4\nFeD36fTvSW41CEnftamXbWL8sAOpalgwVakOkXxu4eMkBW027xTMQvn03xzJx1f9pYd1Lgde7GF5\nKQXsrYLpQwVf9xC9/1zle1kuaQiwh6lKdCrwvoL5s0j6h52uLPj3n9PpX5B83mGnznujruq2vPOG\n808An02n/zXJx1sNpnb69w5bSRXGgqlKdALJu0p/T3JZyWnA4oLnG9Pl1wL/OV32JZJ7o25Mt5uX\nLv86ybttnyE5XXpTuvyu9Os8ly5b388xdj9azPcwnS+Yfgj4L8D/wTf9SJLKoPONN5JUVh5haqix\nDyhJkiRJkiRJkiRJkiRJkiRJkiRJKpf/DwnSKW0YmagWAAAAAElFTkSuQmCC\n",
1458        "text": [
1459         "<matplotlib.figure.Figure at 0x7ff7b42d9d68>"
1460        ]
1461       }
1462      ],
1463      "prompt_number": 83
1464     },
1465     {
1466      "cell_type": "markdown",
1467      "metadata": {},
1468      "source": [
1469       "Let's now look at how accident the number of casualites and vehicles correlate.\n",
1470       "\n",
1471       "First of all, what's the range of number of casualities and vehicles involved in accidents? (The `distinct` function gives the distinct values for a particular key in a cursor.)"
1472      ]
1473     },
1474     {
1475      "cell_type": "code",
1476      "collapsed": false,
1477      "input": [
1478       "sorted(accidents.find().distinct('Number_of_Casualties'))"
1479      ],
1480      "language": "python",
1481      "metadata": {},
1482      "outputs": [
1483       {
1484        "metadata": {},
1485        "output_type": "pyout",
1486        "prompt_number": 84,
1487        "text": [
1488         "[1,\n",
1489         " 2,\n",
1490         " 3,\n",
1491         " 4,\n",
1492         " 5,\n",
1493         " 6,\n",
1494         " 7,\n",
1495         " 8,\n",
1496         " 9,\n",
1497         " 10,\n",
1498         " 11,\n",
1499         " 12,\n",
1500         " 13,\n",
1501         " 14,\n",
1502         " 15,\n",
1503         " 16,\n",
1504         " 17,\n",
1505         " 18,\n",
1506         " 19,\n",
1507         " 20,\n",
1508         " 21,\n",
1509         " 22,\n",
1510         " 23,\n",
1511         " 24,\n",
1512         " 25,\n",
1513         " 26,\n",
1514         " 27,\n",
1515         " 28,\n",
1516         " 29,\n",
1517         " 32,\n",
1518         " 33,\n",
1519         " 35,\n",
1520         " 36,\n",
1521         " 38,\n",
1522         " 40,\n",
1523         " 41,\n",
1524         " 42,\n",
1525         " 43,\n",
1526         " 45,\n",
1527         " 47,\n",
1528         " 48,\n",
1529         " 51,\n",
1530         " 62,\n",
1531         " 63,\n",
1532         " 68,\n",
1533         " 87]"
1534        ]
1535       }
1536      ],
1537      "prompt_number": 84
1538     },
1539     {
1540      "cell_type": "code",
1541      "collapsed": false,
1542      "input": [
1543       "sorted(accidents.find().distinct('Number_of_Vehicles'))"
1544      ],
1545      "language": "python",
1546      "metadata": {},
1547      "outputs": [
1548       {
1549        "metadata": {},
1550        "output_type": "pyout",
1551        "prompt_number": 85,
1552        "text": [
1553         "[1,\n",
1554         " 2,\n",
1555         " 3,\n",
1556         " 4,\n",
1557         " 5,\n",
1558         " 6,\n",
1559         " 7,\n",
1560         " 8,\n",
1561         " 9,\n",
1562         " 10,\n",
1563         " 11,\n",
1564         " 12,\n",
1565         " 13,\n",
1566         " 14,\n",
1567         " 15,\n",
1568         " 16,\n",
1569         " 17,\n",
1570         " 18,\n",
1571         " 19,\n",
1572         " 20,\n",
1573         " 22,\n",
1574         " 28,\n",
1575         " 29,\n",
1576         " 32,\n",
1577         " 34]"
1578        ]
1579       }
1580      ],
1581      "prompt_number": 85
1582     },
1583     {
1584      "cell_type": "markdown",
1585      "metadata": {},
1586      "source": [
1587       "Now let's look at the distribution of the numbers."
1588      ]
1589     },
1590     {
1591      "cell_type": "code",
1592      "collapsed": false,
1593      "input": [
1594       "cas_veh_dict = collections.Counter((a['Number_of_Casualties'], a['Number_of_Vehicles']) \n",
1595       "                                   for a in accidents.find({}, ['Number_of_Casualties', 'Number_of_Vehicles']))\n",
1596       "cas_veh_dict"
1597      ],
1598      "language": "python",
1599      "metadata": {},
1600      "outputs": [
1601       {
1602        "metadata": {},
1603        "output_type": "pyout",
1604        "prompt_number": 86,
1605        "text": [
1606         "Counter({(1, 2): 597834, (1, 1): 361664, (2, 2): 145688, (1, 3): 61780, (3, 2): 39023, (2, 1): 37853, (2, 3): 28895, (4, 2): 13286, (3, 3): 11443, (1, 4): 10894, (3, 1): 8087, (2, 4): 6382, (5, 2): 4698, (4, 3): 4235, (4, 1): 3211, (3, 4): 3135, (1, 5): 2080, (6, 2): 1710, (5, 3): 1672, (4, 4): 1530, (2, 5): 1341, (5, 1): 1010, (3, 5): 755, (6, 3): 659, (5, 4): 627, (1, 6): 571, (7, 2): 549, (4, 5): 424, (2, 6): 414, (6, 4): 331, (3, 6): 263, (7, 3): 246, (5, 5): 232, (8, 2): 223, (6, 1): 208, (1, 7): 171, (4, 6): 147, (6, 5): 144, (2, 7): 137, (7, 4): 119, (3, 7): 107, (8, 3): 105, (9, 2): 98, (5, 6): 88, (1, 8): 71, (7, 1): 69, (2, 8): 67, (6, 6): 66, (7, 5): 59, (5, 7): 56, (9, 3): 49, (8, 4): 44, (10, 2): 44, (4, 7): 40, (6, 7): 35, (11, 2): 33, (3, 8): 33, (8, 5): 31, (1, 9): 30, (4, 8): 28, (8, 1): 28, (9, 4): 26, (5, 8): 25, (4, 9): 23, (2, 9): 22, (8, 6): 20, (7, 6): 18, (7, 8): 18, (10, 3): 18, (13, 2): 18, (3, 9): 18, (7, 7): 18, (10, 4): 17, (9, 1): 17, (12, 2): 16, (2, 10): 16, (1, 10): 15, (9, 5): 15, (10, 1): 14, (10, 5): 13, (6, 8): 13, (11, 3): 12, (11, 1): 11, (14, 2): 10, (11, 4): 10, (5, 9): 9, (15, 2): 9, (6, 9): 8, (12, 1): 8, (22, 2): 8, (2, 11): 7, (7, 9): 7, (13, 1): 7, (1, 11): 6, (17, 2): 6, (2, 12): 6, (19, 2): 6, (5, 10): 6, (12, 3): 6, (16, 2): 6, (8, 7): 6, (8, 8): 6, (13, 4): 5, (14, 1): 5, (13, 3): 5, (9, 7): 5, (6, 10): 4, (15, 1): 4, (1, 12): 4, (21, 2): 4, (3, 11): 4, (10, 9): 4, (18, 2): 4, (15, 3): 4, (11, 7): 4, (25, 2): 4, (17, 3): 4, (11, 5): 3, (10, 7): 3, (12, 6): 3, (19, 3): 3, (4, 10): 3, (26, 1): 3, (5, 14): 3, (4, 11): 3, (12, 4): 3, (9, 8): 3, (18, 1): 3, (3, 10): 3, (9, 6): 3, (10, 8): 2, (6, 16): 2, (26, 2): 2, (8, 9): 2, (4, 12): 2, (27, 2): 2, (12, 7): 2, (11, 9): 2, (13, 6): 2, (14, 3): 2, (19, 1): 2, (16, 3): 2, (5, 13): 2, (4, 13): 2, (9, 11): 2, (11, 8): 2, (16, 1): 2, (10, 6): 2, (14, 14): 2, (3, 13): 2, (29, 2): 2, (18, 3): 2, (20, 2): 2, (11, 6): 2, (11, 11): 1, (45, 1): 1, (41, 2): 1, (62, 2): 1, (1, 17): 1, (16, 28): 1, (48, 1): 1, (63, 2): 1, (32, 2): 1, (15, 4): 1, (5, 11): 1, (87, 5): 1, (18, 4): 1, (51, 34): 1, (13, 8): 1, (40, 2): 1, (3, 12): 1, (47, 3): 1, (4, 15): 1, (9, 9): 1, (29, 1): 1, (36, 2): 1, (11, 16): 1, (21, 3): 1, (13, 13): 1, (20, 3): 1, (23, 3): 1, (6, 14): 1, (17, 6): 1, (14, 5): 1, (1, 16): 1, (38, 4): 1, (21, 5): 1, (33, 2): 1, (16, 4): 1, (6, 11): 1, (17, 1): 1, (14, 6): 1, (43, 1): 1, (13, 19): 1, (10, 10): 1, (16, 13): 1, (6, 20): 1, (42, 1): 1, (13, 9): 1, (68, 1): 1, (3, 15): 1, (8, 10): 1, (4, 22): 1, (36, 3): 1, (42, 2): 1, (12, 20): 1, (1, 14): 1, (43, 4): 1, (23, 2): 1, (28, 2): 1, (3, 16): 1, (42, 8): 1, (7, 14): 1, (6, 18): 1, (17, 10): 1, (22, 4): 1, (23, 1): 1, (24, 4): 1, (2, 14): 1, (16, 5): 1, (14, 7): 1, (24, 3): 1, (22, 1): 1, (28, 4): 1, (10, 11): 1, (4, 18): 1, (14, 13): 1, (12, 5): 1, (15, 8): 1, (5, 32): 1, (15, 5): 1, (3, 14): 1, (8, 14): 1, (35, 4): 1, (5, 12): 1, (10, 12): 1, (45, 3): 1, (11, 18): 1, (40, 4): 1, (2, 16): 1, (51, 2): 1, (9, 10): 1, (5, 15): 1, (17, 4): 1, (29, 5): 1, (26, 29): 1})"
1607        ]
1608       }
1609      ],
1610      "prompt_number": 86
1611     },
1612     {
1613      "cell_type": "code",
1614      "collapsed": false,
1615      "input": [
1616       "cas_veh = pd.DataFrame(\n",
1617       "    {'Casualties': [v for c, v in sorted(cas_veh_dict.keys())],\n",
1618       "     'Vehicles': [c for c, v in sorted(cas_veh_dict.keys())],\n",
1619       "     'Count': [cas_veh_dict[(c, v)] for c, v in sorted(cas_veh_dict.keys())]}\n",
1620       "    )\n",
1621       "cas_veh"
1622      ],
1623      "language": "python",
1624      "metadata": {},
1625      "outputs": [
1626       {
1627        "html": [
1628         "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
1629         "<table border=\"1\" class=\"dataframe\">\n",
1630         "  <thead>\n",
1631         "    <tr style=\"text-align: right;\">\n",
1632         "      <th></th>\n",
1633         "      <th>Casualties</th>\n",
1634         "      <th>Count</th>\n",
1635         "      <th>Vehicles</th>\n",
1636         "    </tr>\n",
1637         "  </thead>\n",
1638         "  <tbody>\n",
1639         "    <tr>\n",
1640         "      <th>0 </th>\n",
1641         "      <td>  1</td>\n",
1642         "      <td> 361664</td>\n",
1643         "      <td> 1</td>\n",
1644         "    </tr>\n",
1645         "    <tr>\n",
1646         "      <th>1 </th>\n",
1647         "      <td>  2</td>\n",
1648         "      <td> 597834</td>\n",
1649         "      <td> 1</td>\n",
1650         "    </tr>\n",
1651         "    <tr>\n",
1652         "      <th>2 </th>\n",
1653         "      <td>  3</td>\n",
1654         "      <td>  61780</td>\n",
1655         "      <td> 1</td>\n",
1656         "    </tr>\n",
1657         "    <tr>\n",
1658         "      <th>3 </th>\n",
1659         "      <td>  4</td>\n",
1660         "      <td>  10894</td>\n",
1661         "      <td> 1</td>\n",
1662         "    </tr>\n",
1663         "    <tr>\n",
1664         "      <th>4 </th>\n",
1665         "      <td>  5</td>\n",
1666         "      <td>   2080</td>\n",
1667         "      <td> 1</td>\n",
1668         "    </tr>\n",
1669         "    <tr>\n",
1670         "      <th>5 </th>\n",
1671         "      <td>  6</td>\n",
1672         "      <td>    571</td>\n",
1673         "      <td> 1</td>\n",
1674         "    </tr>\n",
1675         "    <tr>\n",
1676         "      <th>6 </th>\n",
1677         "      <td>  7</td>\n",
1678         "      <td>    171</td>\n",
1679         "      <td> 1</td>\n",
1680         "    </tr>\n",
1681         "    <tr>\n",
1682         "      <th>7 </th>\n",
1683         "      <td>  8</td>\n",
1684         "      <td>     71</td>\n",
1685         "      <td> 1</td>\n",
1686         "    </tr>\n",
1687         "    <tr>\n",
1688         "      <th>8 </th>\n",
1689         "      <td>  9</td>\n",
1690         "      <td>     30</td>\n",
1691         "      <td> 1</td>\n",
1692         "    </tr>\n",
1693         "    <tr>\n",
1694         "      <th>9 </th>\n",
1695         "      <td> 10</td>\n",
1696         "      <td>     15</td>\n",
1697         "      <td> 1</td>\n",
1698         "    </tr>\n",
1699         "    <tr>\n",
1700         "      <th>10</th>\n",
1701         "      <td> 11</td>\n",
1702         "      <td>      6</td>\n",
1703         "      <td> 1</td>\n",
1704         "    </tr>\n",
1705         "    <tr>\n",
1706         "      <th>11</th>\n",
1707         "      <td> 12</td>\n",
1708         "      <td>      4</td>\n",
1709         "      <td> 1</td>\n",
1710         "    </tr>\n",
1711         "    <tr>\n",
1712         "      <th>12</th>\n",
1713         "      <td> 14</td>\n",
1714         "      <td>      1</td>\n",
1715         "      <td> 1</td>\n",
1716         "    </tr>\n",
1717         "    <tr>\n",
1718         "      <th>13</th>\n",
1719         "      <td> 16</td>\n",
1720         "      <td>      1</td>\n",
1721         "      <td> 1</td>\n",
1722         "    </tr>\n",
1723         "    <tr>\n",
1724         "      <th>14</th>\n",
1725         "      <td> 17</td>\n",
1726         "      <td>      1</td>\n",
1727         "      <td> 1</td>\n",
1728         "    </tr>\n",
1729         "    <tr>\n",
1730         "      <th>15</th>\n",
1731         "      <td>  1</td>\n",
1732         "      <td>  37853</td>\n",
1733         "      <td> 2</td>\n",
1734         "    </tr>\n",
1735         "    <tr>\n",
1736         "      <th>16</th>\n",
1737         "      <td>  2</td>\n",
1738         "      <td> 145688</td>\n",
1739         "      <td> 2</td>\n",
1740         "    </tr>\n",
1741         "    <tr>\n",
1742         "      <th>17</th>\n",
1743         "      <td>  3</td>\n",
1744         "      <td>  28895</td>\n",
1745         "      <td> 2</td>\n",
1746         "    </tr>\n",
1747         "    <tr>\n",
1748         "      <th>18</th>\n",
1749         "      <td>  4</td>\n",
1750         "      <td>   6382</td>\n",
1751         "      <td> 2</td>\n",
1752         "    </tr>\n",
1753         "    <tr>\n",
1754         "      <th>19</th>\n",
1755         "      <td>  5</td>\n",
1756         "      <td>   1341</td>\n",
1757         "      <td> 2</td>\n",
1758         "    </tr>\n",
1759         "    <tr>\n",
1760         "      <th>20</th>\n",
1761         "      <td>  6</td>\n",
1762         "      <td>    414</td>\n",
1763         "      <td> 2</td>\n",
1764         "    </tr>\n",
1765         "    <tr>\n",
1766         "      <th>21</th>\n",
1767         "      <td>  7</td>\n",
1768         "      <td>    137</td>\n",
1769         "      <td> 2</td>\n",
1770         "    </tr>\n",
1771         "    <tr>\n",
1772         "      <th>22</th>\n",
1773         "      <td>  8</td>\n",
1774         "      <td>     67</td>\n",
1775         "      <td> 2</td>\n",
1776         "    </tr>\n",
1777         "    <tr>\n",
1778         "      <th>23</th>\n",
1779         "      <td>  9</td>\n",
1780         "      <td>     22</td>\n",
1781         "      <td> 2</td>\n",
1782         "    </tr>\n",
1783         "    <tr>\n",
1784         "      <th>24</th>\n",
1785         "      <td> 10</td>\n",
1786         "      <td>     16</td>\n",
1787         "      <td> 2</td>\n",
1788         "    </tr>\n",
1789         "    <tr>\n",
1790         "      <th>25</th>\n",
1791         "      <td> 11</td>\n",
1792         "      <td>      7</td>\n",
1793         "      <td> 2</td>\n",
1794         "    </tr>\n",
1795         "    <tr>\n",
1796         "      <th>26</th>\n",
1797         "      <td> 12</td>\n",
1798         "      <td>      6</td>\n",
1799         "      <td> 2</td>\n",
1800         "    </tr>\n",
1801         "    <tr>\n",
1802         "      <th>27</th>\n",
1803         "      <td> 14</td>\n",
1804         "      <td>      1</td>\n",
1805         "      <td> 2</td>\n",
1806         "    </tr>\n",
1807         "    <tr>\n",
1808         "      <th>28</th>\n",
1809         "      <td> 16</td>\n",
1810         "      <td>      1</td>\n",
1811         "      <td> 2</td>\n",
1812         "    </tr>\n",
1813         "    <tr>\n",
1814         "      <th>29</th>\n",
1815         "      <td>  1</td>\n",
1816         "      <td>   8087</td>\n",
1817         "      <td> 3</td>\n",
1818         "    </tr>\n",
1819         "    <tr>\n",
1820         "      <th>30</th>\n",
1821         "      <td>  2</td>\n",
1822         "      <td>  39023</td>\n",
1823         "      <td> 3</td>\n",
1824         "    </tr>\n",
1825         "    <tr>\n",
1826         "      <th>31</th>\n",
1827         "      <td>  3</td>\n",
1828         "      <td>  11443</td>\n",
1829         "      <td> 3</td>\n",
1830         "    </tr>\n",
1831         "    <tr>\n",
1832         "      <th>32</th>\n",
1833         "      <td>  4</td>\n",
1834         "      <td>   3135</td>\n",
1835         "      <td> 3</td>\n",
1836         "    </tr>\n",
1837         "    <tr>\n",
1838         "      <th>33</th>\n",
1839         "      <td>  5</td>\n",
1840         "      <td>    755</td>\n",
1841         "      <td> 3</td>\n",
1842         "    </tr>\n",
1843         "    <tr>\n",
1844         "      <th>34</th>\n",
1845         "      <td>  6</td>\n",
1846         "      <td>    263</td>\n",
1847         "      <td> 3</td>\n",
1848         "    </tr>\n",
1849         "    <tr>\n",
1850         "      <th>35</th>\n",
1851         "      <td>  7</td>\n",
1852         "      <td>    107</td>\n",
1853         "      <td> 3</td>\n",
1854         "    </tr>\n",
1855         "    <tr>\n",
1856         "      <th>36</th>\n",
1857         "      <td>  8</td>\n",
1858         "      <td>     33</td>\n",
1859         "      <td> 3</td>\n",
1860         "    </tr>\n",
1861         "    <tr>\n",
1862         "      <th>37</th>\n",
1863         "      <td>  9</td>\n",
1864         "      <td>     18</td>\n",
1865         "      <td> 3</td>\n",
1866         "    </tr>\n",
1867         "    <tr>\n",
1868         "      <th>38</th>\n",
1869         "      <td> 10</td>\n",
1870         "      <td>      3</td>\n",
1871         "      <td> 3</td>\n",
1872         "    </tr>\n",
1873         "    <tr>\n",
1874         "      <th>39</th>\n",
1875         "      <td> 11</td>\n",
1876         "      <td>      4</td>\n",
1877         "      <td> 3</td>\n",
1878         "    </tr>\n",
1879         "    <tr>\n",
1880         "      <th>40</th>\n",
1881         "      <td> 12</td>\n",
1882         "      <td>      1</td>\n",
1883         "      <td> 3</td>\n",
1884         "    </tr>\n",
1885         "    <tr>\n",
1886         "      <th>41</th>\n",
1887         "      <td> 13</td>\n",
1888         "      <td>      2</td>\n",
1889         "      <td> 3</td>\n",
1890         "    </tr>\n",
1891         "    <tr>\n",
1892         "      <th>42</th>\n",
1893         "      <td> 14</td>\n",
1894         "      <td>      1</td>\n",
1895         "      <td> 3</td>\n",
1896         "    </tr>\n",
1897         "    <tr>\n",
1898         "      <th>43</th>\n",
1899         "      <td> 15</td>\n",
1900         "      <td>      1</td>\n",
1901         "      <td> 3</td>\n",
1902         "    </tr>\n",
1903         "    <tr>\n",
1904         "      <th>44</th>\n",
1905         "      <td> 16</td>\n",
1906         "      <td>      1</td>\n",
1907         "      <td> 3</td>\n",
1908         "    </tr>\n",
1909         "    <tr>\n",
1910         "      <th>45</th>\n",
1911         "      <td>  1</td>\n",
1912         "      <td>   3211</td>\n",
1913         "      <td> 4</td>\n",
1914         "    </tr>\n",
1915         "    <tr>\n",
1916         "      <th>46</th>\n",
1917         "      <td>  2</td>\n",
1918         "      <td>  13286</td>\n",
1919         "      <td> 4</td>\n",
1920         "    </tr>\n",
1921         "    <tr>\n",
1922         "      <th>47</th>\n",
1923         "      <td>  3</td>\n",
1924         "      <td>   4235</td>\n",
1925         "      <td> 4</td>\n",
1926         "    </tr>\n",
1927         "    <tr>\n",
1928         "      <th>48</th>\n",
1929         "      <td>  4</td>\n",
1930         "      <td>   1530</td>\n",
1931         "      <td> 4</td>\n",
1932         "    </tr>\n",
1933         "    <tr>\n",
1934         "      <th>49</th>\n",
1935         "      <td>  5</td>\n",
1936         "      <td>    424</td>\n",
1937         "      <td> 4</td>\n",
1938         "    </tr>\n",
1939         "    <tr>\n",
1940         "      <th>50</th>\n",
1941         "      <td>  6</td>\n",
1942         "      <td>    147</td>\n",
1943         "      <td> 4</td>\n",
1944         "    </tr>\n",
1945         "    <tr>\n",
1946         "      <th>51</th>\n",
1947         "      <td>  7</td>\n",
1948         "      <td>     40</td>\n",
1949         "      <td> 4</td>\n",
1950         "    </tr>\n",
1951         "    <tr>\n",
1952         "      <th>52</th>\n",
1953         "      <td>  8</td>\n",
1954         "      <td>     28</td>\n",
1955         "      <td> 4</td>\n",
1956         "    </tr>\n",
1957         "    <tr>\n",
1958         "      <th>53</th>\n",
1959         "      <td>  9</td>\n",
1960         "      <td>     23</td>\n",
1961         "      <td> 4</td>\n",
1962         "    </tr>\n",
1963         "    <tr>\n",
1964         "      <th>54</th>\n",
1965         "      <td> 10</td>\n",
1966         "      <td>      3</td>\n",
1967         "      <td> 4</td>\n",
1968         "    </tr>\n",
1969         "    <tr>\n",
1970         "      <th>55</th>\n",
1971         "      <td> 11</td>\n",
1972         "      <td>      3</td>\n",
1973         "      <td> 4</td>\n",
1974         "    </tr>\n",
1975         "    <tr>\n",
1976         "      <th>56</th>\n",
1977         "      <td> 12</td>\n",
1978         "      <td>      2</td>\n",
1979         "      <td> 4</td>\n",
1980         "    </tr>\n",
1981         "    <tr>\n",
1982         "      <th>57</th>\n",
1983         "      <td> 13</td>\n",
1984         "      <td>      2</td>\n",
1985         "      <td> 4</td>\n",
1986         "    </tr>\n",
1987         "    <tr>\n",
1988         "      <th>58</th>\n",
1989         "      <td> 15</td>\n",
1990         "      <td>      1</td>\n",
1991         "      <td> 4</td>\n",
1992         "    </tr>\n",
1993         "    <tr>\n",
1994         "      <th>59</th>\n",
1995         "      <td> 18</td>\n",
1996         "      <td>      1</td>\n",
1997         "      <td> 4</td>\n",
1998         "    </tr>\n",
1999         "    <tr>\n",
2000         "      <th></th>\n",
2001         "      <td>...</td>\n",
2002         "      <td>...</td>\n",
2003         "      <td>...</td>\n",
2004         "    </tr>\n",
2005         "  </tbody>\n",
2006         "</table>\n",
2007         "<p>246 rows \u00d7 3 columns</p>\n",
2008         "</div>"
2009        ],
2010        "metadata": {},
2011        "output_type": "pyout",
2012        "prompt_number": 87,
2013        "text": [
2014         "    Casualties   Count  Vehicles\n",
2015         "0            1  361664         1\n",
2016         "1            2  597834         1\n",
2017         "2            3   61780         1\n",
2018         "3            4   10894         1\n",
2019         "4            5    2080         1\n",
2020         "5            6     571         1\n",
2021         "6            7     171         1\n",
2022         "7            8      71         1\n",
2023         "8            9      30         1\n",
2024         "9           10      15         1\n",
2025         "10          11       6         1\n",
2026         "11          12       4         1\n",
2027         "12          14       1         1\n",
2028         "13          16       1         1\n",
2029         "14          17       1         1\n",
2030         "15           1   37853         2\n",
2031         "16           2  145688         2\n",
2032         "17           3   28895         2\n",
2033         "18           4    6382         2\n",
2034         "19           5    1341         2\n",
2035         "20           6     414         2\n",
2036         "21           7     137         2\n",
2037         "22           8      67         2\n",
2038         "23           9      22         2\n",
2039         "24          10      16         2\n",
2040         "25          11       7         2\n",
2041         "26          12       6         2\n",
2042         "27          14       1         2\n",
2043         "28          16       1         2\n",
2044         "29           1    8087         3\n",
2045         "30           2   39023         3\n",
2046         "31           3   11443         3\n",
2047         "32           4    3135         3\n",
2048         "33           5     755         3\n",
2049         "34           6     263         3\n",
2050         "35           7     107         3\n",
2051         "36           8      33         3\n",
2052         "37           9      18         3\n",
2053         "38          10       3         3\n",
2054         "39          11       4         3\n",
2055         "40          12       1         3\n",
2056         "41          13       2         3\n",
2057         "42          14       1         3\n",
2058         "43          15       1         3\n",
2059         "44          16       1         3\n",
2060         "45           1    3211         4\n",
2061         "46           2   13286         4\n",
2062         "47           3    4235         4\n",
2063         "48           4    1530         4\n",
2064         "49           5     424         4\n",
2065         "50           6     147         4\n",
2066         "51           7      40         4\n",
2067         "52           8      28         4\n",
2068         "53           9      23         4\n",
2069         "54          10       3         4\n",
2070         "55          11       3         4\n",
2071         "56          12       2         4\n",
2072         "57          13       2         4\n",
2073         "58          15       1         4\n",
2074         "59          18       1         4\n",
2075         "           ...     ...       ...\n",
2076         "\n",
2077         "[246 rows x 3 columns]"
2078        ]
2079       }
2080      ],
2081      "prompt_number": 87
2082     },
2083     {
2084      "cell_type": "markdown",
2085      "metadata": {},
2086      "source": [
2087       "Plot the data on a bubble chart: the area of the bubble is the number of accidents with that number of casualties and vehicles."
2088      ]
2089     },
2090     {
2091      "cell_type": "code",
2092      "collapsed": false,
2093      "input": [
2094       "plt.scatter(cas_veh['Vehicles'], cas_veh['Casualties'], s=np.sqrt(cas_veh['Count']))\n",
2095       "plt.xlabel('Number of casualties')\n",
2096       "plt.ylabel('Number of vehicles')\n",
2097       "plt.show()"
2098      ],
2099      "language": "python",
2100      "metadata": {},
2101      "outputs": [
2102       {
2103        "metadata": {},
2104        "output_type": "display_data",
2105        "png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEPCAYAAACtCNj2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlFJKKaWUUkoppZRSSimllFJKKaXUufl/0q8Gm6yyCNgAAAAASUVORK5CYII=\n",
2106        "text": [
2107         "<matplotlib.figure.Figure at 0x7ff7b4821b70>"
2108        ]
2109       }
2110      ],
2111      "prompt_number": 88
2112     },
2113     {
2114      "cell_type": "markdown",
2115      "metadata": {},
2116      "source": [
2117       "That shows a few outliers of some very large accidents. How large?"
2118      ]
2119     },
2120     {
2121      "cell_type": "code",
2122      "collapsed": false,
2123      "input": [
2124       "print('Casualties:', cas_veh['Casualties'].max(), 'Vehicles:', cas_veh['Vehicles'].max())"
2125      ],
2126      "language": "python",
2127      "metadata": {},
2128      "outputs": [
2129       {
2130        "output_type": "stream",
2131        "stream": "stdout",
2132        "text": [
2133         "Casualties: 34 Vehicles: 87\n"
2134        ]
2135       }
2136      ],
2137      "prompt_number": 89
2138     },
2139     {
2140      "cell_type": "markdown",
2141      "metadata": {},
2142      "source": [
2143       "Let's limit the plot to smallish values. It looks like 10 vehicles and 20 casualties will capture most of the data."
2144      ]
2145     },
2146     {
2147      "cell_type": "code",
2148      "collapsed": false,
2149      "input": [
2150       "small_cas_veh = cas_veh[cas_veh['Casualties'] <= 20][cas_veh['Vehicles'] <= 10]\n",
2151       "plt.scatter(small_cas_veh['Vehicles'], small_cas_veh['Casualties'], s=np.sqrt(small_cas_veh['Count']))\n",
2152       "plt.xlabel('Number of casualties')\n",
2153       "plt.ylabel('Number of vehicles')\n",
2154       "plt.show()"
2155      ],
2156      "language": "python",
2157      "metadata": {},
2158      "outputs": [
2159       {
2160        "output_type": "stream",
2161        "stream": "stderr",
2162        "text": [
2163         "/usr/lib/python3/dist-packages/pandas/core/frame.py:1686: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
2164         "  \"DataFrame index.\", UserWarning)\n"
2165        ]
2166       },
2167       {
2168        "metadata": {},
2169        "output_type": "display_data",
2170        "png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEPCAYAAABLIROyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xd8U/X+x/FXko6sllL2LjIdbBUR0CoKbply9QqKiuK4\nKG5FBfF6FdzXn+vqVVAQxQUCLlRARVRQ9hDZUJBd6F75/P446SXUAkmb7ym0n+fjkQdNmuT9PbGe\nb853glJKKaWUUkoppZRSSimllFJKKaWUUkoppZRSh2gEzAZWAMuB4cHHRwNbgUXB2wUVUTillFLm\n1AXaB3/2A78DJwKjgDsrqlBKKaUsMQbf+8/gDSATWAU0CN53GMxVSil1DEkBNmFdCYwCNgJLgP8C\nSRVWKqWUUkb5gYVA7+D92lhXAA7gn1iVgFJKKZuZboqJBWYAnwPPl/L7FGA60Cb0wWbNmsm6desM\nF00ppSqddUDzcJ/sNFgQB9a3+5UcevKvF/JzH2BZyReuW7cOEam0t1GjRlV4GfT49Piq4vFV5mMT\nEYBmkZykTXYCdwWuBpZiDfcEeBC4Emt0kAAbgJsMlkEppdRhmKwAfqD0K4zPDWYqpZQKk8kmIHUY\nqampFV0Eo/T4jm+V+fgq87GVxbE6Hl+C7VlKKaXC5HA4IILzul4BKKVUFaUVgFJKVVFaASilVBWl\nFYBSSlVRWgEopVQVpRWAUkpVUVoBKKVUFaUVgFJKVVFaASilVBWlFYBSSlVRWgEopVQVpRWAUkpV\nUVoBKKVUFaUVgFJKVVFaASilVBWlFYBSSlVRWgEopVQVpRWAUkpVUVoBKKVUFaUVgFJKVVFaASil\nVBWlFYBSSlVRWgEopVQVpRWAUkpVUVoBKKVUFaUVgFJKVVFaASilVBWlFYBSSlVRWgEopVQVpRWA\nUkpVUVoBKKVUFaUVgFJKVVFaASilVBWlFYBSSlVRJiuARsBsYAWwHBgefDwZmAWsAb4CkgyWQSml\n1GE4DL533eBtMeAHfgV6A0OA3cA44D6gOnB/ideKiBgsmlJKVT4OhwMiOK+bvAL4E+vkD5AJrAIa\nAJcBE4KPT8CqFJRSStnMrj6AFKAD8DNQB9gRfHxH8L5Sx7z8/Hz69LmaceNeqOiiKBUVMTZk+IGP\ngNuBjBK/k+DtL0aPHv2/n1NTU0lNTTVTOqXClJ6eztSpk9iwYSP33nt7RRdHKebMmcOcOXPK/HqT\nfQAAscAM4HPg+eBjq4FUrCaielgdxa1LvE77ANQxae3atSQnJ5OcnFzRRVHqLyLtAzBZATiw2vj3\nACNCHh8XfGwsVudvEtoJrJRS5XYsVQDdgO+ApRxs5nkA+AWYAjQGNgJXAOklXqsVgFJKRehYqgDK\nQysApZSK0LE0DFQppdQxTCsApZSqorQCUEqpKkorAKWUqqK0AlBKqSpKKwCllKqitAJQSqkqSisA\npZSqorQCUEqpKkorAKWUqqK0AlBKqSpKKwCllKqitAJQUXX55X/nzDN7EggEKrooUZednc0pp3Rm\n2LA7K7ooSkWFHTuCqSrk999X8eefWwgEAjidlev7RV5eHr//vpiaNWtVdFGUigpdDlpFVX5+PoFA\nALfbXdFFMSIrK4v4+HhiYvS7kzr2mNgPwA/kAEVAq+Dtc6CgDOULl1YASikVIRMVwG9Yu3tVB+YB\nC4B84O9lKF+4tAJQSqkImdgQxgFkA32Bl4EBwCllKZxSSqljR7i9dF2wvvHPjPB1SimljlHhnMjv\nwNrM/RNgBdAMmG2yUEoppcyLZBSQD8gyVZAStA9AKaUiZKIP4ExgJbA6eL89Vl9ApfDssy/i9yez\nbNmyii6KUkrZKpwK4HngAmB38P5i4GxjJbJZTk4u2dn7KSwsrOiiKKWUrcK5VPgFOB1YBHQIPrYE\naGeqUNjcBFQZZ60qpaqeSJuAwpnOuBnoGvw5DhgOrIq4ZMcwPfkrpaqicGqKWsALwHnB53+FVQns\nMVgu7QRWSqkImZgJXBG0AlBKqQhFswnoxSP8TrCuApRSSh2njlQB/Ip1oncE/y1W8r5SSqnjkDYB\nAQUFBcTGxtqSJSIUFhbamldUVKTLF0fJnj178Pl8lXa5a3V8MzERbBaQFHI/GfgysmIdux599Ani\n4uJYuHChLXlDh96Ox+Nl48aNtuRdeOEAEhOTSU9PtyWvMtu4cSP16jWkY8fuFV0UpaIinAqgFhB6\n9tgL1DFTHPslJydRvXo9PB6PLXk1ayZTo0YD4uLibMmrVasmtWs31CuAKIiNjSUuzk21aklHf7JS\nx4FwLhV+xVoKelPwfgrwMdDRUJlARwGpY1RhYSEul6v4UlupY4qJiWAjge+B74L3zwJujLhkSlUC\neiWlKpNwa4pawBlYo39+4uC6QKboFYBSSkUomp3AJwb/7QQ0ArYB24HGhN/88yawAwhdanM0sBVr\nbaFFWAvNKaWUstmRaorXgaHAHEof939OGO/fHcgE3gbaBB8bBWQAzx7hdXoFoJRSEYpmH8DQ4L+p\n5SjP91idxiVpD5pSSlWwcHu0zsQ6kYc+/+1y5P4DGAwsBO7i0GGmKory8/PJycmhWrVqtuTl5ORQ\nVFSE3++3JS8zMxOXy2XbMF6lKpNw5gFMBJ4GugGnhdzK6hWgKdbOYtuBZ8rxXuooevS4nBo1arJ3\n715b8tq06Uy9eo1s2WCnoKCAevUa0aZNZ+NZSlVG4VwBdAJOInrr/+wM+fkNYHppTxo9evT/fk5N\nTSU1NTVK8VVL+/ZtycrKsm3pgo4dO7FzZ2Nb9lhwuVx06tSV2rVrGc9S6lg0Z84c5syZU+bXh9MW\n/wFwO9YooLJIwTrJF3cC18P65g8wAutq4qoSr9FOYKWUilA0O4GLv5n7sTaF/wXICz4mwGVhvP9k\nrP2DawJbsEYApWI1/wiwAbgp3MIqpZSKniPVFKkl7hcvDV3881wTBSp+f70CUEqpyJjaESwFaA58\nDXixrhwORFi2SGgFoJRSETKxHPSNWP0ArwXvNwQ+ibhkSimljinhVAC3Yg0BLf7GvwaobaxESiml\nbBFOBZDHwc5fsJp/Kk37TEFBAevWrbMtLzc3l02bNh39iVGSkZFBWlqabXl2++mnn1iyZIlteWlp\naWRkZNiWt2nTJnJzc23LW7duHQUFBbblqYoVTgUwF2tJaC9wPlZzUKlj949Hd975AM2bNy/XWNpI\nDBx4HSkpKaxatcqWvHPOuZSGDRuya9cuW/LstH79erp3P5fTTuvCgQMmu6Qsu3btomHDhpx7bjgD\n4Mpv1apVpKSkMHDgdbbkzZkzh+bNm3PnnQ/YkqcqXjgTwe4DbsBa0fMm4DOsCVyVwtlnn8n8+Qtp\n2rSpLXm9ep3Drl27qVu3ri15F1/ci6SkJBITE23Js1ONGjVo1OgEEhOr2bIURGJiIj16XE7XrvbM\nPK5bty5dupxPr17hrLtYfk2bNqVTp7M5++wzbclTFS+c3uK+wEwObQYyTUcBKaVUhEyMAroM+AN4\nB7iE8BeQU0opdQwLt6aIAy4ErsBa438WcL2pQqFXAEopFTFTE8HAqgR6Addh7QtcI6KSRUYrAKWU\nipCJJqCLgPFYzUD9sXYKq1OGsimllDqGhFNTTAbeB74A7BqQrFcASikVIZNNQHbSCiBK1qxZQ1pa\nGuecY89QwiVLlpCTk8MZZ5xhS95PP/2Ex+OhXbt2tuQpdSzTCkAdonbtJuzatZmlS5fSpk2bo7+g\nHAoKCvB6EygqKmD79m3UqWO2pXDHjh3Uq1cflyuW7OwMYmNjjeYpdayL5n4AqhK48sorWbhwEU2a\nNDGeFRMTQ79+f2ffvnSSk5ON5yUnJ3P++b2pXj2JmBj9U1YqUkeqKb4BegDjgHvtKc7/6BWAUkpF\nKJpXAPWAM7Emgr0XfNPQs/JvZSifUkqpY8SRaooBWJO9ugILS/m9yV5FvQJQSqkImegEfgQYU9YC\nlZFWAEopFSFTo4Aux5r9W7wXsOnloLUCUEqpCJmoAJ4ETgMmBZ//N6wmIZOLhmsFoJRSETKxFMTF\nQE/gTeC/wAVYq4JWCgsXLmTUqEfZu3evLXlTpnzILbeMYM+ePbbkbdy4kfnz59uSBfD999/zxRdf\n2Ja3aNEiVq9ebVueOn5lZ2czc+ZM3fEsQks5dOG3GsHHTBK7nHBCO3E46siDDz5sPKuwsFCcTpc4\nnafKo4+OMZ4nInLiiacJINu2bTOelZ+fL06ny7a8vLw8ASQ5ub7xLHX8GzXqUQHk9ddfr+iiGEOE\n2/WGM3vmCawhn7OxLi3OBu6P6HR+DBs6dDCvvfYWl156kfEsl8tF795XMXfubHr16mk8D+D++29n\nwYLF1K5d23hWbGwst9xyJ3v37rMlLy4ujoceGkNycnXjWer4179/X9as2UjPnvb8v3c8CLetqD5W\nP4AAC4DtxkpkCVZmSimlwqVrASmlVBVlohNYKaVUJaQVgFJKVVFHqwBigN/tKIhSSil7Ha0CKARW\nA+bXElZKKWWrcJqAkoEVwLdYS0BMBz41WSg7TZnyIZdffhVr1qyxJe/ZZ1/koouuYNOmTbbkjR//\nNvfc8yC5ufbs5vnWWxN4/vkXsasTf86cOfzyyy+2ZAF89tlnrFixwra8ymz37t2MHTuWLVu22JKX\nnp7OO++8Q05Oji15lUVqKbezDWfaNnEiKamewKkyePCNxrOys7MFHOJwnCP33HO/8bzc3FxxOBwS\nE1NbPvroI+N5e/bsEXCI0xkvy5cvN55XPBEsKamu8SwRkbS0NAGkZcuOtuRVdg8/PEoAGTRoqC15\nDz30iADy2muv2ZJXEYhwIpgrjOdsDP5bE+sqYGfwlhdJUIRGjx492uDbH5SdncvGjb8xZsz9pKSk\nGM2KiYnhjz/S2Lt3OWPGjKRBgwbG83btyiQ+PpZ7770Dr9drNM/tdrNzZwbNm7fihhuuweUK58+r\n7FwuF8nJtejT51LatWtrNAvA7/fjcsUzaNAVtGrVynheZVe/fj22bNnDXXfdYvz/BYDGjRvhdMZx\n/fVD8Pv9xvMqwqOPPgrwaLjPD2e86I3AUKymoGZAS+AVrN3CTAlWZkoppcJlYh7ArUA34EDw/hrA\n/Dx/pZRSRoVTAeRxaHNPDBG2MymllDr2hFMBzAVGAl7gfOADwt8Q5k1gB7As5LFkYBbWlcRXQFK4\nhVVKKRU94VQA9wO7sE7iNwGfAQ+F+f5vYe0fUPL9ZmH1JXxDJVpZVCmljifhdhbEA62xmn5WA/kR\nZKRgXTG0Cd5fjTWMdAdQF5gTfO9Q2gmslFIRMrUj2Frg38CLwDqgPIvn18E6+RP8t0453qtcRIQ+\nfa7G50vmjTfeMp6Xm5vLGWech89XnY8//sR4HsAbb7zF7bffS16eyVG7Bz311HOMHPkogUDAeFZW\nVhZ9+lzNNdfcTGFhofG8zMxMrrlmGE8//YLxrIqQl5fH//3f/7FhwwZb8g4cOMDzzz/Pjh07jv7k\nKNixYwfPP/88Bw4cOPqT1f/8DjQPud+MyNYHSuHQPoB9JX5f2l6Mtkya2Lp1q8TE+ATekWbNOhjP\n+/HHH8XrPUHgNencuafxvEAgIC5XrDidXvnyyy+N5+3evft/E8GWLVtmPO/TTz+V+PgUiYtLloUL\nFxrP+/DDDyUmpo4AkpubazzPblOnTg1OzLrBlrxXXnlFABk50vxufCIiI0c+LIC88sortuRVBAzs\nCHYA6wqg2HoODgkti+Kmnz+BeliTyv4idCJYamoqqamp5YgsXb169TjnnPP59tvruf3256P+/iW1\na9eO5s3rsHLlcIYPH288z+Fw8Oyzz7NkyUq6d+9uPC85OZlHHnmMffv2c+KJJxrPS01N5ayzOpCQ\n4KdNmzZHf0E5nXfeeVx2WU/atDmR+Ph443l269mzJ2PHjqV379625A0cOJB9+/YxePBgW/Juvvkm\nfD4PAwcOtCXPDnPmzGHOnDllfv2R2or6Bf89D2sxuCnB+wOAzcDNYWakcGgfwDhgDzAWqwM4ib92\nBAcrs8pJRIrb6pRSKmqiuSPYeA5eTjhK+XlIGO8/GavDtybWN/9HgGlYlUljrGUmrgDSS7yuUlcA\nSillgm4JqZRSVVSkFUA4fQAnAP/Aasopfr4Al0VYNqWUUseQcCqAqcAbWO34xWP79Ou5Ukod58K5\nVPgFON10QUqwrQlo//79rF+/nrZt2xpfvhisuQD79++nTh17pj8UFBRQUFBgfCnoYunp6RQVFVGj\nRg1b8pRSB5mYCPYiMBroAnQMuR33du7cSbNmp9CtW18uumiALXlNmrSmceMWPPbYWON5aWlp1K9/\nAklJtWyZeDZ//nzq129KgwYnMH16uMtFld3WrVtp2bIjHTp0Jz295DiC6Pvjjz9o3rw9F1zQj4KC\nAuN5ixcvpkWLjgwdervxLLAmSl177TDmzp1rS97u3bt56KFHbNsdb+bMz+jSpRfz58+3Ja+yeBJI\nw1oUbnbIzSRbJk3MmDFD/P4zBfaKw+GUQCBgNG/atGni83UR+FKaNTO/q9SkSZPE6z1H4Cm59NIr\njec98MBIgREC42TgwCHG89544w2Jjz9DPJ6W8tlnnxnPGzt2rLhcZ0tcXJKsXLnSeN7dd98nDsc5\nAg7Jzs42nlc8Mevssy82nhWa99BDj9iS16VLT4HaMnDgtbbkVQQMNM+vA+Ki/aZHYcuHlZ6eLikp\nJ0lsrE+uuWaYLXktW3YQlytW/vOf/xrP2717t7Ru3UkSE2vLt99+azxv2bJlUq1abfF4qsncuXON\n5+3evVtSUy+RSy4ZaMsJcuvWrdK9+0Vyww3/kKKiIuN5a9eulbPOuljGjHnSeJaIyP79+2XUqEdl\nyZIltuRlZGTIq6++Krt27bIlb/78+TJw4LWyYsUKW/IqAhFWAOG0FU3FWgXUngU7LMFjMa+wsJC9\ne/dSu7Z9e9wUFRXZ0t9QEQKBACJSaY9PqWOZiXkAc4G2wAIObgxjehiobRWAUkpVFibmAYwqc2mU\nUkods3QmsFJKVRImrgAyOdixEAfEBh9LjLRwSimljh3hzAPwAwnBmwfoC7xsslB2EREmTZrE7bff\nzcKFC43nBQIB3nrrLe6/fyRr1649+guiYNmyZXzzzTe2bNACsGrVKhYvXmxLFsDq1atZv369bXk5\nOTm2bD5TLC0tjZycHNvylAqH6f/DbRkyNX78BPF6TxR4VHy+mrJ582ajeWPHPiNeb0dxOO6W6tXr\nS2ZmptG8L774Qjye2uLznSRDhw43miUi8tFHH4vHU1s8nvry4ovmN914883xwbwa8vnnnxvP++KL\nLyQuzid16za1Zeji448/JXFx1aR27Saye/du43mzZs2SOnVOkHvusWeDlh9//FE6dUqVGTNm2pL3\nww8/SM+efWT58uW25FUEIhwGGs4VQL+Q2wCsiWGV4ivJvHkLyM6+FngEl6s1q1evNpr33XcLyM6+\nFZFx5OXFkJaWZjRv7tzvyc29mqysl5g1a47RLIAvv5xNTs5N5OQ8xIwZ3xjPmz79a3Jy7iEnZzCz\nZpmemwhTp35GQcHVHDjgtuUq58MPZ5CfP5asLC/Lli07+gvKaeLED9mxI5k33viv8SyAt99+j19/\n/ZmXXx5vS95//vM2X331CVOmfGhLXmUxHngreHsdGAmYHjRvS2353XffiddbUxITz5UGDVrI/v37\njeZNnz5dvN5akpBwmrRv31UKCgqM5q1atUqSkupKTIxbXn/9TaNZIiKLFy+WpKS64vUmyezZs43n\n/fjjj5KUVFfq1Gkqq1atMp73+++/S8eOZ0u/foMlLy/PeN4XX3whdes2kwsv7G9L3rp162TgwCEy\nbdqnxrNERLZt2yajRo2WP/74w5a8tLQ0eeGFF2Tv3r225FUEDEwEqwjBYzFv7dq1rF69mu7du1Ot\nWjXjeWvWrGHz5s1069YNt9ttPK+wsJCCggI8Ho/xLLD6VUQEpzOci0ulVDRFcyLY4cb/F5+Zx4Qb\nUga2VQBKKVVZRLMCuJu/Xk74gOuxtnj0RVq4CGgFoJRSETK1JWQiMBzr5D8FeAbYGWnhIqAVgFJK\nRSjaE8FqACOAvwNvY+0DsK+shVNKKXXsOFJP3dNYu4FlYC0GN4pKdvJfsWIFKSknExMTR69efcnO\nzjaat2bNGtq3706tWk15+OHHMH2Vs3HjRs47rzft2p3FzJkzjWaBNdHt/fffZ/z48bZsmJKens7o\n0WN48slxtkyWysvLY9KkSXz77bfGs8DqUF+6dCl79uyxJU+pUAEgF6sCKHk7YDjbliFTLVp0EHhV\nIFPc7n7y4IOjjOa1atVJHI5nBVaIz3eiTJ8+3WjeySd3FqdztMBU8XhqyJYtW4zmjRr1T/F624vX\n21WGDLnFaJaIyFlnXSRxcVeJ232JXHnl9cbz+vYdJF5vN/F6G8uUKVOM591778Pi8TSQpKS6sn37\nduN5n3wyVTyeanLddbcZzxIReeedSZKYWEfGjXvOlrzJk9+Xpk3bytdff21LXkUgihPBnICbg8tA\nhN4qxTpAaWmbsFa19pGb25M1azYazduyZT0iA4GTKCzsZnwJgw0b/iAQuB64jJiYRmzevNlo3vz5\ni8jOHkZ29n38/PMio1kAy5cvIz//QXJzh/Pbb+YnSi1Y8CvZ2WPIyenHL7/8ajzvm29+ICdnBAUF\niaxZs8Z43vTpX5Gb24Jp06YZzwL48MOZZGQk8v779uS9++4nbNz4B598Yv5qWJWPLbXl1VcPFY/n\nXIHnxOttKFOnTjWaN2TILeL1niEu1x3i99cyPgHmllvuFJ+vjfh8F0uzZm2M75plTaxLkrg4n3zy\nidnPUkTkqaeel/j46uJ2J8nEie8az5s48V2JjfVI3bpNbZm89MMPP0jr1qfLoEE3SWFhofG8tLQ0\nuf32u2XOnDnGs0RENm7cKA88MNK2pRm2bt0qL774ouzZs8eWvIqATgQLX0FBAS+++BIrVvxBv34X\nc9FFFxnNKyoqYuLEiWzdupX+/fvTqlUro3kiwsyZM9m3bx+XX345iYnmL9wKCwsJBALExdmzi+i2\nbduIiYmxbUe34r/L4GgLpY4ppoaB2s2WCkAppSqTSCsAna+vlFJVlFYASilVRYWzI1iltXnzZkaN\neoItW3bQtWtHRo6812jbdVZWFhMmTGD37j2cd14PzjzzTGNZYLVXL168mMzMTE499VRbFoTTNnKl\nVHkZ7y1PS0uT6tXri8s1UmCKeDyXSI8el0pRUZGRvMzMTGnRor14PL3F6bxfPJ56MmHCO0ayREQC\ngYBceeV14vU2kYSETtKkyUmyc+dOY3ki1rhur7e6xMf75emnnzeaJSLy888/y+mn95Bu3S60ZTno\npUuXSu/ef5dhw+4wvnS4iEhhYaHMnj1bNm3aZDxLRCQnJ0dmzJhhfL6IMocIRwEdq4x/UI88Mlpi\nYm4VkOAtX3y+ZvLrr78ayXv11VfF671MIBDM+02qVatrJEtEZPbs2eLztRbIEhCJjb1VbrvtTmN5\nO3fuFLc7SWCJwHrxeuvKypUrjeUVFBRIYmJtgbfE4XhaUlJOMZYlYp2Mq1evL/C0xMVdKVdddYPR\nPBGRYcNGiM/XSvz+mraclHv0uFx8vk5SrVodW9bMnzp1mlSv3kBee+2/xrNERD788GNJSTlF5s6d\na0teRcDAjmCV0u7d6RQWpoQ8EktMTH32799vJG/fvn3k57fgYAd9C7KyzK2ssXv3bpzOFoAXgIKC\ndmzfvttY3t69e3E6E4FTgKa4XI35888/jeXl5uaSnX0A6I/IFfz5p9lJbnl5eRw4sAe4gfz8PqxZ\ns8FoHsCiRcvIyrqSQKAamzZtMp63YsVysrJuIi8Po//tis2e/QPp6Xv58kvzu7kBfPXVHDZuXM68\nefNtyVNlZ7ymnDp1qni9LQW2Bb+Rz5KEhNrG9nr95ZdfxOOpIzBfYJ/Exd0oPXpcZiRLROTPP/+U\natXqisPxtMAU8XobybRp04zlFRYWSseO3cXrvVA8ngHStOnJkpWVZSxPROSaa4aJz9dUPJ768tBD\nY4xmiYjcdtvdEh+fLB5PdVv2IF60aJF07XqB3HnnAxIIBIznffHFF9Ku3VkycuQYW/IyMjJk0qRJ\ntux3LGI1w3722We27K5WUdAmoPCNGvVPiYvzi9/fVKpVq2N8G8MpUz6QmjUbS1ycT3r27GP8Mnvl\nypVyySV/k27dLpb33ze/dk12dra8/vrr8vLLL8u+ffuM5wUCAVm4cKEsW7bMeFaxrVu32nJsSpUF\nOhM4Munp6ezatYvGjRsTHx9vS6ZSSpmgM4GVUqqKivaGMCZtxFpWuggoAE63uwA//fQTL774XzZv\n3k7Xrh247babaNiwobG8oqIivv32W3bv3k23bt1o1KiRsSyllDqaihwFJEAq0IEKOPmPHfssPXoM\nYPLkVvzww408//x+TjqpEwsWLDCSt2rVKho1akW/fiO56aaPaNmyPSNG3G9sU5hAIMC//vUUtWql\n4PMlM3DgEOMbi6xatYoRI+7lttvuNPY5hiooKOCHH37g559/JhAIGM8Da/RRYWGhLVlKVWYbsLac\nLI3RjpL169eL250ssCVkHoAITJZmzdpFfQREIBCQlJSTxeF4PSRrj/h8p8gHH3wQ1axijz32pHi9\nnYPj8tMkNvY26dChm7HRHUuWLBGfr6Y4HA8JPC5eby359ttvjWSJWB3O7dt3Fb+/rfj9reT88y83\numRyIBCQ6667VVyueHG7E+WDDz4yllVs9uzZcu65l8ttt90lOTk5xvPWr18vY8aMsW3DlIyMDJk+\nfbpto4D2798v33zzjfFl0SsSx9EooPXAImAhMLTE74x+SGPHjpW4uFtKnPxFoEi83kZRn8C0cOFC\n8ftbhkwCK75NkrPPviSqWcVq1GgksPSQY/P5UmTJkiVG8vr2HSTwTEjeu3L66ecZyRIpnlh3sUCR\nQL74/afKp59+aizvq6++Er//ZIEDAr+J250gBQUFxvIyMjLE660u8Ja43RfJyJGjjWWJWBVc3bpN\nxekcJh4ZPX6SAAAgAElEQVRPLVm6dKnRPBGR0047R7zeDtK4cWtbhp22atVRvN5Wkppq5v+5YwER\nVgAV2QfQFdgO1AJmAauB74t/OXr06P89MTU1ldTU1KgFZ2ZmUVBQvZTfOImJqU5WVlbUsgAyMjJw\nOmvw176ZmuzfnxHVrGJZWfuB0DXynbhctYxNdDtwIAuoH/JIfTIzo/s5Hpp3gIKCFKxWTCfQ2Nix\ngTXRDRpjbYjXgsLCAvLy8oiJMfO/UHZ2NoWFAeAy8vLWs23bDiM5xUSEPXu2Ewj0xen8gT///JM2\nbdoYzdy8eSPZ2X/nzz+fIxAI4HK5jOZt2bKe7OxBbNhgz57OdpgzZw5z5syp6GKU2yjgrpD7RmvJ\ng8skFJT4Rr5SEhJqR/1yOysrS7zeZIEVIVkBcbv/Jv/85xNRzSrWt+/VEhNzR8hVxyypVq2O5Obm\nGskbP/5t8XpbCfwmsFK83tPlsceeNJIlIvLHH3+I319L4BFxue6W5OQGRtc62r9/vzRu3Fr8/ovE\n728jgwffZCyr2F13PSguV6zUrXuCLTuQTZz4rpxwQnu54YZ/GFsTK9T8+fPlb3+7Tr788kvjWSIi\nc+fOlRtuuFUWLVpkS15F4DhpAvJifZUC8AHzgJ4hvzf6IQUCAenWrZe43f0F1gVPkrPF620pzz33\nopHMN98cL15vXXE6/yUwSbzeS6VFi/bGFhXbsWOHtGlzhvj9zSUxsbNUq1bH6FZ/gUBAnnnmBalT\np5nUrNlEHnhglPGTyMqVK+XWW0fIiBH32rJgWnp6urz77rvy+eef29JkISKSn59vW5Y6/nGcTARr\nCnwS/DkGmAQ8EfL74LGYk5OTw803387kye+Tn59JcnIj/vnP+7n55mHGMn/99Vdeeum/bNu2m0su\nOZshQ67F5/MZy5PgctD79++nS5cuOtFNqUrueJkHsAFoX0HZbNu2jQEDhvDbb4sJBAYB1cnN/YM7\n73yAlSvX8dxzT0S9bTctLY0PP5zGt99+T05ONrm52TRp0piLL74Yp9PMaFyHw0HdunVJSkoy1lat\nlDp+VbmZwDt37qR9+zPZtWswhYX3AaHfinfg9Q6mV6+afPTRxKhtavLjjz/Sq1dvCgoGkJc3GGv0\n6w/4fM9y/vmn8OGH70S9A+z777/nllvu448/ficmxofbDQ89dC+3336rsc1aVq1axQcffEhBQSG9\ne19Gp06djOQopUoX6RXAscpYG9l1190isbHDSxkCWnzLFp/vJPniiy+iknfgwAFJTKwj8FkpWTni\n9Z4rY8ZEtyP4xx9/FK+3lsB7IR3dv4rX215Gjnw0qlnFHnvsSfF4aktMzJ3idN4vXm9DGTbsDmPt\n13l5eXLffQ9LrVpNpX79lvLEE08Z73NYtGiRPPzwKBk3bpzs2bPHaFaxnJwcWzpkVeXAcdIJfDRG\nPpwDBw6Ix1NdYOsRKgAReF3OPTc6SzW/9NLL4vP1O0LWUqlevYHk5+dHJU9E5PTTewhMKCVrm7jd\n1aJ+8lqyZIl4PHVDltYWgXTx+VoZG+Fx443DxePpFZzrsEC83s5GRx3NnTtXvN6a4nDcL3Fxg6Rh\nw5ZGVwUtLCyU/v0Hi8sVJ9Wq1ZEff/zRWFaxWbNmSa9e/eVf/3rKlo7npUuXyoMPPiTz5s0zniVi\nTXR77bXXZNu2bbbkVQS0Aji8efPmSWLiaUc5+YvAn+Lz1YhK5rnn9g5+Ez98XkJC66hN0Dpw4IDE\nxHgEcg+TdZlMnjw5KlnF7r33QXG5Higl798ycOCQqGaJWCNj4uP9AjtCspZIzZpNop5VrFu3iw6p\nVD2e/vLKK68Yy5s6dar4/Z0EsgWmSPPmHYxliVjDXN3uagKvi893snzyySdG8/Lz8yUhoabACPF4\nqhvbh6NYQUGBVK9eT9zuS6R583ZGsyoSuiPY4QUCARyOcDpDYwgEiqKSWVhYxKH9DH/lcMRFbX2Z\noqIiHA4XEFvq70U8FBQURCWrWF5ePoGAt5TfeMnJyYtqFlj/HYuKCije7cySQF5eTtSziuXl5QNJ\nIWVIIi8v+sdWLDs7G6gOuIH6wfvm5OfnB9dTaoNIDaOT6gAKCwuD/73aIGKtsWRSUVEROTmZ5Oae\nTHq6uZ34VHQYqR137twp8fFJAvuOcgXwkbRr1z0qmaNGPSZu941HyNokXm+yZGZmRiUvEAhI8+Yd\nBD4vJStD3O7kqI+ZtybWtQx+Wy3OKhCf70x59913o5pV7MIL+0ts7C1i7XmcLm73ALnxxuFGskSK\nN7xvIjBJHI5xkpBQWzZu3GgsLysrS045pbP4/SeKx1ND3n33PWNZxf7975elfv1W0r//YFt2zZo2\nbZqcc87l8uab441niVh/p0OH3iYLFy60Ja8ioE1Ah5eWliY+Xz2BJ49wQi4S6CTdu/eIylov27dv\nF7e7usDyUrICEhc3SIYNuz0KR3fQhx9+FDxZLQvJ2icez+VGmmQCgYD07z9IfL6OAm8KTBSf7yzp\n3v2CqPZthNqzZ4+cf35viY31SWysTwYMuMb4FpTvvz9Fzjuvr/TtO8johvfF8vLyZMGCBbZsCK8q\nB7QCKN2OHTukXr1m4nLdIVBL4ONSTsiFArcKdBaPp4f063d1uTvDFi1aJH5/DYHqAi+LtZiYCCwU\nuFQcjkQZMmRY1FeyfOmlV8TtTpKYmI4SE5MqLpdf+vW72thSEEVFRfLBBx/IhRdeIeed10fGjx9v\n7OQf6sCBA1G7elLqeIf2AZSuX7/B7No1gKKi54DPgDuA7sB/gI+AfwLNgLXA5+TkfMpnn/3OSy+9\nUubM5cuXc9ZZvcjMfBWYgbXmXQ2sdt2+QBdEVvL++6u59tphSJTmPnz00cfcd9/DuFynU1jYjcLC\ntsTE9GHGjBmMGPEARUXR6d8I5XQ6OeGEE+jatS1nndWBE044wZbJZwkJCUZnUyul7BfVWvHLL78U\np7O6QF7It/18gY8EBgn0Ebgt+K089Ipgnni9dcr0DTMQCEjLlsVNIqHvWRBsKw9dGvqA+HwnRWU5\n42nTpgWHZC4o5Qpnj3i9qXLttcPKnRMqPT1dune/QLzexuJy3S1O5/3i87WSNm3OkO3bt0c1q1h+\nfr5MnjxZLrvsKunbd5BMmzbNlvHyRUVFujaPOmahTUAHBQIBefHFl8XlShR48Cgdv6XdAgKtpEaN\nBvLTTz9FlD1//nzx+ZoF+xTCyRovXbteUK7jLSwslFq1UgTmHCFnv3g89aO63ntq6sUSF3ejHLq6\nakBiYh6W1q07Rf3EnJeXJ927XyA+XxeB/wq8Ij5fWxkwYLCxk/OOHTvkwgv7i8sVKz5fsi2L3a1e\nvVpeeOEFmTx5stHNbooFAgHZv3+/Tjw7jqEVgCUQCMjNN98hPl8bgRoCG8pQAYjACwLni9dbS2bM\nmBF2/lVX3SBO57gIcnLE7a4hmzdvLvMxz5w5UxISTj1qlss1WoYMubnMOaGWLFkiXm+D4BXVXytQ\nv79d1CeD/fe//xWv9+wSFY41g/vzzz+PalaxU09NlZiYO4N9OOvE6+0i48Y9YyRLROSnn34Sn6+m\nxMffJD5fF7noov5Grzxyc3PlrLMulJgYj9Sq1diWTu6nn35eWrfuLPfd94gtV1WLFi2Sxx57TNas\nWWM8q6KgFYDlzjvvF5/vNIFdAs4IvomXvE0TuETgJ/F6a8k333wTVn7HjucIzIooq1q1M+W7774r\n8zE/+uij4nCMDCNrvrRseVqZc0I9/fTTEhd32xGyHpfhw++KSlax7t0vFphSStbzcvXVQ6OaJSKy\nYcMG8Xhql6hw5kmTJqdEPavYhRcOEHg1mJUnXm8jWbFihbG88ePHi8/XQ6BAHI6npVevfsayREK/\nOHwT1aVXDicvL098vmRxOq+TevWaGc2qSERYAVTKTuDPP/+cV199j6ysz7Em05SXAJ3Jzn6Pfv2u\nDmtzdWtSTWRrMhUV2bOxOTiw/lbKT0QQOdKfkZNAILpfSqyyl/bZRj/r0LzQTFfUPsPSWMdRvECg\nA4fDaTgvgIgL628jNvj3a87B/z88gMt4nvV3GiAQ8BgZBKGiq8w14L59+yQ5uaHANyHf1moIbCrj\nFcCLAkP+dz8+/g7p3fuqI5bhwIEDkpTUWOCpCHJyBRLk//7vpTIf+6effioJCWccNcvl+qcMGnRj\nmXNCLVq0SLzeRvLX3dWKm4A6ysyZM6OSVezVV18Tr7dHiau6XPH52sr06dOjmlWsXbuuweUusgW2\niNd7ljz++FgjWSIi33//vXi9NSU29nbx+VKlR49LjbbNZ2dny+mnnyPx8UlSvXp0+4gOZ8yYJ6Vp\n03YyfPi9tjQB/fzzz3L//Q8avZKqaFT1JqC7735A4uOvL3EiGibwaBlO/gGBjnLoSp5Z4vU2Oezi\nXEVFRdKly3kSG3uZQAsJv+lpokBX8XjqyLffflumYy8sLAxWfvOOkJMpbneDqG6L17VrT4mLG17i\nWAPicv1LmjVrG/UTV25urnTufK74fKkCkwTeEp+vk1x22d+MnSS3bdsmqamXiMsVJ253otxxx33G\nO2aXLl0qY8eOtW1ORSAQkB07dtiSpcygKlcAubm5kpBQW2B1iZPeEoH6UnpH5ZFuPwk0/ctJ3OF4\nWvr2vbrUMjz33L/F5zsz+I24XfDEfrScTIE2Ap8IfC61ajWRAwcORHz8a9asCS6wVVusVTJL5hwQ\nOFdcriSZNCl6C8Lt2rVLWrToIE5nI7FGW40Sp7OF1K/fQjZs2BC1nFB5eXkyfvx4Oe+8PnLhhVfI\nlClTbBkpk5uba0uOUmVBVe4D+PjjjxFpC7Qq8Zu2QGsO3XXyaHKx9qkfTsmPSeRaZs6c/pe+gIyM\nDEaOHE1W1ptYm62NB0YAnx4hJwNrUlgH4HLgAjIyuvP0089HUFbYu3cvZ511AZmZ/wSeA1KBPsAE\n4D2siW9NgVYUFX3LDTfczpw5cyLKKE1mZiZ9+w5i27ZCAoG/B49nD4FAf/bvr8Gll/6N3bt3lzun\npJycHA4cyMDpdP2vHCYXZysWHx8f9c17lFKHKlPtd9VV1wv832G+ZW8TSBF4Rg6dhHW4b+QXC1xx\n2CacxMReMm3atEPyX375FfH5+pR47i/Bq49Lgk1J+cH8LWI1SzUQKDmGfrFUr94gorWIxox5XNzu\nwSHvkSHWKJKrBAYIPCSwMeT3H8gpp3Qp0+dcLBAIyLnnXipu9yA5XB9AbOxdcsopnaOyrlKxn3/+\nWRIT64jXe4XAuwJvi99/sdSu3URWr14dtZxQe/fulTFjHpcOHVKle/eLZdKkSTpeXh1zqMpNQCec\n0D7YbHO4E/smgZMFLgyejEue3A+ItV5PK4Fr5UhNRi7XgzJy5COH5FvDEz8o5flZYk1YOlXAFbwl\nidU3saTU909IOFkWLFgQ1nEXFhZKjRqNBH49SsUWeisQr7eh/Pbbb2X6rEVCJ7sdqWktIH7/GVFb\nXz49PT24w9qnf8lyOF6X+vWbR7WyEbGauBo2bClu99UCXwhMFp/vVBk48FqjE8/OOedScbsTpWbN\nJjJ+/NtGckItWLBAxo4dKx988IEtnbKFhYWSlpamfQ5RRFWtAIqKisTlipNDlyQu7ZYl8IZAB4ET\ngt+OrxG4TKwF2/oKfC1Hv0r4QM49t/chZahWra4cfbRR4CgnTOvm9V4rr776aljHPnv2bElI6BTB\nyd+6OZ2PyD/+UfYx+gMGXCNOZzgjnSbKmWf2KnNOqH//+0Xx+QYcNish4Uz5+OOPo5JVbMSI+yQu\n7qa//B35fM2M7GZVVFQkbdt2CU482yPwi3i9DeWzzz6LelaxmTNnitdbW2JjR4jP10GGDv2HsSwR\nazXXFi3aidtdS+rVaxb1JcpLCgQCMnz4vZKS0lYef3yc0ayKRFWtADIzM4M7YYV7AgyItfbPZLG+\nnX8oVrNMuK//Wjp2PPd/+UVFRQKOMCqOcG+j5eGHHznCER80adIkSUgYWIaMd+SSS66M+LMu1qLF\nqXLkK67i22apXr1BmXNCXXDBFXLkjvWn5Oabo7u8dqNGJ8tf14kScThGyV133RfVLBGRTZs2Bddz\nCr1CfUUGDrwu6lnFOnc+Xw5eve4XlytesrOzjeWNHTtW4uKuFmu02D1yyy13GMsSEfn666/F52st\n8IN4PHVtmelcEYiwAjC/XKNNHA4HkR27A+gUvJWFBDP/+nikE8BKF8DpDO99nE4nZav4JfjasrFe\nG84EngAOR3TGG7hcTuBIu6cVRL2T1lrVNP8vjzud+cTFlb7zWnnzRAqAIg4OQMghJsZc57N1HJnB\ne9k4HI5y/W0cTXx8PE7nfiAPp3MvbndNY1kAHo8HkRxgC1BIXFyc0bzjRaUZBeR2u4M/ZdiUuJsa\nNQ5uEeh0OklObgisj8q7+/1radKkSVjPbdKkCSLLibQSiI1dRqtW4WWUpmvXU4mJ+SqMZ35Bp06n\nljknVP/+vfD73zvMbwW//z0uu+yCqGQVGzy4P273cxz6+e4hLm4CAwf2j2oWQL169ejS5Qzc7muA\nJcBHeDxPcOutQ6KeVWzs2Ifw+e4hIWEAXm9nHnroEeLjj7yVaXkMHTqUzp2LcDr9nHzy7zz44D3G\nsgDOPPNMHn/8brp1m8hrrz1Ps2bNjOap8inT5U+rVqcLfBelJpgj32Ji7pFHH33skPzzzutzlOaJ\n8Jun/P5mYW8UHwgEpEGDVhEee654PLXl999/L9NnLWJNVPJ46os1aupwOfni958iX331VZlzQmVn\nZ0udOk3F4XjtL59ZTMxoI6uPZmRkSNu2XcTnO0fgDXE4xonXmyJ33z0yqjmhMjMzZciQW6RRo5Ol\nbdtuUfv8jmTjxo0yefLkiFe+LQ9dWju6KFtTwDGnTAc/ZMjNAs/aUgEkJqb+ZeXJd955R/z+nlF4\n/3lSr16LiE5kzz33vHg8l0n4fRD/kTPOOL9Mn3Ooq666XrzeXoepBPLE7f67pKZeFNWT8po1a6Rh\nw5bBZS/GCjwufn9bad26k2zbti1qOaFyc3PlnXfekT59Bsm11w6T77//3kiOUuVBVa4Apk2bFtZa\nOOW/pYnHkyT79+8/JD8nJyc4RPGXcrx3QLzei+WZZ56L6NgzMzOlVauOEhPzUBiVwGzxemuWawho\nsfz8fLnqquvF46knLtdDAj8I/ChO5xPi9aZIz569jWzZWFBQIFOnTpVbbx0hw4ffJbNmzdJx+arK\noypXAAUFBcHx8L8ZrQAcjocPu6vWxImTxOc7WSCnjO//tjRtekqZ9u7dvn27NGvWVhyO3gKLSnnv\nPwUeEY+nRtjLWoejqKhIBg26XpzOBHE4GorD0VCczmpywQW9JS8vL2o5JaWlpckrr7wib7zxhuze\nvdtYjlLHC6pyBSAiMnz4CLEmepmqAPYIJB12tcRAICC9e18lDkdPsVb4jOS9vxG3u3q5vpmPHPmI\nuFynCDQUOEOsrS7vEGvbyySBc6V583Zlfv/SjB79L/F6zxDYGXIsB8TjuUhuuMHMePIJE94Rt7u6\neL2DxecbKF5vsrHNYIqtWbNGxox5TF544QXbNqLPzs7WKxsVNqp6BfD222+Lw1FDSt8wJBq3QeJw\neI94Ali7dq04ndUEOgusCuM9CwXGCVSTHj0uKvOxFxUVhcwILhCYKdaOZk8LvC2wT6wZwI1k8eLF\nZc4JlZubK35/LYE1pRzXbnG7k2TXrl1RySq2e7f1vrAyJOt7SUysU6Yrp3D8/vvv4vfXEpfrDnG7\ne0u7dmcancEaCATkpptuF5crXmrUaCjLly83llVsxowZ0qFDqm07dM2bN08GDhxivOIu9t1338mw\nYcOj9rd/LKKqVwCvvfaaxMdfJlBHDl37Jhq39wSaidtdW9LS0g5bhgULFkhCQnuBl8Tai+A6gR/l\nrzOA/xR4RazlKVIF/iPdu19S5mPfs2ePxMVVO+px+P1Xy4QJE8qcE+q3336TxMRTDpuVmHh+1PcD\neO+99yQh4bJSsk4t145qR/Lww6PE6bw7mBUQv//EsJfqKIu1a9eKx1NHYL84HGOlT5/SV5+NJq83\nSaxlLprb0smdnNxA4FGJi/MZq7iLBQIB8XiqCQyXRo1aG82qSERYAVSaeQDFnE4nLlctYCRwHtbE\nj2iYgbUy6Ic4HBxxslFMTAwORwC4BVgJtACGAklAG6zJZw2xVij9GngB+BaoEZx0VDaxsbEEAvlY\nE4gOz+HIKVdOKLfbTVFRJof/u8uI+nhyv9+Pw5Fe4lEhEEjH7/dHNetgpo/Y2K1Yx5lBUZG5LCvP\nj0g+MI/Y2BXUqpVsLKtYcnJtnM7ZBALp1KhRw3henTr18HrnUq1actT+Ho+kVq16eL3zqF+/gfEs\nVT5lrgE//fRTSUwsHor5jEBjgbnl+NZfJPBc8IriZ4EsiY31HLFzc8+ePRIfX00gvcR7ZQgsFmtZ\ngfVScrROTMyDcttt5ds/98QTTxeYfoTj2S9ud/UjXsFEIhAISMOGraX0/Y+XS0JC7ah/u8vLy5Oa\nNRsJTAh+hoXidI6TFi3aG2u62L9/v7Rq1VESEtqK19tAbr55hJGcUFOmfCAnndRFevf+u+zbt894\n3oYNG+TRR8fI119/bTxLxFpkb+LEibJlyxbb8j755JO/jN6rTKjqTUBpaWnidtcIObl+KtZyzP8Q\nqw08kpP/CoFuwdsfwcd+lObNOx21HJdcMlAcjn9HkJUnHk8dWbVqVZmPXcSai+DzdZDDTc6Kjb1L\nLr74inJllPTpp58G1675VKz+jIDAt+L1NpVXX309qlnFlixZIk2bniJebyPxeOpKmzZdjG0+Uyw3\nN1fmzZsny5YtM5qjVFlR1SsAEQl2hIbuiLVH4HqxRsFcL9aWiYcbobNHrIXhzhNrZ63nJXRRLqfz\ncbnuuluOWoa5c+eKz9c8+K0/nArgVTn11HPKddwi1jfyq68eKj5fJzl0yevlEh8/WFJSTo56p6yI\nyGeffSatWp0q8fHJ4nbXksaNT4rqrmOlCQQC8vvvv8u6deuM5ih1vOA4qQAuAFYDfwD3lfL7cn0I\n99//sMTH31rKSfZPgccF2gp4xFoSurdYS0JfLNby0H6Bc8Xaa7ZkJVEoPl9KWJ1/gUBArrrquiPM\nkg29zRS/v1bUvlkGAgGZMGGCtGzZSeLiEsTtriVJSfXk/vsfNt6UsH37dtm6datO8VeqAnAcVAAu\nYC2QAsQCi4ETSzynXB/Cli1bxO2uHjzhH+6kmy0wX+BjsUb3TBVryGbhEV7zvpx44mlhlyM/P18G\nDBgsPt8pAu+EVCizg/+ukri4f0hiYp3DbjJfXnv27JHt27fbuo/t7NmzbcuqCHp8x6/KfGwix8co\noNOxKoCNQAHWhrWXRzOgYcOG3HLLTXi9N3P4z8MDnIG1b+7AYBFaY9VPpdmNx3MHr7/+XNjliI2N\n5f33xzNx4mOcccbbeDyNqVbtYuLibiYx8QwSE1O5/fYEVq78lS5duoR/gBFITk6mbt26tu5jG429\nho9lenzHr8p8bGVREfsBNODQsZlbgc7RDvnXv0bz0Ucd2bz5NUSGlfPdCvF4rmPIkL/RtWvXiF7p\ncDjo3bs3vXv3Zt26daxatYq3336bm2++ma5du+q65EqpClMRFYAtbVTx8fHMmjWVzp1TSU+PQ+S6\nMr5TAR7PYDp1yufZZ58oV5maNWtGs2bNWLhwIeecc0653ksppcorGltXReoMYDRWRzDAA1jbSo0N\nec5aQHdsUEqpyKwDmld0IY4kBquQKUAcpXcCK6WUqqQuBH7H+qb/QAWXRSmllFJKKVWRjjZJ7HjW\nCJgNrACWY60uV9m4gEXA9IouiAFJwIfAKqxV/s6o2OJE3QNYf5vLgHcBc7vC2+NNYAfW8RRLBmYB\na4CvsP6bHq9KO76nsP4+lwAfA9UqoFxlFs4kseNZXaB98Gc/VjNYZTo+gDuBScCnFV0QAyYAxcPJ\nYjjO/uc6ihRgPQdP+u8D11RYaaKjO9CBQ0+Q44B7gz/fBzxpd6GiqLTjO5+D87ue5Dg7vi7AFyH3\n7w/eKqupQI+KLkQUNcRa3/ocKt8VQDWsE2RllYz1haQ6VuU2HWs99eNdCoeeIFcDdYI/1w3eP56l\ncOjxheoDTDzSi4+1/QBKmyRWWRfvTsGqvX+u4HJE03PAPVjDeiubpsAu4C3gN+B1wFuhJYquvcAz\nwGZgG5COVZlXNnWwmk0I/lvnCM893l0HfHakJxxrFcAxv5BRlPix2pJvBzIruCzRcgmwE6v9vyLm\nl5gWA3QEXg7+m0XlujptBtyB9cWkPtbf6N8rskA2OC4WTyujkUA+Vl/OYR1rFUAaVkdpsUZYVwGV\nSSzwEdal2dQKLks0nQlcBmwAJgPnAm9XaImia2vwtiB4/0OsiqCyOBX4EdgDFGJ1IJ5ZoSUyYwdW\n0w9APawvLZXNtcBFHIcVeGWfJObAOimGv6Lc8elsKl8fAMB3QMvgz6M5dPb68a4d1sg0D9bf6QTg\n1gotUXSk8NdO4OLRhfdznHWSliKFQ4/vAqyRXDUrpDRRUJkniXXDah9fjNVUsoiDS2JUJmdTOUcB\ntcO6Ajguh9iF4V4ODgOdgHW1ejybjNWfkY/VtzgEq7P7ayrHMNCSx3cd1vD5TRw8v7xcYaVTSiml\nlFJKKaWUUkoppZRSSimllFJKKaWUUkodzwLA0yH37wZGRem9xwP9ovReRzIAawnob2zICsccDs5E\nfrDE7+bZWxR1vDrWloJQlVM+1sqENYL3o7n+SnneKyaC514P3MCxs3pr6HGXnDDZ1c6CqOOXVgDK\nDgXAf4ARpfxuPId+gy9eHC8VmIu1XtI6rCn7g4BfgKXACSGvOQ9rhu7vwMXBx1xYm2P8gjVz98aQ\n9/0emIY167WkK4Pvv4yDywQ8gnVSfRNrKYGS7gu+ZjHwr+BjQ4PZi7HWDfIEHx8QfO/FWN/iwVq7\n5b4n9YMAAAM0SURBVMWQ95uBNZsarJmcC7CWaRhdItcRLKMHa9bnO8HHQxcYvIeDn0Hx633AzGAZ\nlgFXlHJMSikVFRlAAtZCcYnAXRxsAnqLQyuAjOC/qcA+rOV647AWChwd/N1wDq6nNJ6DS942x5oS\nH491wh8ZfDwe6ySaEnzfTKBJKeWsjzWNvgZWBfINcHnwd7MpffG3C7GaXNzB+9WD/yaHPOcx4Lbg\nz0uxFiED67MAa+OV0ApgOnBWifdzBcvQppTyZHCo4vs9gdeCPzuD79sd6ItVIRdLRFVJegWg7JKB\ntRBeJNtgLsBavTEfa22oL4OPL8c6mYPVFDIl+PNarE1bWmOd/AZjfTP+CeuE3Dz4vF+wTvQlnYZ1\nYt0DFGHtbHZWyO9LW+a6B9aVQW7w/r7gv22wrjSWYq3KeFLw8XlY6+zcQHhNUAOBX7H2IDiZyBZH\n7Bm8LQq+Ryusz2AZ1s5RT2KtT3UggvdUlUgkbaBKldfzWCeyt0IeK+TgFxEn1rf9YnkhPwdC7gc4\n8t9ucfv4bVj7v4ZKxVrL/3CvCz3JOzi0rf1w/Q2lVQzjsZbHXob1DT81+PjNwOlYTVW/Ap049DOA\ng1cTTbGulk4F9mN9bm4i8wSHftsv1iFYhn9iXek8FuH7qkpArwCUnfZhfVu/noMn041YJ0GwTpiR\nrkDpwGpXd2BtanIC1jZ/XwK3cLCiaMnRd/BagNX2XtwE9DesfogjmYW1ymRxG39xk40f+BPreK4O\neX4zrCuQUVg7jDXE+gzaB4+hEVYFAVazWRbWN/Q6WM1NpSmg9ArxS6wVIn3B+w2AWlhNULlYVzhP\nU7n2NVAR0CsAZYfQb87PcLA9HKytFadhdUh+waEdmIf7xh26k5NgbWP4C1Zb9k1YTUZvYDUT/YZ1\nYt2JNRLpSLtAbcdaI3528DUzOPq+Bl9inbwXBnNnAg8BD2Nt97kr+K8/+PxxQIvg+3+N1UQEVv/I\nSmAV1pUBwd8twqrQtgA/HKYM/wk+91esjvLi45uF1WQ0P3g/I/j75lgd5IFgmW8+yjEqpZRSSiml\nlFJKKaWUUkoppZRSSimllFJKKaWUUkoppZRSqiL8P7R4sp8cOs3SAAAAAElFTkSuQmCC\n",
2171        "text": [
2172         "<matplotlib.figure.Figure at 0x7ff7b3fef048>"
2173        ]
2174       }
2175      ],
2176      "prompt_number": 90
2177     },
2178     {
2179      "cell_type": "markdown",
2180      "metadata": {},
2181      "source": [
2182       "It seems that there isn't much of a correlation. We can easily find the Pearson's R correlation coefficient, which confirms what the graph shows us.\n",
2183       "\n",
2184       "The calculation assumes there's one row in the data for each data point. That means we need to expand out the data into one long sequence of (casualties, vehicles) pairs. We'll use some functions from the `itertools` built-in library to do that."
2185      ]
2186     },
2187     {
2188      "cell_type": "code",
2189      "collapsed": false,
2190      "input": [
2191       "# Create an iterator of length 'count' for each (casualties, vehicles) pair\n",
2192       "iters = [repeat(cv, n) for cv, n in cas_veh_dict.items()]\n",
2193       "# Chain all the iterators together into one long one, run them, and store the results in a list.\n",
2194       "all_cv = list(chain(*iters))\n",
2195       "# Finally, find the correlation coefficient\n",
2196       "scipy.stats.pearsonr([cv[0] for cv in all_cv], [cv[1] for cv in all_cv])"
2197      ],
2198      "language": "python",
2199      "metadata": {},
2200      "outputs": [
2201       {
2202        "metadata": {},
2203        "output_type": "pyout",
2204        "prompt_number": 91,
2205        "text": [
2206         "(0.23086557669559266, 0.0)"
2207        ]
2208       }
2209      ],
2210      "prompt_number": 91
2211     },
2212     {
2213      "cell_type": "markdown",
2214      "metadata": {},
2215      "source": [
2216       "# Exercises"
2217      ]
2218     },
2219     {
2220      "cell_type": "markdown",
2221      "metadata": {},
2222      "source": [
2223       "How many accidents occurred in the Milton Keynes district authority (`'Local_Authority_(District)'` = 479)"
2224      ]
2225     },
2226     {
2227      "cell_type": "code",
2228      "collapsed": false,
2229      "input": [
2230       "accidents.find({'Local_Authority_(District)': 479}).count()"
2231      ],
2232      "language": "python",
2233      "metadata": {},
2234      "outputs": [
2235       {
2236        "metadata": {},
2237        "output_type": "pyout",
2238        "prompt_number": 92,
2239        "text": [
2240         "6038"
2241        ]
2242       }
2243      ],
2244      "prompt_number": 92
2245     },
2246     {
2247      "cell_type": "markdown",
2248      "metadata": {},
2249      "source": [
2250       "Which highway authorities are responsible for the roads in the Milton Keynes district authority?"
2251      ]
2252     },
2253     {
2254      "cell_type": "code",
2255      "collapsed": false,
2256      "input": [
2257       "lahs = accidents.find({'Local_Authority_(District)': 479}, ['Local_Authority_(Highway)']).distinct('Local_Authority_(Highway)')\n",
2258       "[(lah, label_of[('Local_Authority_(Highway)', lah)]) for lah in lahs]"
2259      ],
2260      "language": "python",
2261      "metadata": {},
2262      "outputs": [
2263       {
2264        "metadata": {},
2265        "output_type": "pyout",
2266        "prompt_number": 93,
2267        "text": [
2268         "[('E06000042', 'Milton Keynes')]"
2269        ]
2270       }
2271      ],
2272      "prompt_number": 93
2273     },
2274     {
2275      "cell_type": "markdown",
2276      "metadata": {},
2277      "source": [
2278       "Which district authorities are served by Milton Keynes highways?"
2279      ]
2280     },
2281     {
2282      "cell_type": "code",
2283      "collapsed": false,
2284      "input": [
2285       "lads = accidents.find({'Local_Authority_(Highway)': 'E06000042'}, ['Local_Authority_(District)']).distinct('Local_Authority_(District)')\n",
2286       "[(lad, label_of[('Local_Authority_(District)', lad)]) for lad in lads]"
2287      ],
2288      "language": "python",
2289      "metadata": {},
2290      "outputs": [
2291       {
2292        "metadata": {},
2293        "output_type": "pyout",
2294        "prompt_number": 94,
2295        "text": [
2296         "[(479, 'Milton Keynes')]"
2297        ]
2298       }
2299      ],
2300      "prompt_number": 94
2301     },
2302     {
2303      "cell_type": "markdown",
2304      "metadata": {},
2305      "source": [
2306       "Which police forces are responsible for the roads in the Milton Keynes district authority?"
2307      ]
2308     },
2309     {
2310      "cell_type": "code",
2311      "collapsed": false,
2312      "input": [
2313       "pfs = accidents.find({'Local_Authority_(District)': 479}, ['Police_Force']).distinct('Police_Force')\n",
2314       "[(pf, label_of[('Police_Force', pf)]) for pf in pfs]"
2315      ],
2316      "language": "python",
2317      "metadata": {},
2318      "outputs": [
2319       {
2320        "metadata": {},
2321        "output_type": "pyout",
2322        "prompt_number": 95,
2323        "text": [
2324         "[(43, 'Thames Valley')]"
2325        ]
2326       }
2327      ],
2328      "prompt_number": 95
2329     },
2330     {
2331      "cell_type": "markdown",
2332      "metadata": {},
2333      "source": [
2334       "Which district authorities are served by Thames Valley police?"
2335      ]
2336     },
2337     {
2338      "cell_type": "code",
2339      "collapsed": false,
2340      "input": [
2341       "lads = accidents.find({'Police_Force': 43}, ['Local_Authority_(District)']).distinct('Local_Authority_(District)')\n",
2342       "[(lad, label_of[('Local_Authority_(District)', lad)]) for lad in lads]"
2343      ],
2344      "language": "python",
2345      "metadata": {},
2346      "outputs": [
2347       {
2348        "metadata": {},
2349        "output_type": "pyout",
2350        "prompt_number": 96,
2351        "text": [
2352         "[(476, 'Aylesbury Vale'),\n",
2353         " (480, 'Wycombe'),\n",
2354         " (477, 'South Bucks'),\n",
2355         " (479, 'Milton Keynes'),\n",
2356         " (478, 'Chiltern'),\n",
2357         " (473, 'Slough'),\n",
2358         " (481, 'Cherwell'),\n",
2359         " (484, 'South Oxfordshire'),\n",
2360         " (474, 'Windsor and Maidenhead'),\n",
2361         " (483, 'Vale of White Horse'),\n",
2362         " (485, 'West Oxfordshire'),\n",
2363         " (482, 'Oxford'),\n",
2364         " (471, 'West Berkshire'),\n",
2365         " (475, 'Wokingham'),\n",
2366         " (472, 'Reading'),\n",
2367         " (470, 'Bracknell Forest')]"
2368        ]
2369       }
2370      ],
2371      "prompt_number": 96
2372     },
2373     {
2374      "cell_type": "markdown",
2375      "metadata": {},
2376      "source": [
2377       "How many casualties were involved in accident index '201243S022012'"
2378      ]
2379     },
2380     {
2381      "cell_type": "code",
2382      "collapsed": false,
2383      "input": [
2384       "accidents.find_one({'Accident_Index': '201243S022012'}, ['Number_of_Casualties'])"
2385      ],
2386      "language": "python",
2387      "metadata": {},
2388      "outputs": [
2389       {
2390        "metadata": {},
2391        "output_type": "pyout",
2392        "prompt_number": 97,
2393        "text": [
2394         "{'_id': ObjectId('52a9c98292c4e16686d2b607'), 'Number_of_Casualties': 4}"
2395        ]
2396       }
2397      ],
2398      "prompt_number": 97
2399     },
2400     {
2401      "cell_type": "code",
2402      "collapsed": false,
2403      "input": [
2404       "[a for a in accidents.find({'Local_Authority_(District)': 479}, \n",
2405       "                           ['Accident_Index', 'Number_of_Casualties', 'Number_of_Vehicles'],\n",
2406       "                           limit=10)]"
2407      ],
2408      "language": "python",
2409      "metadata": {},
2410      "outputs": [
2411       {
2412        "metadata": {},
2413        "output_type": "pyout",
2414        "prompt_number": 98,
2415        "text": [
2416         "[{'_id': ObjectId('52a9c93e92c4e16686c0b80c'),\n",
2417         "  'Number_of_Casualties': 2,\n",
2418         "  'Accident_Index': '200543N005105',\n",
2419         "  'Number_of_Vehicles': 2},\n",
2420         " {'_id': ObjectId('52a9c93e92c4e16686c0b810'),\n",
2421         "  'Number_of_Casualties': 2,\n",
2422         "  'Accident_Index': '200543N006095',\n",
2423         "  'Number_of_Vehicles': 2},\n",
2424         " {'_id': ObjectId('52a9c93e92c4e16686c0b811'),\n",
2425         "  'Number_of_Casualties': 2,\n",
2426         "  'Accident_Index': '200543N006115',\n",
2427         "  'Number_of_Vehicles': 2},\n",
2428         " {'_id': ObjectId('52a9c93e92c4e16686c0b813'),\n",
2429         "  'Number_of_Casualties': 1,\n",
2430         "  'Accident_Index': '200543N007095',\n",
2431         "  'Number_of_Vehicles': 3},\n",
2432         " {'_id': ObjectId('52a9c93e92c4e16686c0b81d'),\n",
2433         "  'Number_of_Casualties': 1,\n",
2434         "  'Accident_Index': '200543N009105',\n",
2435         "  'Number_of_Vehicles': 2},\n",
2436         " {'_id': ObjectId('52a9c93e92c4e16686c0b81e'),\n",
2437         "  'Number_of_Casualties': 2,\n",
2438         "  'Accident_Index': '200543N009115',\n",
2439         "  'Number_of_Vehicles': 2},\n",
2440         " {'_id': ObjectId('52a9c93e92c4e16686c0b822'),\n",
2441         "  'Number_of_Casualties': 1,\n",
2442         "  'Accident_Index': '200543N010095',\n",
2443         "  'Number_of_Vehicles': 2},\n",
2444         " {'_id': ObjectId('52a9c93e92c4e16686c0b828'),\n",
2445         "  'Number_of_Casualties': 1,\n",
2446         "  'Accident_Index': '200543N011085',\n",
2447         "  'Number_of_Vehicles': 1},\n",
2448         " {'_id': ObjectId('52a9c93e92c4e16686c0b82e'),\n",
2449         "  'Number_of_Casualties': 1,\n",
2450         "  'Accident_Index': '200543N012125',\n",
2451         "  'Number_of_Vehicles': 2},\n",
2452         " {'_id': ObjectId('52a9c93e92c4e16686c0b83a'),\n",
2453         "  'Number_of_Casualties': 1,\n",
2454         "  'Accident_Index': '200543N015095',\n",
2455         "  'Number_of_Vehicles': 2}]"
2456        ]
2457       }
2458      ],
2459      "prompt_number": 98
2460     },
2461     {
2462      "cell_type": "code",
2463      "collapsed": false,
2464      "input": [
2465       "[(v, label_of[('Police_Force', v)]) for k, v in label_of.keys() if k == 'Police_Force']"
2466      ],
2467      "language": "python",
2468      "metadata": {},
2469      "outputs": [
2470       {
2471        "metadata": {},
2472        "output_type": "pyout",
2473        "prompt_number": 99,
2474        "text": [
2475         "[(20, 'West Midlands'),\n",
2476         " (94, 'Fife'),\n",
2477         " (42, 'Essex'),\n",
2478         " (11, 'Durham'),\n",
2479         " (33, 'Leicestershire'),\n",
2480         " (93, 'Tayside'),\n",
2481         " (62, 'South Wales'),\n",
2482         " (31, 'Nottinghamshire'),\n",
2483         " (53, 'Gloucestershire'),\n",
2484         " (97, 'Strathclyde'),\n",
2485         " (44, 'Hampshire'),\n",
2486         " (41, 'Hertfordshire'),\n",
2487         " (13, 'West Yorkshire'),\n",
2488         " (35, 'Cambridgeshire'),\n",
2489         " (37, 'Suffolk'),\n",
2490         " (48, 'City of London'),\n",
2491         " (17, 'Cleveland'),\n",
2492         " (55, 'Dorset'),\n",
2493         " (46, 'Kent'),\n",
2494         " (4, 'Lancashire'),\n",
2495         " (6, 'Greater Manchester'),\n",
2496         " (50, 'Devon and Cornwall'),\n",
2497         " (95, 'Lothian and Borders'),\n",
2498         " (10, 'Northumbria'),\n",
2499         " (32, 'Lincolnshire'),\n",
2500         " (92, 'Grampian'),\n",
2501         " (1, 'Metropolitan Police'),\n",
2502         " (96, 'Central'),\n",
2503         " (61, 'Gwent'),\n",
2504         " (30, 'Derbyshire'),\n",
2505         " (52, 'Avon and Somerset'),\n",
2506         " (21, 'Staffordshire'),\n",
2507         " (43, 'Thames Valley'),\n",
2508         " (12, 'North Yorkshire'),\n",
2509         " (34, 'Northamptonshire'),\n",
2510         " (60, 'North Wales'),\n",
2511         " (3, 'Cumbria'),\n",
2512         " (63, 'Dyfed-Powys'),\n",
2513         " (16, 'Humberside'),\n",
2514         " (54, 'Wiltshire'),\n",
2515         " (98, 'Dumfries and Galloway'),\n",
2516         " (23, 'Warwickshire'),\n",
2517         " (45, 'Surrey'),\n",
2518         " (14, 'South Yorkshire'),\n",
2519         " (36, 'Norfolk'),\n",
2520         " (5, 'Merseyside'),\n",
2521         " (40, 'Bedfordshire'),\n",
2522         " (7, 'Cheshire'),\n",
2523         " (22, 'West Mercia'),\n",
2524         " (47, 'Sussex'),\n",
2525         " (91, 'Northern')]"
2526        ]
2527       }
2528      ],
2529      "prompt_number": 99
2530     },
2531     {
2532      "cell_type": "code",
2533      "collapsed": false,
2534      "input": [
2535       "[(code, label_of[('Casualty_Type', code)]) for t, code in label_of.keys() if t == 'Casualty_Type']"
2536      ],
2537      "language": "python",
2538      "metadata": {},
2539      "outputs": [
2540       {
2541        "metadata": {},
2542        "output_type": "pyout",
2543        "prompt_number": 106,
2544        "text": [
2545         "[(113, 'Goods vehicle (over 3.5 tonnes) occupant'),\n",
2546         " (23, 'Electric motorcycle rider or passenger'),\n",
2547         " (16, 'Horse rider'),\n",
2548         " (10, 'Minibus (8 - 16 passenger seats) occupant'),\n",
2549         " (105, 'Motorcycle - Combination rider or passenger'),\n",
2550         " (5, 'Motorcycle over 500cc rider or passenger'),\n",
2551         " (3, 'Motorcycle 125cc and under rider or passenger'),\n",
2552         " (21, 'Goods vehicle (7.5 tonnes mgw and over) occupant'),\n",
2553         " (98, 'Goods vehicle (unknown weight) occupant'),\n",
2554         " (90, 'Other vehicle occupant'),\n",
2555         " (8, 'Taxi/Private hire car occupant'),\n",
2556         " (103, 'Motorcycle - Scooter rider or passenger'),\n",
2557         " (1, 'Cyclist'),\n",
2558         " (19, 'Van / Goods vehicle (3.5 tonnes mgw or under) occupant'),\n",
2559         " (108, 'Taxi (excluding private hire cars) occupant'),\n",
2560         " (17, 'Agricultural vehicle occupant'),\n",
2561         " (11, 'Bus or coach occupant (17 or more pass seats)'),\n",
2562         " (106, 'Motorcycle over 125cc rider or passenger'),\n",
2563         " (4, 'Motorcycle over 125cc and up to 500cc rider or  passenger'),\n",
2564         " (22, 'Mobility scooter rider'),\n",
2565         " (9, 'Car occupant'),\n",
2566         " (104, 'Motorcycle rider or passenger'),\n",
2567         " (2, 'Motorcycle 50cc and under rider or passenger'),\n",
2568         " (97, 'Motorcycle - unknown cc rider or passenger'),\n",
2569         " (109, 'Car occupant (including private hire cars)'),\n",
2570         " (110, 'Minibus/Motor caravan occupant'),\n",
2571         " (0, 'Pedestrian'),\n",
2572         " (18, 'Tram occupant'),\n",
2573         " (20, 'Goods vehicle (over 3.5t. and under 7.5t.) occupant')]"
2574        ]
2575       }
2576      ],
2577      "prompt_number": 106
2578     },
2579     {
2580      "cell_type": "code",
2581      "collapsed": false,
2582      "input": [
2583       "casualty_class = {'Pedestrian': [0],\n",
2584       "                  'Cyclist': [1],\n",
2585       "                  'Motorcyclist': [2, 3, 4, 5, 23, 97, 103, 104, 105, 106],\n",
2586       "                  'Car occupant': [8, 9, 108, 109],\n",
2587       "                  'Bus occupant': [10, 11],\n",
2588       "                  'Horse rider': [16],\n",
2589       "                  'Other': [17, 18, 22, 90, 110],\n",
2590       "                  'Goods vehicle occupant': [19, 20, 21, 113]}"
2591      ],
2592      "language": "python",
2593      "metadata": {},
2594      "outputs": [],
2595      "prompt_number": 107
2596     },
2597     {
2598      "cell_type": "code",
2599      "collapsed": false,
2600      "input": [],
2601      "language": "python",
2602      "metadata": {},
2603      "outputs": []
2604     }
2605    ],
2606    "metadata": {}
2607   }
2608  ]
2609 }