{
 "metadata": {
  "name": "Statistics"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Statistics"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Some matplotlib and pandas experiments based on \"Head First Statistics\" from O'Reilly Media, Inc."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 207
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "1. First Impression"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Why learn statistics? (p. 3)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "profit_data = [(\"jul\", 2.0), (\"aug\", 2.1), (\"sep\", 2.2), (\"okt\", 2.1), (\"nov\", 2.3), (\"dez\", 2.4)]\n",
      "profit_data = DataFrame(profit_data, columns=[\"month\", \"profit\"])\n",
      "profit_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>month</th>\n",
        "      <th>profit</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td> jul</td>\n",
        "      <td> 2.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> aug</td>\n",
        "      <td> 2.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> sep</td>\n",
        "      <td> 2.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> okt</td>\n",
        "      <td> 2.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> nov</td>\n",
        "      <td> 2.3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>5</strong></td>\n",
        "      <td> dez</td>\n",
        "      <td> 2.4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 208,
       "text": [
        "  month  profit\n",
        "0   jul     2.0\n",
        "1   aug     2.1\n",
        "2   sep     2.2\n",
        "3   okt     2.1\n",
        "4   nov     2.3\n",
        "5   dez     2.4"
       ]
      }
     ],
     "prompt_number": 208
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "profit_fig, profit_axes = plt.subplots(nrows=1, ncols=2)\n",
      "profit_fig.set_size_inches(15,5)\n",
      "profit_axes[0].set_ylim(0, 2.5)\n",
      "profit_data.plot(ax = profit_axes[0], x=\"month\").set_title(\"Stagnating profits.\")\n",
      "profit_axes[1].set_ylim(2.0, 2.5)\n",
      "profit_data.plot(ax = profit_axes[1], x=\"month\").set_title(\"Awesome profits!\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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24IC5p1lFJW7t20uNGlkdPQA4zlP7+6tFewGA5yBxcwE//1zx/Uf79kk+PhXv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\nYjY2m3NihOepaZ9P4gYAcBr6e8fQXrU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7F2q1Glu2bMHvv/9u7P/PGEVERKC1tRWtra1y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\nPvAw1qMAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 209
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The figures use different scaling and offset of the x axes, thus giving a different impression of the profit."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Manic Mango's Diagrams"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mango_data = [(\"sports\", 27500, 0.99), (\"strategy\", 11500, 0.9), (\"action\", 6000, 0.85), (\"shooter\", 3500, 0.95), (\"others\", 1500, 0.85)]\n",
      "mango_data = DataFrame(mango_data, columns=[\"type\", \"sales\", \"satisfaction\"])\n",
      "mango_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>type</th>\n",
        "      <th>sales</th>\n",
        "      <th>satisfaction</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td>   sports</td>\n",
        "      <td> 27500</td>\n",
        "      <td> 0.99</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> strategy</td>\n",
        "      <td> 11500</td>\n",
        "      <td> 0.90</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td>   action</td>\n",
        "      <td>  6000</td>\n",
        "      <td> 0.85</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td>  shooter</td>\n",
        "      <td>  3500</td>\n",
        "      <td> 0.95</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td>   others</td>\n",
        "      <td>  1500</td>\n",
        "      <td> 0.85</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 210,
       "text": [
        "       type  sales  satisfaction\n",
        "0    sports  27500          0.99\n",
        "1  strategy  11500          0.90\n",
        "2    action   6000          0.85\n",
        "3   shooter   3500          0.95\n",
        "4    others   1500          0.85"
       ]
      }
     ],
     "prompt_number": 210
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mango_colors=[\"#F3E761\", \"#F0F0F0\", \"#97C2B9\", \"#E9ABC7\", \"#CAA882\"]\n",
      "mango_fig, mango_axes = subplots(nrows=1, ncols=2)\n",
      "mango_fig.set_size_inches(16,7)\n",
      "_mango_sales = mango_data.sort(\"sales\")\n",
      "mango_axes[0].pie(_mango_sales.sales, labels=_mango_sales.type + \"\\n\" + _mango_sales.sales.astype(str), startangle=90, colors=mango_colors) # autopct='%1.1f%%'\n",
      "mango_axes[0].set_title(\"sales by type\");\n",
      "mango_axes[1].pie(_mango_sales.satisfaction, labels=_mango_sales.type + \"\\n\" + (_mango_sales.satisfaction*100).astype(str) + \"%\", startangle=90, colors=mango_colors)\n",
      "mango_axes[1].set_title(\"satisfied players by type\");\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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Fc2YwaGB/ateqRZu27Wjdpg2BgYHy7aYQotxISUlh0aJFmmLxVeXn55eb58cL\nCgo4fvw427ZtZdu2jZw/fxFvTxu83Y2JGGCB91edqGIho4r+m0qlopqFCdUsTGjsXg+AjKxsTpy/\nzPGYSEasXclfV2/i4+VJy9ZtCQoKwsfHhwoVKmg5uRD/R4rFMsrExITQ0FCOHj1K8+bNNcsHDx5M\nhw4dgAd3DK9evap579q1a9jZ2WFra8u1a9ceWW5ra1tsWZOTk9mxYwdbtmxh586dVK9enTZt2vDB\nBx/g5eWl6Vr7LO7u7txOSi+2nKWNnq4uDWrb0KC2DT1DHhSPZy5e58iZ40weu5lz8dfw8fakTXAI\nnTt3xsnJSduRhRCiyMydO5fly5cDD9q+gwcPEh8fj4eHB61btyY0NJT09HS6du3K6dOn8fLyYtWq\nVcCDQd7GjRtHeno6VapUYcWKFVhZWdG8eXM8PDyIioqiZ8+eVK9enenTp6Ojo4OJiQl79uzR5ikX\nqbS0NH777Te2bfuZHf/7H8bGurRtac2UCTY09vOgQoXyUSgXNcNKFWnsXk9TPN7LyCLm3GWOH/mN\ntSuXcfn6Lfx8vWnZui3t2rXD3d0dlUql5dSiPJNisQxJSkpCV1cXU1NTsrKy+O233/joo4+4efOm\nZvCTTZs24eLiAkDHjh3p1asXY8eO5fr168TFxeHr64tKpcLY2JhDhw7h6+vLypUrGT16dJHlVBSF\n2NhYtm7dytatWzlx4gRNmjShbdu2TJkyBRsbm2fv5DFq1qxJQYHC2fMpNKhvVmR5ywo9XV3cHWvi\n7liTIUB65n2izyZwaO82Fsyfi7GJCV26dadLl67SOAkhSrVjx46xYsUKDh8+TEFBAX5+fqxatYrT\np08THR0NPOiGGh0dTWxsLNbW1gQEBLBv3z58fX0ZNWoUW7ZswcLCgnXr1vH++++zdOlSVCoVubm5\nHDlyBABXV1d27tyJtbU1d+/e1eYpvzJFUTh37hxbt25l+/aNHD0ag7+vLW2DLHnn5xbUtjfWdsQy\nyciwEk28HGni5QhAWnom0Wcvc3z/dhYv/BL9Sgb07N2Hnj170aBBAy2nFeWRFItlyI0bNwgPD6eg\n4MF8Qn379qVly5b069ePmJgYVCoVtWrVYsmSJQA4OTnRrVs3nJyc0NXVZdGiRZoCYdGiRfTv35+s\nrCxCQkKKZHCb+Ph4Vq9ezZo1a8jMzCQ4OJgxY8YQGBhYJJPiqlQqnJ2d2LztshSLz6Gygb6mgRrb\nrw2x8Yk6df9IAAAgAElEQVREHt1HpxXLUOvo8WaXLnTp2g0/Pz/U6icPTy6EECVNVFQUYWFhmq72\nYWFh/Pnnn4XW8/X11XxB6e7uTkJCAiYmJpw5c4ZWrVoBD7qb/vNLzO7du2t+DwgIIDw8nG7duhEW\nVnQjYr9O58+f5/vvl/PDmpXk5t2nbUsbIgZUo9nSLhga6Gk7XrljUtmA5j4NaO7TgHf6tOHMxevs\nOhRJi/8sooplVXr17kuPHj2oXbu2tqOKckKKxTLExcWF48ePF1r+/fffP3Gb9957j/fee6/Qci8v\nL06dOvXKmW7dusXatWtZs2YNly5donPnzixcuBBvb+9iuXPl5+fPvoM/F/l+yzq1Wk3DunY0rGvH\niB4tibvyN7uPnKBfrw2kZ2XTs2cvBg4aTMOGDbUdVQghnkmlUhUaBfxxbc4/BxbR0dEhLy8PAGdn\nZ/bv3//YfRsaGmp+X7x4MYcPH2bbtm14eXlx7NgxzM3Ni+IUilVycjJr167l+/9+w+XLCXQLs2fV\nt964OJtLr5ISRKVSadrm0b1aceL8FXbt3crcOZ9hb29Prz796N69e7E+KiSE3C4QRS49PZ1Vq1YR\nHBxMvXr12LdvH+PHj+fMmTN89tln+Pj4FFtj5OXlxeWrucWy7/JCpVJRr6YVQ7u0YM0nb/HlxB6k\nXTlBqxbN8PJ0Z/HixaSmpmo7phBCPFGTJk34+eefycrKIiMjg02bNhEQEMC9e/eeup1KpaJ+/frc\nvn2bgwcPAg+mbIqNjX3s+vHx8fj6+jJt2jQsLS0fed6/pMnNzWXr1q107dKR2rWr88dvC5j4dlXO\nHu3MzCmeuDa0kEKxBFOr1Xg0sGdC/3ZsWTiG8HZu7P3fOho6NyCwsT9ff/01t27d0nZMUQbJnUVR\nZGJiYli8eDE//vgjfn5+dOnShWXLlj3yLWxxc3Nz43ZSxms7XnlQy9aSYd2CGNKlOYdOXmTT6m+Y\nPGkioaEhDB4ylObNm0s3VSFEieLh4UH//v018wUPGTIET09PAgICcHFxISQkhJCQkMcWR3p6emzY\nsIHRo0eTlpZGXl4e77zzzmMHAZs4cSJxcXEoikKrVq1wdXUt9nN7UTExMfx3xVLW/LCa2vZG9Opi\nx7yZnTE1kekaSitdHR38XR3wd3VgfHgwh05eZNv65bz37iQCGjdm7PiJtGzZUop/USRUyuNmaxfi\nOd2/f59169axaNEiEhMTCQ8Pp0+fPlhbW2slT0FBATY2Nuzb2Y56dR8/xUZRGzr6T1L/NmLq8NL5\nvMrLSL2bwa/7T7F172mycvIZMmQoQ4cNw9LSUtvRhBCi3Lt//z7ff/89Xy/8gtTUJHp2sadn11rU\nqVV+BqkZEBFJ7l0L3n+ro7ajvDb3s3P4df8pftx5HN0K+rwzbgJ9+vSRabLEK5FiUbyUq1evsnjx\nYpYuXYqbmxuDBg2idevWJWLOqaCgFrRvrWLCGLfXcrzyWCw+pCgK5xNusPH3aHYfPkPnzm/wzthx\nuLm9ns9eCCHE/0lKSmLRooUs+noBnu7mDB9ch6YB1qjV5e8OU3ksFh9SFIWjZ/5i3c5jxMYnMnTo\nMEaMHKm1L/JF6SZ9x8QLOXbsGF27dsXNzY2UlBS2b9/O+vXrCQ4OLhGFIoC/fyP2Hfxb2zHKBZVK\nhWMtG94bHMr6OSMwzLlJcOuWNGsayPbt2ykoKNB2RCGEKPPi4+MZMfwt6tWrxaULG9i2oRk//rcJ\nzZvYlMtCsbxTqVT4NKzDnLHd+M8HfbhwPJIGjvXp06vnYwdCLG/27NnDgQMHtB2j1JBiUTyXqKgo\ngoOD6dixI15eXpw8eZJPPvkEBwcHbUcrxMPDg0tX8rQdo9wxNTakf+cmbJw3klZuVowbHYFzg/os\nX76c7OxsbccTQogy5+DBg3R5swP+fh4YVjzKkchQFszxpf5regxDlHw1rKswoX8wP80diYVOCu1D\n2hLY2J+NGzeSn5+v7XivXV5eHrt3737iaMeiMCkWxRMpisLOnTtp1qwZ/fr1IyQkhOjoaCIiIjAy\nMtJ2vCdyd3eXQW60SFdXh+BAN/778SBGdQtg6ddzqFPbnv/85z/k5ORoO54QopyYPXs2zs7OuLi4\n0KtXL7Kzs5k6dSp2dnZ4eHjg4eHBjh07Hrvtjh07cHR0pG7dunz66aea5ZMmTcLNzY3w8HDNslWr\nVvHll18W+/k8VFBQwM8//0xggDc9e7TH3zOJ04c7MWWSO9WqGry2HKJ0Ma5ciT7tA/hp7ghCfWsy\n48OJ1K5Vk7lz55KRUXKvmTIyMggNDcXd3R0XFxd+/PFH7O3tmTRpEq6urvj5+REfHw9AQkICQUFB\nuLm50apVK65evQpA//79GTZsGP7+/nTv3p0lS5Ywb948PDw8iIqKYv369bi4uODu7k6zZs20ebol\nkhSLopCHDZGPjw9vv/02vXv35siRI4SHhz8yJ1VJVadOHXJycrn4V5q2o5RrD7vBzJvQgxkRHVn1\n3QIc6tTiu+++IzdXpjcRQhSfhIQEvv32W44fP86pU6fIz89n7dq1qFQqxo4dS3R0NNHR0QQHBxfa\nNj8/n5EjR7Jjxw5iY2P54YcfOHv2LGlpaURHR3PixAkqVKjA6dOnycrKYsWKFYwcObLYz0lRFH78\n8UcaONbm4+mjGNrfiOio9gwb1ABDA71iP74oG3R1dGjVqCHfTAln6luh7Ni4Eoc6tViyZIlmntGS\nZMeOHdja2hITE8OpU6cIDg5GpVJhamrKyZMnGTlyJGPGjAFg1KhRDBgwgBMnTtC7d29Gjx6t2U9i\nYiIHDhzgp59+YtiwYZq/A4GBgcyYMYOdO3cSExPDli1btHWqJZYUi0JDURS2bduGu7s706ZNY8yY\nMezfv5/u3bujq1t6ZllRq9U0cHRky/bL2o4i/r+Gde2YN6EHU4aEsPTrL6hf14Hvv/++RDZMQojS\nz9jYGD09PTIzM8nLyyMzM1MzcfmzxvU7fPgwDg4O2Nvbo6enR48ePdi8eTM6Ojrk5uaiKAqZmZno\n6ekxZ84cRo8eXezP7EdGRuLn687sWe8w52NHdm9rxRsdaqGrK5dx4uU1rGvHzFFhfDI6jGWL5+Hk\nWJ9NmzY98/+R18nV1ZXffvuNyZMnExUVhbHxgxF9e/bsCUCPHj00zx8ePHiQXr16AdCnTx+ioqKA\nB19ed+3a9ZGpRP55jgEBAYSHh/Pdd9/JdcljyF8ZATwYuCYoKIhx48bx7rvvsnv3bjp06FBq58/z\n9fNj7/6b2o4h/sWtfg2+mtyLSeGtWDDnYxrUr8eaNWvK5XMTQojiY25uzrhx46hRowY2NjaYmprS\nqlUrABYsWKAZxTs1NbXQttevX6d69eqa13Z2dly/fp3KlSsTEhKCp6cnNjY2GBsbc/jwYTp2LL7R\nNk+dOkVoSCsG9O9CxEAz9mxvQ4umNjJ/nihSTnVsWTC5FyO6Nmby+Ldp7O/Lvn37tB0LgLp16xId\nHY2LiwsffPAB06dPL7TOk4rAfzIweHIX7cWLF/Pxxx9z9epVvLy8SE5OfvXgZUjprAREkUlISKBX\nr160b9+eTp06sW/fvidOVFyaeHl58ddl+XaopPJ0sufr9/rwTq+mfPbxh3h5uLF3715txxJClBHx\n8fHMnz+fhIQEEhMTSU9PZ/Xq1URERHDp0iViYmKwtrZm3LhxhbZ9Wvs3YcIEoqOj+fzzz5kyZQoz\nZszgu+++o3v37sycObPI8l+7do0BA/rQqmUgzRplcHRPKF3fqC0jm4pio1KpaOxej/9+PIi23jXo\n9mZnOrYP5ezZs1rNdePGDfT19enduzfjx48nOjoagHXr1mn+27hxYwAaN27M2rVrAVi9ejVNmzZ9\n7D6NjIy4d++e5nV8fDy+vr5MmzYNS0tLrl27VpynVOpIsVhOpaSkMGHCBLy8vKhZsyZHjx5lwIAB\npaq76dM8GOQmU9sxxFM8fKbxmynhdG3hRPeuYXTr8iZXrlzRdjQhRCl39OhRGjdujIWFBbq6uoSF\nhbF//36qVq2KSqVCpVIxePBgDh8+XGhbW1tbzcAY8GBeYTs7u0fWeXjBWq9ePTZs2MC6deuIj4/n\n4sWLr5Q7NTWVyZPG4+baAAvjWI5HdWD4kAZUrFgypqYSZZ+OWk1oU3fWfR5BbbMCmgQ0YvCggSQm\nJmolz6lTp/Dz88PDw4MZM2bwwQcfAA+uY93c3FiwYAHz5s0DHvQaWL58OW5ubqxevfqRgaf++SVQ\nhw4d2LRpE56enkRFRTFx4kRcXV1xcXEhICAAV1fX13uSJVzZqAzEc8vLy2PhwoXMmjWL0NBQ9u/f\nj5WVlbZjFTkHBwfu38/m0uV71KpZckduFQ/+gLdp7EJTr/qs2nYAN1cXRo0azaTJkzE0NNR2PCFE\nKeTo6MiMGTPIyspCX1+fXbt24evry82bNzVt3qZNm3BxcSm0rbe3N3FxcSQkJGBjY8O6dev44Ycf\nHllnypQpfPvtt+Tk5Gi60avVarKysl4qb3Z2Nl9/vZBPP/mYdm1sOfB7CDbW8vdPaE/FCnr0bt+Y\nDs09+H7LfpydGhAREcGkye9iYmLy2nK0adOGNm3aFFo+ceJEPvnkk0eW1ahRg99//73QusuXL3/k\ndd26dTlx4oTmdWBgYBGlLZvkzmI5cuTIEXx9ffn555/ZsmUL8+fPL5OFIoCOjg7169dny/YEbUcR\nz0m/YgUGhzXj+5mDObJnO/Xq1mHNmjUl6kF7IUTp4ObmRr9+/fD29tbcJRgyZIjmDoKbmxt79uzR\n3JFITEwkNDQUAF1dXRYuXEjbtm1xcnKie/fuNGjQQLPvzZs34+Pjg5WVFaampri7u+Pq6kp2dvZj\ni89niYyMxNWlPr/tWMzW9c1ZOMdXCkVRYhhXrsTIni1ZOXMIZw7/gUOd2ixbtkyrbXNpf1SqtFEp\nciVW5qWlpfHee+/x008/MX36dLp161Yu/kcbP348Vy/tYMOq1sV6nKGj/yT1byOmDg8r1uOUNzHn\nLjN/9S5MzKuydPl/H7lYE0KI0i4pKYlx497mjz/+x5wZnoQG19B2pDJlQEQkuXcteP+t4huAqDy6\nkHCDWUu3Y1O9Nt8tW07NmjW1HUkUM7mzWIYpisL69etxcnIiMzOTgwcP0r1793JRKMKDQW7iE2Q+\nv9LK3bEmS6cNINC5GgGN/fl4xgyZn1EIUeopisLy5ctxdq6HcaUzHN4dKoWiKDXq2Vvz3Uf9qW+l\nh6eHO4sWLaKgoEDbsUQxkmKxjLp06RIhISFMnTqVZcuWMX/+fMzMzLQd67Vyc3OTQW5KOR21mq5t\nfFkxYzA7Nq/Fy8Od48ePazuWEEK8lEuXLtGyZRMWfPkBP61swuypnhhV1tN2LCFeiK6uDuEdA1n8\nQV++WfgFLZo1JT4+vtiO9+WXX+Li4kLDhg01g9acOHGCRo0a4erqSseOHR8Z3fSfduzYgaOjI3Xr\n1uXTTz/VLJ80aRJubm6Eh4drlq1ateqRQXHEA1IsljEFBQXMnz8fHx8f/P392bNnD/7+/tqOpRX1\n6tUjKyubq9fTtR1FvCJrS1Pmju9OWDNH2rQKYtLECS89kIQQQrxuBQUFLFjwFT7ebgQF5vLH1ta4\nu1poO5YQr6SWrSX/+bAfnrWN8fH2Yt68uUU+b/Lp06f57rvvOHLkCCdOnGDr1q3Ex8czePBgPvvs\nM06ePMkbb7zB559/Xmjb/Px8Ro4cyY4dO4iNjeWHH37g7NmzpKWlER0dzYkTJ6hQoQKnT58mKyuL\nFStWMHLkyCLNXxZIsViGXL58mZYtW7J27Vp+++03xowZg55e+f3GUldXl7p1Hfhl22VtRxFFQKVS\nEdrUnVWzhxK9fxeuLs5ERUVpO5YQQjxVXFwczZs1Ys2qOezc3Joxw53R1ZXLL1E26KjV9AppxLcf\n9Wf18v8Q0Mifc+fOFdn+z507h5+fH/r6+ujo6NCsWTN++ukn4uLiaNKkCQCtWrXip59+KrTt4cOH\ncXBwwN7eHj09PXr06MHmzZvR0dEhNzcXRVHIzMxET0+POXPmMHr0aHR0ZJqaf5O/VmWAoii8//77\neHl50bx5c7Zv307t2rW1HatE8PX1Y8++G9qOIYqQhWllZo4KY0gnP958oyOTJ02UZxmFECWOoih8\n/fVCGvl70b6Nmh0bg6jn8PqmHBDidaphbcHX7/ammas1AY39mT1rFnl5ea+834YNG7J3716Sk5PJ\nzMxk+/btXLt2jYYNG7J582YA1q9f/8jcqA9dv36d6tWra17b2dlx/fp1KleuTEhICJ6entjY2GBs\nbMzhw4fp2FEGQ3ocKRZLudTUVLp278bnX8yhsrExY8aMkW9F/sHb25uLf+VoO4YoBi18nVg56y32\n/7GdgEb+XLp0SduRhBACeDAKebeunfn2m1ns2tKa4UMaoKMjl1yibFOr1XRp7cOy6QPZsmEVvt5e\nnDx58pX26ejoyKRJk2jTpg3t2rXD3d0dHR0dli5dyqJFi/D29iY9PZ0KFSoU2vZpAzpOmDCB6Oho\nPv/8c6ZMmcKMGTP47rvv6N69OzNnznylzGWN/OUqxfbv34+LmyspBXnMWr+KjPtZ0tf6Xx4MciPP\ntpVVZsaGfD62K00aWuHj7VVo4mwhhHjdjh8/jpenC2ZGl9i1uRUOteVuoihfbCzNmD+xByH+tWnR\nvCn/+c9/XmlexoEDB3L06FH27NmDqakp9evXp379+vz6668cPXqUHj16UKdOnULb2draPnLH8erV\nq9jZ2T2yTnR0NPBgnIsNGzawbt064uPjuXjx4kvnLWukWCyF8vPzmTp1Kh06daT9sEF0fTuCyqYm\nRHwyjfUbNrBlyxZtRywxHB0dycjI4sZNGRW1rFKr1fRo58+8Cd15b+JYBvQPJyMjQ9uxhBDlzMNu\np8FtWzBlkgNzZ3ujr6+r7VhCaIVKpaJTC0+WfBjO3M9m0qd3z5dum2/dugXAlStX2LRpE7169eL2\n7dvAg8GjPv74YyIiIgpt5+3tTVxcHAkJCeTk5LBu3bpCXU0f3lXMycnRDM6jVqtlEL1/kGKxlLly\n5QpNmjVlw/atTFjyFW6BjTTv2dSyp/s7IxgaEcHNmze1F7IE0dXVxcGhDr9sS9B2FFHMHGvZsHzG\nIO5cjcXD3ZWYmBhtRxJClBP/7Hb62y+tCetor+1IQpQINW2qsHRqf9L//gtvTw/Onj37wvvo0qUL\nzs7OdOzYkUWLFmFsbMwPP/xA/fr1adCgAXZ2dvTv3x+AxMREQkNDgQfXgAsXLqRt27Y4OTnRvXt3\nGjRooNnv5s2b8fHxwcrKClNTU9zd3XF1dSU7OxsXF5ciOf+yQKW8yn1h8VpFRkbSpVs3mr7ZkVbd\n30T9hGcT/zvzc27GxXP86DHUavk+4O3Ro7lzK5Iflrcslv0PHf0nqX8bMXV4WLHsX7y4HVEnmL96\nF3O+mMeAAQO0HUcIUYYdP36cbl07E9TUlFkfucvdxBJiQEQkuXcteP8tGbSkpPgl8jiL1u3mqwVf\n07t3b23HEc9JKolSYtmyZYR16UKf98bRple3JxaKAD3HjSIjO5thw4a9xoQll7ePDxfis7UdQ7xG\nwYFuLHq/L9M+fJe3R48ukhHZhBDin6TbqRAvpmNzT76a3Jv3J41j5IgRMpJ5KSHFYglXUFDAhIkT\n+WDqR4ye/ymOXh7P3KaCvj7DP5nO5i2/sHHjxteQsmRzd3fn9u372o4hXrPadlVZOm0gR/ftom3r\nViQnJ2s7khCijMjMzKRHjzf5Zol0OxXiRdSracWy6QOJORRJ61ZBJCUlaTuSeAYpFkuwzMxM3ngz\njK2//crYr+diVbP6szf6/6xqVqfX+LcZMWoU169fL8aUJZ+joyPpGZncui2D3JQ3xpUrMWdcN2yM\nFby9PDhz5oy2IwkhSrlbt24R1CIAVf5Zfv+lFXVqGWs7khClipHhg7bZ3lwHL093Tpw4oe1I4imk\nWCyhbty4QUCTQO5kZzF8zkwqm7740Nu+rYPwbBZIcLt2FBQUFEPK0kFPT49atWrxy/bL2o4itEBX\nR4fRvVoRHupDs6aBmkl8hRDiRV24cIHGjbxpFqDm2wX+0u1UiJeko1YzokdLhnRuRFCLZvz444/a\njiSeQIrFEujEiRN4+fhQ08uN3pPHoveYiUafV/d3RpKLwsCBA4swYenj6+vL7j9vaDuG0KKQJm7M\nGduNYW8N4uMZ019pzichRPkTFRVF0yb+jB1pz4cT3Z464bcQ4vm0DXDly4m9GPv2SKZ8+IG0zSWQ\nFIslzNatW2keFETokHCC+/Z85cZIr2IFIj6Zxv9+3cHatWuLKGXp4+Pjw/mL8txieefsYMeyaQNZ\n/d+lDI8YpplTSQghnmbt2rWEvRHKki996NfTQdtxhChT6teyZum0Afy0bhXDhw8r173hSiIpFksI\nRVGYO28e/QcO5K1ZH+EV1KzI9l3Vzpa+k8byzrixXLlypcj2W5q4u7tzSwa5EUAVMyMWvd+H4wci\n6db1TbKzZaRcIcTjKYrCJ5/MYsKE4fyyrgUtm9tqO5IQZZK5SWUWvtubI1F/0LdPbxkptQSRYrEE\nyMvLY1hEBF8u+pp3Fs6hlpNjkR/DK6gZPi1b0C40pFx+Y+Po6Mi9exkk3ZGCUUBlA33mTujBvb8T\naNu6FWlpadqOJIQoYfLy8hgeMYTVqxawa3NrGjqZazuSEGVaZQN95k3owZW4k4S90Yn79+WarSSQ\nYlHL0tLSaBsczKGTMYxZ8DkW1lbFdqwuo4eh6OrQt1+/YjtGSVWxYkXs7e3Z8j8Z5EY8UEFPl+kj\n36CqYT5NAwO4efOmtiMJIUqI9PR0OnVsR9z5P/h1U0tsbQy1HUmIckG/YgU+HdOF3LSbBLdtzb17\n97QdqdyTYlGL7t69S1DLlhQYGzBk5kdUMizexkivQgUiZk/n9z9+5/vvvy/WY5VEvr6+/LEnUdsx\nRAmio1YzPjyYxs7WNPL35eLFi9qOJITQstTUVIJaBGBpnsj675tibPTyg8wJIV6cnq4uU4d3wkI/\njxbNmnLnzh1tRyrXpFjUkvT0dFq3bYNJDVu6jRmBjq7OazluFRsrwt+bwMTJk7h06dJrOWZJ4ePj\nw7m4LG3HECWMSqVi4BtN6dXGk8CAxpw+fVrbkYQQWpKWlkab1s3xdocFn/uipyeXSUJog45azaQB\n7XCxNyUwoBGJifJlv7bIX0EtyMzMJDikHfpVLejydsRrH37bvWkAjYLb0C40lLy8vNd6bG1yc3Pj\n9m0ZzEQ8XueWXozq0ZxWLYM4c+aMtuMIIV6zu3fv0rZNCzxdC/h0uqdMjSGElqlUKoZ3D6KlV20C\nGvvz119/aTtSuSTF4muWlZVFaIf2UNmA7mNHoVZr558gbMQQdA306dW7t1aOrw1OTk6k3U0nOUUe\nmBaP16axCyO6NaNlUAtiY2O1HUcI8ZrcvXuX4LYtcHXK4/OPvaRQFKIECe8YQPdW7tL7R0ukWHyN\nsrOz6di5E/d11fSaOEZrhSKArp4ew2ZN5c+9e1m6dKnWcrxO+vr61KhRg607yuf0IeL5BAe6EtG1\nKS2DWnDu3DltxxFCFLN79+7RLjgIp/o5zJkphaIQJdGbrX0Y3q0pQS2ac/jwYW3HKVekWHxNcnJy\neOPNMNJyc+gzeRxqndfzjOLTWFhVY+CUybz3wQecP39e23FeCx8fH3b/Kf3exdOFNHFj6JuBBLVo\nxoULF7QdRwhRTNLT0wlp15L6DveZO8sbtVoKRSFKqraNXZg8oC2hIe3kcZHXSIrF1yA3N5eu3btx\n695dwj+c+NoGs3keLo39aNIxhA6dOpaL5xd9fHyIPS+D3IhnC23qzuDOAbRo3pS4uDhtxxFCFLGH\nhWId+0zmf+IjhaIQpUATL0dG9WxB2zatuHr1qrbjlAtSLBazvLw8evXpzZXbfxM+ZRI6urrajlRI\n56EDqWRiTNduXbUdpdi5u7vz9y0Z5EY8nw7NPRjQsRHNmzUtd6MHC1GWZWRk0D60DbVqpPPVZ1Io\nClGatAt0o0uQO21atyQlJUXbcco8KRaLUX5+Pv369+f85QQGTn0fvQolc64mHV1dhs78iIOHDrN4\n8WJtxylWzs7OpKXdI+1ujrajiFKiUwtPerfzpnWrIG7fvq3tOEKIV5SdnU3HDm2pbpPGgs99pVAU\nohTq3b4x3vWsaB/Sjqws6TFWnKRYLCYFBQUMGjyYE+diGTzjA/QqlsxC8SGzqpYMmvouU6dPK9Oj\nQFaqVAk7O1u2/SqD3Ijn17WNL83ca9GubRvS09O1HUcI8ZIURWHQoHCMDG6xcI7cURSiNBvZsyXG\nFXLp0b1ruXiUSlukWCwGiqIwfMQIDsYcZ8jMj6igr6/tSM/F2c+H5mGd6Ni5Mzk5ZffOm7e3D79H\nXtd2DFHKDO3aHDvzCoS90Ync3FxtxxFCvITp0z/iXGwU33zlj46OXAIJUZqp1Wo+fKs9t67GEzFs\nKIqiaDtSmVQq/lLu2bOHAwcOaF4vWbKElStXajHR0321YAE7ft/F0NlT0TeopO04L6TDoHCMqlgQ\n9uab2o5SbHx9fTlzNlPbMUQpo1KpmDSwHdlpfzNk8CBplIQoZVavXs2ypV+zbkUgBgYlb/wAIcSL\n09PVZfbbb7L/z9+ZNm2qtuOUSaWiWNy9ezf79+/XvB46dCh9+/bVYqIni4yMZNr06Qya8QGVDA21\nHeeF6ejqMHTmhxyPiearr77Sdpxi4e7uzt+3ZZAb8eJ0dXSYPqIzRw78yYzp07QdRwjxnKKionhn\nzHDWf9+UalUNtB1HCFGEDCtV5Itx3Vn27X9YsmSJtuOUOVotFt944w28vb1p2LAh3377LQA7duzA\ny8sLd3d3WrduzeXLl1myZAnz5s3Dw8ODqKgopk6dyhdffAFATEwM/v7+uLm5ERYWRmpqKgDNmzdn\n8qLL6ioAACAASURBVOTJ+Pn5Ub9+faKioor9fC5fvkzX7t3p+/54LG2si/14xcXEwoIh0z9g5qxZ\nnDx5UttxilzDhg35f+zdZ1iTZ9sH8H8gQBgh7L33DAFkiKtuBHHvCW5trXXVWRW31da666q1U627\nde896gTZICAbZSqICOT9oKX1rX1USLgyzt+X5+hNct//PIeEnDmv+7xKS5/i2TPFXWpLpEeLp4FV\nU/phy7cb8cMPP7COQwh5h9TUVPTp3R1b1gbDw02fdRxCiBQY6ulg9fSB+GLOLBw6dIh1HIXCtFj8\n7rvvcOvWLfz5559Yu3YtCgsLMWbMGOzfvx/37t3Db7/9BltbW4wbNw5TpkzB3bt30bJlS3A4HHA4\nr25KHzZsGFauXIn79+/D29sb0dGvvu3ncDiora3FjRs38M0339Qfl5bKykpEdO+Gj/r1hHszP6le\nqym4+fuiff/e6NGrF6qqqljHkSgtLS1YWFjgyAnan4c0jJE+H19N648pn03CjRs3WMchhPyH4uJi\nhId1xKyp7ujQ1pJ1HEKIFNmYG2LllH4YGRWJK1eusI6jMJgWi2vWrIFIJELz5s2RlZWFLVu2oE2b\nNrC1tQUA6Onp1T/2bfcHlZeXo6ysDK1atQIADB8+HBcvXqz/ea9evQAAfn5+yMjIkNrrEIvFiBwR\nBb65Kdr17Sm16zS18MjBMDA3Q4+eivOa/tKsWTMackMaxcHKBLNGhqFXzx7Iz89nHYcQ8v9UV1ej\nd68IdG4vwMhhLqzjEEKagIejJeaNi0Cvnj2Qk0Of8ySBWbF4/vx5nDlzBtevX8e9e/fg6+sLkUjU\nqKER//+5GhoaAABVVVWpjtRduWoVbsfcR/+pE+s7nopARVUVoxfNRWzcA6xatYp1HIkKDAxEbAJt\ngUAap3UzN4S38ECfXj1pQiohMkQsFmP06CjoaBVi0VwR6ziEkCbU3McZvdv7ol+f3rSlhgQwKxbL\ny8uhr68PHo+HxMREXL9+HVVVVbh48WJ9F7C4uBgAwOfz8fTp0zeeLxaLoaurC319/fr7EX/88Ud8\n9NFHTfkycOLECaxY+SVGLpwL9dfFqSLRNdDHmMXz8OWqVbh9+zbrOBLj4+ODgkL6cE8ab2Sv1lCt\neYrJn01iHYUQ8tqqVV8i5v55bFtPW2QQooyGd2sBTnU55syexTqK3GP2DhoaGoqamhp4eHhg1qxZ\naN68OUxMTLBlyxb06tULIpEIAwcOBABERETgwIED8PPzqy8M/+rg7dy5E9OnT4ePjw9iYmIwb968\nt15PGh2/1NRUDBoyBJHzZsLA1ETi55cVLiIhOg/ujz79+qKyUjG2nPD29kZp6VNUVtI3TqRxVFRU\nMH9cN/xxaD927tzJOg4hSu/69etYtXIpftnWAtpaaqzjEEIYUFFRwbxx3fDDzh04cuQI6zhyjSOm\nzcIa5NmzZ2gWGAC/Lh3QukcE6zhSV1dXh7WTZ4LPVcPpU6dZx5EILy8vLJ7jiN49HBp1nrGfXkRp\nAR8LJvSSUDIijx5mFWLC0h9x6vRZ+PnJ/5ArQuRRaWkpfEWeWDbfDV272LKOQxiKGn8eL8sNMWdM\nN9ZRCEP3EjMxe90B3L5zFzY2NqzjyCVam9EAdXV1GDx0CEydHNCqe1fWcZqEiooKRkXPQWJKCpYu\nXco6jkT4+/vjFA25IRLiYG2C6cND0aN7BIqKiljHIUTpvLpPMRKd2xtSoUgIAQCI3GwxMDQAfXv3\nQnU1bZnWEFQsNsDixYuRmJaGPpPGK9RAm3fR0RNg3JL5+GbtGoXYLiAoKAgxcTTkhkhO+2BPtPZ1\nxMgRkY0a1kUI+XBbtmxBUuJNLP6CBtoQQv42OLw5eJwqzJzxOesocomKxQ906tQprNu4AVHRs6Gm\nrs46TpNz9PZE2PDB6D9wAJ49k+9C69WQG/qWiUjW+H5tkfjgPrZv3846CiFK48GDB5g753N8v6k5\neDwu6ziEEBmioqKCL8ZEYM+uX3Do0CHWceSOzBWLpaWl6NOnD9zd3eHh4YEbN26guLgYHTt2hIuL\nCzp16oTS0tL6xy9btgzOzs5wc3PDyZMn64/fvn0b3t7ecHZ2xqRJkplSWFJSgmGRkRg0YzL0jAwl\nck551HFgX1g4OqBrhHwvwRUKhSguLkdVFQ25IZKjrsZF9PjumPH5NKSkpLCOQ4jCq6ysRP9+PbB4\nng9cnASs4xBCZJCAr4WFH/fAyBFRSE9PZx1HrshcsThp0iSEhYUhISEBMTExcHNzw/Lly9GxY0ck\nJyejffv2WL58OQAgPj4eu3fvRnx8PI4fP44JEybUL/0aP348tm/fjpSUFKSkpOD48eONzvbxxInw\nCA6AezPlHl6hoqKCEfNn4mFGBqKjo1nHaTA+nw8TExOcOJPNOgpRMA7WJhjZoxUG9u9H+y8SImWf\nTfoYQk91DOrryDoKIUSGeTtbY3hEc/Tp1RMvXrxgHUduyFSxWFZWhkuXLmHEiBEAAC6XC4FAgMOH\nD2P48OEAgOHDh+PgwYMAgEOHDmHgwIFQU1ODnZ0dnJyccOPGDeTl5eHp06cIDAwEAAwbNqz+OQ11\n4MABXLh0Ed3GjmjUeRSFtq4uxi5dgA2bNuLKlSus4zSYn58fTp6lYpFIXp9OAdBUrcaC+W/fzocQ\n0ni7du3CuXNH8PUyf6WaIUAIaZj+oUHQ0xRj6pTJrKPIDZkqFtPT02FsbIyoqCj4+flh9OjRqKio\nQEFBAUxNTQEApqamKCgoAADk5ubCysqq/vlWVlbIycn513FLS0vk5DR86mVhYSHGjBuHQTOnQEOT\n1+DzKBoHT3dEjByOgYMHoby8nHWcBgkODsb92KesYxAFxOFwMGdUOLZu2YxLly6xjkOIwklPT8en\nE8dhx8bm4OvQfoqEkHf762/z/r17cPHiRdZx5IJMFYs1NTW4c+cOJkyYgDt37kBbW7t+yelfOBxO\nk357KBaLMXL0KAR0agdHL48mu668aN+vF2zcXBEWHs46SoOIRCLkF9AyQSIdhnp8zBoZhsGDBsjt\nFyqEyCKxWIyxYyIxcZwrRELlnSFACPlwfG1NTB7SEaNHjqDlqO9BpopFKysrWFlZISAgAADQp08f\n3LlzB2ZmZsjPzwcA5OXlwcTEBMCrjmFWVlb987Ozs2FlZQVLS0tkZ2e/cdzS0rJBmX766SfExsej\nS+Tghr4shcbhcBD1xQxk5eZg9uzZrON8MG9vbxSXlKO6mobcEOlo6ecKfzdrzJo1g3UUQhTGL7/8\ngvz8FHwyxp11FEKIHGoT4A4LQy0sX6YYe4dLk0wVi2ZmZrC2tkZycjIA4PTp0/D09ERERAR27twJ\nANi5cyd69OgBAOjWrRt27dqF6upqpKenIyUlBYGBgTAzM4Ouri5u3LgBsViMH3/8sf45H+Lx48eY\nPGUKBn7+mVJuk/G+tPg6GL8sGtu+244LFy6wjvNBBAIBDA0Ncepsw5cpE/IuHw9oi99271aI/UkJ\nYa24uBjTpn6KNSv8oaYmUx9jCCFygsPhYOrQTliz5pv6uoO8ncxtRrRu3ToMHjwY1dXVcHR0xI4d\nO1BbW4t+/fph+/btsLOzw549ewAAHh4e6NevHzw8PMDlcrFx48b6JaobN25EZGQknj9/jrCwMISG\nhn5wlk8/mwT/Dh/B1s1Foq9REdm6uaDH2JEYMmwoYu/HQE9Pj3Wk9+bn54fjZ1IRHmrLOgpRUAId\nLUwc2B6jRkThzr37UFOj+6sIaaiZM6aiW5glAvyMWUchhMgxUyMBorq3xJhRI3HuwkUakvUfOOK/\n9pogbzhx4gSGjxyBWd9toqE270ksFmPLnIV4UVyCa1evsY7z3tasWYMD+zbh0okuDXr+2E8vorSA\njwUTekk4GVEkYrEYk1fuQkSfoZg5axbrOITIpcuXL2NA/wjcOBcOgS6t+CH/LWr8ebwsN8ScMd1Y\nRyEyrLauDqPmf49ps+cjMjKSdRyZROs33qKiogKjx45Bv8kfU6H4ATgcDobNmYb8x48xffp01nHe\nm0gkQn4h3bNIpIvD4WD68M748ssVePjwIes4hMid6upqjB0TieXRvlQoEkIkQlVFBTNGhGL6tKl4\n8uQJ6zgyiYrFt5j7xRewcnOFR2Az1lHkjqa2NsYvX4idP/6AU6dOsY7zXnx8fFBUVEZDbojUWZoa\nYHBYMMaOHgVa1EHIh1m5cgVsrIDu4XTLACFEctzsLdA5xBNTJk9iHUUmUbH4/9y5cwc//PgDen48\ninUUuWXt7Ig+H49B1MgRKCoqYh3nnfT09KCvr4+zF/JYRyFKYFBYMLIyUvHLL7+wjkKI3EhNTcXq\nr1di1RJfuq+IECJxo3u3xplTJ3H27FnWUWQOFYv/IBaLMXHSJISNGAq+HA1okUUtu4XD1U+E0LCG\n3QfY1Pz8fHHiTNa7H0hII3G5qvg8MhTTp01BRUUF6ziEyDyxWIwJ40di8icesLXms45DCFFAWjwN\nTB3WCWNGjURVVRXrODKFisV/OHnyJB7lZCM4tBPrKHKPw+FgyMypKCotxaRJst/WDwwMwp37Zaxj\nECXh7WINobMlvlq1inUUQmTe7t27kZ+fggmj3FhHIYQosFb+brAzE2DJ4sWso8gUKhZfq6urw/QZ\nM9AlaghUuaqs4ygEnpYmJixfiF9378bRo0dZx/mffH19kVdA9yySpjOu70f45pvVyM/PZx2FEJlV\nXV2N2bOmYuUiEe2pSAiRuslDO2LDhnVIT09nHUVm0Dvva3v37kXlyxfwbdOSdRSFYuloj/6TxmP0\n2DEoLCxkHec/vRpyU46amjrWUYiSsDTRR9c2Ppg7ZzbrKITIrC1btsDJgYcWwWasoxBClICJgS56\nd/BH9IL5rKPIDCoWAbx8+RIzZ89C+MjhdOO8FDQP6wzP4ECEdumCujrZLMYMDAwgEOji/KVc1lGI\nEhke0QIHD+zHgwcPWEchROZUVFRg6ZIFmDfDi3UUQogSGdglGL8fPozk5GTWUWQCFYsAduzYAW0D\nfbg182UdRSFxOBwMmjYJ5c8r8Mknn7CO859EIhGOn8pmHYMoEV0dTQzv1gLTpnzGOgohMmft2jUI\nDjSESGjIOgohRInwtTXRr3MA5s+byzqKTFD6YvH58+eYHx2NsJHDqKsoRRqaPExYvhD79u/HwYMH\nWcd5q6CgYNy+V8I6BlEyvTs2Q0L8A7nZl5SQplBaWoqvv/4Sc6dTV5EQ0vT6hwbh1MmTiIuLYx2F\nOaUvFtetXw8rFyfYe9CUNWkzt7PFwCkTMeGTj5GXJ3t7Gvr6+iI3n4bckKalxuViQr+PMH3qZIjF\nYtZxCJEJK79cjrBOlnBxErCOQghRQtqaGhgcHox5X8xhHYU5pS4Wy8rKsGLFCoSNGMI6itIICu0A\nn5YhMnn/oo+PD57QkBvCQNtAD7yofIojR46wjkIIc/n5+fh280bMnOzJOgohRIn16RiAy5cu4t69\ne6yjMKXUxeLKVavgGRwAcztb1lGUyoCpE/G89iXGjBnDOsobjIyMoKOjg0vXaCsD0rQ4HA6GRQQj\nesE86i4Spbd0STQG9LaHtZUO6yiEECXG01DHkPDm+GLOLNZRmFLaYrGgoADr169H5+GDWEdROuoa\nGpiwfCF+P/IH9u7dyzrOG3x9RTh2Mot1DKKE2gZ6oPhxAU6fPs06CiHMZGRk4Oeff8K0Tz1YRyGE\nEPRs74/bt/7EzZs3WUdhRmmLxUWLFyOgUzsYmpmyjqKUTK2tMGT6Z5g46VNkZclOcRYUFIzbd2nI\nDWl6qioqGB7RHAsXzGMdhRBmFiyYg1GRLjA20mQdhRBCoKGuhmERIZg7eybrKMwoZbGYkZGBH3/6\nCZ0G92cdRak169AW/u3aoEt4uMzcv+jr64ucPBpyQ9joGOKFzIx0XLx4kXUUQppcZmYmfj98GBPH\n0sA5Qojs6NbWF/FxD3DlyhXWUZhQymJx1VdfISQ8FHx9PdZRlF6/TyeglgNERkWyjgLg7yE3slK8\nEuXCVVXFsK6v7l0kRNmsXfM1hgxwgp5Ag3UUQgipp8blIrJ7CObMmsE6ChNKVyxWVFTgp59+Qsvu\nYayjEABqGuoYvzwaJ06ewi+//MI6DkxMTKClpYUr1wtYRyFKKqy1DxLi43Djxg3WUQhpMmVlZfh+\n5w6MG+HMOgohhPxLeCsRHqal4urVq6yjNDmlKxZ//fVXOHp70r2KMsTY0gLDZ0/D1GnTkJmZyToO\nfHx8cOyU7NxHSZSLGpeLQV0CsXLFMtZRCGkyW7duQbs2ljQBlRAik7hcVfRq74uN69exjtLklKpY\nFIvFWLNuHZpHdGEdhfw/vm1aIqhzB3QJC2O+BDQ4OBg3bxczzUCUW3hrEU6fPoPc3FzWUQiRupqa\nGqz5ZhU+GePEOgohhPyn8FY++P2PP1BUVMQ6SpNSqmLx5s2beFJcBI9Af9ZRyFv0/mQMOBpqGDxk\nCNMcIpEIObk05Iawo6PFQ8cQb2ze/C3rKIRI3aZNm1BQWIJJn9/C78fYry4hhJC30dPVRit/N3z/\n/fesozQppSoW123YgJCILlBRUaqXLTe4amoYvywa586fw86dO5nlEIlENOSGMNe7gx82f/stqqur\nWUchRKr27t2L6OhodOg0GBOm3IGj8CA+mXYFRcVVrKMRQsgberQV4duN6yEWi1lHaTJKUzUVFxfj\n0KGDCO7SkXUU8j8Ympshcu7nmDFrJtLS0phkMDMzg4YGDzdvP2ZyfUIAwNHaFDZm+jhw4ADrKIRI\nTVxcHJKTkzFq1Ch88cUXSE1Nxepv1iMt0wRu/gfQqvMx/EHdRkKIjBC6WENFXIvz58+zjtJklKZY\n3LFjB4TNg8HXo+0yZJ1PyxC0CA9FWNdw1NSwWQ7q4yPEkeOPmFybkL/0bCfC2jWrWccgRGo2btyI\n4cOHQ11dHQDA5XIRHh6OP/74Azdv3kS7DoMwfsptOPocwsRpV1BcQt1GQgg7HA4H3dv6YOP6tayj\nNBmlKBbr6uqwfsMGhNB2GXKj5/hRUNfRwYCBA5lcPygoGDduKdcNzET2fNTMHWmpKYiNjWUdhRCJ\ne/r0KX799VcMHz78rT+3sbHBvHnzkJKSitWr1yElwxiufq+6jUdOULeREMJGl5ZCnDp1GgUFyrHN\nmlIUi2fOnAHUuHDwdGcdhbwnVS4X45YtwJWrV7F169Ymv76vry+yaMgNYYzLVUX3j3yxbu0a1lEI\nkbgff/wRrVu3hoWFxf98nJqaGsLDw3HkyBHcvHkTbdsPxLjPbsPJ5xAmfU7dRkJI09LR4qFdsCe2\nb9/GOkqTUIpice369WgeEQoOh8M6CvkABibGGDFvJubOm4ekpKQmvbZIJEJR0VMackOY69bWF7/9\n9huqqugDMVEsW7ZsQVRU1Ac9x8bGBvPnz0dKSiq++notEtOM4Op3AK1Dj+HoSbp1gBDSNHq09cXm\nbzehtraWdRSpU/hiMTs7GxcuXEBgx3aso5AG8GoeiDY9uqJrt25Nev+iubk51NTUcfsuLUUlbJkY\n6MLV3gJ//PEH6yiESExycjLy8/PRqlWrBj1fTU0NXbt2xdGjR3Hjxg20aTsAYz699arbOOMqdRsJ\nIVLl7mABXS11nDx5knUUqVP4YnHzli0IaP8ReFparKOQBuo2Ogra+nro3adPk12Tw+HA29sLfxyn\n+2IIex2D3PDTD9+zjkGIxOzZswfdu3eHqqpqo89la2uLBQsWIDU1Fau+WoPEVEO4+h1Amy7HcfxU\nlgTSEkLIv3VvI8QGJRh0o9DFYk1NDbZs3YoW3WiwjTxT5api7JJ5uHnrT2zYsKHJrhsc3JyG3BCZ\n0C7IA2fPnUdJSQnrKIRIxF/FoiSpqakhIiKivtvYqk0/jJr4J5xEh/DZjKsoLXsh0esRQpRbxxAv\nXL58BdnZ2ayjSJVCF4vXrl2DtkAXFg52rKOQRtIzNsKoBXOwcPEixMXFNck1/fz8kJVDQ24Iezpa\nPAQKnbBv3z7WUQhptKSkJDx+/BjBwcFSu4atrS2io6ORmpqKlSu/QXyKAVxE+9GmyzGcOE3dRkJI\n42nxNNCmmRv279/POopUKXSxePDQQXgEB7KOQSTEI9Af7fr0RLcePVBdXS316wmFQjyhITdERnQK\ndscP33/HOgYhjSbJJajvoqamhm7duuHYsWO4dv06WrXpjxEf34ST6BAmz6RuIyGkcVqKHHFg32+s\nY0iVgheLh+EVQsWiIuk6Yij0TI3Rq1cvqV/LysoKKiqquB9LS1EJeyEiZ8TExir8chei+KSxBPV9\n2NnZ1Xcbv/xyNR4k6cNFtB8fhR2nbiMhpEGChE64dfsOSktLWUeRGoUtFlNSUlBWXgYbV2fWUYgE\nqaiqYvSiL3Dn/j2sXr1aqtf6a8jN78doHDthT0NdDW0DPfHrr7+yjkJIgyUkJKCoqEiqS1DfRV1d\nHd27d8fx48dx9do1tGjVFyM+vgln30OYMusayqjbSAh5T5o8dfh5OuLYsWOso0iNwhaLhw8fhlfz\nIKioKOxLVFoCQwOMWfgFlq9Ygfv370v1WsHBzXH9zydSvQYh76t9oCv27tnFOgYhDfZXV1FW/jbb\n29tj4cKFSE1NxYoVqxGToAun193Gk2epi08IebeWPvY4uH8v6xhSIxvv1lKw7+ABeAQHsI5BpMTV\nX4SOA/uiZ69eUt2s3NfXF4+yacgNkQ1+HnZITExCYWEh6yiENMhvv/3GZAnqu/zVbTxx4iSuXbuG\nkJZ9EDX+Bpx9D2Pa7OsoL5f+ffKEEPnU0s8VJ06eapJ5GiwoZLFYUlKC+/fuwc1fxDoKkaIuwwbC\n0MoC3Xv0kNo1RCIRHj95JrXzE/Ih1LhcBItccOTIEdZRCPlg8fHxKC4uRlBQEOso/5O9vT0WLVqE\n1NRULF/+Fe7G8eEk2o+24cdx6hx1GwkhbzLS58PO0gQXL15kHUUqFLJYPHbsGNz9fKHO47GOQqRI\nRVUVoxfOxYP4eHz55ZdSuYa1tTUAIOYBDbkhsqGFjwMO7FPc5S5Ece3Zswc9evSQmSWo76Kuro4e\nPXrg5MmTuHLlCoJDemP42Otw9j2EaXOo20gI+VsLHwccOKCY21vJxzv2B9p34ADcgpqxjkGaAF9f\nD2OXzMOqr7/Gn3/+KfHz1w+5OU5DbohsCBE54/yFC3jxgoZwEPmyd+9emVyC+j4cHBywePFipKWl\nYdmyr3D3gQ6cRPvQrutxnDmfwzoeIYSx1v6uOHzoEMRiMesoEqdwxeLLly9x6tRJeNOWGUrD2ccb\nXYYORN/+/VBZWSnx8wcFBePqdbpHjMgGAV8LDtZmuHLlCusohLy3wsJCZGVlISBAvmcJqKuro2fP\nnjh58hQuX76CwOBeGDrmKlx8D2P6XOo2EqKs7K2MoSKuk/rgRRYUrli8dOkSzKytIDA0ZB2FNKFO\ng/vBzN4OEd26Sfzcfn5+NOSGyJRAL1scO0r3LRL5cenSJQQFBUFVVZV1FIlxdHTEkiVLkJqahiVL\nV+J2jA6cRPvRrutxnL1A3UZClAmHw0FLP2ccOniQdRSJU7hi8cDBg3APku9vLsmHU1FRwcj5s5CU\nmoLFixdL9NwikQhPiiokek5CGqO50AlHjvzOOgYh7+3ixYto0aIF6xhSoaGhgV69euHUqVO4dOkS\nAoN7YfCoV93Gz7+4gWfPqNtIiDJo5eeMAwcUb6aAQhWLYrEYhw8fhncLdpv9EnZ09AQYt3QB1q5f\nj+vXr0vsvLa2tqitrUNCUonEzklIY7g7WiAnJw8FBQWsoxDyXi5cuICQkBDWMaTOyckJS5YsQVpa\nGhYv+RJ/3tOCg3A/2kecwLlLuazjEUKkyMfVBpmZj5CTo1grCxSqWExISEBV9QtYOtqzjkIYcfTy\nQNeoIeg/cACePZPMlhccDgeenh44dCRTIucjpLFUVVTg42aHq1evso5CyDuVlpYiLS0NIpHybGel\noaGB3r174/Tp07h06RKaBfbAoBFX4OJ3GDPmUbeREEXEVVWFn4cDLl26xDqKRClUsXj+/Hm4+vuC\nw+GwjkIY6jCgD6ycnRDetavEzhkUFIyrN2jIDZEdXo5muHTxAusYhLzTlStX4O/vD3V1ddZRmHBy\ncsLSpUtfdRsXf4kbdzSp20iIgvJ0MMVlKhZl15VrV2Ht5sI6BmGMw+Egav5MpD/KxLx58yRyTn9/\nf2Q8oiE3RHaIXG1w8cJ51jEIeaeLFy+iefPmrGMw91e38cyZM7h48SL8A7pj0IgrcPU7jFnzqdtI\niCIQutjgyuWLrGNIlEIVi9ev34C9hxvrGEQGaPP5GL9sAb7dslkiywFEIhEeP5HMslZCJMHd0RLx\niUlS2S6GEEm6cOGCwg63aShnZ2csW7YMqampWLhoBa7e4sHRZz86RJzAhcvUbSREXrnZmyMpJRUV\nFYozGFFhisWSkhLk5+XC3M6WdRQiI+zc3dB9dBQGDx2CsrKyxp3Lzg41NbVISW3ceQiRFJ66Glzt\nrXDz5k3WUQj5TxUVFXjw4AGaNWvGOopM4vF46NOnD86ePYsLFy7Ct1k3DIi6DFe/w5gdfZO6jYTI\nGQ11NbjYW+HPP/9kHUViFKZY/PPPP2Hv5gpVruLs4UQar22fHrDzdEdYeHijzqOiogIPD3ccOpIh\nmWCESIC3k4XC3UhPFMv169fh7e0NTU1N1lFknrOzM5YvX47U1DREL1yOKzc14CDcj47dTuDS1TzW\n8Qgh78nTwQznzp1jHUNiFKZYvH79OqxcnVnHIDKGw+Egcs7nyM7Lw4yZMxt1ruDgYFy+RlsVENkh\ndLHExfNnWccg5D9dvHhRKbbMkCQej4e+ffu+7jZegI9fBPoNvwRX/8OYs/AmKivp/nlCZMWzyirc\nTcjAr0evYc7a39B94tfYfewq9u7ZwzqaxChMsXj81Emo83ioqaE3UfImTR1tTFgRjR3f78DZZukA\nhwAAIABJREFUsw3/YO3n5490GnJDZIjQxQY3/7wFsVjMOgohb6Us+ytKi4uLC1asWIHU1DQsWLAM\nl66rw957Lzp2p24jIU2t7Fklbsam4YfDlzH9q10IH78KnUYvx5w1+3DyUgoM1K0xf9AUnP3yR5SU\nlLKOKzFc1gEkJe5eDFTuxeL4D79CoKsLHQN9mNjbwMXPB94hwdDV12MdkTBk4+KMXuNHY1hkJGLu\n3YOBgcEHn8PHxwePnyjODctE/unrakNDXQ1ZWVmwsbFhHYeQN9TV1eHWrVsICAhgHUXu8Xg89OvX\nD/369UNSUhK++247+g3/GQJdHnp3N8OcaX7Q0lKYj3SEMFdU+gxJGblIeJiH2JRsJDzMxrOKKujr\n8mGubwYfezeMGB2Jj3wCwFPnvfFcsViMqufPkZ+fDzMzM0avQHIU4p2loKAAqioqSNh2DCXPyhGf\nmYr4zBTcTUvA1R/2YteqddDi8cDX04OumTHsvTzg2TwQdm4uUFFRmOYqeYfWPboi6fZdhIWH4/q1\nax/8fEdHR1RXv0Raehkc7QVSSEjIh3O2M8eDBw+oWCQyJycnBzo6OhAI6P1SklxdXbFixZeIjl6I\nQ4cOYdOmTdjmvRc+3vqYN9MHLYPl/8MpIU1FLBajsLgcSel5SEzPxf2kLCSl56Kq+iUMBAJYGpjB\n11GISeHj0dLTH1zuu0snDocDbyd33LlzB2FhYU3wKqRLIYrFmJgYeNi7gMPhwIAvQEsvf7T08q//\n+cuaGqTmZiI+MxUx6Um4c/MBvv3tEKpfVkMgEEDbUB9mjnZw8/eFV3AgtPg6DF8NkRYOh4Ohs6Zi\n0fCxmDJ1Kr7+6qsPer6Kigrc3Nxw+OgjTP7YW0opCfkw9haGiImJUYg/SESxJCUlwcWF9j6WFh6P\nh/79+6N///5ITEzEd9u3o8+QX6Cnx0Pf7uaYNdWXuo2E/INYLEZuYQkS0/OQkJ6LmOQspGTkoaa2\nDkYCPVgamiPAJRhz+7VCoKt3oxpKQhtn3L59WyH+NivEu0hMTAw8rB3+8+dqXC7cbRzhbuOI3q06\n1x9/XFqMuMwUPMh41YU8s/lH/Lh0FXS0tKGjpweBpSkcvT3h3TwIVk7/fX4iPzS1tTFh+UKsnDAZ\nnTt1QufOnd/9pH8IDg7GpSt/ULFIZIaDpSFi7t1hHYOQf0lMTISTkxPrGErBzc0NX65cieiFC3H4\n8GFs3LgRW77fBx8vPSyY7YOQIOo2EuVSV1eHrPxiJKbnIuHhq8IwNTMfKioqMBTowcbIEm092uLL\n4a3hbS/5lYbedi44ekMxtrZSiGLx3u278LNy/ODnGesZ4CO9IHzkE1R/7MXLaiRnpyMu43UX8vxN\nnP5hN8QQQ1dXAG1jfVg6O8K9mR88g5pBncf7H1cgssjKyQH9Jo7DiFGjcP/uXRgZGb33c/38/HD6\n1EEppiPkwzham2L/hfOsYxDyL9RZbHqamppvdBu3b9+OXoNfdRv79bDAzCki6jYShVNTW4vM3CdI\nTM9DXFoOYpOz8DCrABrqajAU6MPOxBpd/bsgdFxruNt8eL3QEF52zli+f3uTXEvaFOIdI+b+fQwd\n1l4i59JQU4e3vSu87V0xoO2rvfnEYjHyih8jLuOvLmQ8fv9mM74rXQq+Nh98fT3oWZvDWegF7xbN\nYWptKZEsRHpCuoYi8fZdhHbpglsfsHGqSCTC4yeVUkxGyIdxsDJG2sN0vHz5EmpqaqzjEFIvISEB\nbdq0YR1Dabm5uWHlypVYuPDVvY0bN27E5h37IPLWw/xZ1G0k8qn6ZQ0eZhci6R+FYWbuE2jx1GEk\nMICDmS0GtOyNsMDWsDOzYpbT2sQcOXm5qKmpea/7HGWZfKcHUF1djZSHqXC1kt4yUQ6HAwtDE1gY\nmqCjf4v645UvqpD46CHiM1NwPz0Jd45dwB9bd0JVVRW6AgF0jA1h7eYMjwB/uPmLwFVXl1pG8mE4\nHA4Gfz4ZiyPHYuLEiVi3bt17Pc/JyQlVVS+Q8egp7Gz4Uk5JyLvxNNRhZmKIlJQUeHh4sI5DSL3k\n5GTqLMoATU1NDBgwAAMGDEBCQgK2b9+OnoN+gb6+Jvr3tMCsqSLweHL/cZAooKrql0jNLEBSRi4e\npObgQUoWcgqKoaOlCWOBIZwtHDCq0xB0CWwNcwMT1nHfoKGmDiN9A+Tk5MDW1pZ1nEaR+3eHjIwM\nmBmZQFNDo8mvraXBg5+zB/ycPTDk9TGxWIxHhXmIz0xBbEYK7qTGY8/pb1DytAwCvi609QUwsrWC\ns483hC2bw8BUtv5xKxOeliYmLF+IFeMmoVOnToiIiHjnc1RVVeHq6oLDRzLw6Xi6b5HIBkdrUzx4\n8ICKRSIzKioq8PjxY1hZsftmn/ybu7s7Vq1ahUWLFuHgwYPYtGkjNn+3DyKhPhbM9kFwgCnriERJ\nVTx/geSMfCRl5CI2JQdxqVkoLCoDX1sbJnqGcLdywqRuoQgNaA1DXfnYDs/G1ALp6elULLKWm5sL\nc0PZKbg4HA5sTS1ga2qBLoF/L795WlmBhEdpiMtMwb20RNw9cBwHNmyFuro6+AIB+KZGsHV3gWdQ\nAByFXnLfspYXFg52GDD5Y4wdPx6BgYEwNX33H8qgoGBcvHqcikUiMyyM+EhPT2cdg5B6ycnJcHR0\nhKqqKuso5C00NTUxcOBADBw4EPHx8di2bRu6D9gFA31NDOhliRlTfKjbSKSm/NlzJGXkITE9Dw9S\nshGXlo3i0qcQ8HVgpm8CDxtnfDGwFzr6tQRfS5t13AazMTZHeno6PvroI9ZRGkXu3wlyc3NhpmfI\nOsY78bW0EegmRKCbsP5YbW0tMgpyEJeZgtj0ZNx5EI8f/jiFp88rIdDVhbaBHkzsbODi6wNhy+bQ\n1ZePb1LkTfMunZB0+x5Cu3TB7Vu33jkRy9/fH+fP/d5E6Qh5NxMDPjLSH7KOQUi9xMREODs7s45B\n3oOHhwe+/vprLF68+HW3cRM2bt8HPx99zJ9F3UbSOCXlFUhMz0Vieh5ikrOQkJaD8ornMNDlw0zf\nFEI7VwyNGoJ2omBoaijW0EgrAxOkP5T/L3IVolg0Fch+sfg2qqqqcLSwgaOFDbo1/3tAT8nTcsQ/\nSkVcRgruPUzA9Z/3Y/fX66HF0wRfTwC+mRHsPT3gFRwAOw83iY/7VUaDpn2KxVHjMWHCBHz77bf/\n87Gvhtw8b6JkhLybuZEezsTK/x8kojiSkpKoWJQzWlpaGDRoEAYNGoS4uDhs3779VbfRQAsDelpQ\nt5H8T2KxGI9LniIpPRcJ6XmITc5C4sNcVFa9gKFAAHMDU4gcvPBx6Fi09m4GNa7iD2SzMbHArbQ0\n1jEaTe5/63Oyc2AqB53FD6HP10ULTz+08PSrP1ZTW4PU3EeIz0hFTEYS7vwZj817D+PFy2oIBAJo\nG+rBzMEerv4ieDcPghZfh+ErkD/qPB4mLF+I5WM+QefOndGzZ8//fKyzszOeP69CVs4zWFvS/8+E\nPTMjAR49us06BiH1EhIS0L69ZKaUk6bn6en5Rrdx48aN2PTdX91GEYKayc7tP6TpicVi5D0pRVJ6\nXv0ehskZeXhZUwtDgQCWhuZo5hyImb1aItjdR2mbGjYmFvjt9lnWMRpN7ovF3OxsuFop/r1jXFUu\n3Kwd4GbtgF6tOtUff1xWjPiMVMRlpuBOWjzObv0JPy37Ctpa2uDrCaBrYQpHLw94hwTB0tFBaX9h\n34eZrTUGTZuECZ98gsDAQFhavn0LFC6XC2dnJ/x+NBMTRns2cUpC/s3USIDsnFzWMQipl5SUhHHj\nxrGOQRrp/3cbt23bhm79d8PQQAv9e1tgxmfUbVR0dXV1yC4oQVJ6LuIf5iImKQspj/LA4XDqN7dv\n7doaS4a0gsjRnT5n/oONiTnSMzNYx2g0uf8Nz83JhZl3O9YxmDEWGKCNTyDa+ATWH3vxshop2RmI\ny0xFTHoS7ly6jbM//4baurpXXUgjA1g6O8C1mS88A5uBp6XF8BXIlsCO7ZB0+x66hIXh3t27//mm\nFxgYhAtXTlOxSGSCrrYmXr58ifLycujq6rKOQwjy8/Nhbm7OOgaRIE9PT6xevRpLlizBwYMHsWHD\nBmza9qrbGD3bFwH+xqwjkkaqqa3Fo9wiJL4uDGOTs5CWVQA1LhdGAj3YGluji19nrBvbBu62TbO5\nvTwzNzDGk6IivHjxAhoMdm2QFPkvFvNyYWZgxDqGTNFQU4eXvQu87F3Q/6MwAK+WDOQXP0FcZgoe\nZKTgblo8jq7dhu9LloOvzYeOvgD6VuZwFHrBp0UwTG2sGb8KdvpP/hhLR0zAqFGj8N133731MQEB\nAVj99dEmTkbI23E4HJibGuHRo0fw8vJiHYcoObFYjJKSEujp0VA2RfTPbuODBw+wffs2hPfdDSMj\nbQzsY4nPJwmhoSH3Hy8V3suaGqRnP0Zieh7i0nIRm/wImbmPwdNQh7HAAPamtugb0gNdAj+Cgzlt\ngdMQXFUuzI1NkZmZKdd7zsr1b7NYLEZeQb7C3bMoDRwOB+aGxjA3NEYHv5D645UvqpCU9fBVF/Jh\nIu6cuIRj23+EiooKdAUC6BgbwsrVCe4B/vBo5guuujrDV9E01DU0MGH5QiwdNQG7d+9G//79//UY\noVBIQ26ITHl13yIVi4S9yspKqKqqQlNTk3UUImVeXl5YvfobLF78d7dxw5Z98BcZIHq2CM38qNso\nC15Uv0RaViESHuYiLi0HD5KzkF1QBG1NTRjrGcDZ3B6R7QciPLANLIxo+q0k2ZpaICMjg4pFVsrK\nyqDGVYO2Ji2jbCgtDR58nTzg6+QBtO8G4FURnvU4H/Gvu5C3U+Ow7+wabC0vg4DPh7a+HgxtLOEs\nEkIYEgxDc8V7YzGxtsTQGVPw2eTJCAkJgbX1m51WNzc3VFQ8R15+JaOEhLzJUKCN/Px81jEIoa6i\nEtLW1sbgwYMxePBgxMbGYvu2bQjrswdGRtoY1McSn38mhLq6XH/klBuVVS+QklmAxPRcPEjNQVxK\nFvKflIKvrQUTPSO4WjpgYkQnhAa0hpFAn3VchWdpaIJHjx6xjtEocv2bm5ubCzND+tZK0jgcDmxM\nzGFjYo7QgNb1x589r0DCozTEZaTi3sNE3D1wAgc3bIW6mjr4errgmxjBxt0VHoH+cBYJweXK9T8v\n+Ldrg+Q79xEa1gWx92PeuH+Ry+XCyckRh49msAtIyD/oaKqjrKyMdQxCUFxcDH19+hCqrLy9vfHN\nmjVYsnQpDhw4gI0bN2L9ln3wF+lj4Rxf+PvS5zZJeVrxHMmZ+UhKz0NMcjbiUrPwpPQp9Pg6MNEz\ngqe1C2b27Y5OzVpCoM1nHVcp8XnaePr0KesYjSLXn+Zzc3NhZkBvOk1FR1MbAa5CBLgK64/V1dUh\noyAHcRkpiM1Ixp34ePx09DSeVj6DgP9qSw9jO2u4iHwgbBEMgaEBw1fw4fp8Og7LR32CYcOH4acf\nf3rjZ4GBQTh/6Rx0+Yq/VxCRfdo8NZSWlrKOQQh1FgmAV93GIUOGYMiQIYiNjcW2bdvQpfceGBtp\nY1A/K0z/1Ju6jR+gtLwCiRl5SHyYh9iULMSn5aDsaSX0dXVgpm8Cb1s3DBo+EO19Q6DFU6zN7eWZ\nlgYPz549Yx2jUeT6tzQ3Nxem+nS/IksqKipwMLeGg7k1Ipr/PZW29Fk54jPTEJeZgntpCbj560Hs\nWb0BWjwe+AIBdMxMYO/pBq/gANh7yu6oZTV1dYxbHo0lI8bj559/xuDBg+t/1qxZM6xfdwL+PlQs\nEvZ0tHgoLipiHYMQ6iySf/H29saaNWuw9HW3ccOGDVj37X4089XHwjki+Inoi/9/elLyFInpuUhM\nz0PM683tK55XwUAggIWBKUT2nhjbcRTaCAOgrqb4syTkmbaGJp5RZ5Gd3NxcmOrKV6dKWejp6CLE\n0xchnr71x2pqa5CWm4W4zBTEpifjzu04bN3/B6qqX0BXVxfahvowc7CDq58IXiGB0JGRLQCMLcwR\nOXs6pk6fjpCQENjb2wMARCIRCh8/B6DNNiAhAPjaPKSXFrOOQQh1Fsl/+v/dxq1btyK0128wNtLG\nkP5WmDpRubqNYrEYBUVlSEzPQ8LDHMQkZyMpPRfVL2teb25vAT8nP0zrPhHN3X3l/vYeZaTN00JW\nORWLzBQWFMKQT3+Q5AVXlQtXa3u4WtujV8tO9ceflJUgPjMVcZkpuJMaj/Pf/YKfV3wNbU1t8PUF\n0DU3hYOXB7xDgmDl5MCkCylq3QLJd+4jrGs44mIfQEVFBW5ubnj2rBIVlTVNnoeQ/09Hi4eSzBLW\nMQhBcXExFYvknby9vbF27do3uo1rX3cbF831hUioWNuiicViZBcUIyk9r34Pw+TMPIjFgKFAABsj\nS7RwbYmFg1rBz8lDZldckQ+jzdNERVkh6xiNItfFYm1NDbiqqqxjkEYyEuijtTAArYUB9ceqX75E\nSk4G4jJTEJOejDuX7+DcL3tRW1cLgUAAbUN9WDg7ws3fF57BzcDTkv5E3F4fj8by+zEYNGgQdu3a\nBTU1NTg42CMlrRB2JrTkirDF1+ahrIzuWSTsUWeRfAgdHR0MHToUQ4cORUxMDLZu3YpOPfbCxFgb\ng/tbY+onXnLXbaytq8OjvCIkvd7cPiYpC2lZ+eCqqsJQoA9bYyt0FHbA1yNbw8tefrdUIO+mzdPE\ns2zqLDJTV1cHDkeuXwL5D+pqavC0c4annTP6tQkD8Hq5RskTxGWmIi7jVRfy2Ppt+H7RcvC1daCj\npwc9KzM4Cr0gbBEMc1sbiWbiqqlh/LJoLI4chx07diAqKgoBAYE4fHg37EwkeilCPpiOJg+lpTQN\nlbBXVFQEZ2dn1jGIHBIKhVi3bh2WLVuG/fv3Y+PGjVi76a9uox9EQtmbU1FTU4v0nMdITM9FfFou\nYpKzkJFTCA11dRjp6cPexAY9gyLQZWJrOFvasY5Lmpg2T4sG3LD0qljksI5BmgiHw4GZgTHMDIzR\n3rd5/fHnL14gKfsh4jJSEZOeiDunruD4dz9BRUUFuroC6BgbwNLVCR4BfnBr5g91jYbfDG5oZooR\n82Zi1uzZaNmyJQICArBnzx5JvDxCGkVbSwPl5eWsYxBCnUXSaDo6Ohg2bBiGDRuG+/fvv+427oOJ\nsTaGDrDG5I/ZdBv/2tw+KSMPcak5iE3JQlZeEbQ0NWCsZwhnMzsMa9sfYYFtYGVs1uT5iOzR4tE0\nVKbq6uqgQsWi0tPU0IDI0R0iR3cMRgSA1/cGPMlHfEYqHmQk43ZqPPafW4+i8lLo6vChY6AHQ2sL\nOIm84dMiBIbmpu99Pe+QILTsFoauERH49Zdf6AsLIhNUVDioqxOzjkEISktLIRAIWMcgCsLHxwfr\n16/H8uXLsXfvXmzatAnfbNyPQD99RM+RXrfxeVU1Uh/9vbn9g5Qs5D0ugY6WFkz0DOFi6YjxXdqh\nS+BHMNGjYYvk7bR5mlQssiQWi6HCoRuAyb9xOBxYG5vD2tgcnQNa1R+veF6JhEdpiMtMxf2HCbhz\n6DQObdoOda4a+AI98E0MYePhAo/AZnAWCf9z8liPsSOQfPc+ohcuxMuXL5vqZRHyn1Q4HNSJ61jH\nIAQA6Es0InE6OjqIjIxEZGTkP7qNe2FirINhA60x+WNvqKk1bI7Fs8oqpGTmI+FhLmJTcxCfkoXC\nknIIdLRhqmcMd2snTOvdFaHNWkFPRzYmtRP5oM3TwrOKCtYxGkWui8W62jqocOkPEnl/2ppaaObq\njWau3gB6AnjVoc4syEVcZsqrLmRCPH4+egbllc8g4OtC20AfxnbWcPYVQhjSHHpGBlDlcjFu6QIs\nGj4WHA4HYmroEMY44EBM/xCJDOByufQlGpGqv7qNy5Ytq+82rt6wD4H+Blg01xdCr//uNpY9rURS\nRh4S0/MQm5KN+NRslJRXQI//anN7LzsXRA/phw7+LaDN02zCV0UUkTZPE5WVVCwyUyeuo84iaTQV\nFRXYm1vB3twKXYPb1h8vq3j6ekuPVNxLS8CtXw9j7zcbwdPggS8QgG9qDHsvdyTeugsxdXQIaxxQ\nsUhkgpqaGmpra1nHIEqAz+cjKioKUVFRuHfvHrZu3YoO3fbB1IQPdbVqWBlq4eq9ZCQ8/Gtz+xw8\nq6yCvq4uzPVN4ePghhGjI9HWJxAa6hqsXw5RQJrqPFRUVrKO0SjyXSzW1oJDxSKREoE2H809fNHc\nw7f+WE1tDR7mZSMuMwWx6cm4kxoHFXCgoabGMCkhr5eh1tGXFoQ9LpeLmhraf5Y0LZFIhA0bNtTf\n2zht+jSkZyQiPi0flgZm8HMUYUrXTxDiSZvbk6bzsrYGanL+GVGuf1vq6uqgokLLUEnT4apy4WJl\nBxcrO/Rs0REA4DWmC9oEuDFORpTdq+XQ1Fkk7NEyVMISn89HQmIitHV1UVdVjW/GzkVbUTDrWERJ\nVVW/AI/HYx2jUeS6LfdqGqpcvwQi5+6nJuJpZQUCvR1ZRyFKjsMBTUMlMkFNTY06i4SZw4cP4/ud\nOzFx1VK06d8TkzctxYuX1axjESX14mU1NKlYZIe2ziCsfXNgB9oEeEBdTa6b9EQBcDgciGkZKpEB\nXC6X7lkkTDx69Ahjx4/D4GmfwsrJAV2jhqJOXRXrD/3EOhpRUlXVL6Ap54OS5L9YVJHrl0Dk3M3k\n++jS0pt1DEJQ/bIG6hrqrGMQQstQCRM1NTXo2LkTgjp3QGCn9vXHh8yZhnUHf8SjwjyG6Yiyek7L\nUNmqq6ujvZwIM7eSH6Ci6jkCvWgJKmGv8nk1dHR0WMcghAbcECZ69OwJTT0B+nwy9o3jTj7ecBB6\n4vNtXzJKRpRZVfULaGpSZ5EZFRUVmv5HmFl7YCfaBXqCy23YJsCESFJF1QvwqVgkMkBdXZ2KRdKk\nlixZgnsx9zF+WTRU3zLpdGT0HPyZFItTt68wSEeUGRWLjBkZG6P4aSnrGERJ3UqJQWdagkpkROXz\nF+Dz+axjEEKdRdKkzp07hzXr1mH88oXQNdB/62M0dbTRfnBfTN28HM9fvGjihESZUbHImKm5GZ6U\nUbFImt61+Ht4/qIKzTztWUchBABQWVVNxSKRCVQskqZSUFCAIcOGoc/Ho+Hg6f4/Hxs6dCA4PHWs\nPfRDE6Uj5HWxqEXFIjOmpqZ4UlHGOgZRQusO/oD2zb3BVaUlqEQ2VD5/AV1dXdYxCAGPx0NVVRXr\nGETB1dXVoUOnThC2CEbLbuHv9Zyh82dg0+FfkJGfLeV0hLxSVV1NnUWWTExMUFRewjoGUUJ30x4g\ntAUtQSWyo6KqGnxdAesYhMDMzAwFBQWsYxAFN3DQINSqcDBw6qfvPezQwcMNzr5CTN2yAmIx7UtL\npO9VZ1GLdYxGkfti8QkVi6SJXYq9hRcvq+Hrbss6CiH1Kp+/gK6AikXCnoWFBfLz81nHIAps3bp1\nuHj5Ej7+chHUPnDLoKgFs3D/YSJO3LokpXSE/O15dRW0qFhkx8TEBE9KqVgkTWv9oZ/QMYSWoBLZ\n8ux5NfT1DVjHIISKRSJVN27cwOIlSzB28XwYmJp88PN5WlroMGwApm5ejsoXtFyaSFdhWTHMLMxZ\nx2gUuS8WC4ufsI5BlMy9h3EIbSFkHYOQNxSVVcLS0pJ1DEJgaWlJxSKRitLSUvTt3w/hUUPg6ufT\n4PN0GtgX6jpaWL3/e8mFI+Qtcksew9ramnWMRpHrYlFbWxtiiFFR9Zx1FKIkzt69jtq6Wvi42bCO\nQsgbCkueUbFIZIKJiQmKiopoIiqRqLq6OnTs1BGOQi90GNCn0ecbNn8mtv6xCw/zsiSQjpC3y3lS\nABsb+f7MKNfFIofDgbGhMZ6U0VJU0jQ2/v4zOrUQQlVFrn91iAIqLCqDlZUV6xiEgMvlwsjICIWF\nhayjEAUyZswYlFZUYPicz997oM3/YuvmApcAX0z5dhkNuyFSk/M4n4pF1kyMjWnIDWkyMekJ6Bzi\nxToGIW8Qi8XIf1xEnUUiM8zNzWkpKpGY77//Hn8cPYpPVi6GhiZPYucdMX8W4h6l4ujNCxI7JyF/\nefGyGiXlZTA1NWUdpVHkvlg0NTWlziJpEif+vAQx6iB0ke+150TxlFc8h4a6OnR0dFhHIQTAqyE3\neXl5rGMQBfDgwQPMmDULI+bPhImVZL8QU+fx0HnEYEzfsoJuaSISl1/8GOamZlCV84GIcl8smpia\nULFImsTmo7sQ2tIHKrQElciYwqJymJvJ1jeXFy5cwLVr11jHIIxQsUgkobKyEhHdu6ND/17wbh4k\nlWu069MTPAEfX+3dLpXzE+WV/aQA1gpwe4jcf+o1NTfDk/Ji1jGIgqurq0NseiI6h3izjkLIvzwu\nKYeVhL9xb4yamhqcO3cOV69eZR2FMEITUYkkdA4NhbmDHcIih0j1OpHRs/Dd8X1IycmQ6nWIcsl5\nkg8bW/m+XxFQgGLR3MICeSW0fQaRruM3L0JFlQMvZ/n/hogonsKiclhZNfwPUkVFBcLDwyESieDt\n7Y09e/bAzs4OM2bMgFAoRFBQENLS0gAAGRkZaNeuHXx8fNChQwdkZb2aJBgZGYlx48YhODgY/fv3\nx+bNm7F69Wr4+vri8uXL+O233+Dt7Q2RSIQ2bdpI5HUT2UXFImmszz77DNl5uRi5YLbUV/RYOTnC\nLagZJtOwGyJBuU8KYWNnyzpGo8l9sejp6YnEnHTWMYiC23x0F7q09JHIBDZCJC27oATOrm4Nfv7x\n48dhaWmJe/fuITY2FqGhoeBwONDT00NMTAw++eQTfPbZZwCAiRMnIioqCvfv38fgwYPx6aef1p8n\nNzcX165dw759+zBu3DhMmTIFd+/eRcuWLbFo0SKcPHkS9+7dw++//97o10xkm4WFBRWLpMH27t2L\nX3ftwicrl0CL3zT3Ykd+MQNJ2ek4fO1sk1yPKL6cksewsaVikTmhUIj4h8n0TRCRmrpvsWx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nXIYp8Ljw5eqqMku++rVCR7PntGLpuhOopQbJ/2KO7u7ka1N/dvRtcNFeD27duUKFqMs3O3YWkh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\n"
      }
     ],
     "prompt_number": 211
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pie charts are only useful if the values are not all similar, and if they are all parts of a common whole."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mango_regions = [(\"A\", 1000), (\"B\", 5000), (\"C\", 7500), (\"D\", 8000), (\"E\", 9500)]\n",
      "mango_regions = DataFrame(mango_regions, columns=[\"region\", \"sales\"])\n",
      "mango_regions"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>region</th>\n",
        "      <th>sales</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td> A</td>\n",
        "      <td> 1000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> B</td>\n",
        "      <td> 5000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> C</td>\n",
        "      <td> 7500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> D</td>\n",
        "      <td> 8000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> E</td>\n",
        "      <td> 9500</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 212,
       "text": [
        "  region  sales\n",
        "0      A   1000\n",
        "1      B   5000\n",
        "2      C   7500\n",
        "3      D   8000\n",
        "4      E   9500"
       ]
      }
     ],
     "prompt_number": 212
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axes = subplots(nrows=2, ncols=2)\n",
      "fig.set_size_inches(16,11)\n",
      "mango_regions_index = arange(len(mango_regions))\n",
      "mango_width=0.8\n",
      "axes[0,0].bar(mango_regions_index - mango_width/2, mango_regions.sales, width=mango_width, color=\"#cccccc\")\n",
      "axes[0,0].set_xticks(mango_regions_index)\n",
      "axes[0,0].set_xticklabels(mango_regions.region);\n",
      "axes[0,0].set_title(\"sales by region\");\n",
      "mango_data_index = arange(len(mango_data))\n",
      "\n",
      "axes[0,1].barh(mango_data_index - mango_width/2, list(reversed(list(100*mango_data.satisfaction))), height=mango_width, color=\"#cccccc\")\n",
      "axes[0,1].set_yticks(mango_data_index)\n",
      "axes[0,1].set_yticklabels(list(reversed(list(mango_data.type))))\n",
      "axes[0,1].set_title(\"satisfaction by type\");\n",
      "\n",
      "mango_data_sat = list(reversed(list(mango_data.satisfaction*mango_data.sales)))\n",
      "mango_data_notsat = list(reversed(list((1-mango_data.satisfaction)*mango_data.sales)))\n",
      "mango_b1 = axes[1,0].barh(mango_data_index, mango_data_sat, height=mango_width/2, color=\"#8888ff\")\n",
      "mango_b2 = axes[1,0].barh(mango_data_index - mango_width/2, mango_data_notsat, height=mango_width/2, color=\"#88ff88\")\n",
      "axes[1,0].set_yticks(mango_data_index)\n",
      "axes[1,0].set_yticklabels(list(reversed(list(mango_data.type))))\n",
      "axes[1,0].set_title(\"satisfaction by type (absolute)\");\n",
      "axes[1,0].legend([mango_b1, mango_b2], [\"satisfied\", \"unsatisfied\"], loc=\"lower right\");\n",
      "\n",
      "mango_b3 = axes[1,1].barh(mango_data_index - mango_width/2, mango_data_sat, height=mango_width, color=\"#8888ff\")\n",
      "mango_b4 = axes[1,1].barh(mango_data_index - mango_width/2, mango_data_notsat, left=mango_data_sat, height=mango_width, color=\"#88ff88\")\n",
      "axes[1,1].set_yticks(mango_data_index)\n",
      "axes[1,1].set_yticklabels(list(reversed(list(mango_data.type))))\n",
      "axes[1,1].set_title(\"satisfaction by type (absolute)\");\n",
      "axes[1,1].legend([mango_b3, mango_b4], [\"satisfied\", \"unsatisfied\"], loc=\"lower right\");\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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FxMRo0aJF1dZ/7733avDgwbr55pvl5+d33ponTpxYl00AALCQuLg4xcXFOZ+n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\njgEAAAAAtkc4BgAAAADY3v8DVPVhYVFXzssAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 213
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Histogram"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "score_data = [(0,199,5), (200,399,29), (400,599, 56), (600, 799, 17), (800,999,3)]\n",
      "score_data = DataFrame(score_data, columns=(\"points_from\", \"points_to\", \"frequency\"))\n",
      "score_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>points_from</th>\n",
        "      <th>points_to</th>\n",
        "      <th>frequency</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td>   0</td>\n",
        "      <td> 199</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> 200</td>\n",
        "      <td> 399</td>\n",
        "      <td> 29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> 400</td>\n",
        "      <td> 599</td>\n",
        "      <td> 56</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> 600</td>\n",
        "      <td> 799</td>\n",
        "      <td> 17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> 800</td>\n",
        "      <td> 999</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 214,
       "text": [
        "   points_from  points_to  frequency\n",
        "0            0        199          5\n",
        "1          200        399         29\n",
        "2          400        599         56\n",
        "3          600        799         17\n",
        "4          800        999          3"
       ]
      }
     ],
     "prompt_number": 214
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "score_fig, score_axes = subplots(nrows=1, ncols=2)\n",
      "score_fig.set_size_inches(16,4)\n",
      "score_axes[0].bar(score_data.points_from, score_data.frequency, width=score_data.points_to-score_data.points_from+1, color=\"#cccccc\");\n",
      "score_axes[0].set_title(\"Frequency of games by points\");\n",
      "score_axes[0].set_xlabel(\"Points\");\n",
      "score_axes[0].set_ylabel(\"Frequency\");\n",
      "score_list = (score_data.points_to+score_data.points_from)/2\n",
      "score_scores = []\n",
      "for score in score_list:\n",
      "    score_scores.extend(repeat(score, score_data[(score_data.points_from <= score) & (score_data.points_to >= score)].frequency))\n",
      "score_axes[1].hist(score_scores, bins=sorted(set(list(score_data.points_from) + list(score_data.points_to+1))), color=\"#cccccc\")\n",
      "score_axes[1].set_title(\"Frequency of games by points\");\n",
      "score_axes[1].set_xlabel(\"Points\");\n",
      "score_axes[1].set_ylabel(\"Frequency\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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HS5J69Oihw4cP2z4YAABoGNkMAECNJkvt2WefrTZtajY7cuSIrQMBAICTI5sB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\nwLH+H+qiPLw3yUApAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 215
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gamelen_data = [(0,1,4300), (1,3,6900), (3,5,4900), (5,10,2000), (10,24,2100)]\n",
      "gamelen_data = DataFrame(gamelen_data, columns=(\"len_from\", \"len_to\", \"frequency\"))\n",
      "gamelen_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>len_from</th>\n",
        "      <th>len_to</th>\n",
        "      <th>frequency</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td> 4300</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td>  1</td>\n",
        "      <td>  3</td>\n",
        "      <td> 6900</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td>  3</td>\n",
        "      <td>  5</td>\n",
        "      <td> 4900</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td>  5</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> 10</td>\n",
        "      <td> 24</td>\n",
        "      <td> 2100</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 216,
       "text": [
        "   len_from  len_to  frequency\n",
        "0         0       1       4300\n",
        "1         1       3       6900\n",
        "2         3       5       4900\n",
        "3         5      10       2000\n",
        "4        10      24       2100"
       ]
      }
     ],
     "prompt_number": 216
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gamelen_fig, gamelen_axis = subplots(nrows=1, ncols=2)\n",
      "gamelen_fig.set_size_inches(16,4)\n",
      "gamelen_axis[0].bar(gamelen_data.len_from, gamelen_data.frequency, width=gamelen_data.len_to-gamelen_data.len_from, color=\"#cccccc\");\n",
      "gamelen_axis[0].set_title(\"Frequency of games by length\");\n",
      "gamelen_axis[0].set_xlabel(\"Length/h\");\n",
      "gamelen_axis[0].set_ylabel(\"Frequency\");\n",
      "gamelen_axis[0].spines['right'].set_color('none')\n",
      "gamelen_axis[0].spines['top'].set_color('none')\n",
      "gamelen_axis[0].xaxis.set_ticks_position('bottom')\n",
      "gamelen_axis[0].yaxis.set_ticks_position('left')\n",
      "gamelen_list = (gamelen_data.len_to+gamelen_data.len_from)/2\n",
      "gamelen_list2 = []\n",
      "for gamelen in gamelen_list:\n",
      "    gamelen_list2.extend(repeat(gamelen, gamelen_data[(gamelen_data.len_from <= gamelen) & (gamelen_data.len_to >= gamelen)].frequency))\n",
      "gamelen_axis[1].hist(gamelen_list2, bins=sorted(set(list(gamelen_data.len_from) + list(gamelen_data.len_to))), color=\"#cccccc\", normed=True)\n",
      "gamelen_axis[1].set_title(\"Frequency of games by length (normalized)\");\n",
      "gamelen_axis[1].set_xlabel(\"Length/h\");\n",
      "gamelen_axis[1].set_ylabel(\"Frequency\");\n",
      "gamelen_axis[1].spines['right'].set_color('none')\n",
      "gamelen_axis[1].spines['top'].set_color('none')\n",
      "gamelen_axis[1].xaxis.set_ticks_position('bottom')\n",
      "gamelen_axis[1].yaxis.set_ticks_position('left')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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fkpSTk6N+/fo5+tpsNmVnZ8vd3V02m83RbrValZ2dXe72Fi5c6Pg9MjJSkZGR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\nAADASgReAAAAAICVCLwAAAAAACt9AFxNechDCF7+AAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 217
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gamelen_fig, gamelen_axis = subplots(nrows=1, ncols=1)\n",
      "gamelen_fig.set_size_inches(7,4)\n",
      "gamelen_axis.plot([0] + list(gamelen_data.len_to), [0] + list(gamelen_data.frequency.cumsum()));\n",
      "gamelen_axis.set_title(\"Cumulative sum of games by length\");\n",
      "gamelen_axis.set_xlabel(\"Length/h\");\n",
      "gamelen_axis.set_ylabel(\"Cumulative sum\");\n",
      "gamelen_axis.spines['right'].set_color('none')\n",
      "gamelen_axis.spines['top'].set_color('none')\n",
      "gamelen_axis.xaxis.set_ticks_position('bottom')\n",
      "gamelen_axis.yaxis.set_ticks_position('left')\n",
      "gamelen_axis.set_xlim(0)\n",
      "gamelen_axis.set_ylim(0, 22500);\n",
      "gamelen_axis.yaxis.set_major_locator(MultipleLocator(2500.0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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c3r1707t3b/r06VOhk2RlZZnP16xZY/Z8jY6OJjExkcLCQtLS0khNTSUsLAxf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\nIiJShv8PwT4qrk4KbYkAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 218
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "2. Averages"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Arithmetic Mean"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "power_ages = DataFrame([19,20,20,20,21], columns=[\"age\"])\n",
      "power_ages"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>age</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td> 19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> 20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> 20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> 20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> 21</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 219,
       "text": [
        "   age\n",
        "0   19\n",
        "1   20\n",
        "2   20\n",
        "3   20\n",
        "4   21"
       ]
      }
     ],
     "prompt_number": 219
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Arithmetic mean:\n",
      "\\begin{equation}\n",
      "\\textrm{mean}(x_1,\\...,x_n)=\\overline x={1\\over n}\\sum_{i=1}^n x_i\n",
      "\\end{equation}"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean(power_ages.age)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 220,
       "text": [
        "20.0"
       ]
      }
     ],
     "prompt_number": 220
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "power_agefreqs = DataFrame([(19, 1), (20, 3), (21, 1)], columns=[\"age\", \"frequency\"])\n",
      "power_agefreqs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>age</th>\n",
        "      <th>frequency</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td> 19</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> 20</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> 21</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 221,
       "text": [
        "   age  frequency\n",
        "0   19          1\n",
        "1   20          3\n",
        "2   21          1"
       ]
      }
     ],
     "prompt_number": 221
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def avg(frame):\n",
      "    return sum(1. * frame.age * frame.frequency) / sum(frame.frequency)\n",
      "avg(power_agefreqs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 222,
       "text": [
        "20.0"
       ]
      }
     ],
     "prompt_number": 222
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kungfu_agefreqs = DataFrame([(19, 3), (20, 5), (21, 3), (136, 1), (138, 1)], columns=[\"age\", \"frequency\"])\n",
      "kungfu_agefreqs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>age</th>\n",
        "      <th>frequency</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td>  19</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td>  20</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td>  21</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> 136</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> 138</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 223,
       "text": [
        "   age  frequency\n",
        "0   19          3\n",
        "1   20          5\n",
        "2   21          3\n",
        "3  136          1\n",
        "4  138          1"
       ]
      }
     ],
     "prompt_number": 223
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "avg(kungfu_agefreqs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 224,
       "text": [
        "38.0"
       ]
      }
     ],
     "prompt_number": 224
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "agefreq_fig, agefreq_axes = subplots(nrows=1, ncols=2)\n",
      "agefreq_fig.set_size_inches(16,4)\n",
      "agefreq_axes[0].set_title(\"Age in Power-Workout\");\n",
      "agefreq_axes[0].bar(power_agefreqs.age, power_agefreqs.frequency, width=1, color=\"#cccccc\");\n",
      "agefreq_axes[0].set_xlim(18,24)\n",
      "agefreq_axes[0].set_ylim(0, 5)\n",
      "agefreq_axes[1].set_title(\"Age in Kung-Fu\");\n",
      "agefreq_axes[1].get_xaxis().set_visible(False)\n",
      "agefreq_axes[1].get_yaxis().set_visible(False)\n",
      "agefreq_axes[1].spines['bottom'].set_visible(False)\n",
      "agefreq_axes[1].spines['top'].set_visible(False)\n",
      "# Subdivide the right axes vertically: 1 row, 4 columns, select 3rd and 4th.\n",
      "agefreq1 = agefreq_fig.add_subplot(143)\n",
      "agefreq2 = agefreq_fig.add_subplot(144)\n",
      "agefreq1.set_ylim(0, 10)\n",
      "agefreq2.set_ylim(0, 10)\n",
      "agefreq1.set_xlim(18,24)\n",
      "agefreq2.set_xlim(135,140)\n",
      "agefreq1.bar(kungfu_agefreqs.age, kungfu_agefreqs.frequency, width=1, color=\"#cccccc\");\n",
      "agefreq2.bar(kungfu_agefreqs.age, kungfu_agefreqs.frequency, width=1, color=\"#cccccc\");\n",
      "agefreq1.spines['right'].set_visible(False)\n",
      "agefreq1.get_yaxis().tick_left()\n",
      "agefreq2.spines['left'].set_visible(False)\n",
      "agefreq2.get_yaxis().tick_right()\n",
      "agefreq2.tick_params(labelright='off')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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dswFNSYOlMyMjQ0FBQerdu7d+/etfuyITALQo/KemeWMehbuzex9k9/Lr487Z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\n"
      }
     ],
     "prompt_number": 225
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Median"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that the mean may not occur in the data.  The median has the advantage that it exists as a data point:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kungfu_ages = []\n",
      "for index, data in kungfu_agefreqs.iterrows():\n",
      "    kungfu_ages.extend(repeat(data[\"age\"], data[\"frequency\"]))\n",
      "mean(kungfu_ages), median(kungfu_ages)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 226,
       "text": [
        "(38.0, 20.0)"
       ]
      }
     ],
     "prompt_number": 226
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "However, if the number of data points is even, the media is the value halfway between the two values around the middle:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "median([10,20])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 227,
       "text": [
        "15.0"
       ]
      }
     ],
     "prompt_number": 227
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "skew_fig, skew_axes = subplots(nrows=1, ncols=3)\n",
      "skew_fig.set_size_inches(17,4)\n",
      "skew1 = randn(1000000)\n",
      "skew2 = exp(1+0.4*skew1)\n",
      "skew3 = -skew2\n",
      "skew_axes[0].set_title(\"Unskewed normal, mean = median\")\n",
      "skew_axes[0].hist(skew1, bins=30, normed=True, color=\"#cccccc\");\n",
      "skew_axes[0].plot(mean(skew1), 0.2, 'go');\n",
      "skew_axes[0].plot(median(skew1), 0.15, 'rD');\n",
      "skew_axes[0].set_xlim(-4,4)\n",
      "skew_axes[1].set_title(\"Right-skewed log-normal, median < mean\");\n",
      "skew_axes[1].hist(skew2, bins=30, normed=True, color=\"#cccccc\");\n",
      "skew_axes[1].plot(mean(skew2), 0.2, 'go');\n",
      "skew_axes[1].plot(median(skew2), 0.15, 'rD');\n",
      "skew_axes[1].set_xlim(0,10)\n",
      "skew_axes[2].set_title(\"Left-skewed (mirrored) log-normal, median > mean\");\n",
      "skew_axes[2].hist(skew3, bins=30, normed=True, color=\"#cccccc\");\n",
      "skew_axes[2].plot(mean(skew3), 0.2, 'go');\n",
      "skew_axes[2].plot(median(skew3), 0.15, 'rD');\n",
      "skew_axes[2].set_xlim(-10,0);\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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4oNJxq/p/lb0fFhamZ599ViNGjJDT6dSbb76p6667rtJxd+7cWeW2xdf5uuuu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\niIiIiIiISGdY1omIiIiIiIh05h9dId2jxx+JuAAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 228
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Modal/Mode"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "swim_agefreqs = DataFrame([(1, 3), (2, 4), (3, 2), (31, 2), (32, 4), (33, 3)], columns=[\"age\", \"frequency\"])\n",
      "swim_agefreqs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>age</th>\n",
        "      <th>frequency</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td>  1</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td>  2</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td>  3</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> 31</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> 32</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>5</strong></td>\n",
        "      <td> 33</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 229,
       "text": [
        "   age  frequency\n",
        "0    1          3\n",
        "1    2          4\n",
        "2    3          2\n",
        "3   31          2\n",
        "4   32          4\n",
        "5   33          3"
       ]
      }
     ],
     "prompt_number": 229
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The mean and median are both misleading in this case:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "swim_ages = []\n",
      "for index, data in swim_agefreqs.iterrows():\n",
      "    swim_ages.extend(repeat(data[\"age\"], data[\"frequency\"]))\n",
      "mean(swim_ages), median(swim_ages)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 230,
       "text": [
        "(17.0, 17.0)"
       ]
      }
     ],
     "prompt_number": 230
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scipy.stats\n",
      "val, cnt = scipy.stats.mstats.mode(array(swim_ages))\n",
      "val"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 231,
       "text": [
        "array([ 2.])"
       ]
      }
     ],
     "prompt_number": 231
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that 32 is another mode."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "3. Variance and Deviation"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Span, Interquantildistance, Percentils"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "player1 = DataFrame([(7,1),(8,1),(9,2),(10,3),(11,2),(12,1),(13,1)], columns=(\"points\", \"frequency\"))\n",
      "player2 = DataFrame([(7,1),(9,2),(10,5),(11,2),(13,1)], columns=(\"points\", \"frequency\"))\n",
      "player3 = DataFrame([(3,2),(6,1),(7,2),(10,3),(11,1),(13,1),(30,1)], columns=(\"points\", \"frequency\"))\n",
      "player1[\"player\"] = 1\n",
      "player2[\"player\"] = 2\n",
      "player3[\"player\"] = 3\n",
      "player_stats = pandas.concat([player1, player2, player3])\n",
      "player_stats"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>points</th>\n",
        "      <th>frequency</th>\n",
        "      <th>player</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td>  7</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td>  8</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td>  9</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> 10</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> 11</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>5</strong></td>\n",
        "      <td> 12</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>6</strong></td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td>  7</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td>  9</td>\n",
        "      <td> 2</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> 10</td>\n",
        "      <td> 5</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> 11</td>\n",
        "      <td> 2</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td>  3</td>\n",
        "      <td> 2</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td>  6</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td>  7</td>\n",
        "      <td> 2</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> 10</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> 11</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>5</strong></td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>6</strong></td>\n",
        "      <td> 30</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 232,
       "text": [
        "   points  frequency  player\n",
        "0       7          1       1\n",
        "1       8          1       1\n",
        "2       9          2       1\n",
        "3      10          3       1\n",
        "4      11          2       1\n",
        "5      12          1       1\n",
        "6      13          1       1\n",
        "0       7          1       2\n",
        "1       9          2       2\n",
        "2      10          5       2\n",
        "3      11          2       2\n",
        "4      13          1       2\n",
        "0       3          2       3\n",
        "1       6          1       3\n",
        "2       7          2       3\n",
        "3      10          3       3\n",
        "4      11          1       3\n",
        "5      13          1       3\n",
        "6      30          1       3"
       ]
      }
     ],
     "prompt_number": 232
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "player_data = []\n",
      "\n",
      "def get_stats(pstats):\n",
      "    player, stats = pstats\n",
      "    vals = []\n",
      "    for index, data in stats.iterrows():\n",
      "        vals.extend(repeat(data[\"points\"], data[\"frequency\"]))\n",
      "    vals = array(vals)\n",
      "    player_data.append(vals)\n",
      "    return player, mean(vals), median(vals), scipy.stats.mstats.mode(vals)[0][0], amin(vals), amax(vals), percentile(vals,25), percentile(vals,75)\n",
      "\n",
      "player_avgs = DataFrame(map(get_stats, player_stats.groupby(player_stats.player)),\n",
      "                        columns=(\"player\", \"mean\", \"median\", \"mode\", \"minpoints\", \"maxpoints\", \"q1\",\"q3\"))\n",
      "player_avgs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>player</th>\n",
        "      <th>mean</th>\n",
        "      <th>median</th>\n",
        "      <th>mode</th>\n",
        "      <th>minpoints</th>\n",
        "      <th>maxpoints</th>\n",
        "      <th>q1</th>\n",
        "      <th>q3</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 10</td>\n",
        "      <td> 10</td>\n",
        "      <td> 10</td>\n",
        "      <td> 7</td>\n",
        "      <td> 13</td>\n",
        "      <td> 9.0</td>\n",
        "      <td> 11.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> 2</td>\n",
        "      <td> 10</td>\n",
        "      <td> 10</td>\n",
        "      <td> 10</td>\n",
        "      <td> 7</td>\n",
        "      <td> 13</td>\n",
        "      <td> 9.5</td>\n",
        "      <td> 10.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> 3</td>\n",
        "      <td> 10</td>\n",
        "      <td> 10</td>\n",
        "      <td> 10</td>\n",
        "      <td> 3</td>\n",
        "      <td> 30</td>\n",
        "      <td> 6.5</td>\n",
        "      <td> 10.5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 233,
       "text": [
        "   player  mean  median  mode  minpoints  maxpoints   q1    q3\n",
        "0       1    10      10    10          7         13  9.0  11.0\n",
        "1       2    10      10    10          7         13  9.5  10.5\n",
        "2       3    10      10    10          3         30  6.5  10.5"
       ]
      }
     ],
     "prompt_number": 233
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "player_avgs['span'] = player_avgs.maxpoints - player_avgs.minpoints\n",
      "player_avgs['interq'] = player_avgs.q3 - player_avgs.q1\n",
      "player_avgs[['player', 'span', 'interq']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>player</th>\n",
        "      <th>span</th>\n",
        "      <th>interq</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td>  6</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> 2</td>\n",
        "      <td>  6</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> 3</td>\n",
        "      <td> 27</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 234,
       "text": [
        "   player  span  interq\n",
        "0       1     6       2\n",
        "1       2     6       1\n",
        "2       3    27       4"
       ]
      }
     ],
     "prompt_number": 234
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "player_fig, player_axes = subplots()\n",
      "player_fig.set_size_inches(7,4)\n",
      "player_axes.boxplot(player_data, vert=False, whis=1.5);\n",
      "player_axes.set_title(\"Results of basketball players\");\n",
      "player_axes.set_xlim(0,33)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 235,
       "text": [
        "(0, 33)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 235
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Variance, Standard Deviation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\\begin{equation}\n",
      "V(x) = {1 \\over n} \\sum_i(x_i-\\overline x)^2\n",
      "\\end{equation}\n",
      "\n",
      "\\begin{equation}\n",
      "\\sigma(x) = \\sqrt{V(x)}\n",
      "\\end{equation}\n",
      "\n",
      "A discussion for possible reasons why we prefer the mean square error can be found here: http://stats.stackexchange.com/questions/118/why-square-the-difference-instead-of-taking-the-absolute-value-in-standard-devia"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "player_avgs[\"var\"] = var(player_data, axis=1)\n",
      "player_avgs[\"std\"] = std(player_data, axis=1)\n",
      "player_avgs[[\"player\", \"var\", \"std\"]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>player</th>\n",
        "      <th>var</th>\n",
        "      <th>std</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td>  2.727273</td>\n",
        "      <td> 1.651446</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> 2</td>\n",
        "      <td>  2.000000</td>\n",
        "      <td> 1.414214</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> 3</td>\n",
        "      <td> 49.272727</td>\n",
        "      <td> 7.019453</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 236,
       "text": [
        "   player        var       std\n",
        "0       1   2.727273  1.651446\n",
        "1       2   2.000000  1.414214\n",
        "2       3  49.272727  7.019453"
       ]
      }
     ],
     "prompt_number": 236
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Standard Score (z-score)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\\begin{equation}\n",
      "z(x) = \\frac{ x - \\overline{x} }{ \\sigma(x) }\n",
      "\\end{equation}"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scipy.stats.mstats.zscore(player_data[0]);\n",
      "player_points = player_avgs[\"mean\"] + player_avgs[\"std\"]\n",
      "array([scipy.stats.zmap([10, 11, player_points[0], 12, 30],\n",
      " player_data[0]), scipy.stats.zmap([10, 11, 12, 30],\n",
      " player_data[1]), scipy.stats.zmap([10, 11, 12, 30], player_data[2])])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 237,
       "text": [
        "array([ array([  0.        ,   0.60553007,   1.        ,   1.21106014,  12.11060142]),\n",
        "       array([  0.        ,   0.70710678,   1.41421356,  14.14213562]),\n",
        "       array([ 0.        ,  0.14246123,  0.28492247,  2.84922466])], dtype=object)"
       ]
      }
     ],
     "prompt_number": 237
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 237
    }
   ],
   "metadata": {}
  }
 ]
}