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 },
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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "pd.set_option('display.mpl_style', 'default')\n",
      "figsize(15, 3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We saw earlier that pandas is really good at dealing with dates. It is also amazing with strings! We're going to go back to our weather data from Chapter 5, here."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weather_2012 = pd.read_csv('../data/weather_2012.csv', parse_dates=True, index_col='Date/Time')\n",
      "weather_2012[:5]"
     ],
     "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>Temp (C)</th>\n",
        "      <th>Dew Point Temp (C)</th>\n",
        "      <th>Rel Hum (%)</th>\n",
        "      <th>Wind Spd (km/h)</th>\n",
        "      <th>Visibility (km)</th>\n",
        "      <th>Stn Press (kPa)</th>\n",
        "      <th>Weather</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date/Time</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2012-01-01 00:00:00</th>\n",
        "      <td>-1.8</td>\n",
        "      <td>-3.9</td>\n",
        "      <td> 86</td>\n",
        "      <td> 4</td>\n",
        "      <td> 8.0</td>\n",
        "      <td> 101.24</td>\n",
        "      <td>                  Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 01:00:00</th>\n",
        "      <td>-1.8</td>\n",
        "      <td>-3.7</td>\n",
        "      <td> 87</td>\n",
        "      <td> 4</td>\n",
        "      <td> 8.0</td>\n",
        "      <td> 101.24</td>\n",
        "      <td>                  Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 02:00:00</th>\n",
        "      <td>-1.8</td>\n",
        "      <td>-3.4</td>\n",
        "      <td> 89</td>\n",
        "      <td> 7</td>\n",
        "      <td> 4.0</td>\n",
        "      <td> 101.26</td>\n",
        "      <td> Freezing Drizzle,Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 03:00:00</th>\n",
        "      <td>-1.5</td>\n",
        "      <td>-3.2</td>\n",
        "      <td> 88</td>\n",
        "      <td> 6</td>\n",
        "      <td> 4.0</td>\n",
        "      <td> 101.27</td>\n",
        "      <td> Freezing Drizzle,Fog</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-01 04:00:00</th>\n",
        "      <td>-1.5</td>\n",
        "      <td>-3.3</td>\n",
        "      <td> 88</td>\n",
        "      <td> 7</td>\n",
        "      <td> 4.8</td>\n",
        "      <td> 101.23</td>\n",
        "      <td>                  Fog</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "                     Temp (C)  Dew Point Temp (C)  Rel Hum (%)  \\\n",
        "Date/Time                                                        \n",
        "2012-01-01 00:00:00      -1.8                -3.9           86   \n",
        "2012-01-01 01:00:00      -1.8                -3.7           87   \n",
        "2012-01-01 02:00:00      -1.8                -3.4           89   \n",
        "2012-01-01 03:00:00      -1.5                -3.2           88   \n",
        "2012-01-01 04:00:00      -1.5                -3.3           88   \n",
        "\n",
        "                     Wind Spd (km/h)  Visibility (km)  Stn Press (kPa)  \\\n",
        "Date/Time                                                                \n",
        "2012-01-01 00:00:00                4              8.0           101.24   \n",
        "2012-01-01 01:00:00                4              8.0           101.24   \n",
        "2012-01-01 02:00:00                7              4.0           101.26   \n",
        "2012-01-01 03:00:00                6              4.0           101.27   \n",
        "2012-01-01 04:00:00                7              4.8           101.23   \n",
        "\n",
        "                                  Weather  \n",
        "Date/Time                                  \n",
        "2012-01-01 00:00:00                   Fog  \n",
        "2012-01-01 01:00:00                   Fog  \n",
        "2012-01-01 02:00:00  Freezing Drizzle,Fog  \n",
        "2012-01-01 03:00:00  Freezing Drizzle,Fog  \n",
        "2012-01-01 04:00:00                   Fog  "
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 6.1 String operations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You'll see that the 'Weather' column has a text description of the weather that was going on each hour. We'll assume it's snowing if the text description contains \"Snow\".\n",
      "\n",
      "pandas provides vectorized string functions, to make it easy to operate on columns containing text. There are some great [examples](http://pandas.pydata.org/pandas-docs/stable/basics.html#vectorized-string-methods) in the documentation."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weather_description = weather_2012['Weather']\n",
      "is_snowing = weather_description.str.contains('Snow')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This gives us a binary vector, which is a bit hard to look at, so we'll plot it."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Not super useful\n",
      "is_snowing[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "Date/Time\n",
        "2012-01-01 00:00:00    False\n",
        "2012-01-01 01:00:00    False\n",
        "2012-01-01 02:00:00    False\n",
        "2012-01-01 03:00:00    False\n",
        "2012-01-01 04:00:00    False\n",
        "Name: Weather, dtype: bool"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# More useful!\n",
      "is_snowing.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x403c190>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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7N5q0MOoP095r/DrvH9noILIt+D2fk6aOeFCP1qS5KetzB5dcX+ql0wB2y7fs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\nbjtPz8kutvFr4PZeFpWnJo2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7hh2aIE1abke5nzKJLNx4CeqcNJnfnrJroeMW/GF1MiGE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\nAICGiDQAAICGfAAMpyyd0DpvqwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4059c10>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 6.2 Use resampling to find the snowiest month"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If we wanted the median temperature each month, we could use the `resample()` method like this:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weather_2012['Temp (C)'].resample('M', how=np.median).plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x560cc50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RxKKHRQ8XTSx6WPRw0cSih0UPl69NGNT6USgUlM/n1dnZqVQqpXQ6rUSidC+Q\nSQ8XTSx6WPRw0cSih0UPF00selj0cPnYZHSvfoQkEonet0wTicSo/488XPRw0cSih0UPF00selj0\ncNHEoodFD5ePTThHrQ9vrxozRPRw0cSih0UPF00selj0cNHEoodFD5e3TaK9lkn8+HrVmKGih4sm\nFj0serhoYtHDooeLJhY9LHq4fG3CO2qX4MtVY8JCDxdNLHpY9HDRxKKHRQ8XTSx6WPRw+dKEi4n0\n4etVY4aKHi6aWPSw6OGiiUUPix4umlj0sOjh8rUJg1o/fLxqzHDQw0UTix4WPVw0sehh0cNFE4se\nFj1cPjZhUAMAAACAmBndYyYAAAAAeIhBDQAAAABihkENAAAAAGKGQQ0AAAAAYoZBDQAAAABihg+8\nxqCcO3dO77zzjqqqqqJeCmIoCALl83l1dHSoqqpK6XQ66iUhZthHgMvDzwwuhv2jNDCoDdL69et1\n3333Rb2Mosvlcnr22Wd18uRJdXV1qby8XNdee63+5m/+hqGtj1LdR9rb25XNZlVdXa2Kigp1dXUp\nn8+roaFBM2bMiHp5sVGq+4fEPjJYpbyP9KeUe/AzMziluo+wfwzeaN9HGNT6aGpq6vf2ffv2FXkl\n8bBx40Y99NBDqqys1Fe/+lU98sgjam1t1Te/+U0tW7Ys6uVFgn3E2rp1q5YvX65kMqnNmzdr8eLF\nkqTHH3+8JJ8w2D9c7CMW+4hFDxc/Mxb7iMX+4fJ1H2FQ62Pfvn2aN2+eUqlU721BEOjQoUMRrio6\n3d3dqqqqUk9Pj959911JUl1dnb773e9GvLLosI9cXFlZWdRLiBT7x6Wxj7CPvBc9Lo2fGfaRiyn1\n/UPydx9hUOtj4cKF6unp0bRp08ztr7/+ekQritasWbO0bNkynTt3TvPmzeu9fcqUKRGuKlrsI1Z9\nfb1Wr16tTCajyspKbdy4UblcTvX19VEvLRLsHy72EYt9xKKHi58Zi33EYv9w+bqPlAVBEES9CMTb\nmTNnFASBxo0bF/VSEFOFQkH5fF6dnZ1KpVJKp9NKJLioLH6HfQS4PPzM4GLYP0oDgxoAAAAAxMyY\nxx577LGoFxEnx44d03e+8x3l83llMhk9+eST+ulPf6rrrrtOEyZMiHp5RXfs2DE9++yzamtr0+TJ\nk3uv9NjY2KiPfOQjEa8uGuwjVn89fvKTn9CD/aMX+4jFPmLRw8XPjMU+YrF/uHzdR3iPtI/169fr\n4x//uAqh+mh5AAAPuUlEQVSFgr74xS+qoaFB999/vxobG6NeWiTWrVunm2++Wbfeeqt27typHTt2\nSJKOHDkS8cqiwz5i9dfjgQceoAf7Ry/2EYt9xKKHi58Zi33EYv9w+bqPcDGRPgqFgm644QbdcMMN\n2rVrl2pqaiT99gOfS1EikdD06dMlSTU1Ndq1a5eamppUKBQiXll02Ecselj0cNHEoodFDxdNLHpY\n9HD52oRBrY/rr7++98+rVq3q/fNoftt0OPpe3XHmzJmaOHGiXnnllYhWFD32EYseFj1cNLHoYdHD\nRROLHhY9XL424WIiAAAAABAznKMGAAAAADHDoHYJu3btinoJsUIPF00selj0cNHEoodFDxdNLHpY\n9HD50oRB7RIuXOUQv0UPF00selj0cNHEoodFDxdNLHpY9HD50oRBDQAAAABihg+8HoRrr7026iXE\nCj1cNLHoYdHDRROLHhY9XDSx6GHRw+VDE676CAAAAAAxw6GPAEL361//OuolIMbOnj2rs2fPRr0M\nxBT7x++cO3dOp0+fjnoZsRAEgXK5nH75y18qn89HvRygKDj0cZDWr1+vm266KeplFN2xY8f07LPP\nqq2tTZMnT1ZVVZUkqbGxUR/5yEciXl00jh07pu985zvK5/PKZDJ68skn9dOf/lTXXXfdqP9gxaH4\nwQ9+oEOHDungwYO9/2zdulXnz59XTU1N1Msruh//+Me9h1vk83k988wzevXVV1VTU6PKyspoFxeR\nzZs368Mf/rAk6dVXX9UzzzyjlpYWdXd367rrrot4dcW3aNEiHT9+XO973/t01VVXRb2cyLW3t+sb\n3/iGfv3rX+vcuXPauHGjmpubVV5e7sWhS0ORy+W0YcMGvfDCC/rRj36kf//3f9fBgwd1/fXX64or\nroh6eUXX3t6udevW6X/+53+Uz+e1Z88eff/739ekSZNUXV0d9fJio1Rfq0r+vl4tj3oBcdPU1NTv\n7fv27SvySuJh3bp1amho0Lhx47Rz505NmTJFc+bM0ZEjR6JeWmTWr1+vhoYGvf766/riF7+oz3zm\nM7rqqqu0fv16feELX4h6eUW3fft2TZ06VTNnzuy9bcyYMSX5YkKSfvKTn2jWrFmSfjug/NVf/ZXG\njh2rf/7nf9bf//3fR7u4iBw6dKj3z88//7xWr16t8vJyrVixQnfeeWeEK4vG1KlT9clPflLPP/+8\n3n77bc2ePVsf+9jHlEiU5kEu//qv/6ply5aptbVVL7zwglatWqUgCPQP//APvT9LpWbjxo166KGH\nVFlZqa9+9at65JFH1Nraqm9+85tatmxZ1Msruq1bt2r58uVKJpPavHmzFi9eLEl6/PHHNWPGjIhX\nV3y8VnX5+nqVQa2Pffv2ad68eUqlUr23BUFgXmiUkkQioenTp0uSampqtGvXLjU1NalQKES8sugU\nCgXdcMMNuuGGG7Rr167ed43OnTsX8cqi8Y1vfEM/+tGP9POf/1yf/OQn9eEPf1h79uwp2RdYZ8+e\n1W9+8xsFQaDOzs7edwTefffdaBcWoTFjxujAgQO6/vrrVVFRoUQioRMnTqiioiLqpUXmmmuu0QMP\nPKCOjg69+OKLWrlypW666SbNnTs36qUV3fnz5zVu3DhdddVVKi//7cuSsrKykh1cJam7u1tVVVXq\n6enp/d1RV1en7373uxGvLB7KysqiXkKkeK3q8vX1KoNaHwsXLlRPT4+mTZtmbn/99dcjWlG0pkyZ\nYrZnzpypiRMn6pVXXoloRdG7/vrre/+8atWq3j+X4mGPkjR27Fjdeeedmj17tp5//nn94Ac/UE9P\nT9TLisyUKVO0detWSb995+SCq6++OqolRe6zn/2smpqatGnTJp09e1YrV67UpEmTdO+990a9tMhN\nmDBB8+bN01/+5V/qP//zP6NeTiT+7M/+TI888oh+//d/Xx/96Ef1j//4jzp//rz5XVtqZs2apWXL\nluncuXOaN29e7+19n5NLRX19vVavXq1MJqPKykpt3LhRuVxO9fX1US8tErxWdfn6epWrPgII1Tvv\nvKP//u//Vl1dXdRLQcwUCgWdPn1aqVSq952TUvTOO++U9LuJl3Ls2DGdP3++ZIeSC86cOaMgCDRu\n3LiolxILhUJB+XxenZ2dSqVSSqfTJf2uK0oDg9oAcrmcOjo6VFVVpXQ6HfVyIhUEgfL5PD3egyYW\nPVz8DnHR5Hf4mXGxf7hoApS20v1fmgNob29XNptVdXW1Kioq1NXVpXw+r4aGhpI8YZUeLppY9LDo\n4aKJRQ+LHi6aDM769et13333Rb2M2KCHa7Q3YVDrgysLWfRw0cSih0UPF00selj0cNHE4iqHFj1c\nvjZhULuEUr+yUF/0cNHEoodFDxdNLHpY9HCVehOucmjRw+VrE85R66OtrU3ZbLb3ykJdXV29Vxaq\nra2NenlFRw8XTSx6WPRw0cSih0UPF02s/fv3q6enx3k3cdu2bSX5kRb0cPnahEGtH1xZyKKHiyYW\nPSx6uGhi0cOih4smABjUAAAAACBm+F8zl7Br166olxAr9HDRxKKHRQ8XTSx6WPRw0cSih0UPly9N\nGNQuYceOHVEvIVbo4aKJRQ+LHi6aWPSw6OGiiUUPix4uX5owqAEAAABAzIx57LHHHot6EXF37bXX\nRr2EWKGHiyYWPSx6uGhi0cOih4smFj0serh8aMLFRAAAAAAgZjj0EQAAAABihkGtj2PHjmnDhg1q\nbGzUm2++2Xt7Y2NjhKuKDj1cNLHoYdHDRROLHhY9XDSx6GHRw+VrEwa1PtatW6ebb75Zt956q3bu\n3Nl71ZgjR45EvLJo0MNFE4seFj1cNLHoYdHDRROLHhY9XL42KY96AXGTSCQ0ffp0SVJNTY127dql\npqYmFQqFiFcWDXq4aGLRw6KHiyYWPSx6uGhi0cOih8vXJgxqfUyZMsVsz5w5UxMnTtQrr7wS0Yqi\nRQ8XTSx6WPRw0cSih0UPF00selj0cPnahKs+AgAAAEDMcI4aAAAAAMQMg9ogrV+/PuolxAo9XDSx\n6GHRw0UTix4WPVw0sehh0cM12ptwjlofTU1N/d6+b9++Iq8kHujhoolFD4seLppY9LDo4aKJRQ+L\nHi5fmzCo9bFv3z7NmzdPqVSq97YgCHTo0KEIVxUderhoYtHDooeLJhY9LHq4aGLRw6KHy9cmDGp9\nLFy4UD09PZo2bZq5/fXXX49oRdGih4smFj0serhoYtHDooeLJhY9LHq4fG3CVR8BAAAAIGZ4R20A\nuVxOHR0dqqqqUjqdjno5kaOHiyYWPSx6uGhi0cOih4smFj0serh8a8Kg1kd7e7uy2ayqq6tVUVGh\nrq4u5fN5NTQ0aMaMGVEvr+jo4aKJRQ+LHi6aWPSw6OGiiUUPix4ub5sEMFasWBGcOXMmCIIg2LRp\nU9Dd3R10d3cHy5cvj3hl0aCHiyYWPSx6uGhi0cOih4smFj0serh8bcI7apdQVlYW9RJihR4umlj0\nsOjhoolFD4seLppY9LDo4fKlCRcT6aOtrU3ZbFaZTEaVlZXq6upSLpdTfX29amtro15e0dHDRROL\nHhY9XDSx6GHRw0UTix4WPVy+NmFQ60ehUFA+n1dnZ6dSqZTS6bQSiUTUy4oMPVw0sehh0cNFE4se\nFj1cNLHoYdHD5WMTBjUAAAAAiJnRPWaOgGPHjmnDhg1qbGzUm2++2Xt7Y2NjhKuKDj1cNLHoYdHD\nRROLHhY9XDSx6GHRw+VrEwa1PtatW6ebb75Zt956q3bu3KkdO3ZIko4cORLxyqJBDxdNLHpY9HDR\nxKKHRQ8XTSx6WPRw+dqEqz72kUgkNH36dElSTU2Ndu3apaamJhUKhYhXFg16uGhi0cOih4smFj0s\nerhoYtHDoofL1yYMan1MmTLFbM+cOVMTJ07UK6+8EtGKokUPF00selj0cNHEoodFDxdNLHpY9HD5\n2oSLiQAAAABAzHCOGgAAAADEDIMaAAAAAMQMgxoAAAAAxAyDGgAAAADEDFd9BABE5rHHHtM777wj\nSfrABz6gRYsWqaKiYlB/d8eOHbr99tuVTCaH9NgvvviiTp48qfr6euXzea1bt06SlMvllEqlNGHC\nBF199dVasmRJ798pFAr61re+pfvuu09jxowZ0uMCADAYXPURABCZVatWacGCBZo6dap++MMfavfu\n3Xr00UcH9Xc/85nP6Mtf/rImTJhw2Y8bBIG+8IUvaNmyZUqlUuZr69at00033aQ/+qM/uuz7BQAg\nLLyjBgCIhTvuuEP/8R//oTfeeEOZTEaNjY06ceKE3nrrLf3xH/+x7r77bknS2bNntXr1ap08eVJf\n/vKXNWbMGH3uc5/TpEmTJElvvPFG7wedplIp3X///aqqqjKP9Ytf/EIf/OAHnSHtgv7+H+a3vvUt\nvfnmmzp8+LC2bNnSe3s2m9Xbb7+tw4cP62Mf+5jefvttHThwQF/5yleUSCR0+vRpbdiwQR0dHQqC\nQIsWLdLUqVPDygYA8BTnqAEAYqOmpkZHjhzR+PHjtXDhQi1fvlxr1qzRj3/8Y/3v//6vJCmZTGr1\n6tW66qqr9Oijj+pLX/pS75B27tw5PfPMM/rc5z6nVatW6dZbb9W3v/1t53F27typOXPmXNba7r//\nfn3pS1/q92vnz5/XAw88oO9///u6++67lUgklMvlJEn/8i//ok984hN67LHH9NnPflbPPPPMZT0u\nAKA08Y4aACA23vtOViKR0O7du/XWW29p7NixOnnypK6++uqL/v0333xTv/nNb7R27VpJvz2nrO85\nbEeOHFEymVQmkwlt3ddcc40qKys1ceJEpVIpVVZW6syZM5KkvXv36uTJk9q+fbskqaenR52dnQO+\nmwcAgMSgBgCIkUOHDunjH/+4fvWrX+mpp57S7bffrmuvvVZVVVX9Ho7Y15gxYzR58mStXLlywO/5\nt3/7N/3FX/xFmMu+6NoSiYQ+//nPa/z48aE+JgDAbxz6CACIhRdeeEEVFRWaOnWq9u7dq7q6Ot1x\nxx2qqKjQ8ePHne9PJpM6efKkpN8NSlOmTFFPT49effXV3u977xB18uRJ5XI5fehDHxrhf5vf+ehH\nP6qtW7f2bhcKhaI9NgBg9OIdNQBApP7pn/5JQRDoAx/4gB566CFJ0p/8yZ9ozZo1am9v1/vf/359\n6EMf6h3KLrj99tv1la98Re973/t0yy23aPbs2UokEnrkkUfU2Nio7du3q6ysTLfccov+/M//XNJv\nh8ELf76YsrKyy/rahdv6+9rChQu1ZcsWPfrooxo7dqwymYw+/elPX3INAIDSxuX5AQAAACBmOPQR\nAAAAAGKGQQ0AAAAAYoZBDQAAAABihkENAAAAAGKGQQ0AAAAAYoZBDQAAAABi5v8DkYlOXkgkI70A\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x52459d0>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Unsurprisingly, July and August are the warmest."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So we can think of snowiness as being a bunch of 1s and 0s instead of `True`s and `False`s:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "is_snowing.astype(float)[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "Date/Time\n",
        "2012-01-01 00:00:00    0\n",
        "2012-01-01 01:00:00    0\n",
        "2012-01-01 02:00:00    0\n",
        "2012-01-01 03:00:00    0\n",
        "2012-01-01 04:00:00    0\n",
        "2012-01-01 05:00:00    0\n",
        "2012-01-01 06:00:00    0\n",
        "2012-01-01 07:00:00    0\n",
        "2012-01-01 08:00:00    0\n",
        "2012-01-01 09:00:00    0\n",
        "Name: Weather, dtype: float64"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "and then use `resample` to find the percentage of time it was snowing each month"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "is_snowing.astype(float).resample('M', how=np.mean)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "Date/Time\n",
        "2012-01-31    0.240591\n",
        "2012-02-29    0.162356\n",
        "2012-03-31    0.087366\n",
        "2012-04-30    0.015278\n",
        "2012-05-31    0.000000\n",
        "2012-06-30    0.000000\n",
        "2012-07-31    0.000000\n",
        "2012-08-31    0.000000\n",
        "2012-09-30    0.000000\n",
        "2012-10-31    0.000000\n",
        "2012-11-30    0.038889\n",
        "2012-12-31    0.251344\n",
        "Freq: M, dtype: float64"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "is_snowing.astype(float).resample('M', how=np.mean).plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x5bdedd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PF00selj0cNHEoodFDxdNLHpY9LCi0oO7Teaoo6ND6XRaqVRKJSUl6u/vVyaT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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5ff9450>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So now we know! In 2012, December was the snowiest month. Also, this graph suggests something that I feel -- it starts snowing pretty abruptly in November, and then tapers off slowly and takes a long time to stop, with the last snow usually being in April or May."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 6.3 Plotting temperature and snowiness stats together"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can also combine these two statistics (temperature, and snowiness) into one dataframe and plot them together:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "temperature = weather_2012['Temp (C)'].resample('M', how=np.median)\n",
      "is_snowing = weather_2012['Weather'].str.contains('Snow')\n",
      "snowiness = is_snowing.astype(float).resample('M', how=np.mean)\n",
      "\n",
      "# Name the columns\n",
      "temperature.name = \"Temperature\"\n",
      "snowiness.name = \"Snowiness\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We'll use `concat` again to combine the two statistics into a single dataframe."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats = pd.concat([temperature, snowiness], axis=1)\n",
      "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>Temperature</th>\n",
        "      <th>Snowiness</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date/Time</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2012-01-31</th>\n",
        "      <td> -7.05</td>\n",
        "      <td> 0.240591</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-02-29</th>\n",
        "      <td> -4.10</td>\n",
        "      <td> 0.162356</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-03-31</th>\n",
        "      <td>  2.60</td>\n",
        "      <td> 0.087366</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-04-30</th>\n",
        "      <td>  6.30</td>\n",
        "      <td> 0.015278</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-05-31</th>\n",
        "      <td> 16.05</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-06-30</th>\n",
        "      <td> 19.60</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-07-31</th>\n",
        "      <td> 22.90</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-08-31</th>\n",
        "      <td> 22.20</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-09-30</th>\n",
        "      <td> 16.10</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-10-31</th>\n",
        "      <td> 11.30</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-11-30</th>\n",
        "      <td>  1.05</td>\n",
        "      <td> 0.038889</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-12-31</th>\n",
        "      <td> -2.85</td>\n",
        "      <td> 0.251344</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "            Temperature  Snowiness\n",
        "Date/Time                         \n",
        "2012-01-31        -7.05   0.240591\n",
        "2012-02-29        -4.10   0.162356\n",
        "2012-03-31         2.60   0.087366\n",
        "2012-04-30         6.30   0.015278\n",
        "2012-05-31        16.05   0.000000\n",
        "2012-06-30        19.60   0.000000\n",
        "2012-07-31        22.90   0.000000\n",
        "2012-08-31        22.20   0.000000\n",
        "2012-09-30        16.10   0.000000\n",
        "2012-10-31        11.30   0.000000\n",
        "2012-11-30         1.05   0.038889\n",
        "2012-12-31        -2.85   0.251344"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats.plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x5f59d50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vVV01NTUqKSlp82MpvY9aQUGB6urqEsuxWKzNSRoAAAAAoH0pnahFo1GVlpZq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\nAub/AScX4emTXA1lAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5f4ded0>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Uh, that didn't work so well because the scale was wrong. We can do better by plotting them on two separate graphs:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats.plot(kind='bar', subplots=True, figsize=(15, 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "array([<matplotlib.axes.AxesSubplot object at 0x5fbc150>,\n",
        "       <matplotlib.axes.AxesSubplot object at 0x60ea0d0>], dtype=object)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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q1KjYnWG75u01GzdujM7Ozrjyyit3e79r3gAAgAPZnq55G/bfNjlixIhoaWkZ\n7sMCAAAU2rANb7feemtce+21cd9998UFF1wwXIdlGL3xLekDnR4pPXKapPRI6ZHTJKVHSo+cJqky\n9Bi2t8CuuOKK4ToUAABA6Qz7NW9745o3AADgQNYw17wBAACw7wxvVE0Z1hFXkx4pPXKapPRI6ZHT\nJKVHSo+cJqky9DC8AQAAFIBr3mrINW8AAMC+cs0bAABAgRneqJoyrCOuJj1SeuQ0SemR0iOnSUqP\nlB45TVJl6GF4AwAAKIBSXvPW88JL8eyOnVU6o6E7cnRrtI87uN6nAQAAFMiernlrqcO51Fz7uIMN\nTQAAQKlYNjlEZVgzW22apPRI6ZHTJKVHSo+cJik9UnrkNEmVoYfhDQAAoABKec0bAABAUfmcNwAA\ngAIzvA1RGdbMVpsmKT1SeuQ0SemR0iOnSUqPlB45TVJl6GF4AwAAKADXvAEAADQQ17wBAAAUmOFt\niMqwZrbaNEnpkdIjp0lKj5QeOU1SeqT0yGmSKkMPwxsAAEABuOYNAACggbjmDQAAoMAMb0NUhjWz\n1aZJSo+UHjlNUnqk9MhpktIjpUdOk1QZehjeAAAACqCq17ytW7culi1bFieeeGLMnTt34PY1a9bE\n8uXLIyJizpw5MXXq1D0+hmveAACAA9mernlrqeZBdu3aFWeffXasX79+4Lb+/v7o7OyMq6++OiIi\nFi1aFCeddFI0NTVV89AAAAClVtVlk9OmTYsxY8Ykt/X29kZ7e3u0trZGa2trTJgwIXp7e6t52Loo\nw5rZatMkpUdKj5wmKT1SeuQ0SemR0iOnSaoMPYa0bHLNmjXxrW99K7ntwgsvjLe//e3xxBNPxKpV\nqwaWTf7f//t/Y+XKlQP7VSqVOPXUU+Md73jHbh+7q6trX08HAACgVKq2bHLatGkxbdq0N7XvmDFj\nYseOHTF//vyoVCqxdOnSGDdu3D6dJAAAwIGu6r9t8o1v5LW1tUVPT8/Adm9vb7S1tVX7sAAAAKVW\n1V9YsmLFivjpT38aW7dujd/+9rdx2WWXRXNzc8yaNSsWLlwYERGzZ8+u5iEBAAAOCFX9qAAAAABq\no6rvvAFANVx33XXxm9/8JiIijj766Jg3b16MGjXqTX3tAw88EB/+8IejtbV1SMd+8MEHY+vWrTFr\n1qzo7e2NO+64IyIienp6YsyYMTF27Ng4/PDDY8GCBQNf09/fH3feeWdcdtllcdBBBw3puACwN955\nA6DhXH8qmy8LAAAgAElEQVT99TF37tw45phj4p//+Z9j1apV8bnPfe5Nfe2f//mfx0033RRjx47d\n5+NWKpX4/Oc/H1/4wheyj76544474r3vfW+8//3v3+fHBYBq8M4bAA3tIx/5SPzgBz+IDRs2RHt7\ne9xzzz3x/PPPxy9/+cv4wAc+EOeff35EROzcuTMWLlwYW7dujZtuuikOOuig+Mu//MsYP358RERs\n2LAh7rvvvujv748xY8bExz72sey3H//0pz+NY489NhvcXrO7f++8884745lnnomNGzfGsmXLBm7v\n7OyM5557LjZu3Bgnn3xyPPfcc7F+/fr44he/GM3NzfHCCy/E3XffHdu2bYtKpRLz5s2LY445plrZ\nACihqv+2SQCotuOOOy42bdoUI0eOjAsvvDCuuuqqWLx4cXzve9+LX//61xER0draGgsXLozDDjss\nPve5z8UNN9wwMLi9/PLL8eUvfzn+8i//Mq6//vr49//+38fXv/717Dj/9E//FDNnztync/vYxz4W\nN9xww27ve+WVV+LP/uzP4h/+4R/i/PPPj+bm5oHfwPy3f/u38Qd/8Adx3XXXxSc+8Yn48pe/vE/H\nBeDA4503ABre69/xam5ujlWrVsUvf/nLGDFiRGzdujUOP/zwQb/+mWeeiV/96lexZMmSiHj1GrU3\nXhO3adOmaG1tjfb29qqd99vf/vYYPXp0vOUtb4kxY8bE6NGj46WXXoqIiLVr18bWrVvj/vvvj4iI\nXbt2xfbt2/f4rh8AGN4AaHg///nP44Mf/GA89dRTcdttt8WHP/zhmDJlSowbN263Sxnf6KCDDooj\njzwyrr322j3u84//+I/xR3/0R9U87UHPrbm5OT772c/GyJEjq3pMAMrLskkAGtp3v/vdGDVqVBxz\nzDGxdu3amD59enzkIx+JUaNGxbPPPpvt39raGlu3bo2I3w1Pb3vb22LXrl3x4x//eGC/1w9WW7du\njZ6enjjhhBNq/N38zu/93u/F3//93w9s9/f3D9uxASgm77wB0JC+8pWvRKVSiaOPPjo++clPRkTE\n7//+78fixYvjsccei4kTJ8YJJ5wwMKi95sMf/nB88YtfjLe+9a1x6qmnxowZM6K5uTk+85nPxD33\n3BP3339/NDU1xamnnhr/8T/+x4h4dUB87f8H09TUtE/3vXbb7u678MILY9myZfG5z30uRowYEe3t\n7fHxj398r+cAwIHLRwUAAAAUgGWTAAAABWB4AwAAKADDGwAAQAEY3gAAAArA8AYAAFAAhjcAAIAC\nMLwBAAAUgOENAACgAAxvAAAABWB4AwAAKADDGwAAQAEY3gAAAArA8AYAAFAAhjcAAIACMLwBAAAU\ngOENAACgAAxvAAAABWB4AwAAKADDGwAAQAEY3gAAAArA8AYAAFAAhjcAAIACMLwBAAAUgOENAACg\nAAxvAAAABWB4AwAAKADDGwAAQAEY3gAAAArA8AYAAFAAhjcAAIACMLwBAAAUgOENAACgAAxvAAAA\nBWB4AwAAKADDGwAAQAEY3gAAAArA8AYAAFAAhjcAAIACMLwBAAAUgOENAACgAAxvAAAABWB4AwAA\nKADDGwAAQAEY3gAAAArA8AYAAFAAhjcAAIACMLwBAAAUgOENAACgAAxvAAAABWB4AwAAKADDGwAA\nQAEY3gAAAArA8AYAAFAAhjcAAIACMLwBAAAUgOENAACgAAxvAAAABWB4AwAAKADDGwAAQAEY3gAA\nAArA8AYAAFAAhjcAAIACMLwBAAAUgOENAACgAAxvAAAABWB4AwAAKADDGwAAQAEY3gAAAArA8AYA\nAFAAhjcAAIACMLwBAAAUgOENAACgAAxvAAAABWB4AwAAKADDGwAAQAEY3gAAAArA8AYAAFAAhjcA\nAIACMLwBAAAUgOENAACgAAxvAAAABWB4AwAAKADDGwAAQAEY3gAAAArA8AZAXa1YsSJ+7/d+LyZP\nnhxHHXVUXHPNNXU7l0cffTTOPPPMuh0fAAbTVKlUKvU+CQAOTI8//nj8/u//fjz44IPxvve9LyIi\ntm/fHmPGjKnzmQFA4/HOGwB18/Of/zze+ta3xnvf+96B214/uE2ZMiXuueeeOOecc2LixIkxderU\nePzxx5PH+O53vxvve9/74qijjop3v/vd8d/+238buO8HP/hBTJs2LTvuAw88EO95z3sGtv/u7/4u\nJk2aFEceeWRMmjQp2/9rX/tanHbaaXHnnXdGR0dHjB8/Pj796U8n+1Qqlbjpppvi+OOPjylTpsSf\n/dmfxYsvvpjs88Mf/jA++MEPxqRJk2LSpEkxe/bs7Fg33XRTvPOd74zJkyfHlClT4hvf+Mae8gFw\ngDG8AVA3/+E//IeYOHFi/MEf/EE89NBD2f1NTU2xdOnS+OIXvxjPPPNMTJ8+PW666aaB+3/yk5/E\nn/7pn8Ztt90WTz/9dPz93/99fOYzn4lvf/vbERFx/PHHx6ZNm7LH3bRpU7zrXe8a2L7gggti8+bN\n0dnZucdzXbVqVbz44ovxk5/8JFauXBlf+tKXYsOGDQP333rrrfH1r389/vf//t+xYcOGeOmll7IB\nb/bs2XHRRRfF5s2bY/PmzXHPPfck9//Lv/xLLFmyJB566KHYtGlTbNiwIf7kT/5kLxUBOFAY3gCo\nm9bW1njooYfiL/7iL+LGG2+Mjo6OeOSRR5J9Fi5cGMcee2xERHzwgx9MhrG77ror5s2bFx/4wAci\nIuKEE06IT33qU3HbbbdFRERbW1tERLzwwgvxpS99KY4++ujo6+uLzZs3xzvf+c7sfAa7kuB973tf\nfPKTn4zm5uY4/vjjY8KECcm53HnnnXHVVVdFe3t7NDc3x1//9V/HV7/61eQxJk2aFP/n//yfeOKJ\nJyIisuWhbW1t8Zvf/CYefPDBeO6556K5uTlGjhw5eEQADhiGNwDqqrm5OT760Y/GQw89FF/4whfi\nD//wD2P79u273belpSX6+/sHtjdt2hTHHXdcss9xxx0XTz311MD28ccfH08++WTcdttt8aEPfSju\nuuuuPQ5v+2LEiBHJuWzevDk+9alPxdFHHx1HH310nHrqqTFy5Mjo6ekZ2Oc73/lOHH/88TF//vx4\n+9vfHkuWLEke893vfnd0dXXFj370o3jve98bH/zgB+OnP/3pfp0nAOVheAOgYcyZMydeeeWVZDni\nYCZPnhw/+9nPktt+9rOfxZQpUwa2jz/++Piv//W/xqmnnho33HBD3HnnnfGv//qv+z28vdGxxx4b\ny5Yti3/9138d+O/555+P9vb2gX0OO+yw+M//+T/HD3/4w+jq6oq//uu/ju9///vJ47z3ve+NL3/5\ny7Fx48aYM2dOnH322VU9TwCKy/AGQN3s3LlzYPjq7++PO+64Iw4//PA48cQT39TXX3bZZbFs2bLo\n7u6OiIi1a9fGzTffHJdffvnAPscff3x84xvfiCuuuCLa29vj/e9/f6xcubLqw9tf/dVfxRVXXBHr\n168fuG3r1q3JPhs3bhxYmvnyyy/HK6+8kiyL3LVrVzzzzDMR8WqPnTt3xqhRo6p6ngAUV0u9TwCA\nA9czzzwTc+fOjZ6enjjooIPi5JNPjq6urmhp2f3TU1NTUzQ1NQ1sv+c974n//t//e3zyk5+M3t7e\nOPzww2Px4sXJZ7W9853vjNNOO23gt0t+8pOfjO9973sxevToPR5jb8fdnYsuuigOOuigOO+88+K5\n556Lgw46KGbOnDlw/V1ExDXXXBMPPfRQNDc3x2GHHRaLFy+Ok08+eeD+p556Ks4666zo6+uLiIiO\njo74n//zfw56XAAOHD7nDQAAoAAsmwQAACiAvS6bXLNmTSxfvjwiXr2QfOrUqXvc9xvf+EasX78+\nmpub47LLLosJEybs82MAAACQG3R46+/vj87Ozrj66qsjImLRokVx0kkn7XHd/3nnnRcRr/6mr299\n61tx2WWX7fNjAAAAkBt0eOvt7Y329vZobW2NiIgJEyYM3DaY//f//l9MnDhxSI/R1dW1z98EAABA\nmcyYMSO7bdDhbfv27TF69Oi49957IyJi1KhRsW3btkGHt2uvvTZeeOGFuOGGG4b8GNOnT9/7dwMA\nAFBCq1ev3u3tg/7CkjFjxsSOHTvi/PPPj/POOy927NgR48aNG/RA119/ffz5n//5wK9GHspjAAAA\nkBp0eGtra4uenp6B7d7e3mhra9vrgx522GHxyiuv7NdjNLrXPhCW39EkpUdKj5wmKT1SeuQ0SemR\n0iOnSaoMPQZdNtnc3ByzZs2KhQsXRkTE7NmzB+5buXJlHHzwwckSx1tuuSW2bdsWLS0tcckll+z1\nMQAAAHhzGu5Duru6ulzzBgAAHLBWr169219Y4kO6AQAACsDwNkRlWDNbbZqk9EjpkdMkpUdKj5wm\nKT1SeuQ0SZWhh+ENAACgAFzzBgAA0EBc8wYAAFBghrchKsOa2WrTJKVHSo+cJik9UnrkNEnpkdIj\np0mqDD0MbwAAAAXgmjcAAIAG4po3AACAAjO8DVEZ1sxWmyYpPVJ65DRJ6ZHSI6dJSo+UHjlNUmXo\nYXgDAAAoANe8AQAANBDXvAEAABSY4W2IyrBmtto0SemR0iOnSUqPlB45TVJ6pPTIaZIqQw/DGwAA\nQAG45g0AAKCBuOYNAACgwAxvQ1SGNbPVpklKj5QeOU1SeqT0yGmS0iOlR06TVBl6GN4AAAAKwDVv\nAAAADcQ1bwAAAAVmeBuiMqyZrTZNUnqk9MhpktIjpUdOk5QeKT1ymqTK0MPwBgAAUACueQMAAGgg\nrnkDAAAoMMPbEJVhzWy1aZLSI6VHTpOUHik9cpqk9EjpkdMkVYYehjcAAIACcM0bAABAA3HNGwAA\nQIG9qeFtzZo1cc0118Q111wTjz322KD73n333XH99dfHtddeG7/4xS8Gbr/99tvjC1/4Qlx//fXx\nve99b79OuhGUYc1stWmS0iOlR06TlB4pPXKapPRI6ZHTJFWGHi1726G/vz86Ozvj6quvjoiIRYsW\nxUknnRRNTU273f/SSy+NiIjHHnss7r///oHtpqamWLBgQYwfP75a5w4AAHDA2Ovw1tvbG+3t7dHa\n2hoRERMmTBi4bTCHHHJItLSkD99gl9ftl9NOO63ep9BwNEnpkdIjp0lKj5QeOU1SeqT0yBWlydbn\nfhPb+l6s+XHe/rYTY/OG5wfdZ+yhh8RhR4yq+bkM1V6Ht+3bt8fo0aPj3nvvjYiIUaNGxbZt2/Y6\nvD388MNx5plnDmyPHDkylixZEmPGjIl58+ZFW1vbHr+2u7t74A/ba29v2rZt27Zt27Zt27Ztu3zb\nT2/6RXync300gnPnnxyPrVsdEfXtM2rU7gfIvf62yS1btsSKFSti/vz5UalUYunSpXHOOecMOnw9\n8sgj8Ytf/CJmzpyZ3bdx48bo7OyMK6+8crdfW5TfNtnd/bsBk1dpktIjpUdOk5QeKT1ymqT0SOmR\nK0qTzRuej28u/XG9TyMiXh3eJh3zlnqfxtB/22RbW1v09PQMbPf29g46uG3YsCHWrVu328EtImLE\niBHZckoAAAAG96Y+5+3RRx+N5cuXR0TE7NmzY9q0aRERsXLlyjj44IOTd8o+8YlPxBFHHBHNzc0x\nefLkuPjiiyMi4tZbb41f//rXMXLkyLjkkkvirW99626PVZR33gAAgP3nnbfcnt55e1NvgXV0dERH\nR0d2+ymnnJLddtttt+32Ma644oo3cygAAAB2w4d0D9FrFxbyO5qk9EjpkdMkpUdKj5wmKT1SeuQ0\nKR/DGwAAQAG8qWvehlM1rnkbrs+K2JtG/5wIAACoN9e85fbrmrei2db3YkP8ATh3/smGNwAAoCos\nm6RqrKtO6ZHSI6dJSo+UHjlNUnqk9MhpUj6GNwAAgAIwvFE1p512Wr1PoaHokdIjp0lKj5QeOU1S\neqT0yGlSPoY3AACAAjC8UTXWVaf0SOmR0ySlR0qPnCYpPVJ65DQpH8MbAABAARjeqBrrqlN6pPTI\naZLSI6VHTpOUHik9cpqUj+ENAACgAAxvVI111Sk9UnrkNEnpkdIjp0lKj5QeOU3Kx/AGAABQAIY3\nqsa66pQeKT1ymqT0SOmR0ySlR0qPnCblY3gDAAAoAMMbVWNddUqPlB45TVJ6pPTIaZLSI6VHTpPy\nMbwBAAAUgOGNqrGuOqVHSo+cJik9UnrkNEnpkdIjp0n5GN4AAAAKwPBG1VhXndIjpUdOk5QeKT1y\nmqT0SOmR06R8DG8AAAAFYHijaqyrTumR0iOnSUqPlB45TVJ6pPTIaVI+hjcAAIACMLxRNdZVp/RI\n6ZHTJKVHSo+cJik9UnrkNCkfwxsAAEABGN6oGuuqU3qk9MhpktIjpUdOk5QeKT1ympSP4Q0AAKAA\nDG9UjXXVKT1SeuQ0SemR0iOnSUqPlB45TcqnZW87rFmzJpYvXx4REXPmzImpU6fucd+77747tmzZ\nEv39/XH55ZfHhAkT9vkxAAAAyA06vPX390dnZ2dcffXVERGxaNGiOOmkk6KpqWm3+1966aUREfHY\nY4/F/fffH5deeuk+PwbFZV11So+UHjlNUnqk9MhpktIjpUdOk/IZdNlkb29vtLe3R2tra7S2tsaE\nCROit7d3rw96yCGHREtLy349BgAAAL8z6Dtv27dvj9GjR8e9994bERGjRo2Kbdu2RXt7+6AP+vDD\nD8eZZ5455Mfo7u4e+JeC19bq7sv2YaMnDnp+w2ko51/U7devq26E86n3th567G37y1/+crz73e9u\nmPOp97Yeeuxte+3atfHxj3+8Yc6n3tt66LG37ddua5Tz2dN2X19fNJJ69+ju7o5Ro0bt9tyaKpVK\nZU8nvmXLllixYkXMnz8/KpVKLF26NM4555xoa2vb4zf7yCOPxC9+8YuYOXPmkB6jq6srpk+fvsfH\nfzM2b3g+vrn0x/v1GNVw7vyTY9Ixb6n3aQyb7u7fDd3o8UZ65DRJ6ZHSI6dJSo+UHrmiNGmU1+4R\njfP6ffXq1TFjxozs9kGXTba1tUVPT8/Adm9v76CD24YNG2LdunUDg9tQHoPiKsIPh+GkR0qPnCYp\nPVJ65DRJ6ZHSI6dJ+bQMdmdzc3PMmjUrFi5cGBERs2fPHrhv5cqVcfDBByfvkt18881xxBFHxPXX\nXx+TJ0+Oiy++eNDHAAAA4M0ZdHiLiOjo6IiOjo7s9lNOOSW77bbbbtunx6BcivLW/HDRI6VHTpOU\nHik9cpqk9EjpkdOkfHxINwAAQAEY3qga/7KT0iOlR06TlB4pPXKapPRI6ZHTpHwMbwAAAAVgeKNq\nXv+ZIujxRnrkNEnpkdIjp0lKj5QeOU3Kx/AGAABQAIY3qsa66pQeKT1ymqT0SOmR0ySlR0qPnCbl\nY3gDAAAoAMMbVWNddUqPlB45TVJ6pPTIaZLSI6VHTpPyMbwBAAAUgOGNqrGuOqVHSo+cJik9Unrk\nNEnpkdIjp0n5GN4AAAAKwPBG1VhXndIjpUdOk5QeKT1ymqT0SOmR06R8DG8AAAAFYHijaqyrTumR\n0iOnSUqPlB45TVJ6pPTIaVI+hjcAAIACMLxRNdZVp/RI6ZHTJKVHSo+cJik9UnrkNCkfwxsAAEAB\nGN6oGuuqU3qk9MhpktIjpUdOk5QeKT1ympSP4Q0AAKAADG9UjXXVKT1SeuQ0SemR0iOnSUqPlB45\nTcrH8AYAAFAAhjeqxrrqlB4pPXKapPRI6ZHTJKVHSo+cJuVjeAMAACiAlnqfALW39bnfxLa+F2t+\nnL6+vjj00EP3eP/YQw+Jw44YVfPzaBTd3d3+xet19MhpktIjpUdOk5QeKT1ympSP4e0AsK3vxfjm\n0h/X+zTi3PknH1DDGwAAVJNlk1Aj/qUrpUdOk5QeKT1ymqT0SOmR06R8DG8AAAAFYHiDGvHZKik9\ncpqk9EjpkdMkpUdKj5wm5WN4AwAAKIA39QtL1qxZE8uXL4+IiDlz5sTUqVP3uO+6deti2bJlceKJ\nJ8bcuXMHbr/99ttjy5Yt0draGqeffnqcccYZ+3fm0OCsM0/pkdMkpUdKj5wmKT1SeuQ0KZ+9Dm/9\n/f3R2dkZV199dURELFq0KE466aRoamra7f67du2Ks88+O9avX5/c3tTUFAsWLIjx48dX4bQBAAAO\nLHtdNtnb2xvt7e3R2toara2tMWHChOjt7d3j/tOmTYsxY8bs9r5KpTL0M4WCsc48pUdOk5QeKT1y\nmqT0SOmR06R89jq8bd++PUaPHh333ntv3HvvvTFq1KjYtm3bPh9o5MiRsWTJkvgv/+W/DDr8RaR/\n0Lq7u/d5u6+vb5/Pr1aGcv7V3tbDtu3G3F67dm1DnU+9t/XQY2/ba9eubajzqfe2HnqUZbuRXqtG\n1L/H6///jZoqe3k7bMuWLbFixYqYP39+VCqVWLp0aZxzzjnR1ta2x6954oknYtWqVck1b6/ZuHFj\ndHZ2xpVXXrnbr+3q6orp06cPdkp7tXnD8w3zodSTjnlLvU9DDwAAGlajvFaNaJzXq6tXr44ZM2Zk\nt+/1nbe2trbo6ekZ2O7t7R10cIsYfHnkiBEjoqWlZW+HBQAA4HX2Orw1NzfHrFmzYuHChXHjjTfG\n7NmzB+5buXJlrF69Otl/xYoV0dnZGatWrYq77rpr4PZbb701rr322rjvvvviggsuqOK3AI1psLe8\nD0R65DRJ6ZHSI6dJSo+UHjlNyudNvQXW0dERHR0d2e2nnHJKdttZZ50VZ511Vnb7FVdcMYTTAwAA\nIMKHdEPN+GyVlB45TVJ6pPTIaZLSI6VHTpPyMbwBAAAUgOENasQ685QeOU1SeqT0yGmS0iOlR06T\n8jG8AQAAFIDhDWrEOvOUHjlNUnqk9MhpktIjpUdOk/IxvAEAABSA4Q1qxDrzlB45TVJ6pPTIaZLS\nI6VHTpPyMbwBAAAUgOENasQ685QeOU1SeqT0yGmS0iOlR06T8jG8AQAAFIDhDWrEOvOUHjlNUnqk\n9MhpktIjpUdOk/IxvAEAABSA4Q1qxDrzlB45TVJ6pPTIaZLSI6VHTpPyMbwBAAAUgOENasQ685Qe\nOU1SeqT0yGmS0iOlR06T8jG8AQAAFIDhDWrEOvOUHjlNUnqk9MhpktIjpUdOk/IxvAEAABSA4Q1q\nxDrzlB45TVJ6pPTIaZLSI6VHTpPyMbwBAAAUgOENasQ685QeOU1SeqT0yGmS0iOlR06T8jG8AQAA\nFIDhDWrEOvOUHjlNUnqk9MhpktIjpUdOk/IxvAEAABSA4Q1qxDrzlB45TVJ6pPTIaZLSI6VHTpPy\nMbwBAAAUgOENasQ685QeOU1SeqT0yGmS0iOlR06T8jG8AQAAFIDhDWrEOvOUHjlNUnqk9MhpktIj\npUdOk/Jp2dsOa9asieXLl0dExJw5c2Lq1Kl73HfdunWxbNmyOPHEE2Pu3LlDegwAAAByg77z1t/f\nH52dnXHVVVfFVVddFZ2dnVGpVPa4/65du+Lss8/er8eAsrDOPKVHTpOUHik9cpqk9EjpkdOkfAYd\n3np7e6O9vT1aW1ujtbU1JkyYEL29vXvcf9q0aTFmzJj9egwAAABygw5v27dvj9GjR8e9994b9957\nb4waNSq2bdu2TwcYymO8/l8Juru793m7r69vn86xloZy/tXe1qM+26eddlpDnU+9t/XIt1+7rVHO\np97beuixt+3Xa4Tzqfe2Hnrsbfu1a94a5Xz2tN1Ir1Uj6t/jjX+eX6+pMsgaxi1btsSKFSti/vz5\nUalUYunSpXHOOedEW1vbHh/wiSeeiFWrVg1c87avj9HV1RXTp0/f4+O/GZs3PB/fXPrj/XqMajh3\n/skx6Zi31Ps09AAAoGE1ymvViMZ5vbp69eqYMWNGdvug77y1tbVFT0/PwHZvb++gg1tEZNezDeUx\noAwG+1eTA5EeOU1SeqT0yGmS0iOlR06T8mkZ7M7m5uaYNWtWLFy4MCIiZs+ePXDfypUr4+CDD07e\nJVuxYkX89Kc/ja1bt8Zvf/vbuOyyywZ9DAAAAN6cQZdN1oNlk9WnBwAAjapRXqtGNM7r1SEtmwQA\nAKAxGN6gRqwzT+mR0ySlR0qPnCYpPVJ65DQpH8MbAABAARjeoEZe+2wVXqVHTpOUHik9cpqk9Ejp\nkdOkfAxvAAAABTDoRwVAGW197jexre/Fmh+nr68vDj300EH3GXvoIXHYEaNqfi6NoLu7278AvoEm\nKT1SeuQ0SemR0iOnSfkY3jjgbOt7saF+He2BMrwBALB/LJsEhoV/+ctpktIjpUdOk5QeKT1ympSP\n4Q0AAKAADG/AsPBZMzlNUnqk9MhpktIjpUdOk/IxvAEAABSA4Q0YFtbd5zRJ6ZHSI6dJSo+UHjlN\nysfwBgAAUACGN2BYWHef0ySlR0qPnCYpPVJ65DQpH8MbAABAARjegGFh3X1Ok5QeKT1ymqT0SOmR\n06R8DG8AAAAFYHgDhoV19zlNUnqk9MhpktIjpUdOk/IxvAEAABSA4Q0YFtbd5zRJ6ZHSI6dJSo+U\nHjlNysfwBgAAUACGN2BYWHef0ySlR0qPnCYpPVJ65DQpH8MbAABAARjegGFh3X1Ok5QeKT1ymqT0\nSOmR06R8DG8AAAAFYHgDhoV19zlNUnqk9MhpktIjpUdOk/IxvAEAABSA4Q0YFtbd5zRJ6ZHSI6dJ\nSo+UHjlNysfwBgAAUACGN2BYWHef0ySlR0qPnCYpPVJ65DQpn5Y3s9OaNWti+fLlERExZ86cmDp1\n6j7ve/vtt8eWLVuitbU1Tj/99DjjjDP289QBAAAOHHsd3vr7+6OzszOuvvrqiIhYtGhRnHTSSdHU\n1PSm9n1teGtqaooFCxbE+PHjq3n+QEFYd5/TJPX/27vj2Krq+//jr16gYnu5Il+193b7RWRV0BGq\niG6wYMyIzumyjKSdMcsgy5i6aHT7Y2xKURC3GJ1mzqXM0q1oM410iYsO1KmLOhIHq2TUhk0R4lBy\nT3VrDYsAAB4DSURBVFW02F5sabnn94fx6tsPwm3B+7nn3OcjMem9vbRvnp7q/fTc87n0sOjhoolF\nD4seLprEz1FfNhkEgTKZjKqrq1VdXa26ujoFQVD0Y7PZbOHzYRgWNdQnT/Fu3rx5zLf3799f1Pcp\nhfHMf7xv06N8e+zfv997D25zm9vc5ja3uc1tn7fL6bmZ5L/HJz/+tKrwKCuqV199VS+++GLhdhiG\nWrBggc4666wxPbajo0O7d+9WMpnU0qVLlU6nD/v9nn32Wc2dO/dIIx3VG7vf1SPtW4/paxwPVy67\nUP9vxjTfY9DjU8qlh1Q+TUph8+bN/AbwU2hi0cOih4smFj0serii0oTnZq5t27Zp0aJFzv0Tj/YH\nk8mkcrmcli1bpjAM1d7erlQqNebH/uAHP5Akvf766+rs7NTPfvazY/n7AAAAAEBFOerLJtPptHnp\nYxAEn3nWrJjHTpo0SRMnHnXNCCBmovCbv1KjiUUPix4umlj0sOjhokn8HHUVlUgk1NTUpDVr1kiS\nmpubC5978cUXdcIJJxRe5nikx/7mN7/Re++9pxNPPFE//OEPj+tfAgAAAADirqhTYI2NjWpsbHTu\nnz9/ftGP/clPfjKO8QDERVRed19KNLHoYdHDRROLHhY9XDSJH96kGwAAAAAigMUbgJLgN38umlj0\nsOjhoolFD4seLprED4s3AAAAAIgAtn0EUBK87t5FE4seFj1cNLHoYUWpR/++AxrYP/S5f5/9+/fr\npJNO+szPTzlpsqb+X83nPgeOHxZvAAAAQAkN7B8qizelvnLZhSzeIoaXTQIoiaj8NrSUaGLRw6KH\niyYWPSx6oBKweAMAAACACGDxBqAkNm/e7HuEskMTix4WPVw0sehh0QOVgMUbAAAAAEQAizcAJcG1\nCC6aWPSw6OGiiUUPix6oBCzeAAAAACACWLwBKAmuRXDRxKKHRQ8XTSx6WPRAJWDxBgAAAAARwOIN\nQElwLYKLJhY9LHq4aGLRw6IHKgGLNwAAAACIABZvAEqCaxFcNLHoYdHDRROLHhY9UAlYvAEAAABA\nBLB4A1ASXIvgoolFD4seLppY9LDogUrA4g0AAAAAIoDFG4CS4FoEF00selj0cNHEoodFD1SCib4H\nAOBf/74DGtg/9Ll+j6m1X9Abu9894mOmnDRZU/+v5nOdAwAAIKpYvAHQwP4hPdK+1fcYunLZhRW1\neOP6DIseFj1cNLHoYdEDlYCXTQIAAABABLB4AwBPuD7DoodFDxdNLHpY9EAlYPEGAAAAABHA4g0A\nPOH6DIseFj1cNLHoYdEDlYDFGwAAAABEAIs3APCE6zMselj0cNHEoodFD1QCFm8AAAAAEAFHfZ+3\nnp4e/fnPf5Ykffe739Xs2bPH/NixfA0AqBRcn2HRw6KHiyYWPSx6oBIccfGWz+fV1dWllStXSpJ+\n+ctf6stf/rKqqqqKeuzs2bPH9DUAAAAAAId3xJdNBkGgTCaj6upqVVdXq66uTkEQFP3YbDY7pq8B\nAJWE6zMselj0cNHEoodFD1SCqjAMw8/65KuvvqoXX3yxcDsMQy1YsEBnnXVW0Y+VVPTXkKRnn312\n7H8LAAAAAIiRRYsWOfcd8WWTyWRSuVxOy5YtUxiGam9vVyqVGtNj8/l80V/js4YEAAAAgEp3xMVb\nOp1WNpst3A6CQOl0ekyPzefzRX8NAAAAAMDhHfFlk5K0ffv2wk6Rzc3NmjNnjqQPXwp5wgknaO7c\nuUd97GfdDwAAAAAozlEXbwAAAAAA/3iTbgAAAACIgKO+STc+FoahgiDQwMCAUqlUxV+7Rw8XTSx6\nWPRw0cSih0UPF00selj0sOLYg8VbkXp7e9XV1aW6ujrV1NQol8spCIKKvYaPHi6aWPSw6OGiiUUP\nix4umlj0sOhhxbZHiKKsXLkyHB4eDsMwDNevXx8ODQ2FQ0NDYUtLi+fJ/KCHiyYWPSx6uGhi0cOi\nh4smFj0selhx7cGZt3GqqqryPUJZoYeLJhY9LHq4aGLRw6KHiyYWPSx6WHHpwW6TRerp6VFXV5cy\nmYxqa2uVy+WUzWbV1NSkxsZG3+OVHD1cNLHoYdHDRROLHhY9XDSx6GHRw4prDxZvY5DP5xUEgQYH\nB5VMJpVOp5VIVO6GnfRw0cSih0UPF00selj0cNHEoodFDyuOPaI9fYklEonCKddEIhH5f/nHih4u\nmlj0sOjhoolFD4seLppY9LDoYcWxB9e8FSm2O9aMEz1cNLHoYdHDRROLHhY9XDSx6GHRw4ptD7/7\npURHXHesGS96uGhi0cOih4smFj0serhoYtHDoocV1x6ceRunuOxYc7zQw0UTix4WPVw0sehh0cNF\nE4seFj2suPRgw5IixXXHmvGih4smFj0serhoYtHDooeLJhY9LHpYce3B4m0M4rhjzbGgh4smFj0s\nerhoYtHDooeLJhY9LHpYcezB4g0AAAAAIiDaS08AAAAAqBAs3sZo+/btR7xdaejhoolFD4seLppY\n9LDo4aKJRQ+LHlbcerB4G6NcLnfE25WGHi6aWPSw6OGiiUUPix4umlj0sOhhxa0H17wBAAAAQARw\n5g0AAAAAIoA36R6DMAwVBIEGBgaUSqWUTqd9j+QVPVw0sehh0cNFE4seFj1cNLHoYdHDimMPFm9F\n6u3tVVdXl+rq6lRTU6NcLqcgCNTc3Kw5c+b4Hq/k6OGiiUUPix4umlj0sOjhoolFD4seVmx7hCjK\nypUrw+Hh4TAMw3D9+vXh0NBQODQ0FLa0tHiezA96uGhi0cOih4smFj0serhoYtHDoocV1x6ceRun\nqqoq3yOUFXq4aGLRw6KHiyYWPSx6uGhi0cOihxWXHuw2WaSenh51dXUpk8motrZWuVxO2WxWTU1N\namxs9D1eydHDRROLHhY9XDSx6GHRw0UTix4WPay49mDxNgb5fF5BEGhwcFDJZFLpdFqJROVu2EkP\nF00selj0cNHEoodFDxdNLHpY9LDi2CPa05dYIpEonHJNJBKR/5d/rOjhoolFD4seLppY9LDo4aKJ\nRQ+LHlYce3DNW5Fiu2PNONHDRROLHhY9XDSx6GHRw0UTix4WPazY9vC7X0p0xHXHmvGih4smFj0s\nerhoYtHDooeLJhY9LHpYce3BmbdxisuONccLPVw0sehh0cNFE4seFj1cNLHoYdHDiksPNiwpUlx3\nrBkverhoYtHDooeLJhY9LHq4aGLRw6KHFdceLN7GII471hwLerhoYtHDooeLJhY9LHq4aGLRw6KH\nFcceLN4AAAAAIAKivfQEAAAAgArB4m2Mtm/ffsTblYYeLppY9LDo4aKJRQ+LHi6aWPSw6GHFrQeL\ntzHK5XJHvF1p6OGiiUUPix4umlj0sOjhoolFD4seVtx6cM0bAAAAAEQAZ94AAAAAIAJ4k+4xCMNQ\nQRBoYGBAqVRK6XTa90he0cNFE4seFj1cNLHoYdHDRROLHhY9rDj2YPFWpN7eXnV1damurk41NTXK\n5XIKgkDNzc2aM2eO7/FKjh4umlj0sOjhoolFD4seLppY9LDoYcW2R4iirFy5MhweHg7DMAzXr18f\nDg0NhUNDQ2FLS4vnyfygh4smFj0serhoYtHDooeLJhY9LHpYce3Bmbdxqqqq8j1CWaGHiyYWPSx6\nuGhi0cOih4smFj0selhx6cFuk0Xq6elRV1eXMpmMamtrlcvllM1m1dTUpMbGRt/jlRw9XDSx6GHR\nw0UTix4WPVw0sehh0cOKaw8Wb2OQz+cVBIEGBweVTCaVTqeVSFTuhp30cNHEoodFDxdNLHpY9HDR\nxKKHRQ8rjj2iPX2JJRKJwinXRCIR+X/5x4oeLppY9LDo4aKJRQ+LHi6aWPSw6GHFsQfXvBUptjvW\njBM9XDSx6GHRw0UTix4WPVw0sehh0cOKbQ+/+6VER1x3rBkverhoYtHDooeLJhY9LHq4aGLRw6KH\nFdcenHkbp7jsWHO80MNFE4seFj1cNLHoYdHDRROLHhY9rLj0YMOSIsV1x5rxooeLJhY9LHq4aGLR\nw6KHiyYWPSx6WHHtweJtDOK4Y82xoIeLJhY9LHq4aGLRw6KHiyYWPSx6WHHsweINAAAAACIg2ktP\nAAAAAKgQLN4AAAAAIAJYvAEAAABABLB4AwAAAIAIYPEGAAAAABHAm3TjmIyOjurAgQNKpVK+R0EZ\nCsNQQRBoYGBAqVRK6XTa90goMxwjwNjwM4Mj4fiIPxZvx6itrU1XX3217zFKLpvN6uGHH1Z/f79y\nuZwmTpyo6dOn63vf+x4LuU+p1GOkt7dXXV1dqqurU01NjXK5nIIgUHNzs+bMmeN7vLJRqceHxDFS\nrEo+Rg6nknvwM1OcSj1GOD6KE/Xjg8VbkTo7Ow97/44dO0o8SXlob2/XjTfeqNraWt19991avny5\ntm3bpvvuu08rVqzwPZ4XHCPWhg0b1NLSourqaj3wwANatmyZJOn222+vyP+JcHy4OEYsjhGLHi5+\nZiyOEYvjw4rr8cHirUg7duzQ4sWLlUwmC/eFYahdu3Z5nMqfoaEhpVIpjYyM6IMPPpAkzZ07V48+\n+qjnyfzhGDmyqqoq3yN4xfFxdBwjHCOfRI+j42eGY+RIOD7ieXyweCvSkiVLNDIyonPOOcfcv3Pn\nTk8T+XXxxRdrxYoVGh0d1eLFiwv319fXe5zKL44Rq6mpSWvWrFEmk1Ftba3a29uVzWbV1NTkezQv\nOD5cHCMWx4hFDxc/MxbHiMXxYcX1+KgKwzD0PQSiaXh4WGEYavLkyb5HQZnK5/MKgkCDg4NKJpNK\np9NKJNjkFh/jGAHGhp8ZHAnHR/yxeAMAAACACJiwatWqVb6HiIK+vj499NBDCoJAmUxG99xzj154\n4QWdeeaZmjJliu/xSq6vr08PP/ywenp6dNpppxV2mOzo6NB5553neTo/OEasw/V4/vnn6cHxUcAx\nYnGMWPRw8TNjcYxYHB9WXI8PzqMWqa2tTRdddJHy+bxuueUWNTc365prrlFHR4fv0bxobW3V/Pnz\ntXDhQm3atEkbN26UJO3Zs8fzZP5wjFiH63HttdfSg+OjgGPE4hix6OHiZ8biGLE4Pqy4Hh9sWFKk\nfD6vWbNmadasWeru7lZDQ4OkD9+kuhIlEgnNnj1bktTQ0KDu7m51dnYqn897nswfjhGLHhY9XDSx\n6GHRw0UTix4WPay49mDxVqSZM2cWPl69enXh4yifdj0Wn95Vct68eZo2bZq2bNniaSL/OEYselj0\ncNHEoodFDxdNLHpY9LDi2oMNSwAAAAAgArjmDQAAAAAigMXbOHV3d/seoazQw0UTix4WPVw0sehh\n0cNFE4seFj2suPRg8TZOH+2uiA/Rw0UTix4WPVw0sehh0cNFE4seFj2suPRg8QYAAAAAEcCbdB+D\n6dOn+x6hrNDDRROLHhY9XDSx6GHRw0UTix4WPaw49GC3SQAAAACIAF42CaBk3nzzTd8joIwdPHhQ\nBw8e9D0GyhTHx8dGR0f1/vvv+x6jLIRhqGw2q1dffVVBEPgeB/jc8bLJY9TW1qbzzz/f9xgl19fX\np4cfflg9PT067bTTlEqlJEkdHR0677zzPE/nR19fnx566CEFQaBMJqN77rlHL7zwgs4888zIvyHk\neDz55JPatWuXXnvttcI/GzZs0KFDh9TQ0OB7vJJ77rnnCi/XCIJAa9eu1datW9XQ0KDa2lq/w3ny\nwAMP6Nxzz5Ukbd26VWvXrtXmzZs1NDSkM8880/N0pbd06VK99dZbOvXUUzV16lTf43jX29ure++9\nV2+++aZGR0fV3t6uZ599VhMnTozFS5/GI5vNat26dXrqqaf09NNP6+9//7tee+01zZw5UyeccILv\n8Uqut7dXra2teuONNxQEgbZv367HH39cp5xyiurq6nyPVzZ4rhqv56oTfQ8QFZ2dnYe9f8eOHSWe\npDy0traqublZkydP1qZNm1RfX68rrrhCe/bs8T2aN21tbWpubtbOnTt1yy236LrrrtPUqVPV1tam\nm2++2fd4JffYY49pxowZmjdvXuG+CRMmVOQTDEl6/vnndfHFF0v6cNFy5ZVXatKkSfrDH/6gX/zi\nF36H82TXrl2Fj5944gmtWbNGEydO1MqVK3X55Zd7nMyPGTNm6LLLLtMTTzyhffv2adGiRbrwwguV\nSFTmi2QeeeQRrVixQtu2bdNTTz2l1atXKwxD/epXvyr8LFWa9vZ23XjjjaqtrdXdd9+t5cuXa9u2\nbbrvvvu0YsUK3+OV3IYNG9TS0qLq6mo98MADWrZsmSTp9ttv15w5czxPV3o8V7Xi+lyVxVuRduzY\nocWLFyuZTBbuC8PQPPmoJIlEQrNnz5YkNTQ0qLu7W52dncrn854n8yefz2vWrFmaNWuWuru7C2eX\nRkdHPU/mx7333qunn35a//znP3XZZZfp3HPP1fbt2yv2SdfBgwf1zjvvKAxDDQ4OFs4cfPDBB34H\n82jChAl65ZVXNHPmTNXU1CiRSOjdd99VTU2N79G8Of3003XttddqYGBAzzzzjG699Vadf/75+s53\nvuN7tJI7dOiQJk+erKlTp2rixA+frlRVVVXsYlaShoaGlEqlNDIyUvhvx9y5c/Xoo496nqw8VFVV\n+R7BK56rWnF9rsrirUhLlizRyMiIzjnnHHP/zp07PU3kV319vbk9b948TZs2TVu2bPE0kX8zZ84s\nfLx69erCx5X4kklJmjRpki6//HItWrRITzzxhJ588kmNjIz4Hsub+vp6bdiwQdKHZ1g+cvLJJ/sa\nybvrr79enZ2dWr9+vQ4ePKhbb71Vp5xyin70ox/5Hs27KVOmaPHixfr2t7+tf/3rX77H8eIb3/iG\nli9fri9+8Yu64IIL9Otf/1qHDh0y/62tNBdffLFWrFih0dFRLV68uHD/p/+fXCmampq0Zs0aZTIZ\n1dbWqr29XdlsVk1NTb5H84LnqlZcn6uy2ySAkjhw4ID++9//au7cub5HQZnJ5/N6//33lUwmC2dY\nKtGBAwcq+qzj0fT19enQoUMVu1D5yPDwsMIw1OTJk32PUhby+byCINDg4KCSyaTS6XRFn51F/LF4\nG6NsNquBgQGlUiml02nf43gVhqGCIKDHJ9DEooeL/4a4aPIxfmZcHB8umgCVq3J/xTlGvb296urq\nUl1dnWpqapTL5RQEgZqbmyvyolh6uGhi0cOih4smFj0serhoUpy2tjZdffXVvscoG/Swot6DxVuR\n2NHIooeLJhY9LHq4aGLRw6KHiyYWuyta9LDi2oPF2zhV+o5Gn0YPF00selj0cNHEoodFD1elN2F3\nRYseVlx7cM1bkXp6etTV1VXY0SiXyxV2NGpsbPQ9XsnRw0UTix4WPVw0sehh0cNFE+s///mPRkZG\nnLOOf/nLXyry7TXoYcW1B4u3MWBHI4seLppY9LDo4aKJRQ+LHi6aAJWNxRsAAAAARAC/qhmn7u5u\n3yOUFXq4aGLRw6KHiyYWPSx6uGhi0cOihxWXHizexmnjxo2+Rygr9HDRxKKHRQ8XTSx6WPRw0cSi\nh0UPKy49WLwBAAAAQARMWLVq1SrfQ0TV9OnTfY9QVujhoolFD4seLppY9LDo4aKJRQ+LHlYcerBh\nCQAAAABEAC+bBAAAAIAIYPFWpL6+Pq1bt04dHR3au3dv4f6Ojg6PU/lDDxdNLHpY9HDRxKKHRQ8X\nTSx6WPSw4tqDxVuRWltbNX/+fC1cuFCbNm0q7FizZ88ez5P5QQ8XTSx6WPRw0cSih0UPF00selj0\nsOLaY6LvAaIikUho9uzZkqSGhgZ1d3ers7NT+Xze82R+0MNFE4seFj1cNLHoYdHDRROLHhY9rLj2\nYPFWpPr6enN73rx5mjZtmrZs2eJpIr/o4aKJRQ+LHi6aWPSw6OGiiUUPix5WXHuw2yQAAAAARADX\nvAEAAABABLB4O0ZtbW2+Rygr9HDRxKKHRQ8XTSx6WPRw0cSih0UPK+o9uOatSJ2dnYe9f8eOHSWe\npDzQw0UTix4WPVw0sehh0cNFE4seFj2suPZg8VakHTt2aPHixUomk4X7wjDUrl27PE7lDz1cNLHo\nYdHDRROLHhY9XDSx6GHRw4prDxZvRVqyZIlGRkZ0zjnnmPt37tzpaSK/6OGiiUUPix4umlj0sOjh\noolFD4seVlx7sNskAAAAAEQAZ97GKJvNamBgQKlUSul02vc43tHDRROLHhY9XDSx6GHRw0UTix4W\nPay49WDxVqTe3l51dXWprq5ONTU1yuVyCoJAzc3NmjNnju/xSo4eLppY9LDo4aKJRQ+LHi6aWPSw\n6GHFtkeIoqxcuTIcHh4OwzAM169fHw4NDYVDQ0NhS0uL58n8oIeLJhY9LHq4aGLRw6KHiyYWPSx6\nWHHtwZm3caqqqvI9Qlmhh4smFj0serhoYtHDooeLJhY9LHpYcenBhiVF6unpUVdXlzKZjGpra5XL\n5ZTNZtXU1KTGxkbf45UcPVw0sehh0cNFE4seFj1cNLHoYdHDimsPFm9jkM/nFQSBBgcHlUwmlU6n\nlUgkfI/lDT1cNLHoYdHDRROLHhY9XDSx6GHRw4pjDxZvAAAAABAB0V56llBfX5/WrVunjo4O7d27\nt3B/R0eHx6n8oYeLJhY9LHq4aGLRw6KHiyYWPSx6WHHtweKtSK2trZo/f74WLlyoTZs2aePGjZKk\nPXv2eJ7MD3q4aGLRw6KHiyYWPSx6uGhi0cOihxXXHuw2WaREIqHZs2dLkhoaGtTd3a3Ozk7l83nP\nk/lBDxdNLHpY9HDRxKKHRQ8XTSx6WPSw4tqDxVuR6uvrze158+Zp2rRp2rJli6eJ/KKHiyYWPSx6\nuGhi0cOih4smFj0selhx7cGGJQAAAAAQAVzzBgAAAAARwOINAAAAACKAxRsAAAAARACLNwAAAACI\nAHabBACUnVWrVunAgQOSpDPOOENLly5VTU1NUX9248aNuuSSS1RdXT2u7/3MM8+ov79fTU1NCoJA\nra2tkqRsNqtkMqkpU6bo5JNP1k9/+tPCn8nn87r//vt19dVXa8KECeP6vgAAHA27TQIAys7q1av1\n/e9/XzNmzNDf/vY3vfTSS7rpppuK+rPXXXed7rjjDk2ZMmXM3zcMQ918881asWKFksmk+Vxra6vO\nP/98feUrXxnz1wUA4HjgzBsAoKxdeuml+sc//qHdu3crk8moo6ND7777rt5++2199atf1VVXXSVJ\nOnjwoNasWaP+/n7dcccdmjBhgm644QadcsopkqTdu3cX3qA1mUzqmmuuUSqVMt/r3//+t770pS85\nC7ePHO73nffff7/27t2r119/XQ8++GDh/q6uLu3bt0+vv/66LrzwQu3bt0+vvPKK7rzzTiUSCb3/\n/vtat26dBgYGFIahli5dqhkzZhyvbACAGOKaNwBA2WtoaNCePXt04oknasmSJWppadFdd92l5557\nTu+9954kqbq6WmvWrNHUqVN100036bbbbiss3EZHR7V27VrdcMMNWr16tRYuXKg//elPzvfZtGmT\nrrjiijHNds011+i222477OcOHTqka6+9Vo8//riuuuoqJRIJZbNZSdIf//hHff3rX9eqVat0/fXX\na+3atWP6vgCAysOZNwBA2fvkGa9EIqGXXnpJb7/9tiZNmqT+/n6dfPLJR/zze/fu1TvvvKPf/va3\nkj68Ru3T18Tt2bNH1dXVymQyx23u008/XbW1tZo2bZqSyaRqa2s1PDwsSXr55ZfV39+vxx57TJI0\nMjKiwcHBzzzrBwAAizcAQNnbtWuXLrroIv3vf//T7373O11yySWaPn26UqnUYV/K+GkTJkzQaaed\npltvvfUzH/PXv/5V3/rWt47n2EecLZFI6Oc//7lOPPHE4/o9AQDxxcsmAQBl7amnnlJNTY1mzJih\nl19+WXPnztWll16qmpoavfXWW87jq6ur1d/fL+njxVN9fb1GRka0devWwuM+ubDq7+9XNpvV2Wef\n/Tn/bT52wQUXaMOGDYXb+Xy+ZN8bABBNnHkDAJSl3//+9wrDUGeccYZuvPFGSdLXvvY13XXXXert\n7dUXvvAFnX322YWF2kcuueQS3XnnnTr11FO1YMECLVq0SIlEQsuXL1dHR4cee+wxVVVVacGCBfrm\nN78p6cMF4kcfH0lVVdWYPvfRfYf73JIlS/Tggw/qpptu0qRJk5TJZPTjH//4qDMAACoXbxUAAAAA\nABHAyyYBAAAAIAJYvAEAAABABLB4AwAAAIAIYPEGAAAAABHA4g0AAAAAIoDFGwAAAABEwP8HUYXG\ndWSDCskAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5fb9090>"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<style>\n",
      "    @font-face {\n",
      "        font-family: \"Computer Modern\";\n",
      "        src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
      "    }\n",
      "    div.cell{\n",
      "        width:800px;\n",
      "        margin-left:16% !important;\n",
      "        margin-right:auto;\n",
      "    }\n",
      "    h1 {\n",
      "        font-family: Helvetica, serif;\n",
      "    }\n",
      "    h4{\n",
      "        margin-top:12px;\n",
      "        margin-bottom: 3px;\n",
      "       }\n",
      "    div.text_cell_render{\n",
      "        font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
      "        line-height: 145%;\n",
      "        font-size: 130%;\n",
      "        width:800px;\n",
      "        margin-left:auto;\n",
      "        margin-right:auto;\n",
      "    }\n",
      "    .CodeMirror{\n",
      "            font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
      "    }\n",
      "    .text_cell_render h5 {\n",
      "        font-weight: 300;\n",
      "        font-size: 22pt;\n",
      "        color: #4057A1;\n",
      "        font-style: italic;\n",
      "        margin-bottom: .5em;\n",
      "        margin-top: 0.5em;\n",
      "        display: block;\n",
      "    }\n",
      "    \n",
      "    .warning{\n",
      "        color: rgb( 240, 20, 20 )\n",
      "        }  "
     ]
    }
   ],
   "metadata": {}
  }
 ]
}