07_local_south.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "import folium\n",
      "import random\n",
      "import pandas as pd\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Base layer for south"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import folium\n",
      "map_osm = folium.Map(location=[33.7550, -84.3900], zoom_start=7)\n",
      "map_osm.geo_json(geo_path = 'data/south.json')\n",
      "map_osm.create_map(path='south_base.html')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "open up a browser and load south_base.html"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Binding Attribute Data to the Map"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json\n",
      "\n",
      "f = open(r'data/south.json')\n",
      "q = json.load(f)\n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "q.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "[u'type', u'features']"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "features = q['features']\n",
      "len(features)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "1412"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "indices = []\n",
      "values = []\n",
      "for feature in features:\n",
      "    indices.append(str(feature['properties']['FIPS']))\n",
      "    values.append(feature['properties']['HR80'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame({'HR80': values,\n",
      "                   'FIPS': indices} )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = np.array(df.HR80.tolist())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pysal as ps\n",
      "w = ps.queen_from_shapefile('data/south.shp')\n",
      "w.transform = 'r'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = np.array(df['HR80'].tolist())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Local Moran\n",
      "$li_i = \\frac{n-1}{\\sum_j z_j^2}z_i \\sum_j w_{i,j} z_j$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "li_hr80 = ps.Moran_Local(y,w)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "li_hr80.p_sim.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "(1412,)"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sig01 = 1.* (li_hr80.p_sim<=0.01)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sig01.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "155.0"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sig05 = 1.* (li_hr80.p_sim<=0.05)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sig05.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "382.0"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['sig01']  = sig01\n",
      "df['sig05'] = sig05"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Function that shows the map - call this functionto redraw the map.\n",
      "def showmap(name):\n",
      "    sint = str(random.randint(0,10000))\n",
      "    out_file = name+\"_\"+sint+\".html\"\n",
      "    map_osm.create_map(path=out_file)\n",
      "    iframe = \"<iframe src=http://localhost:8015/%s width=1000 height=500></iframe>\"%out_file\n",
      "    print iframe\n",
      "    return HTML(iframe)\n",
      "\n",
      "def resetmap():\n",
      "    return folium.Map(location=[40, -99], zoom_start=4)\n",
      "\n",
      "map_osm = resetmap()\n",
      "showmap('map_0')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<iframe src=http://localhost:8015/map_0_5489.html width=1000 height=500></iframe>\n"
       ]
      },
      {
       "html": [
        "<iframe src=http://localhost:8015/map_0_5489.html width=1000 height=500></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<IPython.core.display.HTML at 0x10c304ad0>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "map_osm = folium.Map(location=[33.7550, -84.3900], zoom_start=5,)\n",
      "map_osm.geo_json(geo_path='data/south.json',\n",
      "                 key_on='feature.properties.FIPS',\n",
      "                 data_out='data.json', data=df,\n",
      "                 columns=['FIPS','HR80'],\n",
      "                 fill_color='YlGn', fill_opacity=0.7,\n",
      "                 line_opacity=0.2,\n",
      "                 legend_name='Homicide Rate HR80')\n",
      "showmap('tmp')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<iframe src=http://localhost:8015/tmp_2641.html width=1000 height=500></iframe>\n"
       ]
      },
      {
       "html": [
        "<iframe src=http://localhost:8015/tmp_2641.html width=1000 height=500></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<IPython.core.display.HTML at 0x10c2fbdd0>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "map_osm = folium.Map(location=[33.7550, -84.3900], zoom_start=5,)\n",
      "map_osm.geo_json(geo_path='data/south.json',\n",
      "                 key_on='feature.properties.FIPS',\n",
      "                 data_out='data.json', data=df,\n",
      "                 columns=['FIPS','sig01'],\n",
      "                 fill_color='YlGn', fill_opacity=0.7,\n",
      "                 line_opacity=0.2,\n",
      "                 threshold_scale=[0,1],\n",
      "                 legend_name='Homicide Rate HR80 - Significant LISA (0.01)')\n",
      "showmap('tmp')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<iframe src=http://localhost:8015/tmp_7597.html width=1000 height=500></iframe>\n"
       ]
      },
      {
       "html": [
        "<iframe src=http://localhost:8015/tmp_7597.html width=1000 height=500></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<IPython.core.display.HTML at 0x10c31d210>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Note that folium only is bound to threshold scales - no discrete or ordinal scale yet, so we have to fake it with two class threshold scales."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "map_osm = folium.Map(location=[33.7550, -84.3900], zoom_start=5,)\n",
      "map_osm.geo_json(geo_path='data/south.json',\n",
      "                 key_on='feature.properties.FIPS',\n",
      "                 data_out='data.json', data=df,\n",
      "                 columns=['FIPS','sig01'],\n",
      "                 fill_color='YlGn', fill_opacity=0.7,\n",
      "                 line_opacity=0.2,\n",
      "                 legend_name='Homicide Rate HR80 - Significant LISA (0.01)')\n",
      "showmap('tmp')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<iframe src=http://localhost:8015/tmp_4115.html width=1000 height=500></iframe>\n"
       ]
      },
      {
       "html": [
        "<iframe src=http://localhost:8015/tmp_4115.html width=1000 height=500></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "<IPython.core.display.HTML at 0x10c320210>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "map_osm = folium.Map(location=[33.7550, -84.3900], zoom_start=5,)\n",
      "map_osm.geo_json(geo_path='data/south.json',\n",
      "                 key_on='feature.properties.FIPS',\n",
      "                 data_out='data.json', data=df,\n",
      "                 columns=['FIPS','sig05'],\n",
      "                 fill_color='YlGn', fill_opacity=0.7,\n",
      "                 line_opacity=0.2,\n",
      "                 legend_name='Homicide Rate HR80 - Significant LISA (0.05)')\n",
      "showmap('tmp')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<iframe src=http://localhost:8015/tmp_9847.html width=1000 height=500></iframe>\n"
       ]
      },
      {
       "html": [
        "<iframe src=http://localhost:8015/tmp_9847.html width=1000 height=500></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "<IPython.core.display.HTML at 0x10c320fd0>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "li_hr80.q"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "array([3, 3, 3, ..., 3, 3, 3])"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cold_spots = 1. * (li_hr80.q==3) * (li_hr80.p_sim < 0.01)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cold_spots.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "63.0"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Cold spots in pandas'y way\n",
      "lisa_df = pd.DataFrame({'q': li_hr80.q, 'p_sim': li_hr80.p_sim})\n",
      "cold = lisa_df[(lisa_df['q']==3) & (lisa_df['p_sim']<0.01)]\n",
      "print cold.info()\n",
      "print '\\n~~~~~~~~~~\\n'\n",
      "# Hot spots in pandas'y way\n",
      "hot = lisa_df[(lisa_df['q']==1) & (lisa_df['p_sim']<0.01)]\n",
      "print hot.info()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 63 entries, 6 to 1286\n",
        "Data columns (total 2 columns):\n",
        "p_sim    63  non-null values\n",
        "q        63  non-null values\n",
        "dtypes: float64(1), int64(1)None\n",
        "\n",
        "~~~~~~~~~~\n",
        "\n",
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 51 entries, 199 to 1401\n",
        "Data columns (total 2 columns):\n",
        "p_sim    51  non-null values\n",
        "q        51  non-null values\n",
        "dtypes: float64(1), int64(1)None\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hot_spots = 1. * (li_hr80.q==1) * (li_hr80.p_sim < 0.01)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hot_spots.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "51.0"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['hot_01'] = hot_spots\n",
      "df['cold_01'] = cold_spots"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.hot_01"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "0     0\n",
        "1     0\n",
        "2     0\n",
        "3     0\n",
        "4     0\n",
        "5     0\n",
        "6     0\n",
        "7     0\n",
        "8     0\n",
        "9     0\n",
        "10    0\n",
        "11    0\n",
        "12    0\n",
        "13    0\n",
        "14    0\n",
        "...\n",
        "1397    0\n",
        "1398    0\n",
        "1399    1\n",
        "1400    0\n",
        "1401    1\n",
        "1402    0\n",
        "1403    0\n",
        "1404    0\n",
        "1405    0\n",
        "1406    0\n",
        "1407    0\n",
        "1408    0\n",
        "1409    0\n",
        "1410    0\n",
        "1411    0\n",
        "Name: hot_01, Length: 1412, dtype: float64"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.cold_01"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "0     0\n",
        "1     0\n",
        "2     0\n",
        "3     0\n",
        "4     0\n",
        "5     0\n",
        "6     1\n",
        "7     0\n",
        "8     1\n",
        "9     0\n",
        "10    1\n",
        "11    1\n",
        "12    0\n",
        "13    0\n",
        "14    1\n",
        "...\n",
        "1397    0\n",
        "1398    0\n",
        "1399    0\n",
        "1400    0\n",
        "1401    0\n",
        "1402    0\n",
        "1403    0\n",
        "1404    0\n",
        "1405    0\n",
        "1406    0\n",
        "1407    0\n",
        "1408    0\n",
        "1409    0\n",
        "1410    0\n",
        "1411    0\n",
        "Name: cold_01, Length: 1412, dtype: float64"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "map_osm = folium.Map(location=[33.7550, -84.3900], zoom_start=5,)\n",
      "map_osm.geo_json(geo_path='data/south.json',\n",
      "                 key_on='feature.properties.FIPS',\n",
      "                 data_out='data.json', data=df,\n",
      "                 columns=['FIPS','hot_01'],\n",
      "                 fill_color='RdPu', fill_opacity=0.7,\n",
      "                 line_opacity=0.2,\n",
      "                 threshold_scale=[0,1],\n",
      "                 legend_name='Homicide Rate HR80 - Hot Spots (0.01)')\n",
      "showmap('tmp')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<iframe src=http://localhost:8015/tmp_1377.html width=1000 height=500></iframe>\n"
       ]
      },
      {
       "html": [
        "<iframe src=http://localhost:8015/tmp_1377.html width=1000 height=500></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "<IPython.core.display.HTML at 0x10c322310>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "map_osm = folium.Map(location=[33.7550, -84.3900], zoom_start=5,)\n",
      "map_osm.geo_json(geo_path='data/south.json',\n",
      "                 key_on='feature.properties.FIPS',\n",
      "                 data_out='data.json', data=df,\n",
      "                 columns=['FIPS','cold_01'],\n",
      "                 fill_color='YlGnBu', fill_opacity=0.7,\n",
      "                 line_opacity=0.2,\n",
      "                 threshold_scale=[0,1],\n",
      "                 legend_name='Homicide Rate HR80 - Cold Spots (0.01)')\n",
      "showmap('tmp')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<iframe src=http://localhost:8015/tmp_2001.html width=1000 height=500></iframe>\n"
       ]
      },
      {
       "html": [
        "<iframe src=http://localhost:8015/tmp_2001.html width=1000 height=500></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "<IPython.core.display.HTML at 0x10d2c1750>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dbf = ps.open('data/south.dbf')\n",
      "dbf.header"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "['NAME',\n",
        " 'STATE_NAME',\n",
        " 'STATE_FIPS',\n",
        " 'CNTY_FIPS',\n",
        " 'FIPS',\n",
        " 'STFIPS',\n",
        " 'COFIPS',\n",
        " 'FIPSNO',\n",
        " 'SOUTH',\n",
        " 'HR60',\n",
        " 'HR70',\n",
        " 'HR80',\n",
        " 'HR90',\n",
        " 'HC60',\n",
        " 'HC70',\n",
        " 'HC80',\n",
        " 'HC90',\n",
        " 'PO60',\n",
        " 'PO70',\n",
        " 'PO80',\n",
        " 'PO90',\n",
        " 'RD60',\n",
        " 'RD70',\n",
        " 'RD80',\n",
        " 'RD90',\n",
        " 'PS60',\n",
        " 'PS70',\n",
        " 'PS80',\n",
        " 'PS90',\n",
        " 'UE60',\n",
        " 'UE70',\n",
        " 'UE80',\n",
        " 'UE90',\n",
        " 'DV60',\n",
        " 'DV70',\n",
        " 'DV80',\n",
        " 'DV90',\n",
        " 'MA60',\n",
        " 'MA70',\n",
        " 'MA80',\n",
        " 'MA90',\n",
        " 'POL60',\n",
        " 'POL70',\n",
        " 'POL80',\n",
        " 'POL90',\n",
        " 'DNL60',\n",
        " 'DNL70',\n",
        " 'DNL80',\n",
        " 'DNL90',\n",
        " 'MFIL59',\n",
        " 'MFIL69',\n",
        " 'MFIL79',\n",
        " 'MFIL89',\n",
        " 'FP59',\n",
        " 'FP69',\n",
        " 'FP79',\n",
        " 'FP89',\n",
        " 'BLK60',\n",
        " 'BLK70',\n",
        " 'BLK80',\n",
        " 'BLK90',\n",
        " 'GI59',\n",
        " 'GI69',\n",
        " 'GI79',\n",
        " 'GI89',\n",
        " 'FH60',\n",
        " 'FH70',\n",
        " 'FH80',\n",
        " 'FH90']"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      " df['STATE_NAME'] = dbf.by_col('STATE_NAME')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['County'] = dbf.by_col('NAME')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hot_01 = df['hot_01']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hot  = df[hot_01>0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hot"
     ],
     "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>FIPS</th>\n",
        "      <th>HR80</th>\n",
        "      <th>sig01</th>\n",
        "      <th>sig05</th>\n",
        "      <th>hot_01</th>\n",
        "      <th>cold_01</th>\n",
        "      <th>STATE_NAME</th>\n",
        "      <th>County</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>199 </th>\n",
        "      <td> 21025</td>\n",
        "      <td> 19.603231</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>    Kentucky</td>\n",
        "      <td>  Breathitt</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>231 </th>\n",
        "      <td> 21193</td>\n",
        "      <td> 37.516413</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>    Kentucky</td>\n",
        "      <td>      Perry</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240 </th>\n",
        "      <td> 21051</td>\n",
        "      <td> 29.301453</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>    Kentucky</td>\n",
        "      <td>       Clay</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>247 </th>\n",
        "      <td> 21131</td>\n",
        "      <td> 38.077319</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>    Kentucky</td>\n",
        "      <td>     Leslie</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>285 </th>\n",
        "      <td> 21095</td>\n",
        "      <td> 20.689600</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>    Kentucky</td>\n",
        "      <td>     Harlan</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>305 </th>\n",
        "      <td> 21013</td>\n",
        "      <td> 19.419361</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>    Kentucky</td>\n",
        "      <td>       Bell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>712 </th>\n",
        "      <td> 28027</td>\n",
        "      <td> 15.349333</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>    Coahoma</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>804 </th>\n",
        "      <td> 48387</td>\n",
        "      <td> 10.351324</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>  Red River</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>831 </th>\n",
        "      <td> 13045</td>\n",
        "      <td> 14.197991</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>    Carroll</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>850 </th>\n",
        "      <td> 28015</td>\n",
        "      <td> 10.229133</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>    Carroll</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>852 </th>\n",
        "      <td> 13189</td>\n",
        "      <td> 23.365326</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>   McDuffie</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>862 </th>\n",
        "      <td> 13245</td>\n",
        "      <td> 19.637099</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>   Richmond</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>868 </th>\n",
        "      <td> 13077</td>\n",
        "      <td> 11.884147</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>     Coweta</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>876 </th>\n",
        "      <td> 13141</td>\n",
        "      <td> 14.085499</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>    Hancock</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>897 </th>\n",
        "      <td> 28051</td>\n",
        "      <td> 23.218691</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>     Holmes</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>905 </th>\n",
        "      <td> 13163</td>\n",
        "      <td> 21.735587</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>  Jefferson</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>907 </th>\n",
        "      <td> 13033</td>\n",
        "      <td> 25.841129</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>      Burke</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>908 </th>\n",
        "      <td> 28007</td>\n",
        "      <td> 20.135917</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>     Attala</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>914 </th>\n",
        "      <td> 13285</td>\n",
        "      <td> 14.665787</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>      Troup</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>924 </th>\n",
        "      <td> 01123</td>\n",
        "      <td> 13.789775</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td> Tallapoosa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>927 </th>\n",
        "      <td> 48063</td>\n",
        "      <td> 10.781671</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>       Camp</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>930 </th>\n",
        "      <td> 28163</td>\n",
        "      <td> 23.157459</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>      Yazoo</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>932 </th>\n",
        "      <td> 22017</td>\n",
        "      <td> 23.115310</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>   Louisiana</td>\n",
        "      <td>      Caddo</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>939 </th>\n",
        "      <td> 01065</td>\n",
        "      <td> 10.681022</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td>       Hale</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>951 </th>\n",
        "      <td> 01119</td>\n",
        "      <td> 15.771627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td>     Sumter</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>963 </th>\n",
        "      <td> 28099</td>\n",
        "      <td> 11.209663</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>    Neshoba</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>966 </th>\n",
        "      <td> 48459</td>\n",
        "      <td> 12.822755</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>     Upshur</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>968 </th>\n",
        "      <td> 28089</td>\n",
        "      <td> 20.025793</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>    Madison</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>969 </th>\n",
        "      <td> 48315</td>\n",
        "      <td> 35.392535</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>     Marion</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>978 </th>\n",
        "      <td> 48203</td>\n",
        "      <td> 15.944386</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>   Harrison</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>995 </th>\n",
        "      <td> 28149</td>\n",
        "      <td> 14.204454</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>     Warren</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1022</th>\n",
        "      <td> 01113</td>\n",
        "      <td> 20.412760</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td>    Russell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1033</th>\n",
        "      <td> 48365</td>\n",
        "      <td> 12.867529</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>     Panola</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1037</th>\n",
        "      <td> 22031</td>\n",
        "      <td> 18.139179</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>   Louisiana</td>\n",
        "      <td>    De Soto</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1058</th>\n",
        "      <td> 13259</td>\n",
        "      <td> 33.921303</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>    Stewart</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1059</th>\n",
        "      <td> 13307</td>\n",
        "      <td> 14.238929</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>    Webster</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1060</th>\n",
        "      <td> 13261</td>\n",
        "      <td> 10.217984</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>     Sumter</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1062</th>\n",
        "      <td> 01005</td>\n",
        "      <td> 24.236549</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td>    Barbour</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1093</th>\n",
        "      <td> 13239</td>\n",
        "      <td> 28.284542</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>    Quitman</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1101</th>\n",
        "      <td> 13243</td>\n",
        "      <td> 17.362920</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>   Randolph</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1112</th>\n",
        "      <td> 01099</td>\n",
        "      <td> 10.301238</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td>     Monroe</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1119</th>\n",
        "      <td> 01067</td>\n",
        "      <td> 13.070187</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td>      Henry</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1120</th>\n",
        "      <td> 13061</td>\n",
        "      <td> 18.763486</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>       Clay</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1134</th>\n",
        "      <td> 13095</td>\n",
        "      <td> 17.540724</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>  Dougherty</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1141</th>\n",
        "      <td> 13037</td>\n",
        "      <td> 17.491691</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>    Calhoun</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1184</th>\n",
        "      <td> 01003</td>\n",
        "      <td> 11.456795</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td>    Baldwin</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1288</th>\n",
        "      <td> 48473</td>\n",
        "      <td> 13.469374</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>     Waller</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1332</th>\n",
        "      <td> 48039</td>\n",
        "      <td> 10.220909</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>   Brazoria</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1391</th>\n",
        "      <td> 12099</td>\n",
        "      <td> 11.267840</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Florida</td>\n",
        "      <td> Palm Beach</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1399</th>\n",
        "      <td> 12011</td>\n",
        "      <td> 15.615793</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Florida</td>\n",
        "      <td>    Broward</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1401</th>\n",
        "      <td> 12087</td>\n",
        "      <td> 12.133105</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Florida</td>\n",
        "      <td>     Monroe</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "       FIPS       HR80  sig01  sig05  hot_01  cold_01   STATE_NAME      County\n",
        "199   21025  19.603231      1      1       1        0     Kentucky   Breathitt\n",
        "231   21193  37.516413      1      1       1        0     Kentucky       Perry\n",
        "240   21051  29.301453      1      1       1        0     Kentucky        Clay\n",
        "247   21131  38.077319      1      1       1        0     Kentucky      Leslie\n",
        "285   21095  20.689600      1      1       1        0     Kentucky      Harlan\n",
        "305   21013  19.419361      1      1       1        0     Kentucky        Bell\n",
        "712   28027  15.349333      1      1       1        0  Mississippi     Coahoma\n",
        "804   48387  10.351324      1      1       1        0        Texas   Red River\n",
        "831   13045  14.197991      1      1       1        0      Georgia     Carroll\n",
        "850   28015  10.229133      1      1       1        0  Mississippi     Carroll\n",
        "852   13189  23.365326      1      1       1        0      Georgia    McDuffie\n",
        "862   13245  19.637099      1      1       1        0      Georgia    Richmond\n",
        "868   13077  11.884147      1      1       1        0      Georgia      Coweta\n",
        "876   13141  14.085499      1      1       1        0      Georgia     Hancock\n",
        "897   28051  23.218691      1      1       1        0  Mississippi      Holmes\n",
        "905   13163  21.735587      1      1       1        0      Georgia   Jefferson\n",
        "907   13033  25.841129      1      1       1        0      Georgia       Burke\n",
        "908   28007  20.135917      1      1       1        0  Mississippi      Attala\n",
        "914   13285  14.665787      1      1       1        0      Georgia       Troup\n",
        "924   01123  13.789775      1      1       1        0      Alabama  Tallapoosa\n",
        "927   48063  10.781671      1      1       1        0        Texas        Camp\n",
        "930   28163  23.157459      1      1       1        0  Mississippi       Yazoo\n",
        "932   22017  23.115310      1      1       1        0    Louisiana       Caddo\n",
        "939   01065  10.681022      1      1       1        0      Alabama        Hale\n",
        "951   01119  15.771627      1      1       1        0      Alabama      Sumter\n",
        "963   28099  11.209663      1      1       1        0  Mississippi     Neshoba\n",
        "966   48459  12.822755      1      1       1        0        Texas      Upshur\n",
        "968   28089  20.025793      1      1       1        0  Mississippi     Madison\n",
        "969   48315  35.392535      1      1       1        0        Texas      Marion\n",
        "978   48203  15.944386      1      1       1        0        Texas    Harrison\n",
        "995   28149  14.204454      1      1       1        0  Mississippi      Warren\n",
        "1022  01113  20.412760      1      1       1        0      Alabama     Russell\n",
        "1033  48365  12.867529      1      1       1        0        Texas      Panola\n",
        "1037  22031  18.139179      1      1       1        0    Louisiana     De Soto\n",
        "1058  13259  33.921303      1      1       1        0      Georgia     Stewart\n",
        "1059  13307  14.238929      1      1       1        0      Georgia     Webster\n",
        "1060  13261  10.217984      1      1       1        0      Georgia      Sumter\n",
        "1062  01005  24.236549      1      1       1        0      Alabama     Barbour\n",
        "1093  13239  28.284542      1      1       1        0      Georgia     Quitman\n",
        "1101  13243  17.362920      1      1       1        0      Georgia    Randolph\n",
        "1112  01099  10.301238      1      1       1        0      Alabama      Monroe\n",
        "1119  01067  13.070187      1      1       1        0      Alabama       Henry\n",
        "1120  13061  18.763486      1      1       1        0      Georgia        Clay\n",
        "1134  13095  17.540724      1      1       1        0      Georgia   Dougherty\n",
        "1141  13037  17.491691      1      1       1        0      Georgia     Calhoun\n",
        "1184  01003  11.456795      1      1       1        0      Alabama     Baldwin\n",
        "1288  48473  13.469374      1      1       1        0        Texas      Waller\n",
        "1332  48039  10.220909      1      1       1        0        Texas    Brazoria\n",
        "1391  12099  11.267840      1      1       1        0      Florida  Palm Beach\n",
        "1399  12011  15.615793      1      1       1        0      Florida     Broward\n",
        "1401  12087  12.133105      1      1       1        0      Florida      Monroe"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[['STATE_NAME', 'County']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 1412 entries, 0 to 1411\n",
        "Data columns (total 2 columns):\n",
        "STATE_NAME    1412  non-null values\n",
        "County        1412  non-null values\n",
        "dtypes: object(2)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 1412 entries, 0 to 1411\n",
        "Data columns (total 2 columns):\n",
        "STATE_NAME    1412  non-null values\n",
        "County        1412  non-null values\n",
        "dtypes: object(2)"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head()"
     ],
     "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>FIPS</th>\n",
        "      <th>HR80</th>\n",
        "      <th>sig01</th>\n",
        "      <th>sig05</th>\n",
        "      <th>hot_01</th>\n",
        "      <th>cold_01</th>\n",
        "      <th>STATE_NAME</th>\n",
        "      <th>County</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 54029</td>\n",
        "      <td> 6.597720</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> West Virginia</td>\n",
        "      <td>    Hancock</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 54009</td>\n",
        "      <td> 3.213677</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> West Virginia</td>\n",
        "      <td>     Brooke</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 54069</td>\n",
        "      <td> 7.601796</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> West Virginia</td>\n",
        "      <td>       Ohio</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 54051</td>\n",
        "      <td> 4.005640</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> West Virginia</td>\n",
        "      <td>   Marshall</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 10003</td>\n",
        "      <td> 7.116872</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>      Delaware</td>\n",
        "      <td> New Castle</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "    FIPS      HR80  sig01  sig05  hot_01  cold_01     STATE_NAME      County\n",
        "0  54029  6.597720      0      0       0        0  West Virginia     Hancock\n",
        "1  54009  3.213677      0      0       0        0  West Virginia      Brooke\n",
        "2  54069  7.601796      0      0       0        0  West Virginia        Ohio\n",
        "3  54051  4.005640      0      0       0        0  West Virginia    Marshall\n",
        "4  10003  7.116872      0      0       0        0       Delaware  New Castle"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.tail()"
     ],
     "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>FIPS</th>\n",
        "      <th>HR80</th>\n",
        "      <th>sig01</th>\n",
        "      <th>sig05</th>\n",
        "      <th>hot_01</th>\n",
        "      <th>cold_01</th>\n",
        "      <th>STATE_NAME</th>\n",
        "      <th>County</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1407</th>\n",
        "      <td> 51083</td>\n",
        "      <td> 15.034136</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Virginia</td>\n",
        "      <td>        Halifax</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1408</th>\n",
        "      <td> 51163</td>\n",
        "      <td>  3.967782</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Virginia</td>\n",
        "      <td>     Rockbridge</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1409</th>\n",
        "      <td> 51161</td>\n",
        "      <td>  4.127839</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Virginia</td>\n",
        "      <td>        Roanoke</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1410</th>\n",
        "      <td> 51199</td>\n",
        "      <td>  5.280349</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Virginia</td>\n",
        "      <td>           York</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1411</th>\n",
        "      <td> 51153</td>\n",
        "      <td>  3.000030</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Virginia</td>\n",
        "      <td> Prince William</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "       FIPS       HR80  sig01  sig05  hot_01  cold_01 STATE_NAME          County\n",
        "1407  51083  15.034136      0      0       0        0   Virginia         Halifax\n",
        "1408  51163   3.967782      0      0       0        0   Virginia      Rockbridge\n",
        "1409  51161   4.127839      0      0       0        0   Virginia         Roanoke\n",
        "1410  51199   5.280349      0      0       0        0   Virginia            York\n",
        "1411  51153   3.000030      0      0       0        0   Virginia  Prince William"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[[0,2,4]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 1412 entries, 0 to 1411\n",
        "Data columns (total 3 columns):\n",
        "FIPS      1412  non-null values\n",
        "sig01     1412  non-null values\n",
        "hot_01    1412  non-null values\n",
        "dtypes: float64(2), object(1)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 1412 entries, 0 to 1411\n",
        "Data columns (total 3 columns):\n",
        "FIPS      1412  non-null values\n",
        "sig01     1412  non-null values\n",
        "hot_01    1412  non-null values\n",
        "dtypes: float64(2), object(1)"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[df.hot_01 > 0 ].all()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "FIPS           True\n",
        "HR80           True\n",
        "sig01          True\n",
        "sig05          True\n",
        "hot_01         True\n",
        "cold_01       False\n",
        "STATE_NAME     True\n",
        "County         True\n",
        "dtype: bool"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[0:10]"
     ],
     "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>FIPS</th>\n",
        "      <th>HR80</th>\n",
        "      <th>sig01</th>\n",
        "      <th>sig05</th>\n",
        "      <th>hot_01</th>\n",
        "      <th>cold_01</th>\n",
        "      <th>STATE_NAME</th>\n",
        "      <th>County</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 54029</td>\n",
        "      <td> 6.597720</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> West Virginia</td>\n",
        "      <td>    Hancock</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 54009</td>\n",
        "      <td> 3.213677</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> West Virginia</td>\n",
        "      <td>     Brooke</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 54069</td>\n",
        "      <td> 7.601796</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> West Virginia</td>\n",
        "      <td>       Ohio</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 54051</td>\n",
        "      <td> 4.005640</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> West Virginia</td>\n",
        "      <td>   Marshall</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 10003</td>\n",
        "      <td> 7.116872</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>      Delaware</td>\n",
        "      <td> New Castle</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 24043</td>\n",
        "      <td> 2.063326</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>      Maryland</td>\n",
        "      <td> Washington</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td> 24001</td>\n",
        "      <td> 3.724487</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>      Maryland</td>\n",
        "      <td>   Allegany</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td> 24015</td>\n",
        "      <td> 6.067626</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>      Maryland</td>\n",
        "      <td>      Cecil</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td> 24023</td>\n",
        "      <td> 5.031826</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>      Maryland</td>\n",
        "      <td>    Garrett</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td> 24025</td>\n",
        "      <td> 3.426300</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>      Maryland</td>\n",
        "      <td>    Harford</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "    FIPS      HR80  sig01  sig05  hot_01  cold_01     STATE_NAME      County\n",
        "0  54029  6.597720      0      0       0        0  West Virginia     Hancock\n",
        "1  54009  3.213677      0      0       0        0  West Virginia      Brooke\n",
        "2  54069  7.601796      0      0       0        0  West Virginia        Ohio\n",
        "3  54051  4.005640      0      0       0        0  West Virginia    Marshall\n",
        "4  10003  7.116872      0      0       0        0       Delaware  New Castle\n",
        "5  24043  2.063326      0      1       0        0       Maryland  Washington\n",
        "6  24001  3.724487      1      1       0        1       Maryland    Allegany\n",
        "7  24015  6.067626      0      0       0        0       Maryland       Cecil\n",
        "8  24023  5.031826      1      1       0        1       Maryland     Garrett\n",
        "9  24025  3.426300      0      0       0        0       Maryland     Harford"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hot = df[df['hot_01']>0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hot"
     ],
     "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>FIPS</th>\n",
        "      <th>HR80</th>\n",
        "      <th>sig01</th>\n",
        "      <th>sig05</th>\n",
        "      <th>hot_01</th>\n",
        "      <th>cold_01</th>\n",
        "      <th>STATE_NAME</th>\n",
        "      <th>County</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>199 </th>\n",
        "      <td> 21025</td>\n",
        "      <td> 19.603231</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>    Kentucky</td>\n",
        "      <td>  Breathitt</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>231 </th>\n",
        "      <td> 21193</td>\n",
        "      <td> 37.516413</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>    Kentucky</td>\n",
        "      <td>      Perry</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240 </th>\n",
        "      <td> 21051</td>\n",
        "      <td> 29.301453</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>    Kentucky</td>\n",
        "      <td>       Clay</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>247 </th>\n",
        "      <td> 21131</td>\n",
        "      <td> 38.077319</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>    Kentucky</td>\n",
        "      <td>     Leslie</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>285 </th>\n",
        "      <td> 21095</td>\n",
        "      <td> 20.689600</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>    Kentucky</td>\n",
        "      <td>     Harlan</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>305 </th>\n",
        "      <td> 21013</td>\n",
        "      <td> 19.419361</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>    Kentucky</td>\n",
        "      <td>       Bell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>712 </th>\n",
        "      <td> 28027</td>\n",
        "      <td> 15.349333</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>    Coahoma</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>804 </th>\n",
        "      <td> 48387</td>\n",
        "      <td> 10.351324</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>  Red River</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>831 </th>\n",
        "      <td> 13045</td>\n",
        "      <td> 14.197991</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>    Carroll</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>850 </th>\n",
        "      <td> 28015</td>\n",
        "      <td> 10.229133</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>    Carroll</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>852 </th>\n",
        "      <td> 13189</td>\n",
        "      <td> 23.365326</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>   McDuffie</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>862 </th>\n",
        "      <td> 13245</td>\n",
        "      <td> 19.637099</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>   Richmond</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>868 </th>\n",
        "      <td> 13077</td>\n",
        "      <td> 11.884147</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>     Coweta</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>876 </th>\n",
        "      <td> 13141</td>\n",
        "      <td> 14.085499</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>    Hancock</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>897 </th>\n",
        "      <td> 28051</td>\n",
        "      <td> 23.218691</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>     Holmes</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>905 </th>\n",
        "      <td> 13163</td>\n",
        "      <td> 21.735587</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>  Jefferson</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>907 </th>\n",
        "      <td> 13033</td>\n",
        "      <td> 25.841129</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>      Burke</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>908 </th>\n",
        "      <td> 28007</td>\n",
        "      <td> 20.135917</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>     Attala</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>914 </th>\n",
        "      <td> 13285</td>\n",
        "      <td> 14.665787</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>      Troup</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>924 </th>\n",
        "      <td> 01123</td>\n",
        "      <td> 13.789775</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td> Tallapoosa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>927 </th>\n",
        "      <td> 48063</td>\n",
        "      <td> 10.781671</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>       Camp</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>930 </th>\n",
        "      <td> 28163</td>\n",
        "      <td> 23.157459</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>      Yazoo</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>932 </th>\n",
        "      <td> 22017</td>\n",
        "      <td> 23.115310</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>   Louisiana</td>\n",
        "      <td>      Caddo</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>939 </th>\n",
        "      <td> 01065</td>\n",
        "      <td> 10.681022</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td>       Hale</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>951 </th>\n",
        "      <td> 01119</td>\n",
        "      <td> 15.771627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td>     Sumter</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>963 </th>\n",
        "      <td> 28099</td>\n",
        "      <td> 11.209663</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>    Neshoba</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>966 </th>\n",
        "      <td> 48459</td>\n",
        "      <td> 12.822755</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>     Upshur</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>968 </th>\n",
        "      <td> 28089</td>\n",
        "      <td> 20.025793</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>    Madison</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>969 </th>\n",
        "      <td> 48315</td>\n",
        "      <td> 35.392535</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>     Marion</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>978 </th>\n",
        "      <td> 48203</td>\n",
        "      <td> 15.944386</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>   Harrison</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>995 </th>\n",
        "      <td> 28149</td>\n",
        "      <td> 14.204454</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>     Warren</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1022</th>\n",
        "      <td> 01113</td>\n",
        "      <td> 20.412760</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td>    Russell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1033</th>\n",
        "      <td> 48365</td>\n",
        "      <td> 12.867529</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>     Panola</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1037</th>\n",
        "      <td> 22031</td>\n",
        "      <td> 18.139179</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>   Louisiana</td>\n",
        "      <td>    De Soto</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1058</th>\n",
        "      <td> 13259</td>\n",
        "      <td> 33.921303</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>    Stewart</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1059</th>\n",
        "      <td> 13307</td>\n",
        "      <td> 14.238929</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>    Webster</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1060</th>\n",
        "      <td> 13261</td>\n",
        "      <td> 10.217984</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>     Sumter</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1062</th>\n",
        "      <td> 01005</td>\n",
        "      <td> 24.236549</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td>    Barbour</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1093</th>\n",
        "      <td> 13239</td>\n",
        "      <td> 28.284542</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>    Quitman</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1101</th>\n",
        "      <td> 13243</td>\n",
        "      <td> 17.362920</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>   Randolph</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1112</th>\n",
        "      <td> 01099</td>\n",
        "      <td> 10.301238</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td>     Monroe</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1119</th>\n",
        "      <td> 01067</td>\n",
        "      <td> 13.070187</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td>      Henry</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1120</th>\n",
        "      <td> 13061</td>\n",
        "      <td> 18.763486</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>       Clay</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1134</th>\n",
        "      <td> 13095</td>\n",
        "      <td> 17.540724</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>  Dougherty</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1141</th>\n",
        "      <td> 13037</td>\n",
        "      <td> 17.491691</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>    Calhoun</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1184</th>\n",
        "      <td> 01003</td>\n",
        "      <td> 11.456795</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td>    Baldwin</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1288</th>\n",
        "      <td> 48473</td>\n",
        "      <td> 13.469374</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>     Waller</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1332</th>\n",
        "      <td> 48039</td>\n",
        "      <td> 10.220909</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>   Brazoria</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1391</th>\n",
        "      <td> 12099</td>\n",
        "      <td> 11.267840</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Florida</td>\n",
        "      <td> Palm Beach</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1399</th>\n",
        "      <td> 12011</td>\n",
        "      <td> 15.615793</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Florida</td>\n",
        "      <td>    Broward</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1401</th>\n",
        "      <td> 12087</td>\n",
        "      <td> 12.133105</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Florida</td>\n",
        "      <td>     Monroe</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "       FIPS       HR80  sig01  sig05  hot_01  cold_01   STATE_NAME      County\n",
        "199   21025  19.603231      1      1       1        0     Kentucky   Breathitt\n",
        "231   21193  37.516413      1      1       1        0     Kentucky       Perry\n",
        "240   21051  29.301453      1      1       1        0     Kentucky        Clay\n",
        "247   21131  38.077319      1      1       1        0     Kentucky      Leslie\n",
        "285   21095  20.689600      1      1       1        0     Kentucky      Harlan\n",
        "305   21013  19.419361      1      1       1        0     Kentucky        Bell\n",
        "712   28027  15.349333      1      1       1        0  Mississippi     Coahoma\n",
        "804   48387  10.351324      1      1       1        0        Texas   Red River\n",
        "831   13045  14.197991      1      1       1        0      Georgia     Carroll\n",
        "850   28015  10.229133      1      1       1        0  Mississippi     Carroll\n",
        "852   13189  23.365326      1      1       1        0      Georgia    McDuffie\n",
        "862   13245  19.637099      1      1       1        0      Georgia    Richmond\n",
        "868   13077  11.884147      1      1       1        0      Georgia      Coweta\n",
        "876   13141  14.085499      1      1       1        0      Georgia     Hancock\n",
        "897   28051  23.218691      1      1       1        0  Mississippi      Holmes\n",
        "905   13163  21.735587      1      1       1        0      Georgia   Jefferson\n",
        "907   13033  25.841129      1      1       1        0      Georgia       Burke\n",
        "908   28007  20.135917      1      1       1        0  Mississippi      Attala\n",
        "914   13285  14.665787      1      1       1        0      Georgia       Troup\n",
        "924   01123  13.789775      1      1       1        0      Alabama  Tallapoosa\n",
        "927   48063  10.781671      1      1       1        0        Texas        Camp\n",
        "930   28163  23.157459      1      1       1        0  Mississippi       Yazoo\n",
        "932   22017  23.115310      1      1       1        0    Louisiana       Caddo\n",
        "939   01065  10.681022      1      1       1        0      Alabama        Hale\n",
        "951   01119  15.771627      1      1       1        0      Alabama      Sumter\n",
        "963   28099  11.209663      1      1       1        0  Mississippi     Neshoba\n",
        "966   48459  12.822755      1      1       1        0        Texas      Upshur\n",
        "968   28089  20.025793      1      1       1        0  Mississippi     Madison\n",
        "969   48315  35.392535      1      1       1        0        Texas      Marion\n",
        "978   48203  15.944386      1      1       1        0        Texas    Harrison\n",
        "995   28149  14.204454      1      1       1        0  Mississippi      Warren\n",
        "1022  01113  20.412760      1      1       1        0      Alabama     Russell\n",
        "1033  48365  12.867529      1      1       1        0        Texas      Panola\n",
        "1037  22031  18.139179      1      1       1        0    Louisiana     De Soto\n",
        "1058  13259  33.921303      1      1       1        0      Georgia     Stewart\n",
        "1059  13307  14.238929      1      1       1        0      Georgia     Webster\n",
        "1060  13261  10.217984      1      1       1        0      Georgia      Sumter\n",
        "1062  01005  24.236549      1      1       1        0      Alabama     Barbour\n",
        "1093  13239  28.284542      1      1       1        0      Georgia     Quitman\n",
        "1101  13243  17.362920      1      1       1        0      Georgia    Randolph\n",
        "1112  01099  10.301238      1      1       1        0      Alabama      Monroe\n",
        "1119  01067  13.070187      1      1       1        0      Alabama       Henry\n",
        "1120  13061  18.763486      1      1       1        0      Georgia        Clay\n",
        "1134  13095  17.540724      1      1       1        0      Georgia   Dougherty\n",
        "1141  13037  17.491691      1      1       1        0      Georgia     Calhoun\n",
        "1184  01003  11.456795      1      1       1        0      Alabama     Baldwin\n",
        "1288  48473  13.469374      1      1       1        0        Texas      Waller\n",
        "1332  48039  10.220909      1      1       1        0        Texas    Brazoria\n",
        "1391  12099  11.267840      1      1       1        0      Florida  Palm Beach\n",
        "1399  12011  15.615793      1      1       1        0      Florida     Broward\n",
        "1401  12087  12.133105      1      1       1        0      Florida      Monroe"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[df['hot_01']>0]"
     ],
     "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>FIPS</th>\n",
        "      <th>HR80</th>\n",
        "      <th>sig01</th>\n",
        "      <th>sig05</th>\n",
        "      <th>hot_01</th>\n",
        "      <th>cold_01</th>\n",
        "      <th>STATE_NAME</th>\n",
        "      <th>County</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>199 </th>\n",
        "      <td> 21025</td>\n",
        "      <td> 19.603231</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>    Kentucky</td>\n",
        "      <td>  Breathitt</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>231 </th>\n",
        "      <td> 21193</td>\n",
        "      <td> 37.516413</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>    Kentucky</td>\n",
        "      <td>      Perry</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240 </th>\n",
        "      <td> 21051</td>\n",
        "      <td> 29.301453</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>    Kentucky</td>\n",
        "      <td>       Clay</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>247 </th>\n",
        "      <td> 21131</td>\n",
        "      <td> 38.077319</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>    Kentucky</td>\n",
        "      <td>     Leslie</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>285 </th>\n",
        "      <td> 21095</td>\n",
        "      <td> 20.689600</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>    Kentucky</td>\n",
        "      <td>     Harlan</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>305 </th>\n",
        "      <td> 21013</td>\n",
        "      <td> 19.419361</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>    Kentucky</td>\n",
        "      <td>       Bell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>712 </th>\n",
        "      <td> 28027</td>\n",
        "      <td> 15.349333</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>    Coahoma</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>804 </th>\n",
        "      <td> 48387</td>\n",
        "      <td> 10.351324</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>  Red River</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>831 </th>\n",
        "      <td> 13045</td>\n",
        "      <td> 14.197991</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>    Carroll</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>850 </th>\n",
        "      <td> 28015</td>\n",
        "      <td> 10.229133</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>    Carroll</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>852 </th>\n",
        "      <td> 13189</td>\n",
        "      <td> 23.365326</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>   McDuffie</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>862 </th>\n",
        "      <td> 13245</td>\n",
        "      <td> 19.637099</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>   Richmond</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>868 </th>\n",
        "      <td> 13077</td>\n",
        "      <td> 11.884147</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>     Coweta</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>876 </th>\n",
        "      <td> 13141</td>\n",
        "      <td> 14.085499</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>    Hancock</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>897 </th>\n",
        "      <td> 28051</td>\n",
        "      <td> 23.218691</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>     Holmes</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>905 </th>\n",
        "      <td> 13163</td>\n",
        "      <td> 21.735587</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>  Jefferson</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>907 </th>\n",
        "      <td> 13033</td>\n",
        "      <td> 25.841129</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>      Burke</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>908 </th>\n",
        "      <td> 28007</td>\n",
        "      <td> 20.135917</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>     Attala</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>914 </th>\n",
        "      <td> 13285</td>\n",
        "      <td> 14.665787</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>      Troup</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>924 </th>\n",
        "      <td> 01123</td>\n",
        "      <td> 13.789775</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td> Tallapoosa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>927 </th>\n",
        "      <td> 48063</td>\n",
        "      <td> 10.781671</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>       Camp</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>930 </th>\n",
        "      <td> 28163</td>\n",
        "      <td> 23.157459</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>      Yazoo</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>932 </th>\n",
        "      <td> 22017</td>\n",
        "      <td> 23.115310</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>   Louisiana</td>\n",
        "      <td>      Caddo</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>939 </th>\n",
        "      <td> 01065</td>\n",
        "      <td> 10.681022</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td>       Hale</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>951 </th>\n",
        "      <td> 01119</td>\n",
        "      <td> 15.771627</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td>     Sumter</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>963 </th>\n",
        "      <td> 28099</td>\n",
        "      <td> 11.209663</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>    Neshoba</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>966 </th>\n",
        "      <td> 48459</td>\n",
        "      <td> 12.822755</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>     Upshur</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>968 </th>\n",
        "      <td> 28089</td>\n",
        "      <td> 20.025793</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>    Madison</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>969 </th>\n",
        "      <td> 48315</td>\n",
        "      <td> 35.392535</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>     Marion</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>978 </th>\n",
        "      <td> 48203</td>\n",
        "      <td> 15.944386</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>   Harrison</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>995 </th>\n",
        "      <td> 28149</td>\n",
        "      <td> 14.204454</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Mississippi</td>\n",
        "      <td>     Warren</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1022</th>\n",
        "      <td> 01113</td>\n",
        "      <td> 20.412760</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td>    Russell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1033</th>\n",
        "      <td> 48365</td>\n",
        "      <td> 12.867529</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>     Panola</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1037</th>\n",
        "      <td> 22031</td>\n",
        "      <td> 18.139179</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>   Louisiana</td>\n",
        "      <td>    De Soto</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1058</th>\n",
        "      <td> 13259</td>\n",
        "      <td> 33.921303</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>    Stewart</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1059</th>\n",
        "      <td> 13307</td>\n",
        "      <td> 14.238929</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>    Webster</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1060</th>\n",
        "      <td> 13261</td>\n",
        "      <td> 10.217984</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>     Sumter</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1062</th>\n",
        "      <td> 01005</td>\n",
        "      <td> 24.236549</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td>    Barbour</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1093</th>\n",
        "      <td> 13239</td>\n",
        "      <td> 28.284542</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>    Quitman</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1101</th>\n",
        "      <td> 13243</td>\n",
        "      <td> 17.362920</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>   Randolph</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1112</th>\n",
        "      <td> 01099</td>\n",
        "      <td> 10.301238</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td>     Monroe</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1119</th>\n",
        "      <td> 01067</td>\n",
        "      <td> 13.070187</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td>      Henry</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1120</th>\n",
        "      <td> 13061</td>\n",
        "      <td> 18.763486</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>       Clay</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1134</th>\n",
        "      <td> 13095</td>\n",
        "      <td> 17.540724</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>  Dougherty</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1141</th>\n",
        "      <td> 13037</td>\n",
        "      <td> 17.491691</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Georgia</td>\n",
        "      <td>    Calhoun</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1184</th>\n",
        "      <td> 01003</td>\n",
        "      <td> 11.456795</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Alabama</td>\n",
        "      <td>    Baldwin</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1288</th>\n",
        "      <td> 48473</td>\n",
        "      <td> 13.469374</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>     Waller</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1332</th>\n",
        "      <td> 48039</td>\n",
        "      <td> 10.220909</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>       Texas</td>\n",
        "      <td>   Brazoria</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1391</th>\n",
        "      <td> 12099</td>\n",
        "      <td> 11.267840</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Florida</td>\n",
        "      <td> Palm Beach</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1399</th>\n",
        "      <td> 12011</td>\n",
        "      <td> 15.615793</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Florida</td>\n",
        "      <td>    Broward</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1401</th>\n",
        "      <td> 12087</td>\n",
        "      <td> 12.133105</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>     Florida</td>\n",
        "      <td>     Monroe</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "       FIPS       HR80  sig01  sig05  hot_01  cold_01   STATE_NAME      County\n",
        "199   21025  19.603231      1      1       1        0     Kentucky   Breathitt\n",
        "231   21193  37.516413      1      1       1        0     Kentucky       Perry\n",
        "240   21051  29.301453      1      1       1        0     Kentucky        Clay\n",
        "247   21131  38.077319      1      1       1        0     Kentucky      Leslie\n",
        "285   21095  20.689600      1      1       1        0     Kentucky      Harlan\n",
        "305   21013  19.419361      1      1       1        0     Kentucky        Bell\n",
        "712   28027  15.349333      1      1       1        0  Mississippi     Coahoma\n",
        "804   48387  10.351324      1      1       1        0        Texas   Red River\n",
        "831   13045  14.197991      1      1       1        0      Georgia     Carroll\n",
        "850   28015  10.229133      1      1       1        0  Mississippi     Carroll\n",
        "852   13189  23.365326      1      1       1        0      Georgia    McDuffie\n",
        "862   13245  19.637099      1      1       1        0      Georgia    Richmond\n",
        "868   13077  11.884147      1      1       1        0      Georgia      Coweta\n",
        "876   13141  14.085499      1      1       1        0      Georgia     Hancock\n",
        "897   28051  23.218691      1      1       1        0  Mississippi      Holmes\n",
        "905   13163  21.735587      1      1       1        0      Georgia   Jefferson\n",
        "907   13033  25.841129      1      1       1        0      Georgia       Burke\n",
        "908   28007  20.135917      1      1       1        0  Mississippi      Attala\n",
        "914   13285  14.665787      1      1       1        0      Georgia       Troup\n",
        "924   01123  13.789775      1      1       1        0      Alabama  Tallapoosa\n",
        "927   48063  10.781671      1      1       1        0        Texas        Camp\n",
        "930   28163  23.157459      1      1       1        0  Mississippi       Yazoo\n",
        "932   22017  23.115310      1      1       1        0    Louisiana       Caddo\n",
        "939   01065  10.681022      1      1       1        0      Alabama        Hale\n",
        "951   01119  15.771627      1      1       1        0      Alabama      Sumter\n",
        "963   28099  11.209663      1      1       1        0  Mississippi     Neshoba\n",
        "966   48459  12.822755      1      1       1        0        Texas      Upshur\n",
        "968   28089  20.025793      1      1       1        0  Mississippi     Madison\n",
        "969   48315  35.392535      1      1       1        0        Texas      Marion\n",
        "978   48203  15.944386      1      1       1        0        Texas    Harrison\n",
        "995   28149  14.204454      1      1       1        0  Mississippi      Warren\n",
        "1022  01113  20.412760      1      1       1        0      Alabama     Russell\n",
        "1033  48365  12.867529      1      1       1        0        Texas      Panola\n",
        "1037  22031  18.139179      1      1       1        0    Louisiana     De Soto\n",
        "1058  13259  33.921303      1      1       1        0      Georgia     Stewart\n",
        "1059  13307  14.238929      1      1       1        0      Georgia     Webster\n",
        "1060  13261  10.217984      1      1       1        0      Georgia      Sumter\n",
        "1062  01005  24.236549      1      1       1        0      Alabama     Barbour\n",
        "1093  13239  28.284542      1      1       1        0      Georgia     Quitman\n",
        "1101  13243  17.362920      1      1       1        0      Georgia    Randolph\n",
        "1112  01099  10.301238      1      1       1        0      Alabama      Monroe\n",
        "1119  01067  13.070187      1      1       1        0      Alabama       Henry\n",
        "1120  13061  18.763486      1      1       1        0      Georgia        Clay\n",
        "1134  13095  17.540724      1      1       1        0      Georgia   Dougherty\n",
        "1141  13037  17.491691      1      1       1        0      Georgia     Calhoun\n",
        "1184  01003  11.456795      1      1       1        0      Alabama     Baldwin\n",
        "1288  48473  13.469374      1      1       1        0        Texas      Waller\n",
        "1332  48039  10.220909      1      1       1        0        Texas    Brazoria\n",
        "1391  12099  11.267840      1      1       1        0      Florida  Palm Beach\n",
        "1399  12011  15.615793      1      1       1        0      Florida     Broward\n",
        "1401  12087  12.133105      1      1       1        0      Florida      Monroe"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[df['cold_01']>0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 63 entries, 6 to 1286\n",
        "Data columns (total 8 columns):\n",
        "FIPS          63  non-null values\n",
        "HR80          63  non-null values\n",
        "sig01         63  non-null values\n",
        "sig05         63  non-null values\n",
        "hot_01        63  non-null values\n",
        "cold_01       63  non-null values\n",
        "STATE_NAME    63  non-null values\n",
        "County        63  non-null values\n",
        "dtypes: float64(5), object(3)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 63 entries, 6 to 1286\n",
        "Data columns (total 8 columns):\n",
        "FIPS          63  non-null values\n",
        "HR80          63  non-null values\n",
        "sig01         63  non-null values\n",
        "sig05         63  non-null values\n",
        "hot_01        63  non-null values\n",
        "cold_01       63  non-null values\n",
        "STATE_NAME    63  non-null values\n",
        "County        63  non-null values\n",
        "dtypes: float64(5), object(3)"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df[df['cold_01']>0].to_string() "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "       FIPS       HR80  sig01  sig05  hot_01  cold_01      STATE_NAME        County\n",
        "6     24001   3.724487      1      1       0        1        Maryland      Allegany\n",
        "8     24023   5.031826      1      1       0        1        Maryland       Garrett\n",
        "10    54061   4.443023      1      1       0        1   West Virginia    Monongalia\n",
        "11    54077   1.094331      1      1       0        1   West Virginia       Preston\n",
        "14    54103   4.571638      1      1       0        1   West Virginia        Wetzel\n",
        "15    24021   3.194183      1      1       0        1        Maryland     Frederick\n",
        "16    54065   6.224131      1      1       0        1   West Virginia        Morgan\n",
        "20    54095   0.000000      1      1       0        1   West Virginia         Tyler\n",
        "23    54073   0.000000      1      1       0        1   West Virginia     Pleasants\n",
        "24    54033   6.005233      1      1       0        1   West Virginia      Harrison\n",
        "26    54017   4.484506      1      1       0        1   West Virginia     Doddridge\n",
        "27    54091   2.009969      1      1       0        1   West Virginia        Taylor\n",
        "29    54085   2.913244      1      1       0        1   West Virginia       Ritchie\n",
        "36    51107   4.063129      1      1       0        1        Virginia       Loudoun\n",
        "44    54105   6.772315      1      1       0        1   West Virginia          Wirt\n",
        "46    21015   1.454270      1      1       0        1        Kentucky         Boone\n",
        "51    54097   4.268579      1      1       0        1   West Virginia        Upshur\n",
        "52    54021   3.999680      1      1       0        1   West Virginia        Gilmer\n",
        "55    21117   6.080151      1      1       0        1        Kentucky        Kenton\n",
        "66    54007   7.197351      1      1       0        1   West Virginia       Braxton\n",
        "68    21191   3.033336      1      1       0        1        Kentucky     Pendleton\n",
        "75    21081   0.000000      1      1       0        1        Kentucky         Grant\n",
        "82    54075  10.081661      1      1       0        1   West Virginia    Pocahontas\n",
        "105   21209   1.528141      1      1       0        1        Kentucky         Scott\n",
        "118   21073   4.781257      1      1       0        1        Kentucky      Franklin\n",
        "120   21211   4.286694      1      1       0        1        Kentucky        Shelby\n",
        "122   21011   6.650042      1      1       0        1        Kentucky          Bath\n",
        "141   21005   0.000000      1      1       0        1        Kentucky      Anderson\n",
        "149   21027   7.907795      1      1       0        1        Kentucky  Breckinridge\n",
        "169   21059   5.429576      1      1       0        1        Kentucky       Daviess\n",
        "192   21183   6.126043      1      1       0        1        Kentucky          Ohio\n",
        "238   51121   6.822428      1      1       0        1        Virginia    Montgomery\n",
        "248   21001   2.188232      1      1       0        1        Kentucky         Adair\n",
        "294   40003   9.420187      1      1       0        1        Oklahoma       Alfalfa\n",
        "310   51035   4.889378      1      1       0        1        Virginia       Carroll\n",
        "321   40153   9.446439      1      1       0        1        Oklahoma      Woodward\n",
        "339   40103   2.880267      1      1       0        1        Oklahoma         Noble\n",
        "346   40047   7.959249      1      1       0        1        Oklahoma      Garfield\n",
        "347   47073   6.856986      1      1       0        1       Tennessee       Hawkins\n",
        "351   37171   4.485638      1      1       0        1  North Carolina         Surry\n",
        "387   05007   4.267213      1      1       0        1        Arkansas        Benton\n",
        "398   47057   7.959724      1      1       0        1       Tennessee      Grainger\n",
        "415   05055   7.589557      1      1       0        1        Arkansas        Greene\n",
        "522   47003   8.358409      1      1       0        1       Tennessee       Bedford\n",
        "611   48129   8.179959      1      1       0        1           Texas        Donley\n",
        "620   05127   6.883497      1      1       0        1        Arkansas         Scott\n",
        "684   48191   5.958765      1      1       0        1           Texas          Hall\n",
        "746   48101   0.000000      1      1       0        1           Texas        Cottle\n",
        "822   48125   9.418857      1      1       0        1           Texas       Dickens\n",
        "825   48009   0.000000      1      1       0        1           Texas        Archer\n",
        "875   48237   4.499640      1      1       0        1           Texas          Jack\n",
        "889   48503   3.493511      1      1       0        1           Texas         Young\n",
        "1050  48093   5.283876      1      1       0        1           Texas      Comanche\n",
        "1090  48229   0.000000      1      1       0        1           Texas      Hudspeth\n",
        "1091  48141   8.821301      1      1       0        1           Texas       El Paso\n",
        "1131  48451   7.863119      1      1       0        1           Texas     Tom Green\n",
        "1239  48267   8.204118      1      1       0        1           Texas        Kimble\n",
        "1242  48435   0.000000      1      1       0        1           Texas        Sutton\n",
        "1246  48043   4.401602      1      1       0        1           Texas      Brewster\n",
        "1252  48377   6.425084      1      1       0        1           Texas      Presidio\n",
        "1284  48137   0.000000      1      1       0        1           Texas       Edwards\n",
        "1285  48465   3.712986      1      1       0        1           Texas     Val Verde\n",
        "1286  48265   5.791059      1      1       0        1           Texas          Kerr\n"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hot_01"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "0     0\n",
        "1     0\n",
        "2     0\n",
        "3     0\n",
        "4     0\n",
        "5     0\n",
        "6     0\n",
        "7     0\n",
        "8     0\n",
        "9     0\n",
        "10    0\n",
        "11    0\n",
        "12    0\n",
        "13    0\n",
        "14    0\n",
        "...\n",
        "1397    0\n",
        "1398    0\n",
        "1399    1\n",
        "1400    0\n",
        "1401    1\n",
        "1402    0\n",
        "1403    0\n",
        "1404    0\n",
        "1405    0\n",
        "1406    0\n",
        "1407    0\n",
        "1408    0\n",
        "1409    0\n",
        "1410    0\n",
        "1411    0\n",
        "Name: hot_01, Length: 1412, dtype: float64"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "array([ 6.59772049,  3.21367741,  7.6017962 , ...,  4.12783918,\n",
        "        5.2803488 ,  3.00003   ])"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w = ps.queen_from_shapefile('data/south.shp')\n",
      "w.transform = 'r'\n",
      "wy = ps.lag_spatial(w,y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(y,wy, alpha=0.5, lw=0)\n",
      "vlines(y.mean(),wy.min(),wy.max())\n",
      "hlines(wy.mean(),y.min(),y.max())\n",
      "xlabel('y')\n",
      "ylabel('wy')\n",
      "lm = ps.spreg.OLS(wy[:, None], y[:, None])\n",
      "plot(y, lm.predy, color='red')\n",
      "title('Moran Scatter Plot')\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEZCAYAAACaWyIJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXl4lOXZPnzOZN8XsgIJSdgJOyogO4g7SkVRq6JC9a3+\n2rcWW5dqX7GfVexiW22rtkXBDcUq7qICBgUUZDMJZIGQfU8m2ySZ/fn+OLlzz0wmG5KF5D6PY47M\n8iz380zmvPbr0mmapkFBQUFBYchC398LUFBQUFDoXyhBoKCgoDDEoQSBgoKCwhCHEgQKCgoKQxxK\nECgoKCgMcShBoKCgoDDEoQSBgsIghl6vx+nTp/t7GQoDHEoQKPQqkpKS4Ofnh9raWpf3Z8yYAb1e\nj6Kion5amYTFYsH999+PhIQEhISEIDk5Gb/85S9/8HGTkpKwe/futtcFBQXQ6/VwOBw/+NjuxwwJ\nCWlb+9NPP93j42zevBkLFiw4Z+tSOL+gBIFCr0Kn0yElJQVbt25tey8jIwOtra3Q6XRndUy73X6u\nlgcAeOqpp3DkyBF89913aGpqQlpaGmbNmvWDj6vT6eCpXvNsazhtNluHnzU0NKCpqQlbt27F7373\nO3z++edndQ6FoQklCBR6HbfeeiteeeWVttdbtmzBmjVrXAixoaEBa9asQUxMDJKSkvD73/++7fPN\nmzdj3rx5WL9+PaKiovD444/j9OnTWLp0KaKiohAdHY1bb70VDQ0NbcdLSkrCn//8Z0ybNg3h4eG4\n6aabYDabPa7v0KFDWLlyJeLi4gAAo0aNwq233tr2eXFxMa677jrExMQgKioKP//5zwEAeXl5Ha7h\ntttuQ1FREVasWIGQkBD88Y9/xKJFiwAA4eHhCAkJwYEDBwAAL730EiZNmoTIyEhcfvnlLlaSXq/H\nP//5T4wdOxbjx4/v8l7PmTMHqampyMzMbPdZR/c4KysL99xzD7755huEhIQgMjKyy/MoDDJoCgq9\niKSkJG3nzp3a+PHjtaysLM1ms2kjR47UCgsLNZ1OpxUWFmqapmm33XabtnLlSs1oNGoFBQXauHHj\ntE2bNmmapmkvv/yy5u3trf3973/X7Ha71traqp06dUrbuXOnZrFYtOrqam3hwoXafffd53Le2bNn\na+Xl5ZrBYNAmTpyovfDCCx7X+MQTT2iJiYnaP//5Ty09PV1zOBxtn9lsNm3q1Kna+vXrtZaWFs1k\nMml79+7VNE3r1hp27drV9rqgoEDT6XSa3W5ve++9997TxowZo2VnZ2t2u1174okntIsvvrjtc51O\np1166aVaXV2dZjKZ2q09Pz9f0+l0ms1m0xwOh7Z3714tMDBQ2717d9v+eXl5Xd7jzZs3a/Pnz+/O\nV6owCKEEgUKvQgiCJ554Qnv44Ye1Tz/9VLv00ks1m83WJghsNpvm6+urZWVlte334osvaosXL9Y0\njYIgMTGx0/Ns375dmzFjhst5X3/99bbXDzzwgPbTn/7U4752u137xz/+oc2bN0/z8/PThg8frm3Z\nskXTNE3bv3+/Fh0d7ULePVmDsyAQpO18rMsvv7yNjMVaAgMDtaKiIk3TSORffvllh+cUxwwPD9ci\nIiK0iRMnas8991zb50IQdOceK0EwdOHd3xaJwuCHTqfDbbfdhgULFiA/P7+dW6impgZWqxWjRo1q\ney8xMRGlpaVtrxMSElyOWVlZiV/84hfYu3cvmpqa4HA42rk0hKsHAAICAlBWVuZxfXq9Hvfeey/u\nvfdemM1mbNq0CWvXrsVFF12E4uJijBo1Cnp9ey9qd9bQFQoLC/GLX/wC999/v8v7paWlbdfsfu2e\nUFtb63GNAt25xwpDFypGoNAnSExMREpKCj799FNcd911Lp9FRUXBx8cHBQUFbe8VFRVh5MiRba/d\nA8u/+c1v4OXlhczMTDQ0NODVV1/tNBunu4FpPz8/3HvvvYiIiEBWVhYSExNRVFTkMUDd1Rrcz+lp\nDYmJifjXv/6Furq6tkdzczPmzJnT47V3hq7u8bk4h8L5CyUIFPoMmzZtwu7duxEQEODyvpeXF1av\nXo1HHnkERqMRhYWF+Mtf/uISsHWH0WhEUFAQQkNDUVpaij/+8Y+dnlvrJFPnb3/7G/bs2YPW1lbY\nbDZs2bIFRqMRM2bMwEUXXYT4+Hg89NBDaGlpgclkwv79+7u1htjYWOTl5bW9jo6Ohl6vd3nvpz/9\nKZ588kmcOHECAAO6b7/9dqfXcjbo6h7HxsaipKQEVqv1nJ9bYeBDCQKFPkNKSgpmzpzZ9tpZC33u\nuecQFBSElJQULFiwALfccgvuvPPOtu3cNdbHHnsMR44cQVhYGFasWIFVq1Z1qtV6OoZAYGAg7r//\nfsTHxyM6OhrPP/883nnnHSQlJUGv1+PDDz/EqVOnkJiYiISEBGzbtq1ba3j44YfxxBNPICIiAs88\n8wwCAwPxyCOPYN68eYiIiMDBgwexcuVKPPjgg7jpppsQFhaGKVOm4LPPPvN4jzq7tu581tk9XrZs\nGVJTUxEXF4eYmJguz6kwuKDTOlOVzgFMJhMWLVoEs9kMi8WCa6+9Fk899RQMBgNuvPFGFBYWIikp\nCdu2bUN4eHhvLkVBQUFBwQN6XRAAQEtLCwIDA2Gz2TB//nz86U9/wgcffICoqCg88MADePrpp1FX\nV4eNGzf29lIUFBQUFNzQJ66hwMBAACzlt9vtiIiIwAcffIDbb78dAHD77bfjvffe64ulKCgoKCi4\noU8EgcPhwPTp0xEbG4slS5YgNTUVlZWViI2NBcBAVWVlZV8sRUFBQUHBDX1SR6DX63Hs2DE0NDTg\nsssuw5dffunyeWeBPAUFBQWF3kWfFpSFhYXhqquuwuHDhxEbG4uKigrExcWhvLzcY6bCmDFjXFLt\nFBQUFBS6xujRo3Hq1Klub9/rrqGamhrU19cDAFpbW/HFF19gxowZuOaaa7BlyxYAbEK2cuXKdvvm\n5eVBYxuMQfl47LHH+n0N6vrUtanrG3yPnirQvW4RlJeX4/bbb4fD4YDD4cBtt92GZcuWYcaMGVi9\nejU2bdrUlj6qoKCgoND36HVBMGXKFBw5cqTd+5GRkdi5c2dvn15BQUFBoQuoyuJ+xOLFi/t7Cb2K\nwXx9g/naAHV9Qw19UlB2tuhowpOCgoKCQsfoKXcqi0BBQUFhiEMJAgUFBYUhDiUIFBQUFIY4lCBQ\nUFBQGOJQgkBBQUFhiEMJAgUFBYUhDiUIFBQUFIY4lCBQUFBQGOJQgkBBQUFhiEMJAgUFBYUhDiUI\nFBQUFIY4+nQwjYKCOxwOYN8+oLQUGDUKmDMHUMPqFBT6FkoQKPQrvvoKSEvj8+xsCoE5c/p1SQrn\nGZqagM8+A4xGYPp0PhR6BiUIFPoVRUXtXytBoNATbNsGFBfzeWEhEB4OJCX165LOO6gYgUKvYsOG\nDZ1+PmKE6+uRI3tvLQqDE+Xl8rmmARUV/beW8xVqHoFCr6Kr79BuB/bsAcrKGCOYP1/FCBR6htdf\nB06e5HO9HvjJT4Dhw/t3Tf2NnnKnEgQKvQr1HSr0NsxmxpqMRmDqVGD06P5eUf9DCQKFAQX1HSoo\n9D3UhDIFBQUFhR5BCQIFBQWFIQ4lCBTOGvX1QHU1MzUUFBTOX/S6ICguLsaSJUuQmpqKyZMn49ln\nnwXAtMKRI0dixowZmDFjBnbs2NHbS1E4h/jqK+CvfwX+8Q/gv/9VwkBB4XxGrweLKyoqUFFRgenT\np8NoNGLWrFl47733sG3bNoSEhGD9+vUdL04FGgckWluBP/zBlfxvvx1ITm6/rfoOFRT6Hj393fV6\nZXFcXBzi4uIAAMHBwZg4cSJKS0sBQBHEeQz3r059lQoK5y/6NEZQUFCAo0ePYs6ZHgLPPfccpk2b\nhnXr1qG+vr4vl6LwAxAQACxcKF+PH+/ZGlBQUDg/0Gd1BEajEYsXL8ajjz6KlStXoqqqCtHR0QCA\n3/72tygvL8emTZtcF6fT4bHHHmt7vXjxYixevLgvlqvQDdTUAFYrEBfXcTWwcg0pKPQ+0tLSkCa6\nNwJ4/PHHB15BmdVqxdVXX40rrrgC9913X7vPCwoKsGLFCmRkZLguTpHIeQ/1HSoo9D0GXEGZpmlY\nt24dJk2a5CIEyp06RW3fvh1Tpkzp7aUoKCgoKHhAr1sEe/fuxcKFCzF16lTozvgPnnzySWzduhXH\njh2DTqdDcnIyXnzxRcTGxrouTmmT5z3Ud6ig0PdQvYYUBhTUd6ig0PcYcK4hBQUFBYWBDSUIFBQU\nFIY41KjKIYqMDMBgAMaMaT8lTEFBYWhBxQiGIL78klPBAMDLC7jjDiAhoXfOpb5DBYW+h4oRKHSJ\nzEz53G4HsrP7by0KCgr9DyUIhiDCw11fh4X1zzoUFBQGBpQgGIK45hoOig8OBi64gA8FBYWhCxUj\nUOhVqO9QQaHvoWIECgoKCgo9ghIECgoKCkMcShAoKCgoDHGogjKFTlFbC7z1Fv9OmABcdx1rDxQU\nFAYPlCBQ6BQffghUVfH58eOsQr74Yr7WNKC0FPD25nCa8wmHDwM5OUBkJLB0KeDr298rUlDoPyhB\noNApjEbX183N/KtpwJtvkkwBYPZs4Ior+nZtZ4vjxyngBFpbgR/9qP/Wo6DQ31AxAoVOMXMmYDIB\nJ08Cp0/LVhRFRVIIAMCBA0BTU/+s0RkOB1BSAlRWdrxNSUnnrxUUhhqUIFDoFBdcAOj1QGAg3T+f\nfAKYzXzPHR3NLe4rOBzA1q3Af/4DPP88sHOn5+3c+yr1Vp+lgQazma48YdUpKAgo15BCp6itpf9c\ndChtbOTQ+oQEYMYM4OhRvr9kCSuV+xP5+bRcBPbuBebNAwICXLebNAm49lpaNMOGAYsX9+ky+wX1\n9cDLLwMNDfw+f/xjICmpv1elMFCgBMEgh8MB2GxnHwwNCwP8/KhNAnweEcHn114LLFzILKLQ0HOz\n3h8CdytFp+vYSpkxg4+hgm++oRAAAIuFHWjvvLN/16QwcKAEwSBGbi7w3//yhz9tGrByZc/dN4GB\nwC23kDg0jZp/YKD8XAiFgYCkJGDKFM5a0OmAZcsAf//+XtXARH+78RQGFlSvoUGMp59mRozAjTcC\nEyf27Rr64zs0GJjSOhCslIGChga6hurradXdcguQmNjfq1LoLfT0d6csgkEKh0O6cwTcXw9WREb2\n9woGHsLCgHvvpZAMC2sfN1EY2lBZQ/2AwkLmsaelAVZr75xDrwfmzpWvhw0Dxo/vnXP1BMePA+++\nC3z1FYfiKPQdfH2Z+aWEgII7lEXQx6ioAF55RZJgRQVw0029c67lyzmTuKUFGD26b/3le/eS7AHG\nKsaNY5bO22/LbZqagKuu6rs19RZaW1lrER6ufO8K5yd63SIoLi7GkiVLkJqaismTJ+PZZ58FABgM\nBixfvhzjxo3DpZdeivr6+t5eyoBAQYGrJpyX17vnS04GUlP7RgiYzcC+fQxQf/IJg9QAX9tsTO90\nhvvrgQKrFfjoI+Bf/wJ27JDfV309C9WcXa+ZmcCf/gT87W/A668rK0fh/ESvWwQ+Pj74y1/+gunT\np8NoNGLWrFlYvnw5Xn75ZSxfvhwPPPAAnn76aWzcuBEbN27s7eX0O2JiOn99vsLhoKVTWso6g4MH\ngaAgflZZSaHg3o8oPr7v19kd7NoFHDrE52VldKX4+gKff04hMGYM8/D1egoMQf6nTtH1NXVq/61d\nQeFs0OuCIC4uDnFnGCA4OBgTJ05EaWkpPvjgA+zZswcAcPvtt2Px4sVDQhCkpABXX81CrOBg4Mor\nz+3xNY1FVRYLMHYsM0T6AvX1FAICBoN0k1RVkUynT2dVa04OEBUFXHppz89jtQLffsvjTJ0KDB/u\neTtNY6ZMQEDP74FosidQUcE1C0vg1Cm6uyZMaG8B2Gw9O5eCwkBAn8YICgoKcPToUcyePRuVlZWI\njY0FAMTGxqKys+Ywgwy9OSf4vfeA77/n85gY4Cc/6ZvOmkFBPI/FQnIcPpx5/QcPMj5gswE+Pqz0\nnTfP8zFaWujC8tS+QuCdd4DsbD4/fBi4+24gOtp1G6uVbpqCAp7zhhu4hu5i9Gj2VRJITnbtqwRI\nobB0KfDZZ3weF0c3nILC+YY+EwRGoxGrVq3C3/72N4SEhLh8ptPpoOsgyrZhw4a254sXL8biodAP\noBs4eJABWV9fWhhJSQxaCiEAULPNz++bbCE/P9Yp7NhBoRAcTGIHmM/v49PxviYT8NprbP4WHMwc\n947cRs4tJKxWkr27IDh2jO+LbT7+uGtBYDAA+/fTipk3jwKptBQYNYrFeDYb8MUXFAApKfJ4c+dS\ncDQ3AyNHdn6dCgq9hbS0NKSlpZ31/n0iCKxWK1atWoXbbrsNK1euBEAroKKiAnFxcSgvL0dMB85y\nZ0GgQJSXA59+KrXSN98Efv1rkpC3t6t7oi8zhUaPBv7f/+PzfftYwASwP9GBA2xV7Qnffis7gBqN\nvLa1az1vO2yYq+smKqr9Nj1115hMXKvonnryJHPuZ82S21x8MV1BZjNwxpBtw2CJ8yicv3BXkh9/\n/PEe7d/rWUOapmHdunWYNGkS7rvvvrb3r7nmGmzZsgUAsGXLljYBodA1GhpcM1dMJhKUtzf76vv4\nULO9+GJqtB2hro4EnZV19ms5fJgB04wM1/dLSqghA1xLbm7Hx3AvdBPZRp5w003UyGNjOf8gObn9\nNlOnUmCIcy9Z4vlYmZms5cjIcG2hXV9PC8EZTU285yEhwKZNwO9+x6wi93kNAlYr8N13vL8mU8fX\no6AwENDrFsG+ffvw2muvYerUqZhxpsvXU089hYceegirV6/Gpk2bkJSUhG3btvX2UgYNEhPpbmls\n5OuUFNn/JzWV3TUdjs5HShoMJDJBUhdf3D5429gIVFdT43Xz5gGg1v/FF3x+6BA1b9HIzV1T96S5\nC8yaRXdOa6sUYHa75/VHRgJr1nR8LID34u676doJCWnvOgJY47B7N5/bbBQ+4h4GBLAmQODbbxkH\n0DSu0d+f6ywrY6trdx3G4QBefZUzGwDem7vvdnUbWSwUlh2tT0GhL9HrgmD+/PlwOBweP9vZUcN4\nhU4RGMggcHo6YwQzZ7p+rtN1PVc4K8tVUz161FUQFBeTzCwW+v/XrJGtqAVOnXJ9nZcnBcHo0cCZ\npDCkprIBXEeIiqIrpqSEmvQrr/B5airw8MO0dHoKPz8KyI6QmSmfe3vT569pvHdLl0qXmtUqhYC4\nxuHDZRsL515OAnV1UggAFKZlZdI6a22lVVFTw/NdcQVw0UU9v8ZzAYMB+OADCv2pU4dGS26F9lCV\nxecpQkOB+fPPfn93Dd/99f790kUjCsVWr3bdJjratShMaLbV1czaEdk/ZnPXmUshIWyI99vfyhz+\nigq6eO69t/vX1V2Eh7vGGsaOdW3JIaBprm644cNlWqxe7xpHEAgMdI3V6HSusxrS0ykExPHT0vpP\nELzzjkz7TUuj9TdpUv+sRaH/oATBEMWUKex5lJ5OoeI+s1dYFBUVJC1No3bs7N5YvpwuHKHtCsFU\nWOjaQykvT2rb1dWMcYwY4bnnjXMtAiCzf7oDh4PtsouKGJ9YurRjy+jqq4Ht27meceM6DmT7+gKL\nFknr5oILaN1UVlIouBfJAbyuVatYXW23c3sRswDaWzhdWW+9CfdYiPtrhaEBJQiGKHQ6YMUKPjxh\nyRJq5tnZJMPjx5mZdNttUgv28fG8v3sWTXQ0tePjx4H336dQCAsD1q1r3yp69myZw+/t3bPhMV9/\nzQdAYeTlRWHgCaGhwO23d++4S5bQTWWxkPz1+vZuMndMnNhxy++UFJ7fYKDQ6M9+SxMmyClz3t60\njBSGHpQgUPAIMcJR0xgLqKlhhsyHH7JOYMKEjvdNTASuuYYZRQB95r//PY+TnEyCbmggAS1a5Lrv\nXXfRjfL99/TDe3K9CJw6RWsjJobrefddZidFRLANRHn5D74NbThXKaL5+ZyrbDYzDrF2bf+22lix\ngudvbKRLyD01VmFoQAmCIYCjRyVhzp/feeWuMxITqbGKFEmhvZeWdi4IAAawZ85ktoxwE9XV0YoQ\nQVNPQWC9Hrj8cgaLW1uBN94ALrmkfTwkN5eEKvz3H37Idba2ysyeyy7r3nX2JdLSaFnodLSSjhxx\ntQisVpmKO2VK7xeo6fX9F59QGDhQgmCQ4/vv6Y4RMJm63+Nn0iT60uvqaBGILJyeTray2eiLDw+X\nxJ2QAFx4oeftMzNds3EOHnQVBA4Hs56cg7jC2tDraW1MnMg01IEGZyFsNMoUYIDxhFdeYcYWQCFx\n5539G0M4VzCZ6Gq022nlOQfPzwYOB12NFgv/T3ttxkJpKVPXXn2VJqd7MG2QQAmCQY7CQtfXPQm+\nAgyOTptGTdZg4I+up37kY8dIet7edBmtWcMYgabxx2yzkbhFZpHzTGT314cOsY1FURFJJSGB748d\ny9cjRvBxzTVdzwZoaWGgOyLCNZjbm1i2jBlVR4+ycM3Hh5bWVVdRWAohANAqqqoauF1auwuHA9iy\nRbrqjh0DfvrT9s0ACwtZuJecLDvXdoRt22TPqW++oUvxnDRY1DSmUv3sZ8wIEEhK6txPeZ5DCYJB\nDncS6ahbZ2fw8WGGUE+wY4eMEcTH8/dlNpP4RVmJ+4953Tqea+ZM+tKzskiS117LbZqamInjcPCY\neXnUBJOT6evOzeVvNyWFMYLOUFfHXH6jkVr6qlV90zBu5EhWR9fUUHh5ebECec4cCjy9Xt4fvb69\nUDwfYTC4xmvq6piN5lz17lzgFxpKYvdUxAiwr5P4vwF4LwsLe9ZY0AVVVcCjjwL//rfr+3fdBTzx\nxJDoIaIEwSDHBRfQLD99mv/Pl1zSe+fSNKC2liS+bZtMBc3OpoZnMpHc/vtfruvwYan5VVRQG05J\nITmuXk1CdHalmEySJHU6kv2tt8oMnmnT5Pk++4xCb8oUz2s9dEjGPhwOElFfdQ719+d1V1bymiIj\neT2hobRkduzgdpddRsvpfEdwsOxMC/A7dc8W++Yb+byxkQrDnDmej+frS4XBOUW5KwvCBZrGpljP\nP+/6fmws8Pe/UysYYqPmlCAY5NDpgAUL+OhNOBzAW2+xXXN2NrVz8WNvbJQN8ZKSaHnn5VEQjB1L\nMiwr4zZ33CGJXQgBh4PXERXFimUx1S0xsX0ef3o6XbkCLS2eawTcg7BnU718toiN5flEjye7XVZ5\nT5/Ox2CCvz8F+2ef8VqXLqU7zn0b57hQZ24eHx9y9QcfUBgsWNB1Oi9On+Y/jyfk5g75vFklCBR+\nECoqqE2XlFCjDwriD7q6WgYEU1Komev1NOFtNv7Qx48nGVosJMeWFloSv/ylPP6ePXx4eTFw/eMf\nU9BoGjOX3AOp7nMDcnI8C4LZs1lzcPgwBdaFF7Ktd0rK2bnPeorgYJmtExjI+9AX5+0vjBnTubvu\n2mupSLS2Mg4lrLuOMGFC15lrePRR5i2745JLaHYNhij8OYISBEMIdjszcux2ukF+aHDNZGKWS24u\nNfHqahaP+fqS9MUY6vnzaebv3UtXR2oqP4+Npd+/uJh+40OH6DISVcjl5awUBmgVfPABhUdnLhz3\noK/za5uNQqW6mscTrR4KC2llLFxIUl6zpvOurWI9H31EoRQRQQ1V9B/qDsLD5f0BBocL6IcgKQl4\n4AFq+Gc9SKmsrGPTYNeujqsLFXq/DbXCwICmMe9++3YS6ksvufpYzwYGAwn81CkGbUVHTodDVuAC\ndA0lJgIPPgj84Q+yWtjfn8JA/PDNZloUwj3r7CpoauKcgAMH6NsXQ2/csXAhSbyykgqfcxO1HTto\nBWRnU/vMz6eLprmZpFxXJ4WlOzSNqbhffknBtW0bB940NDAW8sEHPbt3117LjKfgYAq/QZyQ0m3o\ndGchBH70I+6o07kKgVmz+A8lmkUpIdAplEUwRNDQ4NottLJSBme7iy+/pDDR61koNmkSrQqHQw5s\ncTho1ufkSEFTX0/XUUICf+h33EEyDwgA/vlPknF1NX2/zl1KExMpUHJy5IznX/+aAmLcODajc+9q\najCQmGNjSep//jPX6OfnOnPA318OsPH2ptAQFpJ7IBNgRotoX/Hii7y22lqmsc6axfvbE0REMEvq\nfMCpUxwWZLez3UZXbpteRU5Oxz6h555j2qdCj6EEwRCBv3/76WWBgSRwTy4ik4lB1+JiWfz15z/L\nTJuHHqIwiInhMYxGktvkyUztzMyUmn16OgnknXe4hkWLZL//FSuonQcEMJbnrBl7e7Og6rXXuJ68\nPGb6Wa3MRf/Nb5j4MXMmz5+WJl1UERG0RI4do5XQ2sp4YVISBdno0byunBy6j5KS6NbqqAvpgQM8\nnr+/7Iqq1/O49fWdt9nuCI2NFK5mM+MFSUk9P0Zvw2ym9SMyft5/nymwfVV3AUCmmHlCdXXnwy4U\nugUlCIYI/P1pRX/0EYXBzJkk4Lo6umMmTSIRXnABCW73bjlVLDeX/vq6OmrOej0L03btoosmMJDB\nYH9/+vB/9jMSyMGD3H/4cOC99yTRFRYye8/LizUAv/41t/eUM+/jwzUVFpK0bTa6csxmPt+yhRwh\nXDMjRnC7Cy5of8xRo/h+UxMJvzutFRobmVH4/vtcb0wMBdywYVy7wcDBNB1NQesIBQUUrBYLM59y\nc4F77uljgu0GmptdJ8Y5HLwnvbrO0lI53s4dV13Ff2KFcwolCPoJFRV0QfRlwVBqqgy0bt1KYm9u\nZtbP998zq6O0lALD2dVhsbDdQXU1yTcmhtp6SAg1YuGGbW2lC+i113hM4eNPT6dbJyaG12swkIyF\nVeDl1fF90DQKmaoqCqP0dB5Xr+f++/ZRAAm3j8FAbV8Upo0YIYls0iTm6Tvj+HHeh3Hj2tcN2e3A\nf/4DfP45jye6hS5bxuu3WoErr+y5+zk/nzEa0VPIZKJQKS8feIIgPJycLGZKR0T0UnbT2LHtJx0J\nFBb2vK+JQo+gBEEfw2IB7rsPOHGC5LJ+PSdUAcxg+eILarJz51K77i2IvHWDgVqecBllZXE9I0cy\nqKrTkQQC+nXnAAAgAElEQVTCwljgdOwYny9dyn2sVq731CmSY2kpr8HLS1aTBgeTQIuK6N4NDe26\n10x2Nq0Iq5VZR2IGQGMjXUAABYCXFx9BQRRqVis54+67qWkbjTyO2UwhWFEhaw927ZJ+/z17OPXN\nufum0Sj783t7y6EtN99MYSOym9xht1NwBQZ6zgbKyZHCr6WFAnbsWM+zDfobej1bjx85Ii3Jc9LK\nobMMnylTKPEV+gxKEPQxtm/n/3hLC8ngH/8g8WoaNWmRUlhczH4svTXPds4cniMggOsQWp6mAX/5\nC4nTy4sCadQo2bNo2TL61pcvZy+ulhZqs42NDCLu2sXfuK+vFDYBAYwd6PUk0qVLOy/gstkYTxDB\n5j17aK3Y7ST5kSMpZOx2unf8/Tlm8fRpxhiuvVZq1t98QyGVm8sePzNnAvPm0epx5hqrlULQWRAE\nBTElNCWFx9brub8IsLsLgYMHGccQI0S9vBgDcZ+pINJMp06ldRATQ+HSXVd3YyNTbUXn0N62Kv38\nPMdNBDoSiO2QmOjaTMkZhw6p1Kl+hBIEfQzRd0VkrIiWCSaTa1653S7z8m02ukDq68+u6ZsnTJwI\n/M//UHMVefQhIXSTNDWRbE6c4GfLlknt1dubwdf6eq5D07h9WRmJqaWFxKzXSyK3WnncP/9Zul9s\nNhKlJwKxWtuntjY3s4I0N5eWlJg5EB5OfsnM5OerVzNesHUrhUF9PV1JZWU8Tk0N3WBz5tAycXaB\niWwhg4Gppq2t5CZfXwrDSy/lfp7WfOAAM2vKy6nxJydzn88+ay8ILryQ58jNpTC45prud+M0m+lW\nEv8rJ07we6yrowUzYkTvt64WSE9nCq3Dwf+Rdi0hGhqk/88TnNvHKvQrlCDoY4weTQ1L5MGL7pkB\nAXQNVFTwta+v1NI/+oguGYB/b7/93GSYxMby4dyP5/nnSewnTlA4Wa0krKuvpiYbGcnfdnExSe/k\nSf6eo6JIpOPGkaREDODoUQZoY2L40DQGXo8do8C44Yb2lf8BAXTjHD/O11FRJFY/P1YWNzZSW/f1\npSuqtZVEWlMDPPOMHDxfW8s1OnORcGuYTLy/ohXGtGmSsN94Q84U/vprXnNoKN0jM2bI4zc3k3R9\nfdt3da2v77goTafjzIXLL+/R1wWA/x/OCkNVFTOP9u3jvY2J4bAbscbeQksLv0eh0Hz2GQVz1A1L\npO/OHYO4jfP5DiUI+hgjRtB1UVJCMnMeZ3jbbQyyinRCQWCitw7AH3t+fu+lGi5ZAmzeTLIJCiKh\nfPMNrZMVK+hWAUiOmkZiFdq/3c6kjooKErSI/YWFSYF34gSFgNlMzfybb+gCE6T46afkkbIy7jNq\nFLXdZ5+lO2rSJFoA334re/kLj4JIMU1Kkk3IpkzhGg0GkldcHHv5fPopSTQggILmiitI0Ha762D5\nwkLuHxrKe5CdTaHx7rvUiL29+X3Gx9O15OUlrR29vv1wnIYGXntUlGtDvbw8utUAatcjR1IBKC/n\nPbjySh4zLIx/BQH7+fFeCOW6qorr6u1hM62tXIOXzYzf/v6M1NngYUOl9Z8XUIKgjzFpEoOfokBq\n1Sr5WVCQDBw7Iy7OtRiqN8cJ2mwkqLg4klZWFrXe3Fxg40b6iu+4gy6r4cP5+OYbEnx5OWMB115L\ngVBYSE1x6lQSYkEBsH8/Sbm4mBp7TQ2Pu3s3hcGePWzLrGkUAIcPsy+QlxddPklJtA6EH97Pj+eJ\ni6OAEVbIiBG8jhkz6Hq58EKec+xYusD+/W8KktZWEnNNDe+rl5eMieh0vHbngK+vL62gDz9k8NvL\ni9fxpz/RPXXsGPePj+d9cC6+OnCALidNoxX04x9z/+Zm4M03pTvsrbe4TmER1dRQEIn6ixtu4P3y\n8qK76p13ZH0H0ActdNatw7CXXvLI+/jHP1jpp3BeodcFwdq1a/Hxxx8jJiYGGWfy5TZs2ID//Oc/\niD4TCX3qqadw+dnYyechdDr62IcPpyDoKF3aGStXkkDq6ugymTSp5+dtaaHGHBHReWDv669JdtOn\n02opLSWxZGRwv+RkYOdO/tbHj2eMr7CQhFpZyefffksiFNbOdddRoGzbRtLNzSUJNzdLrbiyktlG\nJpNUIlta+NxioebucJDwzGbeu6wsvhcbyxiGjw9J2WTiOebNo1DIyZGuuIwMxhEqKqRlEhJCq0Tg\n5pvZF6m1leS7fz/XEBrK81dWSleQw8Fj2u3yvgkUFkpB4HAwDVVcW14e1zVpEgWuc0zEYpEtvAWE\nlQK0b7h21VUUBjYbBeU5r/x1ODqVLkcPO5A6WXf2PYIU+h29LgjuvPNO/PznP8eaNWva3tPpdFi/\nfj3Wr1/f26cfcGhqAv71L6nhz5vX9dCXoCBXy8ETCgtJbomJ7YfRZGYyW8luZ8bLj3/ccdaO8/sj\nRpCAt2/nejWNQmDkSAqFG2+khpqRQdK02fi3tpaB2iNHeJyqKq5B00iWs2ZxvRkZcs5wXByFQ2io\n7F0vsguFvzsujo/UVLqvRKA9IoLnFllKzc38rKiIBNvYKNtwWyy8T84uIG9vumXWrpXnc57bMH8+\n4wZpabyOsDDeF+EeiY/nX/fWFO6v3b0kRUV0N8XE0NUmUlUjIylQxKAWoPNU4okTgfvv53q6EvRd\nQdN4H/03boDud4973uiRRziw5QxmeN5K4TxCrwuCBQsWoMDDfERtiPoOs7Jc3Tzffdfz6V/uOHwY\n+OMfSSShocCGDXTRCHz8sfQpnz5NMuuo5/0VV5D0WlpIxPPm0UoQgeOmJmrDhYV0gSxdKt09DQ1S\nkJSVyYKyb791da/4+DCobLXKoPSuXTzeiBF0PTU2cpuZM2VAeto0Hn/qVK5fFOUNG0YyFoLF15du\nqoMHea6gIClkALmWkSO5xpYWXsMdd7j67QUcDlozzc1y/dOmMVZTWsrr2L2b9665WU5JE/EUgMdd\nvlxaBd7edKkJ0r7sMnn8OXNo8YSF8TqSkjpvuSyspsDAHyAENA3Q66ED4HH8rwh8KAxK9Fv30eee\new7Tpk3DunXrUO+cBjHI0dk83rPF22+TFC0WarmvvOL6udCcBYRQABiwffddukIcDpLjLbdQS7XZ\nZCZKWJgk3ZEjSc4AffQPPUT3z6JFdHXExVEwiPO8+SaPMXo0r3fMGHJKSQndXcXFPE9AAIXJvn20\nKr7/nuSYlwf87W+0ip55htcqirpCQmTdwvz5FCY6HY9bVcXjiG6lwcEcaD9lCre12UjaViu3+fxz\nz/e3ro4uJLud5zp5ktuHhFBQpabSRdbYyN5IDz1E95N7GufcuYxXLF7MQLgzaefn0wq55BKu+ZNP\nKHSXL5dCwGjkWty/y61bWfsh4hTdxtNPy86dbhLw2LQ12PaWJsvGlRAY1OiXYPE999yD//u//wMA\n/Pa3v8X999+PTZs2edx2w4YNbc8XL16Mxc59hc9DpKYymyY9naR4LrLp3I0r58ZyALV2EaSMi5PW\nwq5dDJrq9STQ1lYS0VtvyYyc6mrmqf/1r9R+4+PlwHeBoCA2gGtoIDG98IIMYFZVkWBjY6Xmnp9P\nS6C4mNvY7SRakcVz/LgMlH7yCYm6sJDrLy2lcHjoIcZZ1q+niyU3lwIqMJDnCQqiYLTZSKw+Psyk\nWbiQx12xgvEDX1+6t0aPJsGLUNWxYwxi19XRlZWaSsvIauX2gYE8rnP2lvt9d8eOHbSOAF5rZKS0\noISVUlZGQS6EaFUVhaxzoHnyZDlN8cQJ2RPKZmOmkbM12A6dmAzvvd6MY7lSMxlt9ryd1Uph3dRE\noToQm+UNNaSlpSGto7TdbqBfBEGMU1OXn/zkJ1ixYkWH2zoLgsEAnY7B36uvPnfjEW+6SbqcwsN5\nfGfMnk2ia24meXp7k+yffJL7REczXTQpSfrUBRwOkvOqVcxXr6mRffudUVbGfRMSqPHu3Cl98EFB\nJHabjeSsadxe0/iZjw/fN5spqPz9pb+/tpbrFsLOZKJF0NzM45eUSGW2uJjXYjbzPfEQtQ+HDlEQ\nfP01XTmtrXRFiToGUcVttVK7FjUd+/czOyc6mutITaV1JFJiAWb5+PiwDsNopKWwbBnPKdxBxcW8\nPk2jIA0N5f/DyJGye+np06739uRJruezz+Q9yMxkNtTo0e2Fj7Di2vj+hRfYzc4Thg9vi0rn5gLf\nb6U7LTGR96WjmcHvvivHbB47xtYc7nEphb6Fu5L8+OMdxHc6QL8IgvLycsSf+c/Zvn07pnQ0YXwQ\nQhRUff89SfCGG7qehtUVZsxgnn1+PonGuTZBICpKtjDIyqIryMuLRF9TQ7IPC6OGnJTEOIDJRC30\ntdeo7QcEcK2NjUyfvOgiXs8rrzBOERFBcrruOvr3GxuprcfHc/+GBp4zKooatXgdHU03y/jxdBvt\n2yfXLboSWK1ca1AQ3wsJIbnrdJIghQDQ6Uj+zc0815w5srdPQwMtofp6+t9ra0nE06fz3Lt28dr2\n7eO5xD3LyOC1lZZSMGRnMwA9dSothoQEdik1GGjxffIJv2e9nvf19GmS7PDhXK/Vyu/+F79wVdLd\nW4pER/O6O3LvTZpEa0EIrSVLAJ2+k0BBZWW77nqtrXQvigZ/zc1MBOiogt25rsVu5/9KfDyF66FD\ndMGtXDm4R28ONvS6ILj55puxZ88e1NTUICEhAY8//jjS0tJw7Ngx6HQ6JCcn48UXX+ztZQwYiFxz\ngJrj9u1sQvdDkZTkaqKXllJbHjFCpqiKVM+GBtkQzeEg4Y8ezbx3gFlF33zDtZlM/LFrGkkuMFBa\nEGYztcOXX+Z+ERHc9k9/oktDtG/Q66ULSPRYGjmSloPIFqquppBxOHgdNhuF0kUXkfjfe4/nXLiQ\nFpDoj7R2LXsI6XQkc39/nisujgHbK66g4AsLIzkJq0SkfPr48JytrRQA778vg7YlJfw8Lo5Cp7CQ\nWnxdHY+h19PFN3s2nzc2UrCITp01Nbyu5GQKs9BQCgRN4/Pt2xlj+elPpTAYP57WR3o6t7nqKm6z\nYIFskJeUJK0YPz/gJyFvwfuem/iGJ0Wwi8SMlhaZvhoQwEdnqaAxMfIaxevcXBZD2mz8P9m27dz8\nXyv0DXpdEGzdurXde2tFnt4QhPP4RaDjkYs/BDk5dP04HCSY1atJbm+/LbNqLBZqf8XF1N7vv1+m\nRDY2MqiamSkDqMJd09xMUm1upltJ+PIBEmRRET8zGEigp07RTZKbSxJsbCTpXHopLRmdjsf+/ntJ\nhvHxwF13yfnHAF0nxcW0PPbvp0AICGA19nXXkYDKyhisrakhgUdGUrhcf72r1p2UxGMAjCdoGi0f\nYR15eTGmERxMjf83v6FbTFQOi0wkETAWgmPyZEmQ3t4Uws3N8nMxlrOyUloax4/zukSX5bw8WbMx\ne7aMHSxbRpeUxUIhqveSF9TuR5yTwy+1m4iIcG01PWxY59r86tUymD19OoXSd99R+Gdn8z5GRXEu\nxblyfyr0LtTX1MeYOJGanUghvfDCc3t8o5FuDeFK0DRaIPX1/LE2NlKTnzePvt2gIMkZhYVMHTWb\nSaJLlpAgRd/8yEiSW3AwLQPRa2fMGNlOIjWVLpCyMnmNu3eTJIRl4esrK3QBGdANDyfRxcSQjESV\ns7c3CXrLFukTLymRox5FO4nvv+e2vr78q9fTMhKZNnPmkFjXrCH5NjbynCUl0rVis3GdTU28h5Mn\nUzP38+MxReqrzcb3o6Kka++aa7j2rVsppAIDmZLq48MU3uhoCteaGgoDoX0LIeVeYfzmm9Sqg4IA\nfPUV4hYt6viL/wHp2Ho978mRI7z2GTM6twhCQ2mVOWPsWLomxf+dtzcD2VOnnvWyFPoQShD0MUJC\nqMHu2kViOJsRhx1BBEGzs0kmJhMtjqYmumlEbr9OR0JqbOSP/ttvqe0ePkyiFENYysuBTZtYo3Dq\nFMl8wgQKg5Mn6ZLR66nBx8RQg732WpJEVZUM+JaVyYphvZ7Pv/ySgkaQdkwMz2G3k1zffJO+Z29v\naqAtLVII2O10Q8TEsKGdCFTW11MoibIVs5lCUBBtVhYrov39aQF98w3fP3SI5Hz6NEnXbidBT5hA\nwfp//0dtfsYMEvjSpbR2oqPl3GaA1zZ7NtdVWsq/Eybw/JMm0Sq6+GLOPC4upqCoq5Ouu6wsWgg+\nPvw/+fs/dMCjHXzZu3d7HIvW1MT4j80m19IVamtpiURF8f6dDcLDafmVl0t3WldZVAoDB0oQ9DEM\nBpKc0Ug3CiB98z8EdXUMVtbVkTyPHycxRUeTNOvr+ZlOxx9pfDyJLyqKWj/A9fj60qdtNFJQNDSQ\nHKdNo4Z3+rTMTBGTv6ZMoYXhcFAAhYTwuOL6hIYr/P4+PiTB/HxZBRwRQa3Sbud5MjJ4HUVFtHAe\neIDX43BQiJnNFFwZGfSxR0bS2kpOli2zFy6UawB4D2pq6PYID2f8oL6eAq26mtcbHExBJ/oB2WwU\neqdO8b3oaFpxYtKbMywWVjyLltzXXiuFUEoKH+npvJfjxsngt83GdXy7ORtvbPUQ6T+Dxzdo0DTW\nQFziYTSm3U6rSWRrZWVxJKiwmDyhqopT2MR3ecklPP7Z4Mor2SYEoADydI8UBiaUIOhj5OSQwIQv\n2mg8N4Jg/35qqwBdHRaLbBfd3EziF37+YcNIcnFx1OBEumFSEt0rtbUk7Oho1yyWiROpvY4fL6eP\nARQ6WVk8Tmwszy0mnwEkbYDXK+YXlJeThBITSb7p6ST1mhquJTiYD1EPsG8fXS9ffUXBInLlLRa6\ntCIjKUjWrKGAiY2lUPjrXyXJ+frys/37eT/mziXBJyVxveK6R4/m9VitMrVVuEpEBfV//0uhGBfH\n1NqgIN47MfdA9BZy7/sj0ncDzpTvbnhcBzwORAP4mdt3+ubVryF71i0wm3nPos54f3bs4FonTXJt\nId7Y6NqTqKWFLi/3Nt/OyMhwnUl8+PDZC4J582hRNDfz/0T1Hjp/oARBH6OqSs7+Fal33YXBQPIR\nU7MEvvySWTUmk2yD7O9PIhCphxYLCUH0ATKbSZbbt3N/USU7aZIcLJOTQ1dSQgL3E0VQXl6u5Cj6\n5QAURiKGIDKFRLvq8HBZg+DrK+cWVFTw+JWVZ1obn2m3bDbLdFERDBYVy6dPU+MUVo9AcjIflZUs\nrgoN5VrDwvj36ae5z+jRsp+Rtzc19IoKPg8O5ucjRjAY2tDAz3Q6Enl2trQ0Tp8mMTv3giou5joj\nI2UbjaIi7jsrvgyP/raDEY0ANjxGti8qkgHkoCBZK1Fby5qCfft4Tf/zP7IoUQhP0YnU27vrGcju\nA3G6OyCnI/RmZ1yF3oMSBH0MMXFMkE5nPWScUVVFf73Qri+9lP7m48fZujkggATu40PNuLmZpOHv\nTzIQefgA3zcYuG9CAgmxqIjC4IorZM672cw1hofz+Dk5PMapU7Jwy9eXZB4aynM5HCTisDCSvtXK\nY4gOBaK1c1AQ/ehlZVxPY6NrrrzNRmESH09CTklhvrzNRqGTnk5yvOceap/NzfSNi2yot9+W2TqB\ngawY/tWvZLA8PZ3br1rFgOa//8375O/PmMG8eVxferpspDdlipy57IycHBLzyJG8hrw86YJ77jlg\n/f06dNgQ9MknOfMTDNaOOMx787Of8Tsxm2n9fPYZXWKZmbyXYhTo++9LQeDjA9x6K4v5bDamnHY2\nIAxgjKW4mBZQZCStLoWhhy4Fwfr167Fu3TqkKoffOUNIiOxD0930uowMKQQAktXFF7fvWHnyJInz\n2DFZLxAZycBhRAR7+ldXy2Ky1lZuExhIEoyM5LmamkiMSUl0NzQ1kWRFOmRFhTxfdTXJb+xYnnP8\neNe1ihTLlhYZHM7Pp3CJjqbAcE56sdt5rmnT+PzwYe7/+eck8qYmCgjRaG7xYuDVV2XmzzffSCsE\n4Hnz82Vfp9JSHicggGmQc+bw/gAkxaYmEvm338rmegkJJPY776QVd/w4z1FSwm0tFjnL4OIpTXjy\n76FAWgdfZgcZPjNn8iHgPMN41SoWrjU3U0iL2IO7+yUujsKgu/DyYnptb6ChQabpzp3btVBS6D90\nSUMTJ07E3XffDavVirVr1+Lmm29GmHMrSYUeQbRhrq/n8+6a0u4BP0FqY8fSIrDZuE1oKMkzMJBE\nqNPxs7AwErLBILM5xDFra0nONhuDjcOH87PhwymwAgKo2Z88KQk+JoZCQpB7WBi1YjFf2W6X7imz\nWRKXGMQiXEyaJquAbTYKJx8fat9jxlBrt9spaCorZetnEWOIiCCpCyEAyMwkIQjEJDgxmrK5mZ8J\nt0lNDYlepLb6+VEYiDiOaA+RnMxW1ElJUiCkpXHNGx7vuJq3YNQibL4jDQAJ8TIP22ianPfsqb+b\nmAXx858Djz5KSyo4uOPuEf0Nq5WFhqKfpAhci+9EYWChS0Fw11134a677kJ2djY2b96MKVOmYP78\n+bjrrruwxEP6mkLnmDyZbousLBLsddd1vY/wi4vBKBER0oSPi2N17YkTJJGdO2XA0mqVfXYEwSYn\nUyP29qZbQKej+8NioetlxAieLzmZBD5+PF0Mf/0rjykIXswqaWigReDjw792O8+r00lhJYKjJhOP\n6eNDUhZNL0U/f4uF5DZlisw6qqggSTY0cNugIHmM2lpq4NnZtICsVvr6/fzoOhMCbuFCCkEvL/rd\nRYM7h4PbzplD6+bAAbp35s2jxRUbKzOehGDYt4+PG35kw8JFPljYwXd2wyoHgoJ1ePJJIDsdiC/k\nvV26tP22FgszlwoLeZ9+/GPXPkbOSExkO5GKCq7PufnfQEJtretsZRHIHuEUHjGbaXWZTEzN7U6q\nq0LvoFuOCbvdjuzsbGRlZSE6OhrTpk3DM888gxdeeAFvvfVWb69xUCEvj5kvoiPm22/LoSmeUFND\n/7XQxD0NshEjIwGS3aefkpSNRhLruHEyl/3kSbpcgoJk5XFcHDtxlpfL5m/l5XRTaBqzYerqJJGb\nzSTIiAi+p9eTKK1W1wwUEbQUwVuzWfbZaWkh2V92GYkvLY1EmJxMwfX++9JFFRNDQWAyUWMPDKRl\ncOyYrHS2WmXsY+ZMknVgIDX3mBiZTTN2LMlWtN2+8kqePzCQglFg9WpWFH//Pc85cSLwh83RCGo5\ncyC3Vg5mvxC88Xwj8vP5WpfN69m0yXUmsyd89x23BXhdH3/sOjHNHeHhA9/NEhZGISv+b0WXV2ds\n3SqTJY4epXWjnA39gy4FwS9/+Ut8+OGHWLp0KR555BFcdGYq9oMPPojxnY1NUvCIHTtkEzWdznUK\nlSdkZbn6248day8ISku5zahRdD1MmSKnhe3ZQwIdO5aE09JCkhfZMsePU1CEhZG4q6vlHN+AAGrO\nFRVcs3PzMx8fatDjxlG4eHm5DtwB5LYjR0rhIfLmGxspkObP5zXOmgXcfju3/8UvZDVwQIDMfho1\nipaDECqtrdTcxYzlhATei4AAWjkGA4XJzTdTEw8PZyD8yy9li/1TpzwPo/n2WyAnW8MXOzsZ2WGx\ntAV7/ADcoVGzT0vjfQsJ4TW//z6tNtEueuxYV/ePs/D09Pp8REAA51rs2sXXS5a4ujctFteMOZOJ\nFuAQ6j85oNClIHjzzTfx8MMPY/ny5Zjo1tbywIEDvbawwQqTyXXQuDt5usM9nS8kxPX1F1/Ibp2J\nicyjF/uEh8tWALt384d2+jS16/R0km98PAXDlVeSkE+c4A/W15fbAHJ4i05HAvPy4jY6Ha9FaPh+\nftLPD8gCsMJCkrLFwte+vmf65eiBP/xBDqQZM4aWTVoaj2cwSL+90ci1RkTIYLemyTRcg4Gc7Ocn\nU3TFnILduyl0FiyQ849DQ3mMggJ+B/7+PF7rguUI3LcTCwGPbp8brtdw661sBucp0L9gAc/v40OL\nRzTce+MN2YZj9GiSpBBAM2YwIG408v5MmMA1uX/X5xsSE2mReYKPD78D0fJcp+s61VWh99ClIHj9\n9dexd+9e/O///i9OnTqFmTNnYsGCBbjvvvsQPtDt0wGISZPoqqivJxF1ZVSJIfIZGdTanQfZmM2u\nLZuLiuh68nRMvZ5EZLXKLKC8PJLNqFF0i0ydyjRFu52ZNwYDXS+i5bEgdm9vPq6/XlaShofzPYdD\nNtIT21dUUHsXHTtbWnju6mo5+UvEG06fpounpYX7BAW5DrMXef4Wi/Tf+/qSVIKCKNx8fWWlsJeX\nDHQPHy4rh41GOSA+OoaBXh0A94Fxi6fXo6AuDBERFJapPnRnuQsBh4MV4yIYLWo6ALq7jh6V24rA\nd14ehW1YGN1TVVV06+3fz8yg66/vfnpxRxAT2kTgf6BAp6Mw/OQT3qu5c1Xb6v5El4Jg6dKlWLhw\nIQ4dOoTdu3fjhRdeQGZmJu5TPWbPClOmUCPMz6cWevHFnW+v03Ga1pVXkiidLQQxeEVo4A0NJBCT\niRpwQwOJpamJPu6gIL4n2iBbrdKV8uGH/DGmpLCNdEMDSdpkktMM7XYeQ1Tx/uxnDNQKC6ewkNot\nQEIHXGcX+/nx3GIimdksB9XodNwuOJiE76wNR0VJsg8I4Oz0t96iW8tspksoIIDCaN065uOLYTBG\nI49dWUnLYMYM4I916zD1u5c6vOdJozSMHk3rI6gViNRRwAgt39+//T6FhdL1I6ylpUtJblFRdOkJ\ngVZbC/zzn7S+xPhOs5kWkXAD2mwUst0RBA4H3XTV1fxehOF+6BDjDWLc6Nq1ntfeX4iN7dhiUOhb\ndCkIli1bhubmZsydOxfz58/HoUOHXCaMKfQMZjM1UlEU1J10OqORPWxqakiisbEkjyVLKCA++YSf\nHT4sm6ytWEEhIXztpaVs2fzVV9zXaiUpiJYOn35KK8BuZzxAp5Ok5O3N7cX42ro6krbVyvMfOEDr\nQbhBxo0j4ZrNwL/+xWsUhWVCi7dYJPGLnjtxcSTfmBiZISTmJDc18XgTJlCTFLnyGzdSKBQX8/hr\n15Zxw9EAACAASURBVPLYYmaASJU1mXWAEUBB+/u7NOk0ss3JbU3y/M4IwBUr6ForKKBw8fPznPUD\ntI8zBAWxJ5FoJXHppSR2o5HXbTTK6WuzZ0sSd0Z3B9Hv3Cnz9Y8cYUxk/HgKPiF8qqpofZwJ8Sko\nuKBLQTB16lQcOnQImZmZCA0NRUREBObOnYsA8R+u0CMYjSS1qioSsRje0hn27SNh22wMYtbVUWN+\n6y22TPj1r5ne6ePD4xkMbEDn5UWhM2UKtfjISOZy79pF4hQuoJwcnqeoiARoMpGwhUvIx0e6g3x8\npD/Xx0fGB8xmEpuoFp40SaY2iiBhVJSsuvX25vHj4kiiEREkSz8/CoayMn4eHs571tzMbWNiuE4R\nOC4qItl6e3PddXW87ntb/4Tfm3/d4T29f72GnBy63RobSZgiGO7jw+tbuZLrGT++axdeYiKtqY8/\n5uubbpJCAKC1deGFtGK2b+f/gJgCZ7NReF5wAV2AVVW8HvekgI7gPDFMvB4/vr1w8hQUV1AAuiEI\n/vKXvwAAmpqasHnzZtx5552oqKiA2TmVRaHbMBqptYkiK/d2BZ4gCNlsZmzBaCRRtLRQGDz1lCTb\npiYSi8lE4hetJBYsYLBV1DAAJB4x7lH4s6uqpOYPSOIXrZFNJj5fs4b75ObSxy3y8nU6Cof0dOnz\njYiQvYPEfAFB3larHGTz1Vck08pKftbQIAPFYjpaUxPdauHhTO0UTfUcDqCyqmMVemXctygdORtB\nQRRSjmZaLaLwrrBQtsRITeVgnJ6kMoqAdVISr/H77ynoQ0IoAMQ8g1GjeE0NDdItNnEirY99+3hf\nJk5kuml3zx8TIxsOitcArbV33+X/w6hR7RvgKSgIdCkInnvuOXz99dc4fPgwkpOTsXbtWizoLPFd\noVOcPk2isFpJpMJ10xlmz6Y/WTRrM5kY/BRFW2Yzg8pffinTP0WlsHNvoM8/p7tA+PQrKtj5VJBI\nenr77gcREbIJnNDSIyLoilixguTyxhtye71epp6OHcveOFFRdAEdP8737XbZf6iyktfR1MS1FxdT\nyxcFZsIS8vHhcXNyeI5nngHw7rv4MHMVOsKUyRqGDaNVNDwMQBnPK/oiXXABfdSvvcZ7JprRJScz\nJgDI0Z4pKZ4bshUXA3/+M4WT0UjS1+tpadXV8V5lZzN2odNRgF19NYV3cjItiYICCsGvvpLHHTaM\nLaG7g6uu4v0qKZEzlAEKvORkKgwREcoiUOgYXQoCk8mE+++/HzNnzoTPQEo7OE8hiFwETbszvGPY\nMA5Uqayk++TGG0lofn50a2zcSMIfM4bEcvgwSV6kdg4bxhTRd94hcUVFkZSam0k2grjy82VBmbBC\nAAoRUdksehQdPsw+9sOGUbMW7gyRnz9hAquot2/neUpKSEg6nUwfnTaNOfZGI++Jt7fs3CkEkkjV\ndzgoLA5+pwMWeU7t/J/It3F09PXw8aHgevhW/vX15XorKylYGhooOAMDSfB33knBWlrKezN1Kq95\n71763wEKAXcrwWpl+ut333F9BgMFjJhjICqrhftJ7BsS4upqstlc5yYAMqOpO6ispIJhMsnhN+Lc\nYgqagkJn6FIQ/PrXHftZFXqO+HgSnhgg091eQ4GB1O68valpt7aScFtbOSjc4ZAD26dNY1Dw669J\nrMKF4+fHhyj1nziR7qO1axkEPXhQunj0ehJXTAwFTHExyUlk+Hz3nSxkA3jcyEjZS2n5ch4L4DWe\nPCnXnJzM4rG5c5muKkjf11cGk4Wb6UJ8hz2NZyKchvb3JTxMa9PkRYC7uZmZRKtXSxfTvn28jvp6\n+tD1et7HlBQW+bW0UJt3NnbFBDOAwiozk5XdAMn7/fcZqDcYuPbgYAqC1FRZ0wDwPUHMAIVgbCzX\nYbPx+5owwbXAKjqabh2jkfezs56Pn38up8FVVTGO1FFQW0HBE1Qb6j5GeTldICIPXhQZdRfDh7Ma\nd/9+kkR9vRygkpdH9010tEydFAFVq5Wa4eTJ/Dt7Nh/p6XQh/PGPJC4xiMXbm/uXldGdUVrKOER9\nPY8dGMhrEcSt18sZvk1NJDsRCP/iCykw9HpqxxkZJL7YWL5XW8t1GQxAdY2Tr9/NYnp98lM4ueoh\nlJbS2rCcqTrOzycZR0RwvcLqAWQcwGzm38RECo60NBL55Mkk5I8+4rqTk2lBZGby2ElJFGDOqZe7\nd9NicM6gslh470eMILkfPMj7eOWVrjn8oufTwYMU0KdOsf33woVygtqJE7JnVH4+hbIYaekOZ+vN\n02sFha6gBEEf48gRGXzV6UiAnnD6NMlGp+Nc46Qkvu/lxRm6L79MLddkkgHe1lZZqdzQQGJNSqKP\nWsQTamsZF4iMBB57jMQr+sKI/v+Fha61CUePktASE3m83FwKCbPZdYaAyDhqagJ+9ztZIyFcTcKa\nEEPaU1JInoGV+cgqd5q044b/d6+Gpia6QKZPBwJA4p04kWTZ2CjJTwykDwnhGnNySKr5+bRqCgp4\nnbt2SZdJVZVs5xwfT3fZhx/yHh07Jl1yzsHWigpea3Cw9L2LzKpDh3if/vd/PV+PxUIhcuIEjy2E\nbUwMrRhNc209omkUuh0JgsWLaRUajXTVqRRRhZ5CCYI+xujRJCCRQeOprN5oZEMuoW1v3Qrcd58k\nLj8/CoTRo0nkTU2yOhegxSHmB9tsUjM2GHje/fvpqhG5/8K3PXEiiU0EmZ3z2EV6pphX7Nx3SEBU\nFfv4ULMV1bSi6E1kIxkMgNWmA2rgER94/wg36N8FQPI78iSPtXs3j6VpvG+i6ZzQxv38eI98fGTv\nmk8+Idnm5lJbj4mh5SSC6SNGyGlgPj4UgkePyroJb29ZH7FjBwO9AIn+ww9lmm14OK0RkTJbXNzx\n/4BeL2M4gGxzLQbp6HQyiAzwe+1ICAByEJDBQGWgokI1b+sOGhtplen1tI47mu1ssfB/zeGgMjAY\nYy69LgjWrl2Ljz/+GDExMcjIyAAAGAwG3HjjjSgsLERSUhK2bds2ZNpVrFnDAOTJkyQuT/3khbtH\nwGyWg1QEwsLorrngAjlgXbhdRC/+wECZGioCuX5+dGcIt5F4CJ/53r2uGUciVpCYyOPZ7XImgEh/\nFNDpJHmGhUlN2c8PCLHVoQaRfMNDgDxhpIbAQK69oQHwsXHdMTHcf9Eirun4cbpShD/e15cPTeP9\nSUnhua+/XmZD2Wy8ZoOBxxJprX5+JFirlfGV48elIBBN+5qapCVw9CgzdETH1rAwOcpSzIPIyGBN\ngFtbLhd4e8uagpYWGdB17n564410XTWfSXONj+/4eEeO8PrET2jHjq7rHoY6zGZ2hhXuy6wsjv30\n1Drk1VelYD98GLj77oHVruNcoNcTyu68807s2LHD5b2NGzdi+fLlyM3NxbJly7Bx48beXsaAQXg4\n8Mtf0j1z6638wbsjOtq1Za8oBnPGFVdQE/Ty4rYLF8p/Ym9vkuScOXLUY0gIyTM+ngQu2lDb7fwb\nEEBtNjT0TGuFIP6zC6I1GhmADQ2VZO9e+SreLynh/l+m6aABaGzSocbhegGNCIFepyE4SMPweK0t\n3iEKypyzhvbs4blGjaLvX8xLHjOGRL5wIQXF3Ll83Hwz3VJi5m9Tk+xg2tpKEh83jse46CIOexEa\nua8v12+x0Orw9aU7LT2dQlZcs9nMc8+ezWMEBjLgK3omrV7tem9OnmR1+Btv0GK75RbOorj6aq7/\nV79irEIgIIDf8fXXdz58HvBsmSl0jooK12LO6mrPqdwGg6t1V11NN91gg07TOpibdw5RUFCAFStW\ntFkEEyZMwJ49exAbG4uKigosXrwY2dnZ7Ren06EPltenyM/nFDAASEvbgD17Hu98h/MI3gCsnXze\nzY4JCgoDEosWPYalSzfgZz9rr5gNNPSUO/slRlBZWYnYM3mTsbGxqHQuixzkqKykG6GhAZg1awOW\nL9+ARx7p3r5vvcUgq6Zxhu1tt1H7+/e/2e/n1Clq7DNnSs36gw9kr5+GBmr7ZWXUfsSMABHE9feX\nk8RsNloQbf13/OjqCQighhodTe33zbc6p3cdgNGjNZSXA15O9Qm+vjxeYKB0RYmCrZYW2QspNJSZ\nRSNGUIuLjKSGXl4uXVDLl/NY48axi6czXnqJsQUxW3jaNFoFzq6WG26gm+bFF6XrKzWVFsWhQ9xH\nuAIuv5zHyszkGktK2MJD3DOAFkd9vayNuOgi2QtI4Fe/8lygdrawWHh/QkIG7tSygYacHBZh6vVM\nBhAJGe7IyqK7zW5nIsFAFwJng34PFut0Oug66a61YcOGtueLFy/G4sWLe39RvYjYWPoZDQYS6WWe\nBth6QHk5CV+QzebNzHkfMUKmRUZEyMKoyEg5RtLLi24Oq1X237dYZCYPILOORD+hgADpEnLuDBod\npSFtT8cexVFxZjRbfWGznRm+nqdraz8NyA6kgKwoFvdFbKPXyw6lDQ0UaLGxvK7iYrqdxHhL4f8X\n5CfiGgIlJYwbeHnxnptMDGR7e1OYARQsdjvdS+Hh/JuQwLUlJspiL72e7x87xteiCtpqpUtq7Fje\nIzHMB+D6Tp92ndYVEyMDk199RSEkKo67mkFw4gSPn5IiXV8Avyvn1wpdozs9pADGezqL+QwEpKWl\nIS0t7az37xdBIFxCcXFxKC8v77SbqbMgGAwoKaHmLvrpdJYN4oy6OtcqZLudhKbXM0CZn8/MGEG6\nRUXUlkWqqujPLzJTAM++ZItFCg6hte+rSMEoez5ggsdMn5tv0mAwUAiZmuUAmupqfj5iBAm4tVXO\nMxCtKEQ8QAy0F1XBJSVyu6oqxgmCgri22loSrpcXNe/aWhKvzQY8/zyL1YS2HR9PAZKczPsVGEih\nkp1Ni+OiixggF1PB7HZaA97eXM/NNzNoazTSMoiNpeZYUiIFxLBhXMPll1MYfPKJ6/0JCWFM6OBB\n3tMFC/i9ZGbKNFEx/GfNmo7/B/bvZ/EYQAFyyy1dxw8UhgbcleTHH++Zy7lfBME111yDLVu24MEH\nH8SWLVuwcuXK/lhGv+Cjj6jd22z84Tv3l+kM48aRzNLTSVZjx7IQ7OWX5cwB0XxNEHhICLVPMahF\npDoKN5BICXV2JQo3Ud7pjq20px+oRWtAJMxmXo/+hKyUHj6c6ZkiEA2Q5ESAWpzL+byaJucZNzeT\nrEVev7BcxJCZyEjuW1/Pa5s5k5qwKPaqrmaNQEKCzArx9WVQNzmZriZR+LZiBY8nmvCZzRQoTU3S\nwrBaaSEEBtISeOopfncRERRwkZEyBVik7y5YQCugpobfwfLlXOu117reRyEoO3rtjsxM+dzhkPMM\nFBR+KHpdENx8883Ys2cPampqkJCQgN/97nd46KGHsHr1amzatKktfXSoQLhlhDYuhrZ0BW9vZpHU\n1ZEwx46llrx5sxx/KUhcNKcTWrdORw1WuILsdjntS+T479KWYAnSAAv4cMPsizRccokcL3j0ILVt\nnY6kXF/P44h++84Dc6qqpOADSI5eXrImAZBuIBHbmDKFlo4YLek8+UzTuK2/v2xtIQRBTg4FY0kJ\njx8QQBfQ/PlMwyws5Nr8/OheETMhTp4kefv702983XVsIXH0qBwyk5bG/Ssruc+PfkTrTtOY8ZOc\nLK/v3nv5XQvLxRPGjGEbEHGf3OcRuCM8XFYbi9cKCucCvS4Itm7d6vH9naKb1xCDGBkoCr16EjAs\nKGDeusCePSQyLy/pVxc+/rg4asuiH1F2tmubB29vwGzpWOsfjxyc1I1rq0FIbqL74/hxrmHyZLpG\nxJAVQepidrHDIV1ZFotMuxSxCNF8T0w/0zQZuLZYKGREMNZqJbmWlFCTDwykK0wEfcVsg8ZGErWI\nL4hmdg4H8N57UlOPimLVr9DkV68GfvMb6fZJT6fGL6rANY3BQoNBtgRpauK51q/n+twDiKL+ojMk\nJNAVlJVFUp89u/Ptr7yS11VZSSHS1XS7/kBTEwVxYyOb96kq5/MD/R4sHmq48EISQGUlCbuzH0pm\nJjXtceNIZsePk/wmTODr+nqZ0yyEgRjortNRO66uppar1wN/wv24z/EMd/Cg9ft4a23FZZoGQJNE\n3dAgB8k0NFAzTU/nOsQgG9GZ1NeXa/Dz43qFlSKOK8gecA0cC0FVXc1re/FFup7EHOCRI+UQHNHk\nzWKhkLjuOnZX9fGR8xrEcXU6WlINDSRQgOsfPpzCzMtLBgMdDl5Xfj63mTyZhO7jQ6Ej3EghIVz7\nDx0wn5TUcbaKO4KDmSk2kPHOO7IiuqSEAm7cuH5dkkI3oARBH2P4cElmNlvHRPLFF3Iw/ccf00XR\n1ES/c1ERtcewMP49fFgWgpWXS5LMzwcMdR1r/dcE7cKh0KWw2Xhsh8W1D5LNJgPHEyawN9HLL9MH\nX19PrS8mhiQr3FGiUllYEq2tMn1VQLSqBlxHYIp4gND6bTZWXh8/zmv19+es3+PHKRj8/WWfnsxM\naWHV1kq3SUwMu3c6CweAa/7Xv6jlAzx+Q4MUrImJfJ2bS+E9Zw413Lw87hsX98Oqd0+eZPDY358p\niYOlJYR7JnhlpRIE5wOUIOhjbN9OghEuhz17PG+Xni6f5+UxiBkeTqIrKyNBmUwkufh4kr6XF3BJ\n+at48nTHqSeJCRpaW0lqvr7ArVM4TWvXLumrNpnoWw8MJJFecQV78Y8cSb/66dMkw8BAXosgdkH4\nCQl8X2QojRlDTVpYDcKf7+8v/f3CtQWQtIuLOU1t3Di6tfR6+vmTkthm2deXAispidp8TQ2PPXMm\n3xMDWjIzuW98vBSsosuqmHtQUUEB8POf81wZGbLmwGJhbCEoiPc9IoLXYbXKSWA9RVUV60HE9VZU\ncIToYMDo0TKordfLuInCwIYSBH2MykqZNumcR++O0FD5mUibBEh4zc10n4gagZ27Otb6fxH4b3wQ\n8xMEBvLcjRU8d0YGSfOuuxgUTUtzbV/c2sp1+vjw/KmpTF/MyKCgEL2GxDYiAO1wcH1NTZLwi4tJ\nvjqdzPEPDeV2ISEyniAa5dntcgpbTg6DqA4HJ4H5+UnLSJw3PFy2dhD9iS69lFk9x49TcFVVMU/f\n25uCIj+f68jNlVbUhx+y5Ud+vhRimiab55WU0BIQ1sapU2wP0VNUVLjea9GWXGRKnc9YuZIWXWMj\n3WrdTY9W6F8oQdDHGD1aFkLpdHJ4iTt+9CP6W+vr2W9m8mRqyE1NwFz/o/j48ExuWNR+34vnaigq\nItnqdMD4BGrvorpWZBR5ezMIKsZGukPTeL4vv2Sb4xMnuK1oax0ZSRIVbaaFxi/iH8LC8PJyPb6/\nP7Vs0SHUZCLxOsc4xNjK0FBq84GB3EbTZBqqmGEsxnaKgS8TJ1IT3btXEr7RyPTPn/yEr6OieAzR\nlnr8eBmIvecevq9p/A4EbDYeRwgCT51ju4Phw2U1NUDhMhiEAMDrOs9rPocklCDoY4ipXQLOvnNn\nREWxG6LAVVfrcH8Hx3zc5//De6mPoqmJP8TqHJKm0UiNW8wSiIykptbSQtfO119TOIhmdEVFrkVr\nmkZybm0F/vpX7iPy+GNjqYHX17uOthTxAVGrAPB8opVESAibxyUkyPnKEydS2FitPJ+wLGw2asrv\nv0/BER3NY7S00Kc+ahT/irTZ6moGU8X0tMZGChMxpU1YIwUF7Cip01HQjBwpST0sjPdj8mRep4+P\n/I6Skhgrqa4meXe3KtzTd3vrrVynvz+wZMnZHUdB4VxBCYI+RlGR1JRFAZVHFBd32jMgKFBry7ix\n2QDfM22oBWnZbHKIvNHIVEh/fwZSnQn35EmZCRQaKoOnzmhu5ucWCwlWVA2LcYzCnQPISmHneQbC\n6rnoIsYb3n2X1bdNTTx3RIRrJ1NNk620AZ7r+++5doeDxH3zzYwBvPQSXS1eXvTpv/IKffnNzbQA\nTp2iBTFsGN1Jmkb3lljv1KncPziYabGTJsl1+PsztXTHDm4TGsr7eeedP7xPUE+yhRQUehtKEPQx\nvL1dWzu4NAjspOfSqxE/x4P+zyIujgQVV0ltWvjFhWtB5OYLV4zNRs1YzPUNDaUVINwxNhtJToyo\n9PKS1ceA9NtbLFIYiLx9s1lm4jgXkDkXiQEUOMHBXMf27dzW15fX0dAgWzWI2gLhIhLH1OvpshEN\n8AoLeQ1Tp5LcT5/mdn5+PE9NDd1YYmKXcBtFRsqaBIGgIMYOPLUDBxifCAlhhlFrK+MFlZXsSa+g\nMFjQ6/MIFFzhzPUhaITJrJPJ7u7QNGSka7jheg2/DX0WNhs18gkTmFJ63XX0x44axYePD0krIIAE\nJyqLxTjEoCAOP3EeWuNczCVGLZ45ddtcAyG8hAtG1A4ArmMoBZznIgAUNuJYorJZDH4RvYdSUvie\ncC2FhsoCubAwuY/Y7+hRuqVE5o9ez9jFgQMUAiUlsvZBdFkdP57CZt48uqP8/OhuMhiAjRsZB/Hk\nqisrcxXe7q8VFM53KIugjyG6eTo0D8R/ySUsIHCCIHJnLfmSSygMnn2WbpOXXpL9fOx2Ztr4+fGz\n/7+9cw+K+sry+GmgoUGah8gbFOTRyENEMU7cxIiKmIcYYyabymtNzO5OUqmtZGa2UrOzW3F2a9Qk\nu5syk/kjm91MZZJZdczEURPDaoykjEqMCEEFFRUEERBEeYlAd//2j+8cbjeCyqO7oft8qij8df8e\n93bjOfeeZ22tEqD19RB8zzyD8tQ+PqqtJQtMgwE/VitW1z09MGE0NKiaQBz1ZNvFjE0tPE5/f4R+\nHj8OQW4yIYrH1xe+hvPn7XMWrl9X5Sus1r/0Mg6A8mLfBpfjYIf0/Pm4jgvtTZmCe7EpqKVFldEI\nCEDvZyI8k3cA27cjsogICiQ8/FabfUyM/Y4nNta+wul40tICc9bUqdJlTHAeogicTGSk/eo5wF8b\nqKEzFGyztloh4EJCYNc/dw4Cv70dgqq3Fzbz5cth0965E4KfM2B7exHLHxqqkpg+/xyCknvnEkGg\nBwZCkPb2IvqmtxfCvL9fCX5uQt9nk6HMZRX8/JQ9nQi296AgXB8XBxNVZCSUBHf9CgyEsOY8Ce68\n1tGB5y9YAJs/F9ObMQOvG43KHKbXwwnNDeWvXMFnYzLh3nV1t8b+d3TcetzZiXuxszsqCj6J0lLc\nZ8mSkX3nd0tTEwrl8a4kLw8F7wTB0YgicDK8ckYTRyK/O5gYAgJgj/6v/4IQTkyEAH73XZg0fvgB\nr0+bBkH/xz8iZJKTvtgx7O0NIV1VhZ+bN1Vpa/YJcFtKsxkrbb0ewmnaNDyTM4i9vFS9JFvYeTx7\ntr25KCYGO5OZM1Vj+dRUVF5lU8+MGaq5i6ZBYTU04D0O25w5EwrFzw+7gatXISiPHoVCSk4mWruW\n6F//VfUh7uxUq3fbcM+yMiiK0FDVilCnwzP/4z9wzUMPqT7CU6eizLQjC72dOmVvmiovF0UgOAdR\nBE6G49oZ28Si4SgshMmgvR0mkaoqOEQvXsT77GDt7YVyuH5dCW22yXNl0M5OrNTZfs52fyJVUsLW\nP8AKo7VVhYGGheEZfC6R8k/MmoX7Xr2q7stNeE6dwv2MRghWiwXz6u3F/Z55Bp3X2tqIvvoK2bc6\nnepl8PjjWN17eaGAXGMj7lNQgPnNmIH3f/QjPLenBwljAQHYKSUm4jP7n/+BPyExEe/dey/m+s03\nUKS+vkig274dgVvffqsyvRcvdlyc/OByI2OtYyQId4soAicTG2t/PFyJYluCgpDk1NiI5ueapiJt\nWHhzBBCXhB68WmfYActOYiIlsL28lPBnfwHf37bCaWsrhCwrDNsmLgkJRJ9+qmz3RBD2qanITDYa\nYeIqL8fql8Nnu7thKnr6aQhtf38IX27Y0teHcxobsUrm0tsZGTgnKAj9BYKCYP45fx5C32hEbsGp\nU0T/9m/YCdy4ges7OhDSyo3quR9Aby8yrYODcU13t4rkLS7GboQ7jI0nubnYkbAJr7Bw/J8hCEMh\nisDJDLZR3230icGgGqtERWH129EBU0VAAFbgnZ23RvAQKQGfmgozCCsB/q1pWNHzroJrB/n6QuBZ\nLPbv85j1eiiB1FT4JGJiYO4pLoYpiKOGmppwHfcBbm7G+6wEOMO6t1c5vEtLoQD0enxmbW04r6kJ\nph+LBUlwYWHYRWRm4npvb/Qg/qd/wucSG0v0n/+pisq1tamdDVdNDQuDYgsNxXlNTaqgXmUljsPD\nlc/DURFDXl7IKF+92jH3vx3d3ejPrNNB0fFcBc9AFIGT2bbN/ni4zOKhaGuDMDtyBCtuPz+sePv7\n4SjmdpW2kai+vhB6AQG4vrcXgpDzDmwjf3gsOp1qgh4cDFs6d/oiwvmcMMY2/T17sJpPTYVgjY2F\ncK2rg7kmOBihnd99h3tz4bcpU5TAT0qCY/nwYSg2f38IfIMBTnOdDk7yEyfgEG5pgT/i4EFkJl+9\nCuWweLF9jZuGBtyDazaFhKiM5PvugymJP9fsbLzPyosV5dmzeG/hQvcz2fT3I/Ls6lUcnzwJv5SP\nSAePQb5qJ2MboTNSdu9WZpkLF7BKrauDYL94EYKbaxj5+6sy12FhEIDc45gI54SEKPNNT4/yV/T3\nQ3i2tUEwG41KMNoqAd4pBAdj5XzoEKKR/vmfid57T1VW9fGBMuHQULMZY4+Px+6mtRXC+F/+BeNj\n/0NODgR0dDTm1tQEYWUwKKV07RqUAwvnxkaMJSBAdTRLTFTzzs7GuTk5UBjs/A0NJfrJT/C5Go3w\nI/D458+Ho/qll0ZfcXQi09yslAARvqu2NvecqzA0ogiczLx5qgH53WI2Y8W9Ywfsx9zLoLERQptr\n8nR3q3LQnAQWFYW2jz09MB1xFJFeD3MKr7grKlQ1UW4uQwSnakiIKgrHmccBASp+/8QJCNDubgja\n5mZck5+PVXZHhyrRwCYprhKamQkh/Pzzyu6eng4h3NEBobxoESqllpRg/L29EP7t7RDysbGquZoq\n5gAAHQdJREFUjwCzdi2cvDodru/tRdhtWBieaQsXuIuMVB3gEhJg4uIEupwc9xWMQUHqeyXC38ZY\nS2gIkwtRBE4mJcVeEQQE3PmaXbsgULu6VOMPtuv39Ng7f6dMgYDu64MD198fCqC9XXX2IsJq+M03\nsWr+858hJNlMY2v20ekgONmxywljfX2qwmhfH5QP1/RPSoIzmNs3pqZCMVy/jnvcuAHB+vbbuFdY\nGO7FsLmKxxsVhXnk5SGf4IsvECpqMmEeZ8/C5Ga14j65uRDajz1m/znGxMAv8Omn+ByTkpCY98kn\nqnT244/jtcuXobRbW/H5TsS2kONFUBDmvX8/vu/ly+/u71JwH0QROBnuMWzrcL0dO3cijPLKFQgq\no1H5ALgwG0fw8G4gMRErcE2D36C6WtUS0ukQevnf/63MKQYDhKtej2s4/0DTYGtPSYGwOHxYFaXj\nbOTAQJwXGIhdAbeFNBhUxu60aRC6P/ygVuhPPWXflUvT4GiurcUuZMoUNb7SUlxPBAH14x/bf0Ym\nE4rZ7d2LZ93uM/3yS9U4paUFUUQdHfjNPZK5SUxYmMo9cFQm8URh1izVrlPwPNz8z3viwcJ2cGTP\nUDQ3Q0Bx7Xyu8280YtUcEKCihLheUFMTBGl7OwQyZ8peuwbhHRqK1e/WrchAvngR8fLsdA4IgBD3\n94fSqqlBRc8jR7Ci5oYy/HPzJlbsHBbK/oS6OmVeYHNOTAwUyfnz8HdUVWFcRLj/gQMqGc423+JO\nESwdHVAC/f3YlXzyyfBO+NZW++PeXmViI8Lqv7oaTmhm/vxbm9MLgjshOwInw20Z70YRMBERqklL\nYCBMRGYzTCxs8ydS5aC5nj/3/eVnca/k6moId44mys5GPH10NBRPTw/MOHwvb288l2PvOUeBbcnv\nvAOlYrVCARw7BsE5dy4ieq5cwT2PHVOVRD/8EDb8+HgkkjU0qPnOnKls/rGxdy7p0NpqL/g5cW4o\n4Z2aav+sRx+FUqyvhyJLTsau6rHHYA7y8nJf34AgMKIInIzRaB/rf7vM4shI2KlLS2FSycmBmaak\nBAK9vR3ncTE67jrGPQIGJ6tpmoq6OXtWJYh1danSEn5+qsAa7zS4ry9XAuUWmj4+sP37+yPccOtW\n/DswELsEVhhc/K61VYWmcnnr9nb4KrgXQkwMxrB2LUxIQ5lkrFaVpzB9Oj4n2yihqVOHbwb/wAN4\nfnMzFI7JBJPIxx+rJj0PPohzo6Lu8GUKgpvgUkWQkJBAQUFB5O3tTXq9no4ePerK4TiF8+ftdwNc\nznk4HnkEwio1FcKbCIIuORk2/8uXlcP42jX85hLTYWGw05vNahfC/WS5fLNej3DL+HgIY70ewrir\nS/keiNRuY+FCRNNcv45x3H8/BGZsLNE//qOqF/THP8LvQKSqixoMGHNHB54VHo5oJaMRq25Ng1DP\nylKr8f5+XM8x7VYrTD8XLuB47lxk4K5dC/OSjw/GdLuM7blz7Y/9/NDCsqdHKcLBNDRgDgkJjskq\nFgRX4lJFoNPpqLi4mKZ6kAF2JLWGLBaiLVsQKqnTQVjzavrKFZUZGxenTBp1dfi9cCGUzqVLypQ0\nZQoE2cmTKiKorw8KJC8P9z9yBM8ND8e1RNgtREaqmkdpaRjDvfdiHM8+i53Aww9DIIeEoEDb5cuI\neOKVv7c3nmc2I8vYzw/CPzERz4mNhYlq3jwcHzgABzI7uDs6YDI6d075H44fR+RQRATRqlVj+26G\n80V89x2czET4/F98cfgdhyBMRlxuGtJGYix3A2bPRmglc7tolBMnENLX1wdBzKvdkydhIjpyBEK1\nvx+O1/nzoTj6+lSmaECAavISEABncVCQWu1zc5rmZpigOjogoC0WlaAWGKj6BdfX472pU+Gg7enB\nNT4+8AfExKDkQ3CwWsVzOWci3DMiAiadv/s75EacPw+lxjV/iGCK4oQui4XorbdUw/uKCigMX1/7\n3YKjOHRI/buzE8+//37HPtMRdHYS/elPUN4pKajNJNnDApGLo4Z0Oh0tW7aMcnNz6YMPPnDlUJxG\ncrL98e3KFfzf/2EHUV8PIVtTg9V9WRkEf1sbhPCVK1ipV1VB6F66pJK99Hq8b7Vipb1+PUI3ud0k\ndwSrq8P9OjuhQMxmCPuEBGVO+vOfcV5DA1bi9fXKGd3Zif4IHDIaHIyEMiLc12iEMpk2DTsGHx8o\nmCefhI2+sRFCfcsW7CS4zSUR/s3dzIKCoBS7u1XMe2kpMoF37ryzqW00cO/k4Y4nC198Ad/KjRsI\n5T1yxNUjEiYKLl0PHDp0iKKjo6mlpYXy8/MpLS2N7h+01Fq/fv3AvxcvXkyLHVUD2EkM/s83uDEK\nEUw1O3YQff01TCqXLqkM4j17lN3eYMD1nD+QloaVfVgYhP+ZMwgP1elUo3q+LjQU53DBt44OFYVE\npNpLGo2wjYeGwtnb0oLXDAb89vHBdWYzlE5jI8JX09LsTUvcPWz6dChDdsjq9Xgmm4d6e+EMX7UK\n57KpKz5e7SwyMpCJHBKCZ+3ciddZMa1ZMz7fFbNyJRzhN25g7IN9DJMFDi4Y7liYvBQXF1NxcfGo\nr3epIoiOjiYiovDwcFq9ejUdPXr0torAHeDMYGYoy9hnn0Go6fVYTXNXLl9fmCVMJtW5q6tL1RM6\nfZro/fcREnnypMod4Jr+Fy6opK7wcNVpjPsTcH8BrgWUkoJdwaVLKpuYdwDJyfixWrELCApSGcTc\nV/n0aTWngABE6fz7v6tcBYbLYbCZTK/HWJ57DsqL6yQVF2M3sGwZQl2JYEKyZfDxeDB9OtHPf475\n2457spGVpcJyvbygUAX3YPAi+Ve/+tWIrneZIrhx4wZZLBYyGo3U3d1Ne/fupTfeeMNVw3Ea8+bB\nrMIMZWbgJKuMDNjP2XnLoZmZmfAdGAxw2F64AIEZGIiImk8+gdAKDlYhohYLVt4XLuC8pCQoEW4r\nGRMDJcWF4WbPhk2eCIL54EG87u+vMnenTyf627+FgOZsXSLcb/du+xWnjw/GPTguoKoKvpDSUpiN\nFixA2ChfM2sWHLXffYfXrl+37zQ2c6Z6j48dAZfynswsXIjP/8oVfE62FVoFz8ZliqC5uZlW/6Xw\nutlspqeffpqWL1/uquE4jVWrkMzV24vV+FANytPT0X7R3x9Oyc5OrHQNBkTo5OVBGBYVYTXe3Ixz\nuJNXXx+ENjtxOb9Ap0MUzt/8DdErryizS0MDHLM3b6rCdLW1SuC3tUGRmM1QLuHhuNeCBRDehYUq\nDyAlBc+wVQJz52KnMLjOvsWC3Y9erxrEPPTQraUnDhzArsTbG8rnzBkVWWQyoeREdTXG4s41gcaD\ntDT8CIItLlMEiYmJVG4bPuMhnDiBVRm3lxzKufngg4jNb2/Hf1qLBbuExESs5nt7Iai/+AKrO46e\n6exUiVq8E/D3h2Dt6lI+hcpKOFnz8oj27YMJJyJC9f7lEhbnziESqaJCtbzs78cYsrNVSW1fX9yP\nCEqJlQAXLvvrv4ZyG+wY5/sRqRW3rZOYCPc6eVIli3FLS1uMRvguuHVlevqovpq74sQJ+F1iY9WO\nSRAmOxI85mS4RERPD4R3aOit5+h0tzokbbfxx47hx2BQCVAGg6r9wyGhHDU0YwYigdj5296uCtdx\nlnBLi/ID3LiB+wUEqAJ0ej1e0zTsBAwG1b7RluBgKJ+eHvVaZubQSVi+vpgnm8rCw1VxOebSJfge\nKisxDqNROZaJMN7//V+lUP/0JyhRR6SmHD+OvAgifP43b8I0N16YzSrjOzXV/QvdCRMHUQROpqND\nNYHhjFvm+nXkGPj5oZTycFU0eSUdFIRrOAY/Lg5mHq5syopAr8cKtroa55tMqndydray73MP4v5+\njPGJJyCAuVNYfDx+5sxR3cQGw+arAwfUOIbL8j1zBoKvqwsKYc2aW30m4eEQ6gsXYnyhofbnsPKz\n/Ww463m8OXfu1uPxUgQWC8pcXLyIY5MJobW23eYEwVGIInAyVVWqjg+RMnl0daE0NJtbzp6FLX8o\n5s6FE/XKFcT59/ZiFdzVBUFeW6tq+c+ahRV0SQkUR1oaFEZFBQT5zJnIK4iKglC9dg2Cdt48tHFs\nboZQNRrRReypp7BavR0xMWhbyRE8mzZhV/LUU0qI9/aiLwCbmqqrsVMZXOAtMhIKoqQE165YYS8c\ng4Mxdn5WUJCKKBpvwsNvfzwWGhuVEiCCkmxrs3eMC4KjEEXgZDienoUZ/66rs29jWVOjHL+7dyPJ\nKiwMVTFDQ4lefx0C9/BhrIh9fPDbaoXg7u+HkPT2xsqV6wix/b6xEaGk33yD83lcRiPOmz4d0Tgx\nMRDO3d1IELuTErCFyzIQIVrpm2+UL6Gnx75iqKZh/jodzFixscqnkJExfKijlxcUZkkJVtW5uY5r\nvL5oET5j9hEsXTp+9x68E+L8DkFwBqIInExqKgRcTw+EGK9eQ0JUlzEi2Oc5L+DECVwzfz6ye59/\nHrb9S5cgsK1WnKPXQyHExkIJBAfjvbAw7DxYufj5wd5dUYEVf10dhPK1a2pFPXUq3uvrU1VHR2pu\nYZNNczPmUluL1woLcb/ERFV7ibOXP/lEham+8MLwq25uoOPnByWWlzfir2LEeHurRLjxJjwciuXr\nr/F3sWKFtIsUnIcoAieTl4dM2AsXYE9/5BG8HhODom2HDkG4PfwwnKBnz6r6+VxRlJvJHD+uHKfX\nr8Oc09UFQRoZCUGyciWUR18flE1ICKJ4fv97mB4uXYKZxscHwpdrz/j6ItR1xw44MTMzRx52yPbz\nM2cgRKOiMOaMDPgYnn4auxKzGXkLrASIoLC+/x7hpIOxWjH/qioIzYcewk5gPDl+HJ/79OnwoziD\n++/HZ8Y9nQXBWYgicDKJibDRc1SPrZDJzbUXaJ2dKrpI0+ATiIxUUTLe3tgJhIRAQUyZonoBnD2L\ncNBTp4h+8hPVh+C++1Tvgq4u+xDOkBD8vnkTZpAZM7CD6esbXelldianpWEHwKaOvj789vFR+QBE\nqp/ycMdMdTWUABGUwpdfIpRzvITnkSOo80SERDez2X6cjkSKwAmuQP7snMzZsyjNMH06/tNfvjz8\nuQ88gB1CVJRqIj9nDlbYOh1W0W1tuBfb14mwmj10CAK+uRlO6A8/tL/3ggXYKRgM8AH4+CCpS9NQ\nwmHGDJzHUUdjYeVKlf0bHX1r4T2moIDoD3+AAoyOJvqrvxr6PN412B6PZxHbwdFB5887TxEIgisQ\nReBk/P3tHYG3c2zm56u+xVOmQFAnJ8NW39aG+8ycCZ/Bjh2w9UdFIRy0uxv36Ouzj0ZhzpzB7oLj\n/U0mmCZiYqAkxpMHH0SSV28vdkTDKZaoKKLXXsOYuLnOUKSkqIJ0ROhHMJ4r6YgICH9mPKODBGEi\notMmcEMAnU7ndv0KNA1C+8QJKIEf/9g+QWoompogFCMjcdzZCfOFpiGkk0sycH+Bn/8cpRusVvws\nWoRdgcEAuzc3vGlvV+Wfn3nmzr2BR4OjvkOLBXPhHc1wlJZihR8Rgc/hbsxH/f0wDbGPYPlysdkL\nk4uR/r8TReAizGYIF0ckDO3eDbv56dPwDSxahHyDmBjsBIjs2zL6+aF43LRp4z8WV36HFRVQiMyC\nBY6L+hGEicRI/9+JachF3M6Uwc3ifX1Hl1D00EMwH332GVa3U6eijPOxYzABca+C+HgIxyeeuPNz\nLBbUJaqthUJ58MGx+w4czWCTGJuSBEGwRxTBBMNqhdmGm8gsWaLKMt8t3t5wtNbUKMcnJ7GZzYi4\n4cb1hw9jt1BQcPvaNt9+i6QtIigpHx8V2tnSgmb1bW3IZF69emKYUmJjYRqyPRYE4VakrJWLGK5p\n/fnzSgkQoWbP4Iqcd0tennJGT5uGuH2LBUogOBhKor4eZqSiotvfq6Vl+ONdu3BsscBRfezY6MY7\n3sydCwWXlIT4/IICV49IECYmsiNwMpqGhLIffkBkzBNPqFBNoqF9BqP1I8TGEv3DP6hmLr6+2F1s\n345yD+3tyDsIDoY/YajkLSY52b75jG0IqG1pjKGOxwo7wUfDvffeuTCc1Yq5mc2IbprsDWgEYaSI\nInAyJ0+iwigRQjx37CB69VX1/syZsONzrsDSpcMnVtly5gyiXOLjEV7J+Pvbh6gajURr1yJ5bO9e\nhEa2tqJcw8GDqPI5lFlnzhy8fvEifAS2ZbLnzSP66iv829cX+Q7jQWsr0bZtcHibTCg+54iEq23b\nlBP9yBE4zu/mMxcEd0EUgZOxrdNPpKqPMl5eKD/c0gJhxNm+t6OsTDVwJ4LAzMoa/nwvLxSvCwmB\nAG9qgqDdvx/KacWKoa/Lyhr6vvfdhxyAtjaYYcarYubnnysTVFUVMqXHuwNZZ6dSAkR4Xl3d8Elv\nguCOiCJwMrNmYeXNDWHuuefWc3S628fGD+bUKfvjysrbKwJmyRIoJtvVLxeBGwk3b8Ipe/kyhGhh\n4a0r6p4erLbNZpTRuJsCdo42OREhdNbHB+NiRlNOQxAmM6IInIzRSPT3fw9HbWDg+Kw8B3c5G6rr\n2XAMrt0/mlr++/er2j/t7fA55Oer961WFLlrbMTxDz8QvfzynQVubq5yYvv6oqTGeOPri93R7t1Q\nBg884Lh+BoIwURFF4AICA4fu7jVali2DSYfLUi9efPfXzp0L81R1NfwF3C9gJFy7dvvjzk6lBIgw\n1suX7X0ZQ/GjH2FndPUqfCeOatKSnu7YPseCMNERReAG+Pkh+mi03HcffkZLerp9obZZs+zfnzIF\nEVLsD/HyuvveBjNn4kcQBMchJSaEceH0aazyB0ct8XfY0AAzj9mM4nayAhcExyG1hoQJhXyHguB8\nRvr/zqWZxUVFRZSWlkYpKSn05ptvunIogiAIHovLdgQWi4VMJhN99dVXFBsbS/Pnz6ctW7bQLBsD\ns6wmJy6apoq6zZgxfOavfIeC4HwmTfXRo0ePUnJyMiUkJBAR0ZNPPkk7d+60UwTCxETTkI17+jSO\nMzKIHn/cMSW1BUFwPC4zDTU0NFB8fPzAcVxcHDVwl3ZhQtPcrJQAERLaWltdNx5BEMaGy3YEurtc\nPq5fv37g34sXL6bFIwmSFxzCUPV+hqsB9MYbbzh2MIIgUHFxMRUXF4/6epf5CEpKSmj9+vVU9JfU\n0Y0bN5KXlxe9/vrranBiX56w7N+PUhlESGAT/SwIE4dJEz5qNpvJZDLR/v37KSYmhu655x5xFk8y\nOEEsIMC14xAEwZ5J4yz28fGh9957jwoKCshisdC6devEUTzJEAUgCO6BJJQJgiC4GZMqoUwQBEFw\nPaIIBEEQPBxRBIIgCB6OKAJBEAQPRxSBIAiChyOKQBAEwcMRRSAIguDhiCIQBEHwcEQRCIIgeDii\nCARBEDwcUQSCIAgejigCQRAED0cUgSAIgocjikAQBMHDEUUgCILg4YgiEARB8HBEEQiCIHg4oggE\nQRA8HFEEgiAIHo4oAkEQBA9HFIEgCIKHI4pAEATBw3GJIli/fj3FxcVRTk4O5eTkUFFRkSuGIQiC\nIJCLFIFOp6Of/vSnVFZWRmVlZbRixQpXDMPlFBcXu3oIDsWd5+fOcyOS+XkaLjMNaZrmqkdPGNz9\nj9Gd5+fOcyOS+XkaLlMEv/nNbyg7O5vWrVtH169fd9UwBEEQPB6HKYL8/HzKysq65WfXrl300ksv\nUU1NDZWXl1N0dDT97Gc/c9QwBEEQhDug01xso6mtraWVK1fSiRMnbnkvOTmZzp8/74JRCYIgTF6S\nkpLo3Llzd32+jwPHMiyNjY0UHR1NREQ7duygrKysIc8byUQEQRCE0eGSHcFzzz1H5eXlpNPpKDEx\nkd5//32KjIx09jAEQRAEmgCmIUEQBMG1TLjM4u3bt1NGRgZ5e3vT8ePH7d7buHEjpaSkUFpaGu3d\nu9dFIxw7RUVFlJaWRikpKfTmm2+6ejhj5oUXXqDIyEg7E19bWxvl5+dTamoqLV++fFJHhtXX11Ne\nXh5lZGRQZmYmvfvuu0TkPnO8efMmLViwgObMmUPp6en0i1/8gojcZ35ERBaLhXJycmjlypVE5F5z\nS0hIoNmzZ1NOTg7dc889RDTy+U04RZCVlUU7duygRYsW2b1eWVlJ27Zto8rKSioqKqKXX36ZrFar\ni0Y5eiwWC73yyitUVFRElZWVtGXLFqqqqnL1sMbE888/f0t2+KZNmyg/P5/Onj1LS5cupU2bNrlo\ndGNHr9fTO++8Q6dOnaKSkhL67W9/S1VVVW4zR4PBQAcOHKDy8nKqqKigAwcO0Lfffus28yMi2rx5\nM6Wnp5NOpyMi9/r71Ol0VFxcTGVlZXT06FEiGsX8tAnK4sWLtdLS0oHjDRs2aJs2bRo4Ligo0I4c\nOeKKoY2Jw4cPawUFBQPHGzdu1DZu3OjCEY0PNTU1WmZm5sCxyWTSmpqaNE3TtMbGRs1kMrlqaOPO\nqlWrtH379rnlHLu7u7Xc3Fzt5MmTbjO/+vp6benSpdrXX3+tPfLII5qmudffZ0JCgtba2mr32kjn\nN+F2BMNx+fJliouLGziOi4ujhoYGF45odDQ0NFB8fPzA8WSdx51obm4eCACIjIyk5uZmF49ofKit\nraWysjJasGCBW83RarXSnDlzKDIycsAM5i7ze+211+jtt98mLy8l7txlbkTYESxbtoxyc3Ppgw8+\nIKKRz88l4aP5+fnU1NR0y+sbNmwYsOHdDbzNm0xMxjGPFZ1O5xbz7urqojVr1tDmzZvJaDTavTfZ\n5+jl5UXl5eXU3t5OBQUFdODAAbv3J+v8Pv/8c4qIiKCcnJxhy0pM1rkxhw4doujoaGppaaH8/HxK\nS0uze/9u5ucSRbBv374RXxMbG0v19fUDx5cuXaLY2NjxHJZTGDyP+vp6u52OuxAZGUlNTU0UFRVF\njY2NFBER4eohjYn+/n5as2YNPfvss/Too48SkfvNkYgoODiYHn74YSotLXWL+R0+fJh27dpFe/bs\noZs3b1JHRwc9++yzbjE3hnOywsPDafXq1XT06NERz29Cm4Y0m8jWwsJC2rp1K/X19VFNTQ1VV1cP\neMgnE7m5uVRdXU21tbXU19dH27Zto8LCQlcPa9wpLCykjz76iIiIPvroowHhORnRNI3WrVtH6enp\n9Oqrrw687i5zbG1tHYgq6enpoX379lFOTo5bzG/Dhg1UX19PNTU1tHXrVlqyZAl9/PHHbjE3IqIb\nN25QZ2cnERF1d3fT3r17KSsra+Tzc5D/YtR89tlnWlxcnGYwGLTIyEhtxYoVA+/9+te/1pKSkjST\nyaQVFRW5cJRjY8+ePVpqaqqWlJSkbdiwwdXDGTNPPvmkFh0dren1ei0uLk778MMPtatXr2pLly7V\nUlJStPz8fO3atWuuHuaoOXjwoKbT6bTs7Gxtzpw52pw5c7Qvv/zSbeZYUVGh5eTkaNnZ2VpWVpb2\n1ltvaZqmuc38mOLiYm3lypWaprnP3C5cuKBlZ2dr2dnZWkZGxoA8Gen8JKFMEATBw5nQpiFBEATB\n8YgiEARB8HBEEQiCIHg4oggEQRA8HFEEgiAIHo4oAkEQBA9HFIEgCIKHI4pAEATBwxFFIAgj4I03\n3qDNmzcPHP/yl78caFQjCJMVySwWhBFw8eJFeuyxx6i0tJSsViulpqbS999/T6Ghoa4emiCMGpdU\nHxWEycqMGTMoLCyMysvLqampiebOnStKQJj0iCIQhBHy4osv0u9+9ztqbm6mF154wdXDEYQxI6Yh\nQRgh/f39lJmZSRaLhaqrqyd1UxNBIJIdgSCMGL1eT0uWLKHQ0FBRAoJbIIpAEEaI1WqlkpIS+vTT\nT109FEEYFyR8VBBGQGVlJaWkpNCyZcsoKSnJ1cMRhHFBfASCIAgejuwIBEEQPBxRBIIgCB6OKAJB\nEAQPRxSBIAiChyOKQBAEwcMRRSAIguDh/D8+W7p3B7myuwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c320bd0>"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c = 2*hot_01 + df.cold_01\n",
      "scatter(y,wy, c=c, s=(sig01+1)*50)\n",
      "vlines(y.mean(),wy.min(),wy.max())\n",
      "hlines(wy.mean(),y.min(),y.max())\n",
      "xlabel('y')\n",
      "ylabel('wy')\n",
      "title('Moran Scatter Plot')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "<matplotlib.text.Text at 0x10f414650>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEZCAYAAACaWyIJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c31d090>"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sig01.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "155.0"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['wHR80'] = wy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df[df['cold_01']>0].to_string() "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "       FIPS       HR80  sig01  sig05  hot_01  cold_01      STATE_NAME       County     wHR80\n",
        "6     24001   3.724487      1      1       0        1        Maryland     Allegany  3.153441\n",
        "8     24023   5.031826      1      1       0        1        Maryland      Garrett  3.527749\n",
        "10    54061   4.443023      1      1       0        1   West Virginia   Monongalia  2.932325\n",
        "11    54077   1.094331      1      1       0        1   West Virginia      Preston  3.642804\n",
        "14    54103   4.571638      1      1       0        1   West Virginia       Wetzel  3.831961\n",
        "16    54065   6.224131      1      1       0        1   West Virginia       Morgan  3.416385\n",
        "19    54003   6.413683      1      1       0        1   West Virginia     Berkeley  4.182615\n",
        "20    54095   0.000000      1      1       0        1   West Virginia        Tyler  2.992347\n",
        "22    54037   4.400150      1      1       0        1   West Virginia    Jefferson  4.153122\n",
        "23    54073   0.000000      1      1       0        1   West Virginia    Pleasants  1.682967\n",
        "24    54033   6.005233      1      1       0        1   West Virginia     Harrison  2.769722\n",
        "26    54017   4.484506      1      1       0        1   West Virginia    Doddridge  2.914966\n",
        "27    54091   2.009969      1      1       0        1   West Virginia       Taylor  3.119190\n",
        "28    54107   2.135657      1      1       0        1   West Virginia         Wood  3.390607\n",
        "29    54085   2.913244      1      1       0        1   West Virginia      Ritchie  3.061795\n",
        "36    51107   4.063129      1      1       0        1        Virginia      Loudoun  4.390625\n",
        "37    54001   0.000000      1      1       0        1   West Virginia      Barbour  4.416850\n",
        "44    54105   6.772315      1      1       0        1   West Virginia         Wirt  3.429076\n",
        "46    21015   1.454270      1      1       0        1        Kentucky        Boone  2.026717\n",
        "51    54097   4.268579      1      1       0        1   West Virginia       Upshur  3.601592\n",
        "52    54021   3.999680      1      1       0        1   West Virginia       Gilmer  3.727101\n",
        "55    21117   6.080151      1      1       0        1        Kentucky       Kenton  1.922059\n",
        "68    21191   3.033336      1      1       0        1        Kentucky    Pendleton  4.054054\n",
        "75    21081   0.000000      1      1       0        1        Kentucky        Grant  3.831520\n",
        "78    21161   1.876349      1      1       0        1        Kentucky        Mason  3.277904\n",
        "82    54075  10.081661      1      1       0        1   West Virginia   Pocahontas  4.290141\n",
        "83    21187   3.735246      1      1       0        1        Kentucky         Owen  4.484162\n",
        "105   21209   1.528141      1      1       0        1        Kentucky        Scott  4.641435\n",
        "118   21073   4.781257      1      1       0        1        Kentucky     Franklin  2.340248\n",
        "120   21211   4.286694      1      1       0        1        Kentucky       Shelby  4.284222\n",
        "122   21011   6.650042      1      1       0        1        Kentucky         Bath  4.665551\n",
        "141   21005   0.000000      1      1       0        1        Kentucky     Anderson  4.925832\n",
        "164   21101   3.264054      1      1       0        1        Kentucky    Henderson  3.416317\n",
        "169   21059   5.429576      1      1       0        1        Kentucky      Daviess  2.347524\n",
        "192   21183   6.126043      1      1       0        1        Kentucky         Ohio  3.074290\n",
        "238   51121   6.822428      1      1       0        1        Virginia   Montgomery  3.835457\n",
        "294   40003   9.420187      1      1       0        1        Oklahoma      Alfalfa  1.989812\n",
        "310   51035   4.889378      1      1       0        1        Virginia      Carroll  4.608768\n",
        "321   40153   9.446439      1      1       0        1        Oklahoma     Woodward  1.191327\n",
        "338   47091   7.275373      1      1       0        1       Tennessee      Johnson  5.068751\n",
        "346   40047   7.959249      1      1       0        1        Oklahoma     Garfield  4.817456\n",
        "347   47073   6.856986      1      1       0        1       Tennessee      Hawkins  4.841746\n",
        "351   37171   4.485638      1      1       0        1  North Carolina        Surry  5.059591\n",
        "385   05135   4.564022      1      1       0        1        Arkansas        Sharp  4.676720\n",
        "387   05007   4.267213      1      1       0        1        Arkansas       Benton  3.998879\n",
        "398   47057   7.959724      1      1       0        1       Tennessee     Grainger  4.754336\n",
        "463   48341   6.033183      1      1       0        1           Texas        Moore  5.183679\n",
        "611   48129   8.179959      1      1       0        1           Texas       Donley  4.519983\n",
        "620   05127   6.883497      1      1       0        1        Arkansas        Scott  4.185357\n",
        "684   48191   5.958765      1      1       0        1           Texas         Hall  3.357944\n",
        "744   48345   0.000000      1      1       0        1           Texas       Motley  4.777826\n",
        "746   48101   0.000000      1      1       0        1           Texas       Cottle  3.629756\n",
        "822   48125   9.418857      1      1       0        1           Texas      Dickens  4.771036\n",
        "825   48009   0.000000      1      1       0        1           Texas       Archer  3.635333\n",
        "875   48237   4.499640      1      1       0        1           Texas         Jack  4.549122\n",
        "889   48503   3.493511      1      1       0        1           Texas        Young  4.178185\n",
        "953   48417   8.514261      1      1       0        1           Texas  Shackelford  4.808184\n",
        "1050  48093   5.283876      1      1       0        1           Texas     Comanche  3.728266\n",
        "1091  48141   8.821301      1      1       0        1           Texas      El Paso  0.000000\n",
        "1131  48451   7.863119      1      1       0        1           Texas    Tom Green  4.059151\n",
        "1239  48267   8.204118      1      1       0        1           Texas       Kimble  2.120236\n",
        "1242  48435   0.000000      1      1       0        1           Texas       Sutton  3.830180\n",
        "1246  48043   4.401602      1      1       0        1           Texas     Brewster  2.746418\n",
        "1252  48377   6.425084      1      1       0        1           Texas     Presidio  1.467201\n",
        "1284  48137   0.000000      1      1       0        1           Texas      Edwards  4.227311\n",
        "1285  48465   3.712986      1      1       0        1           Texas    Val Verde  1.446759\n",
        "1286  48265   5.791059      1      1       0        1           Texas         Kerr  2.151593\n"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df[df['hot_01']>0].to_string() "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "       FIPS       HR80  sig01  sig05  hot_01  cold_01      STATE_NAME       County      wHR80\n",
        "199   21025  19.603231      1      1       1        0        Kentucky    Breathitt  20.213720\n",
        "231   21193  37.516413      1      1       1        0        Kentucky        Perry  18.053386\n",
        "240   21051  29.301453      1      1       1        0        Kentucky         Clay  19.064651\n",
        "247   21131  38.077319      1      1       1        0        Kentucky       Leslie  26.731707\n",
        "285   21095  20.689600      1      1       1        0        Kentucky       Harlan  20.361694\n",
        "305   21013  19.419361      1      1       1        0        Kentucky         Bell  19.268191\n",
        "359   37185  14.374897      1      1       1        0  North Carolina       Warren  16.083794\n",
        "712   28027  15.349333      1      1       1        0     Mississippi      Coahoma  18.435638\n",
        "804   48387  10.351324      1      1       1        0           Texas    Red River  19.645064\n",
        "831   13045  14.197991      1      1       1        0         Georgia      Carroll  16.907577\n",
        "850   28015  10.229133      1      1       1        0     Mississippi      Carroll  20.176860\n",
        "868   13077  11.884147      1      1       1        0         Georgia       Coweta  17.892569\n",
        "876   13141  14.085499      1      1       1        0         Georgia      Hancock  16.891151\n",
        "905   13163  21.735587      1      1       1        0         Georgia    Jefferson  19.314330\n",
        "908   28007  20.135917      1      1       1        0     Mississippi       Attala  16.953643\n",
        "914   13285  14.665787      1      1       1        0         Georgia        Troup  18.389184\n",
        "924   01123  13.789775      1      1       1        0         Alabama   Tallapoosa  16.972680\n",
        "930   28163  23.157459      1      1       1        0     Mississippi        Yazoo  18.021208\n",
        "932   22017  23.115310      1      1       1        0       Louisiana        Caddo  19.128359\n",
        "963   28099  11.209663      1      1       1        0     Mississippi      Neshoba  18.659732\n",
        "966   48459  12.822755      1      1       1        0           Texas       Upshur  19.088636\n",
        "968   28089  20.025793      1      1       1        0     Mississippi      Madison  19.401358\n",
        "969   48315  35.392535      1      1       1        0           Texas       Marion  17.886479\n",
        "978   48203  15.944386      1      1       1        0           Texas     Harrison  19.890318\n",
        "995   28149  14.204454      1      1       1        0     Mississippi       Warren  17.991778\n",
        "1022  01113  20.412760      1      1       1        0         Alabama      Russell  17.192806\n",
        "1024  13283  10.952303      1      1       1        0         Georgia     Treutlen  17.745963\n",
        "1033  48365  12.867529      1      1       1        0           Texas       Panola  19.027194\n",
        "1037  22031  18.139179      1      1       1        0       Louisiana      De Soto  19.654971\n",
        "1058  13259  33.921303      1      1       1        0         Georgia      Stewart  18.068855\n",
        "1059  13307  14.238929      1      1       1        0         Georgia      Webster  19.959551\n",
        "1060  13261  10.217984      1      1       1        0         Georgia       Sumter  17.826729\n",
        "1062  01005  24.236549      1      1       1        0         Alabama      Barbour  18.211972\n",
        "1093  13239  28.284542      1      1       1        0         Georgia      Quitman  21.470889\n",
        "1101  13243  17.362920      1      1       1        0         Georgia     Randolph  22.019482\n",
        "1112  01099  10.301238      1      1       1        0         Alabama       Monroe  17.154104\n",
        "1119  01067  13.070187      1      1       1        0         Alabama        Henry  18.363221\n",
        "1120  13061  18.763486      1      1       1        0         Georgia         Clay  20.308494\n",
        "1134  13095  17.540724      1      1       1        0         Georgia    Dougherty  17.875898\n",
        "1141  13037  17.491691      1      1       1        0         Georgia      Calhoun  19.320700\n",
        "1162  13007  17.507003      1      1       1        0         Georgia        Baker  17.276367\n",
        "1184  01003  11.456795      1      1       1        0         Alabama      Baldwin  17.564441\n",
        "1254  48339  12.193192      1      1       1        0           Texas   Montgomery  17.506628\n",
        "1288  48473  13.469374      1      1       1        0           Texas       Waller  17.365347\n",
        "1317  22075  10.237117      1      1       1        0       Louisiana  Plaquemines  20.492185\n",
        "1332  48039  10.220909      1      1       1        0           Texas     Brazoria  19.022988\n",
        "1379  12093  24.674299      1      1       1        0         Florida   Okeechobee  15.611664\n",
        "1391  12099  11.267840      1      1       1        0         Florida   Palm Beach  20.597051\n",
        "1392  12051  21.506533      1      1       1        0         Florida       Hendry  16.138073\n",
        "1397  12021  22.875931      1      1       1        0         Florida      Collier  18.494922\n",
        "1399  12011  15.615793      1      1       1        0         Florida      Broward  21.631943\n",
        "1401  12087  12.133105      1      1       1        0         Florida       Monroe  26.876699\n"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}