GIS_Mapping.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import arcgis\n",
    "from arcgis.gis import GIS\n",
    "from IPython.display import display\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from scipy.special import erf\n",
    "import arcgis\n",
    "arcgis.env.out_spatial_reference = 4326\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_count = pd.read_csv('./Illinois_Counties_1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Youngest</th>\n",
       "      <th>Oldest</th>\n",
       "      <th>Mean Age</th>\n",
       "      <th>Num Female</th>\n",
       "      <th>Num Male</th>\n",
       "      <th>Counties</th>\n",
       "      <th>Cook County Cases</th>\n",
       "      <th>Chicago Deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007</td>\n",
       "      <td>101</td>\n",
       "      <td>~NaN~</td>\n",
       "      <td>0.1</td>\n",
       "      <td>87</td>\n",
       "      <td>50</td>\n",
       "      <td>38</td>\n",
       "      <td>63</td>\n",
       "      <td>48</td>\n",
       "      <td>33</td>\n",
       "      <td>~NaN~</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>20</td>\n",
       "      <td>~NaN~</td>\n",
       "      <td>16</td>\n",
       "      <td>86</td>\n",
       "      <td>54</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>~NaN~</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>5</td>\n",
       "      <td>~NaN~</td>\n",
       "      <td>~NaN~</td>\n",
       "      <td>~NaN~</td>\n",
       "      <td>~NaN~</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>~NaN~</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010</td>\n",
       "      <td>61</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>92</td>\n",
       "      <td>57</td>\n",
       "      <td>27</td>\n",
       "      <td>34</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>88</td>\n",
       "      <td>62</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012</td>\n",
       "      <td>290</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>89</td>\n",
       "      <td>57</td>\n",
       "      <td>143</td>\n",
       "      <td>147</td>\n",
       "      <td>55</td>\n",
       "      <td>174</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>117</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>92</td>\n",
       "      <td>59</td>\n",
       "      <td>61</td>\n",
       "      <td>56</td>\n",
       "      <td>76</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Cases Deaths Youngest Oldest Mean Age  Num Female  Num Male  \\\n",
       "0  2007    101  ~NaN~      0.1     87       50          38        63   \n",
       "1  2008     20  ~NaN~       16     86       54          12         8   \n",
       "2  2009      5  ~NaN~    ~NaN~  ~NaN~    ~NaN~           2         3   \n",
       "3  2010     61      4       18     92       57          27        34   \n",
       "4  2011     34      3        9     88       62          15        19   \n",
       "5  2012    290     12        9     89       57         143       147   \n",
       "6  2013    117     11       14     92       59          61        56   \n",
       "\n",
       "   Counties  Cook County Cases Chicago Deaths  \n",
       "0        48                 33          ~NaN~  \n",
       "1        28                  9          ~NaN~  \n",
       "2        36                  1          ~NaN~  \n",
       "3        30                 30              1  \n",
       "4        19                 22              1  \n",
       "5        55                174              5  \n",
       "6        76                 60              7  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year                  int64\n",
       "Cases                 int64\n",
       "Deaths               object\n",
       "Youngest             object\n",
       "Oldest               object\n",
       "Mean Age             object\n",
       "Num Female            int64\n",
       "Num Male              int64\n",
       "Counties              int64\n",
       "Cook County Cases     int64\n",
       "Chicago Deaths       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_count.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,'# of Cases'),\n",
       " Text(0.5,0,'Year'),\n",
       " Text(0.5,1,'Cases of WNV in Cook County')]"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lAAAAuB3BDQAAwCAIbgAAAAZBcAMAADAIghsAAIBBENwAAAAMokDO4wYAQEGX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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x177a16358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.style.use('fivethirtyeight')\n",
    "ax = sns.barplot(x = 'Year', y = 'Cook County Cases', data = df_count)\n",
    "ax.set(ylabel='# of Cases', \n",
    "       xlabel='Year', \n",
    "       title='Cases of WNV in Cook County')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gis = GIS('https://www.arcgis.com', 'atcarl', 'pass84GIS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "toolboxes = gis.content.search('travel', 'Geoprocessing Toolbox', \n",
    "                               outside_org=True, max_items=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "map1 = gis.map('Chicago', zoomlevel=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dark-gray',\n",
       " 'dark-gray-vector',\n",
       " 'gray',\n",
       " 'gray-vector',\n",
       " 'hybrid',\n",
       " 'national-geographic',\n",
       " 'oceans',\n",
       " 'osm',\n",
       " 'satellite',\n",
       " 'streets',\n",
       " 'streets-navigation-vector',\n",
       " 'streets-night-vector',\n",
       " 'streets-relief-vector',\n",
       " 'streets-vector',\n",
       " 'terrain',\n",
       " 'topo',\n",
       " 'topo-vector']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map1.basemaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c0bbf848e5d54d87911059217d69eb00"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "map1.basemap = 'topo'\n",
    "map1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[41.884250000000065, -87.63244999999995]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map1.center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "map2 = gis.map('Chicago', zoomlevel=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "279fd15abd6247b58620e5688c5bad95"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "map2.basemap = 'topo'\n",
    "map2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_spray = pd.read_csv('./spray_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Date</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2011-08-29</td>\n",
       "      <td>42.391623</td>\n",
       "      <td>-88.089163</td>\n",
       "      <td>2011</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-08-29</td>\n",
       "      <td>42.391348</td>\n",
       "      <td>-88.089163</td>\n",
       "      <td>2011</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-08-29</td>\n",
       "      <td>42.391022</td>\n",
       "      <td>-88.089157</td>\n",
       "      <td>2011</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-08-29</td>\n",
       "      <td>42.390637</td>\n",
       "      <td>-88.089158</td>\n",
       "      <td>2011</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-08-29</td>\n",
       "      <td>42.390410</td>\n",
       "      <td>-88.088858</td>\n",
       "      <td>2011</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        Date   Latitude  Longitude  Year  Month  Day\n",
       "0           0  2011-08-29  42.391623 -88.089163  2011      8   29\n",
       "1           1  2011-08-29  42.391348 -88.089163  2011      8   29\n",
       "2           2  2011-08-29  42.391022 -88.089157  2011      8   29\n",
       "3           3  2011-08-29  42.390637 -88.089158  2011      8   29\n",
       "4           4  2011-08-29  42.390410 -88.088858  2011      8   29"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spray.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "portal_gis = GIS(\"https://acarl.maps.arcgis.com\", \"atcarl\", \"pass84GIS\")\n",
    "spray = portal_gis.content.search(\"spray_clean\", \"Feature Layer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://acarl.maps.arcgis.com/home/item.html?id=cedef9ad460748ac90edfcff3daf1237' target='_blank'>\n",
       "                        <img 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' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://acarl.maps.arcgis.com/home/item.html?id=cedef9ad460748ac90edfcff3daf1237' target='_blank'><b>spray_clean</b>\n",
       "                        </a>\n",
       "                        <br/>City pesticide spry for killing mosquitos. <img src='https://acarl.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by atcarl\n",
       "                        <br/>Last Modified: March 05, 2018\n",
       "                        <br/>0 comments, 5 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"spray_clean\" type:Feature Layer Collection owner:atcarl>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spray[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "map1.add_layer(spray[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "portal_gis = GIS(\"https://acarl.maps.arcgis.com\", \"atcarl\", \"pass84GIS\")\n",
    "parks = portal_gis.content.search(\"Park_Locations\", \"Feature Layer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://acarl.maps.arcgis.com/home/item.html?id=357f9ad79a474e09bbcb50a56bf8704c' target='_blank'>\n",
       "                        <img src='data:image/png;base64,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' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://acarl.maps.arcgis.com/home/item.html?id=357f9ad79a474e09bbcb50a56bf8704c' target='_blank'><b>Park_Locations</b>\n",
       "                        </a>\n",
       "                        <br/><img src='https://acarl.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by atcarl\n",
       "                        <br/>Last Modified: March 05, 2018\n",
       "                        <br/>0 comments, 0 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"Park_Locations\" type:Feature Layer Collection owner:atcarl>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parks[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "map1.add_layer(parks[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c0bbf848e5d54d87911059217d69eb00"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "map1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "portal_gis = GIS(\"https://acarl.maps.arcgis.com\", \"atcarl\", \"pass84GIS\")\n",
    "water = portal_gis.content.search(\"Hydro\", \"Feature Layer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://acarl.maps.arcgis.com/home/item.html?id=c72f81a2b05f4919ba7959dfa863f196' target='_blank'>\n",
       "                        <img src='data:image/png;base64,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' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://acarl.maps.arcgis.com/home/item.html?id=c72f81a2b05f4919ba7959dfa863f196' target='_blank'><b>Hydro</b>\n",
       "                        </a>\n",
       "                        <br/><img src='https://acarl.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by atcarl\n",
       "                        <br/>Last Modified: March 05, 2018\n",
       "                        <br/>0 comments, 3 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"Hydro\" type:Feature Layer Collection owner:atcarl>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "water[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "map2.add_layer(water[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "279fd15abd6247b58620e5688c5bad95"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "map2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "map2.add_layer(spray[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "portal_gis = GIS(\"https://acarl.maps.arcgis.com\", \"atcarl\", \"pass84GIS\")\n",
    "traps = portal_gis.content.search(\"Mos_traps\", \"Feature Layer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://acarl.maps.arcgis.com/home/item.html?id=e298b0eb4b0244b185f117393bab221c' target='_blank'>\n",
       "                        <img src='data:image/png;base64,R0lGODlhBQAFAID/AMDAwAAAACH5BAEAAAAALAAAAAAFAAUAQAIEhI+pWAA7' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://acarl.maps.arcgis.com/home/item.html?id=e298b0eb4b0244b185f117393bab221c' target='_blank'><b>Mos_traps2</b>\n",
       "                        </a>\n",
       "                        <br/><img src='https://acarl.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by atcarl\n",
       "                        <br/>Last Modified: March 06, 2018\n",
       "                        <br/>0 comments, 2 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"Mos_traps2\" type:Feature Layer Collection owner:atcarl>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traps[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "map3 = gis.map('Chicago', zoomlevel=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "map3.add_layer(traps[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "map3.add_layer(spray[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d851c5f3351d4a7ba548834951f42c55"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "map3.basemap = 'topo'\n",
    "map3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "portal_gis = GIS(\"https://acarl.maps.arcgis.com\", \"atcarl\", \"pass84GIS\")\n",
    "gardens = portal_gis.content.search(\"Gardens\", \"Feature Layer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://acarl.maps.arcgis.com/home/item.html?id=6d7ea0364347427b8667796a7093ccae' target='_blank'>\n",
       "                        <img src='data:image/png;base64,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' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://acarl.maps.arcgis.com/home/item.html?id=6d7ea0364347427b8667796a7093ccae' target='_blank'><b>Gardens</b>\n",
       "                        </a>\n",
       "                        <br/><img src='https://acarl.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by atcarl\n",
       "                        <br/>Last Modified: March 06, 2018\n",
       "                        <br/>0 comments, 4 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"Gardens\" type:Feature Layer Collection owner:atcarl>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gardens[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "map4 = gis.map('Chicago', zoomlevel=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dfdc4c36e8a842e1a509283769195f90"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "map4.basemap = 'topo'\n",
    "map4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "map4.add_layer(gardens[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "map4.add_layer(spray[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xtrain_df = pd.read_csv('./X_train_clean_v1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Block</th>\n",
       "      <th>Trap</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Id</th>\n",
       "      <th>Year_x</th>\n",
       "      <th>Month_x</th>\n",
       "      <th>Day_x</th>\n",
       "      <th>DayofYear</th>\n",
       "      <th>...</th>\n",
       "      <th>GR</th>\n",
       "      <th>HZ</th>\n",
       "      <th>MIFG</th>\n",
       "      <th>RA</th>\n",
       "      <th>SN</th>\n",
       "      <th>SQ</th>\n",
       "      <th>TS</th>\n",
       "      <th>TSRA</th>\n",
       "      <th>VCFG</th>\n",
       "      <th>VCTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>T002</td>\n",
       "      <td>41.954690</td>\n",
       "      <td>-87.800991</td>\n",
       "      <td>1</td>\n",
       "      <td>2007</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>T002</td>\n",
       "      <td>41.954690</td>\n",
       "      <td>-87.800991</td>\n",
       "      <td>2</td>\n",
       "      <td>2007</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>149</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "      <td>T007</td>\n",
       "      <td>41.994991</td>\n",
       "      <td>-87.769279</td>\n",
       "      <td>3</td>\n",
       "      <td>2007</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>149</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>79</td>\n",
       "      <td>T015</td>\n",
       "      <td>41.974089</td>\n",
       "      <td>-87.824812</td>\n",
       "      <td>4</td>\n",
       "      <td>2007</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>149</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>79</td>\n",
       "      <td>T015</td>\n",
       "      <td>41.974089</td>\n",
       "      <td>-87.824812</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>149</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Block  Trap   Latitude  Longitude  Id  Year_x  Month_x  Day_x  \\\n",
       "0           0     41  T002  41.954690 -87.800991   1    2007        5     29   \n",
       "1           1     41  T002  41.954690 -87.800991   2    2007        5     29   \n",
       "2           2     62  T007  41.994991 -87.769279   3    2007        5     29   \n",
       "3           3     79  T015  41.974089 -87.824812   4    2007        5     29   \n",
       "4           4     79  T015  41.974089 -87.824812   5    2007        5     29   \n",
       "\n",
       "   DayofYear  ...    GR   HZ  MIFG   RA   SN   SQ   TS  TSRA  VCFG  VCTS  \n",
       "0        149  ...   0.0  1.0   0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0  \n",
       "1        149  ...   0.0  1.0   0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0  \n",
       "2        149  ...   0.0  1.0   0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0  \n",
       "3        149  ...   0.0  1.0   0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0  \n",
       "4        149  ...   0.0  1.0   0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Unnamed: 0',\n",
       " 'Block',\n",
       " 'Trap',\n",
       " 'Latitude',\n",
       " 'Longitude',\n",
       " 'Id',\n",
       " 'Year_x',\n",
       " 'Month_x',\n",
       " 'Day_x',\n",
       " 'DayofYear',\n",
       " 'culex',\n",
       " 'erraticus',\n",
       " 'pipiens',\n",
       " 'restuans',\n",
       " 'salinarius',\n",
       " 'tarsalis',\n",
       " 'territans',\n",
       " '60018',\n",
       " '60106',\n",
       " '60605',\n",
       " '60607',\n",
       " '60608',\n",
       " '60609',\n",
       " '60611',\n",
       " '60612',\n",
       " '60613',\n",
       " '60614',\n",
       " '60615',\n",
       " '60617',\n",
       " '60618',\n",
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     "execution_count": 108,
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    "df_2008 = xtest_df[xtest_df['Year_x']==2008]\n",
    "df_2009 = xtrain_df[xtrain_df['Year_x']==2009]\n",
    "df_2010 = xtest_df[xtest_df['Year_x']==2010]\n",
    "df_2011 = xtrain_df[xtrain_df['Year_x']==2011]\n",
    "df_2012 = xtest_df[xtest_df['Year_x']==2012]\n",
    "df_2013 = xtrain_df[xtrain_df['Year_x']==2013]\n",
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       "      <td>41.994991</td>\n",
       "      <td>-87.769279</td>\n",
       "      <td>3</td>\n",
       "      <td>2007</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>149</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>79</td>\n",
       "      <td>T015</td>\n",
       "      <td>41.974089</td>\n",
       "      <td>-87.824812</td>\n",
       "      <td>4</td>\n",
       "      <td>2007</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>149</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>79</td>\n",
       "      <td>T015</td>\n",
       "      <td>41.974089</td>\n",
       "      <td>-87.824812</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>149</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Block  Trap   Latitude  Longitude  Id  Year_x  Month_x  Day_x  \\\n",
       "0           0     41  T002  41.954690 -87.800991   1    2007        5     29   \n",
       "1           1     41  T002  41.954690 -87.800991   2    2007        5     29   \n",
       "2           2     62  T007  41.994991 -87.769279   3    2007        5     29   \n",
       "3           3     79  T015  41.974089 -87.824812   4    2007        5     29   \n",
       "4           4     79  T015  41.974089 -87.824812   5    2007        5     29   \n",
       "\n",
       "   DayofYear  ...    GR   HZ  MIFG   RA   SN   SQ   TS  TSRA  VCFG  VCTS  \n",
       "0        149  ...   0.0  1.0   0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0  \n",
       "1        149  ...   0.0  1.0   0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0  \n",
       "2        149  ...   0.0  1.0   0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0  \n",
       "3        149  ...   0.0  1.0   0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0  \n",
       "4        149  ...   0.0  1.0   0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2007.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Unnamed: 0',\n",
       " 'Block',\n",
       " 'Trap',\n",
       " 'Latitude',\n",
       " 'Longitude',\n",
       " 'Id',\n",
       " 'Year_x',\n",
       " 'Month_x',\n",
       " 'Day_x',\n",
       " 'DayofYear',\n",
       " 'culex',\n",
       " 'erraticus',\n",
       " 'pipiens',\n",
       " 'restuans',\n",
       " 'salinarius',\n",
       " 'tarsalis',\n",
       " 'territans',\n",
       " '60018',\n",
       " '60106',\n",
       " '60605',\n",
       " '60607',\n",
       " '60608',\n",
       " '60609',\n",
       " '60611',\n",
       " '60612',\n",
       " '60613',\n",
       " '60614',\n",
       " '60615',\n",
       " '60617',\n",
       " '60618',\n",
       " '60619',\n",
       " '60620',\n",
       " '60621',\n",
       " '60622',\n",
       " '60623',\n",
       " '60625',\n",
       " '60626',\n",
       " '60628',\n",
       " '60629',\n",
       " '60630',\n",
       " '60631',\n",
       " '60632',\n",
       " '60633',\n",
       " '60634',\n",
       " '60636',\n",
       " '60637',\n",
       " '60638',\n",
       " '60639',\n",
       " '60640',\n",
       " '60641',\n",
       " '60642',\n",
       " '60643',\n",
       " '60645',\n",
       " '60646',\n",
       " '60647',\n",
       " '60649',\n",
       " '60651',\n",
       " '60652',\n",
       " '60654',\n",
       " '60655',\n",
       " '60656',\n",
       " '60659',\n",
       " '60660',\n",
       " '60666',\n",
       " '60707',\n",
       " '60803',\n",
       " '60827',\n",
       " 'Station',\n",
       " 'Tmax',\n",
       " 'Tmin',\n",
       " 'Tavg',\n",
       " 'Depart',\n",
       " 'DewPoint',\n",
       " 'WetBulb',\n",
       " 'Heat',\n",
       " 'Cool',\n",
       " 'Sunrise',\n",
       " 'Sunset',\n",
       " 'SnowFall',\n",
       " 'PrecipTotal',\n",
       " 'StnPressure',\n",
       " 'SeaLevel',\n",
       " 'ResultSpeed',\n",
       " 'ResultDir',\n",
       " 'AvgSpeed',\n",
       " 'BCFG',\n",
       " 'BR',\n",
       " 'DZ',\n",
       " 'FG',\n",
       " 'FG+',\n",
       " 'FU',\n",
       " 'GR',\n",
       " 'HZ',\n",
       " 'MIFG',\n",
       " 'RA',\n",
       " 'SN',\n",
       " 'SQ',\n",
       " 'TS',\n",
       " 'TSRA',\n",
       " 'VCFG',\n",
       " 'VCTS']"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df_2007.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [],
   "source": [
    "tot7 = len(df_2007)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [],
   "source": [
    "cul7 = (sum(df_2007['culex']) / tot7 * 100)\n",
    "err7 = (sum(df_2007['erraticus']) / tot7 * 100)\n",
    "pip7 = (sum(df_2007['pipiens']) / tot7 * 100)\n",
    "res7 = (sum(df_2007['restuans']) / tot7 * 100)\n",
    "sal7 = (sum(df_2007['salinarius']) / tot7 * 100)\n",
    "tar7 = (sum(df_2007['tarsalis']) / tot7 * 100)\n",
    "ter7 = (sum(df_2007['territans']) / tot7 * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [],
   "source": [
    "x7 = [cul7, err7, pip7, res7, sal7, tar7, ter7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst7 = pd.DataFrame(x7, index=['Culex', 'Erraticus', 'Pipiens', 'restuans', \n",
    "                              'Salinarius', 'Tarsalis', 'Territans'], columns=['col'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Culex</th>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Erraticus</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pipiens</th>\n",
       "      <td>82.812910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restuans</th>\n",
       "      <td>54.001574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salinarius</th>\n",
       "      <td>1.075833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tarsalis</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Territans</th>\n",
       "      <td>2.860142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   col\n",
       "Culex       100.000000\n",
       "Erraticus     0.000000\n",
       "Pipiens      82.812910\n",
       "restuans     54.001574\n",
       "Salinarius    1.075833\n",
       "Tarsalis      0.000000\n",
       "Territans     2.860142"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17c39bcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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+q+bNm+u7776z13nlyhWFh4fLYrHoww8/THWChZubm0JCQtSzZ09NnjxZ3333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\nifLpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17bc71940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "lst7.plot(kind = 'bar') \n",
    "plt.xticks\n",
    "plt.ylabel=('% of Species')\n",
    "plt.xlabel=('Species') \n",
    "plt.title('% of Species found in All Traps 2007');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "tot8 = len(df_2008)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "cul8 = (sum(df_2008['culex']) / tot8 * 100)\n",
    "err8 = (sum(df_2008['erraticus']) / tot8 * 100)\n",
    "pip8 = (sum(df_2008['pipiens']) / tot8 * 100)\n",
    "res8 = (sum(df_2008['restuans']) / tot8 * 100)\n",
    "sal8 = (sum(df_2008['salinarius']) / tot8 * 100)\n",
    "tar8 = (sum(df_2008['tarsalis']) / tot8 * 100)\n",
    "ter8 = (sum(df_2008['territans']) / tot8 * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    "x8 = [cul8, err8, pip8, res8, sal8, tar8, ter8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst8 = pd.DataFrame(x8, index=['Culex', 'Erraticus', 'Pipiens', 'restuans', \n",
    "                              'Salinarius', 'Tarsalis', 'Territans'], columns=['col'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17c516d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1a+3du1fffPONOnbsqHr16iklJUV79+5VtWrVjPSM5ZT1hsuS9Oyzz951Xnd3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\nPyW8BgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17c4e1320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "lst8.plot(kind = 'bar') \n",
    "plt.xticks\n",
    "plt.ylabel=('% of Species')\n",
    "plt.xlabel=('Species') \n",
    "plt.title('% of Species found in All Traps 2008');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tot9 = len(df_2009)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cul9 = (sum(df_2009['culex']) / tot9 * 100)\n",
    "err9 = (sum(df_2009['erraticus']) / tot9 * 100)\n",
    "pip9 = (sum(df_2009['pipiens']) / tot9 * 100)\n",
    "res9 = (sum(df_2009['restuans']) / tot9 * 100)\n",
    "sal9 = (sum(df_2009['salinarius']) / tot9 * 100)\n",
    "tar9 = (sum(df_2009['tarsalis']) / tot9 * 100)\n",
    "ter9 = (sum(df_2009['territans']) / tot9 * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x9 = [cul9, err9, pip9, res9, sal9, tar9, ter9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lst9 = pd.DataFrame(x9, index=['Culex', 'Erraticus', 'Pipiens', 'restuans', \n",
    "                              'Salinarius', 'Tarsalis', 'Territans'], columns=['col'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17c7c13c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WLVpox44d+v7779W2bVvVqVNHKSkp2rFjh6pWrWqkZyynbDdclqRnnnnmpvN6\neHjo2Wef1cyZM7VkyRKH9nQlZcuW1aOPPqrNmzerU6dOqlWrlvLmzauDBw/q5MmTeuCBB3T69OlM\n75Bw+fJl++eVnJys3377Tampqerdu7e9NzpPnjwaPHiw3nnnHQ0aNEhVqlSxX6Zk9+7dSk1NVf/+\n/W85uHvGjBm6evWq8uXLp927d2d6mLJ27dr2k3eefPJJNW/eXD/88IO9zosXLyoyMlIWi0UffPCB\nwwkWHh4eCgsLU8+ePTVhwgT98MMPCggI0J49e3T27NkML//Ru3dvRUREaPHixdqxY4fKlSunI0eO\nKDo6WsWKFdPHH398V94Q+25CsIKDc+fOac6cOWrVqpXD9XRsRo8erU8//VQ///yzrFarnn766XRd\n8jfTq1cvBQUFaf78+dq/f7/++OMPFS9eXJ06dVKHDh1ydAFSmxIlSmju3LlauXKlNm3apIMHD2rb\ntm2yWq0qVqyYXnrpJbVq1SrTQcA1a9ZUixYt9MUXX2jz5s2677771KpVK3Xu3DndmWZFihTRl19+\nqfnz52vt2rWKjIxUnjx5FBAQoKefflpt2rRJd4+5Tp06qVKlSpo7d6727t2ry5cvq2TJkurSpYtC\nQ0NvOSj14Ycf1pw5c/T1118rIiJCERERKlCggIKCgtS6dWs1a9bM4XBG+/bt5e/vr6VLl+rgwYPa\nt2+fChUqpGbNmikkJCTHg6EfeOABzZ49W19//bU2bNigTZs2KX/+/AoODlarVq3SHRLy8PDQxIkT\nNW3aNK1fv14REREqUaKEevTooY4dO972LWcy895776lWrVpasmSJduzYIV9fX7Vs2VI9evRwiT1+\n2w2XS5Ys6XBdt8y0aNFCM2fO1F9//aWdO3dmu5cxt3344YeaO3eu1qxZo8jISOXPn1/333+/WrRo\noQ4dOmjgwIH69ddf7Rfivd60adM0efJk/fLLL7JarQoODlanTp3S3XamWbNmyp8/v+bPn28/nFqw\nYEE1bNhQHTp0yNKOni3AX758Od0to67n7e1tD1bStTOja9SooeXLl2vbtm3Knz+/GjRooNdffz3D\nz7FKlSqaOXOmpk2bZr+KfqlSpfTyyy+rY8eO6cY2FipUSDNmzNCUKVMUERGhzZs3q0SJEurYsaM6\nderkcO0uuCZLfHx81s4JBu5ytnsFPvXUUxo1apSzywHuCbZ7BUrXhhlwthvcXZb7Ey9evKj27dvb\nz/DZvn27OnTooNatW2vy5Mn2MTcHDx5USEiIWrdurffffz/DAa0AAAB3oywFq71796p79+72wYhJ\nSUkaOXKkPvnkEy1YsED79+9est7yAAAgAElEQVS3D2IOCwtT//797bcfuP42BAAAAHezLAWrpUuX\nasCAAfZj+n/88YceeOABBQQEyMvLS88++6zWrl2rU6dOKSkpSUFBQZKujQnI7NR6AACAu02WDmbf\neJfws2fPOgyc9Pf3V1xcXIbTTd2eBMhtLVq0cLlbhgB3Oy8vL23fvt3ZZQDGZOuczRuvjmv7+8b7\nP1mtVk4LBQAA94xspZ7ixYs79ETFxcWpWLFiKl68uMN9lOLi4oycPg8AAOAOsnVea/Xq1e1X1g0I\nCNCqVavUsmVLlSxZUt7e3oqMjFStWrUUHh6e4f2R7oQ9cVfUbeM/TnntrJjWpLBqFM3+zYbdRWpq\nqmJiYhQQEJDpzY2ROdovZ2i/7KPtcob2yz53b7tsBSsfHx+FhYVpyJAhSk5OVqNGjfTkk09KkkaO\nHKlRo0bp0qVLqly5stq1a2e04Ky6nGrVgXjXvdTD5dR75/Jhmd2+AllD++UM7Zd9tF3O0H7Z585t\nd1vBavny5fb/16tXT/PmzUs3T6VKlTRr1qycVwYAAOBmGFkOAABgCMEKAADAEIIVAACAIQQrAAAA\nQwhWAAAAhmTrcgsAAAA3cywxRX9fSr3t51mtViV7F9e52KuyWNJfNqmEr6fK+LlufHHdygAAgNv6\n+1Kqnl6Zk/sFJ2Y4dc3z/kaD1YgRI1S7dm1j94rlUCAAAIAh9FgBAIC7ztSpU/Xjjz/K09NTzz//\nvB5//HGNHj1a58+fV968eTVgwABVq1bN+OsSrAAAwF3l559/1o4dOzR37lxJ0r///W/NmzdPAwYM\nULNmzbRnzx4NGTJEixYtMv7aHAoEAAB3lV9//VXNmjWTj4+PfHx8NHnyZF29elXNmjWTJNWoUUMF\nChTQ0aNHjb82wQoAANxVPD09Hf4+ceJEunmsVqtSUtKfdZhTBCsAAHBXqV27ttavX6+rV68qOTlZ\nYWFhkqSffvpJkrRnzx7FxsaqfPnyxl+bMVYAAMC4Er6eWvO8/20/z2q1KjkpWT55fWSxWDJc7q08\n9thj+vPPPxUSEqK0tDS1atVK9evX10cffaQZM2YoT548Gj16tLy9vW+7vlshWAEAAOPK+Hll63pT\nqampOnLkuMoFlEt3SO92dO/eXd27d3eYNmXKlHTzvffee9l+jYxwKBAAAMAQghUAAIAhBCsAAABD\nCFYAAACGEKwAAAAMIVgBAAAYQrACAAAwhGAFAABgCMEKAADAEIIVAACAIQQrAAAAQwhWAAAAhhCs\nAAAADCFYAQAAGEKwAgAAMIRgBQAAYAjBCgAAwBCCFQAAgCEEKwAAAEMIVgAAAIYQrAAAAAwhWAEA\nABhCsAIAADCEYAUAAGAIwQoAAMAQghUAAIAhBCsAAABDCFYAAACGEKwAAAAMIVgBAAAYQrACAAAw\nhGAFAABgCMEKAADAEIIVAACAIQQrAAAAQwhWAAAAhhCsAAAADCFYAQAAGEKwAgAAMIRgBQAAYAjB\nCgAAwBCCFQAAgCEEKwAAAEMIVgAAAIYQrAAAAAwhWAEAABhCsAIAADDEy9kFAMD1jiWm6O9LqUaW\nZbValexdXOdir8piScnx8kr4eqqMH5tNAJljCwHApfx9KVVPr4w1vNREI0tZ87w/wQrATeVoC7F6\n9Wp9+eWXkqSGDRuqT58+OnjwoD744ANdvHhRNWvW1JAhQ+TlxYYIAADc/bI9xiopKUljxozR559/\nrrlz5+r333/X9u3bFRYWpv79+2vJkiWSZP8XAADgbpftYJWamqrU1FQlJyfb/+/l5aWkpCQFBQVJ\nklq0aKF169YZKxYAAMCVZfsYXf78+dWjRw+1bdtWPj4+Cg4OlpeXl4oVK2afx9/fX7GxtzdWIjXV\n3KBVV2a1Wo29V1eWlpbm8C9uz73Yfq687t4r6610b373TKL9ss/V287T0/Omj2c7WB0+fFjh4eFa\nvny5/Pz8NGzYMG3bts1hHqvVKg+P2+sUi4mJ0ZUrV7Jbll2yd/EcLyM3JScl68iR484u446Jjo52\ndglu7V5qP1ded++19Va6t757uYH2yz5XbDtvb2+VLVv2pvNkO1hFRESodu3aKlKkiKRrh/3mzZun\nuLg4+zxxcXHy9/e/reUGBARktyQH52KvytSZQLnBJ6+PygWUc3YZuS4tLU3R0dEKDAy87ZCNe7P9\nXHndvVfWW+ne/O6ZRPtln7u3XbaDVcWKFbVy5UolJiYqf/782rx5s2rWrKn169crMjJStWrVUnh4\nuBo2bHhby71VF1tWmbhmTW6yWCzG3qs78PDwuKfer2n3Uvu58rp7r6230r313csNtF/2uWvbZTtY\nNWjQQM8++6xCQ0Pl7e2tqlWrKjQ0VE2bNtWoUaN06dIlVa5cWe3atTNZLwAAgMvK0QWmQkNDFRoa\n6jCtUqVKmjVrVo6KAgAAcEfud/ASAADARRGsAAAADCFYAQAAGEKwAgAAMIRgBQAAYAjBCgAAwBCC\nFQAAgCEEKwAAAEMIVgAAAIYQrAAAAAwhWAEAABhCsAIAADCEYAUAAGAIwQoAAMAQghUAAIAhBCsA\nAABDCFYAAACGEKwAAAAMIVgBAAAYQrACAAAwhGAFAABgCMEKAADAEIIVAACAIQQrAAAAQwhWAAAA\nhhCsAAAADCFYAQAAGEKwAgAAMIRgBQAAYAjBCgAAwBCCFQAAgCEEKwAAAEMIVgAAAIYQrAAAAAwh\nWAEAABhCsAIAADCEYAUAAGAIwQoAAMAQghUAAIAhBCsAAABDCFYAAACGEKwAAAAMIVgBAAAYQrAC\nAAAwhGAFAABgiJezCwDuNscSU/T3pVQjy7JarUr2Lq5zsVdlsaQYWWYJX0+V8WPVB4DcwNYVMOzv\nS6l6emWs4aUmGlvSmuf9CVYAkEs4FAgAAGAIwQoAAMAQghUAAIAhBCsAAABDCFYAAACGEKwAAAAM\nIVgBAAAYQrACAAAwhGAFAABgCMEKAADAEIIVAACAIQQrAAAAQwhWAAAAhhCsAAAADCFYAQAAGEKw\nAgAAMIRgBQAAYIhXTp68adMmTZs2TUlJSapfv7769++v7du367///a+Sk5P15JNPqmfPnrJYLKbq\nBQAAcFnZ7rE6ceKEPvroI40ZM0bz5s3Tn3/+qc2bN2vkyJH65JNPtGDBAu3fv1+bN282WS8AAIDL\nynaw+vnnn9WsWTPdf//98vLy0qhRo+Tr66sHHnhAAQEB8vLy0rPPPqu1a9earBcAAMBlZftQ4PHj\nx+Xt7a0BAwbo5MmTaty4scqVK6dixYrZ5/H391dcXNxtLTc1NTW7JTmwWq1GlpNbrFarsffqytLS\n0hz+vRfw3csZV24/V287k+7Fddck2i/7XL3tPD09b/p4toNVamqqIiIiNHXqVPn6+mrAgAHy8fFx\nGE9ltVpve3xVTEyMrly5kt2y7JK9i+d4GbkpOSlZR44cd3YZd0x0dLSzS7hj+O7ljCu3n6u3XW64\nl9bd3ED7ZZ8rtp23t7fKli1703myHayKFi2qunXrqkiRIpKkxx9/XGvXrpWHx/+OLsbFxTn0YGVF\nQEBAdktycC72qqREI8vKDT55fVQuoJyzy8h1aWlpio6OVmBgoMN3427Gdy9nXLn9XL3tTLoX112T\naL/sc/e2y3awevTRR/Xee+8pISFB+fPn19atW9W0aVN9/fXXOnr0qAICArRq1Sq1bNnytpZ7qy62\nrLJYUowsJ7dYLBZj79UdeHh43DPvl+9ezrhy+7l62+WGe2ndzQ20X/a5a9tlO1g99NBDCg0NVffu\n3ZWSkqK6deuqdevWCgwM1JAhQ5ScnKxGjRrpySefNFkvAACAy8rRdaxefPFFvfjiiw7T6tWrp3nz\n5uWoKAAAAHfkfgcvAQAAXBTBCgAAwBCCFQAAgCEEKwAAAEMIVgAAAIYQrAAAAAwhWAEAABhCsAIA\nADCEYAUAAGAIwQoAAMAQghUAAIAhBCsAAABDCFYAAACGEKwAAAAMIVgBAAAYQrACAAAwhGAFAABg\nCMEKAADAEIIVAACAIQQrAAAAQwhWAAAAhhCsAAAADCFYAQAAGEKwAgAAMIRgBQAAYAjBCgAAwBCC\nFQAAgCEEKwAAAEMIVgAAAIYQrAAAAAwhWAEAABhCsAIAADCEYAUAAGAIwQoAAMAQghUAAIAhBCsA\nAABDCFYAAACGEKwAAAAMIVgBAAAYQrACAAAwhGAFAABgCMEKAADAEIIVAACAIQQrAAAAQwhWAAAA\nhhCsAAAADCFYAQAAGEKwAgAAMIRgBQAAYAjBCgAAwBCCFQAAgCEEKwAAAEMIVgAAAIYQrAAAAAwh\nWAEAABhCsAIAADCEYAUAAGAIwQoAAMAQghUAAIAhBCsAAABDCFYAAACGEKwAAAAMIVgBAAAYQrAC\nAAAwxEiwGj9+vEaMGCFJOnjwoEJCQtS6dWu9//77SklJMfESAAAALi/HwWr79u1auXKl/e+wsDD1\n799fS5YskST7vwAAAHe7HAWr8+fPa8qUKXrttdckSadOnVJSUpKCgoIkSS1atNC6detyXCQAAIA7\n8MrJk0ePHq0ePXrozJkzkqSzZ8+qWLFi9sf9/f0VGxt7W8tMTU3NSUl2VqvVyHJyi9VqNfZeXVla\nWprDv/cCvns548rt5+ptZ9K9uO6aRPtln6u3naen500fz3awWrZsme6//37Vq1dP4eHhktJvEK1W\nqzw8bq9TLCYmRleuXMluWXbJ3sVzvIzclJyUrCNHjju7jDsmOjra2SXcMXz3csaV28/V2y433Evr\nbm6g/bLPFdvO29tbZcuWvek82Q5WP/30k2JjY9WxY0clJCTo8uXLslgsiouLs88TFxcnf3//21pu\nQEBAdktycC72qqREI8vKDT55fVQuoJyzy8h1aWlpio6OVmBg4G2HbHfFdy9nXLn9XL3tTLoX112T\naL/sc/e2y3awmjRpkv3/4eHh+u233xQWFqZXXnlFkZGRqlWrlsLDw9WwYcPbWu6tutiyymJx7bMR\nLRaLsffqDjw8PO6Z98t3L2dcuf1cve1yw7207uYG2i/73LXtcjTGKiMjR47UqFGjdOnSJVWuXFnt\n2rUz/RIAAAAuyUiwatGihVq0aCFJqlSpkmbNmmVisQAAAG7F/Q5eAgAAuCiCFQAAgCEEKwAAAEMI\nVgAAAIYQrAAAAAwhWAEAABhCsAIAADCEYAUAAGAIwQoAAMAQghUAAIAhBCsAAABDCFYAAACGEKwA\nAAAMIVgBAAAYQrACAAAwhGAFAABgCMEKAADAEIIVAACAIQQrAAAAQwhWAAAAhhCsAAAADCFYAQAA\nGEKwAgAAMIRgBQAAYAjBCgAAwBCCFQAAgCEEKwAAAEMIVgAAAIYQrAAAAAwhWAEAABhCsAIAADCE\nYAUAAGAIwQoAAMAQghUAAIAhBCsAAABDCFYAAACGEKwAAAAMIVgBAAAYQrACAAAwhGAFAABgCMEK\nAADAEIIVAACAIQQrAAAAQwhWAAAAhhCsAAAADCFYAQAAGEKwAgAAMIRgBQAAYAjBCgAAwBCCFQAA\ngCEEKwAAAEMIVgAAAIYQrAAAAAwhWAEAABhCsAIAADCEYAUAAGAIwQoAAMAQghUAAIAhBCsAAABD\nCFYAAACGEKwAAAAMIVgBAAAYQrACAAAwhGAFAABgiFdOnjx37lytWLFCHh4eqlatmgYPHqy//vpL\nH3zwgS5evKiaNWtqyJAh8vLK0csAAAC4hWz3WO3bt0/h4eGaOXOm5s2bp5SUFC1atEhhYWHq37+/\nlixZIkn2fwEAAO522Q5WBQoU0MCBA5UvXz5ZLBZVrFhRhw8fVlJSkoKCgiRJLVq00Lp164wVCwAA\n4MqyfYyuTJkyKlOmjCTp3LlzWrRokV5++WUdP37cPo+/v79iY2Nva7mpqanZLcmB1Wo1spzcYrVa\njb1XV5aWlubw772A717OuHL7uXrbmXQvrrsm0X7Z5+pt5+npedPHczz46eTJk+rXr59atmypWrVq\n6ZdffrE/ZrVa5eFxe51iMTExunLlSk7LUrJ38RwvIzclJyXryJHjt57xLhEdHe3sEu4Yvns548rt\n5+ptlxvupXU3N9B+2eeKbeft7a2yZcvedJ4cBauDBw+qX79+CgkJUbt27XTq1CnFxcXZH4+Li5O/\nv/9tLTMgICAnJdmdi70qKdHIsnKDT14flQso5+wycl1aWpqio6MVGBh42yHbXfHdyxlXbj9XbzuT\n7sV11yTaL/vcve2yHaz++ecfvfXWW3r77bfVtGlTSVLJkiXl7e2tyMhI1apVS+Hh4WrYsOFtLfdW\nXWxZZbGkGFlObrFYLMbeqzvw8PC4Z94v372cceX2c/W2yw330rqbG2i/7HPXtst2sPrmm2908eJF\nTZ8+XdOnT5ckNWrUSCNHjtSoUaN06dIlVa5cWe3atTNWLAAAgCvLdrDq1auXevXqleFjs2bNynZB\nAAAA7sr9Dl4CAAC4KIIVAACAIQQrAAAAQwhWAAAAhhCsAAAADCFYAQAAGEKwAgAAMIRgBQAAYAjB\nCgAAwBCCFQAAgCEEKwAAAEMIVgAAAIYQrAAAAAwhWAEAABhCsAIAADCEYAUAAGAIwQoAAMAQghUA\nAIAhBCsAAABDCFYAAACGEKwAAAAMIVgBAAAYQrACAAAwhGAFAABgCMEKAADAEIIVAACAIQQrAAAA\nQwhWAAAAhhCsAAAADCFYAQAAGEKwAgAAMIRgBQAAYAjBCgAAwBCCFQAAgCEEKwAAAEMIVgAAAIYQ\nrAAAAAwhWAEAABhCsAIAADCEYAUAAGAIwQoAAMAQghUAAIAhBCsAAABDCFYAAACGEKwAAAAMIVgB\nAAAYQrACAAAwhGAFAABgCMEKAADAEIIVAACAIQQrAAAAQwhWAAAAhhCsAAAADCFYAQAAGEKwAgAA\nMIRgBQAAYAjBCgAAwBCCFQAAgCEEKwAAAEMIVgAAAIYQrAAAAAwhWAEAABhCsAIAADCEYAUAAGBI\nrgWrH374Qe3atVPr1q21cOHC3HoZAAAAl+GVGws9c+aMJk+erK+//lo+Pj7q0qWLgoODVaFChdx4\nOQAA4ETHElP096VUI8uyWq1K9i6uc7FXZbGkGFlmCV9PlfHLlciTTq68yvbt21W3bl0VKlRIkvTk\nk09q3bp1dzRY5fO0qEqhO9OI2ZHP0+LsEu4Yb29vZ5dwR/HdyxlXbj9Xb7tTl1IUeznNyLKsVquu\n5C+hff+kyGIx84Ppn89DJX1d87PNDffStu98cpre+iXe8FKTjC1pWpPCkp+xxd2UJT4+3mp6oTNn\nztTly5fVs2dPSdKyZcv0xx9/aOjQoaZfCgAAwGXkyhirtLQ0WSyOe3Y3/g0AAHC3yZVgVbx4ccXG\nxtr/jouLU7FixXLjpQAAAFxGrgSrevXq6ddff9W5c+d0+fJlrV27Vo888khuvBQAAIDLyJVRhMWL\nF1fPnj3Vs2dPpaSkqGXLlqpevXpuvBQAAIDLyJXB6wAAAPcirrwOAABgCMEKAADAEIIVAACAIQQr\nAAAAQwhWAAAAhhCsbsPVq1edXYJbsbXX8ePHtXnzZqWlmbmH2b0mMTFRUVFRzi7DbSUmJjq7BLfD\nupt9KSnXbhp84sQJRUREyGrlxPusiouLkyTt3r1bS5cuVVKSuXsF3kmegwcPHu7sIlxR3759Vb9+\nfeXLl0+StG/fPvXp00f/+te/nFyZe5g+fbqWLVum6tWrq1u3boqJidHu3bvVuHFjZ5fmFpYtW6YF\nCxYoKChIHTt21MaNGxUXF6c6deo4uzSXt2nTJi1fvlw1atRQx44dNXPmTPn6+qpatWrOLs0tsO5m\n34wZM/Ttt9/qoYceUpcuXRQdHa29e/fSdlnwySefKCIiQmXLllX//v3l6emptWvXqlmzZs4u7bbR\nY5WJevXq6bXXXtP69es1efJkDRs2TH379nV2WW7jp59+0rBhw7R69Wo1b95cn332mQ4cOODsstzG\n/Pnz1atXL61Zs0ZNmjTRvHnztGXLFmeX5RY+//xzNW/eXGvWrFHVqlW1fPlyrVixwtlluQ3W3ez7\n8ccf9c4772j16tV65plnNGXKFO3fv9/ZZbmFnTt36t1339WPP/6oF154QcOHD9fJkyedXVa2EKwy\n0aFDB/Xt21dDhgzRd999p6lTp3Jbntvg5eWlvHnzasuWLWrQoIGsVquSk5OdXZbb8Pb2VrFixRQR\nEaFHHnlEXl5eunLlirPLcgteXl6qVKmStm7dqsaNG8vX19d+eAa3xrqbfV5eXvL19bWvt7Rd1nl6\nespisWjz5s165JFHdOXKFV2+fNnZZWULwSoTU6ZM0dixY/Xhhx+qTZs26tq1q9avX+/sstzGQw89\npJCQEF24cEG1a9fWm2++qbp16zq7LLdRqlQpvf322/rrr79Ur149jRgxQuXLl3d2WW7hvvvu0/jx\n47V79241bNhQU6ZMUdGiRZ1dlttg3c2+6tWrq3PnzoqPj1dwcLD69Omj2rVrO7sst/DYY4/p+eef\nl6+vr2rUqKHXX39dzzzzjLPLyhZuaZOJwYMHa/DgwSpUqJAk6eDBgxo5cqTmzJnj5Mrcg9VqVVRU\nlAICApQ3b17t2rVLQUFBzi7LbSQnJ2v79u2qWrWq/P39tXr1aj3xxBPKkyePs0tzefHx8Vq9erXq\n1Kmj8uXLa9asWWrZsqV9XcbNse5mn9Vq1cGDB1W2bFnlzZtXO3fuVHBwsLPLchsJCQkqWLCgpGuD\n/0uXLu3kirKHYHUT8fHxio6O1sMPP6yEhAT5+fnJyytX7lt915k+fXqG07t27XqHK3FPKSkp2rp1\nqxISEhzOKnr++eedWJX7iIqKStd2/MBlDevu7VuxYoVefPFFffXVVxk+3rlz5ztckfs5ffq0Fi9e\nnG69HTp0qBOryh5SQiY2b96scePGKTU1VdOnT9crr7yisLAwNWnSxNmluYXrV4yUlBRFRESoRo0a\nTqzIvYSFhSkmJkbly5eXxWKRJFksFoJVFowZM0YbNmxQQECAve2ka4PacWusu7fPNv6R8VTZN3To\nUFWpUkVVq1Z1WG/dET1WmQgNDdX48eP1xhtvaM6cOTp27JgGDx6sefPmObs0t5ScnKw333xTX3zx\nhbNLcQtt2rTRwoUL5eHBMMjb1apVK82dO9d+qRTkDOsu7oQOHTrcNb+v9FhlwmKxOIzJKFOmjBOr\ncX+XLl3S6dOnnV2G2wgICFBSUpJ8fX2dXYrbKVGiBGPRDGLdvbVHHnkkw14Wq9Uqi8XCpVKyoHLl\nyjp06JAqVqzo7FJyjGCViRIlSmjjxo2Srg2oW7hwoUqWLOnkqtxHy5Yt7Rsaq9WqhIQEvfrqq06u\nyn3kz59fr7zyih5++GH5+PjYpw8bNsyJVbmHgIAAdevWTXXr1pW3t7d9OmOEsiajdbdTp05Orsq1\nbd682dkluL2oqCiFhITI39/fYb1dsmSJE6vKHg4FZiI+Pl7jxo1TRESE0tLSVKdOHQ0YMED+/v7O\nLs0tnDp1yv5/i8UiPz8/+fn5ObEi9xIeHp7h9BYtWtzhStzPtGnTMpzerVu3O1yJe2LdzT7bGamX\nL1+W1WpVWlqaTpw4obCwMGeX5vJiYmIynB4QEHCHK8k5ghVyxYEDB/TFF19o7NixioqK0ogRIzR8\n+HCVK1fO2aW5jaioKB0+fFhPPPGEoqOj74oucmewWq06ceKEW26g76RNmzapcePGWrlyZYaPc+LE\nrfXu3Vv+/v7229hERESoWrVqeu+995xdmsu7cuWKtm3bpkuXLjmEUnfcIeJQ4A2u7wbPyLJly+5g\nNe7r448/1ptvvilJKl++vPr166cPP/ww01O54WjFihVavHixEhMTVa9ePfXr109du3bVSy+95OzS\nXN7SpUs1ceJEh6s2ly5d2i0PKdxJ+/fvV+PGjfXbb7+le4wzUrPm7Nmz+uyzzzRu3Dg99dRTeu21\n19SrVy9nl+UWwsLCFB8fr+PHj+vhhx9WZGSkatas6eyysoVgdYMpU6Y4u4S7QlJSksN1g2rVquW2\ndyp3hoULF2r69Onq2rWrChcurNmzZ6tHjx4EqyyYPXu2vvnmG02aNEm9evVSRESEIiMjnV2Wy+ve\nvbskqVChQnrrrbecXI17sh0yDQwM1OHDh1W9enVuRZVFhw4d0pIlSzRmzBi1bNlSvXv31jvvvOPs\nsrKFYHUD2wB1usNzplixYlq8eLGee+45SdKaNWsYn3YbPD09lTdvXvvfhQsX5tILWVS4cGGVKFFC\nFStWVFRUlFq3bq2FCxc6uyy3sW3bNqWlpfF9y4bg4GANGTJEvXr1Ut++ffXnn38qf/78zi7LLRQu\nXFjStVAaFRWlZ5991m13xglWmbi+OzwlJUWRkZEKDg4mWGXRu+++qzFjxuizzz6Tp6enateu7ZZX\n0HWWSpUqacGCBbp69ar+/PNPLVy4UJUqVXJ2WW7Bx8dH27dvV7ly5bRp0yZVrVpViYmJzi7LbRQs\nWFBt2rRR1apVHcI9Z6Te2htvvKGjR4/qgQce0IgRI/T7778rNDTU2WW5hcDAQP3nP//Riy++qJEj\nRyo2NtbZJWUbg9ezKD4+XsOGDdPEiROdXQruAUlJSZo5c6bDWaldu3Zl7zcLoqKitHz5cvXp00eD\nBw/Wjh071K1bN3Xo0MHZpbkFzkjNvvPnz+vgwYOqW7euZs+erT///FPdunVT2bJlnV2ay0tJSdGu\nXbtUu3ZtrVu3Tjt27NDLL7/sliftEKyy6MqVK2rfvj0DYG+hX79+GjduXKYnATD4H3B958+fV1JS\nkqxWq1JTU3XixAnVq1fP2WW5vD59+ig4OFjVqlXTf//7X73yyisKDw/X1KlTnV2ayxszZowGDhzo\nMG348OEaPny4cwrKAQ4FZqJnz54Ofx8/flyNGzd2UjXuwzbYkJMAsufVV1/V7NmzVb9+fYdgaruC\n89atW51YnXsg1OfM1OQFX1sAAArPSURBVKlT7YehCxUqpNjYWFWtWpVglQVnz55VaGioxo4dq+ef\nf14vvPCCFi1a5OyyXNqHH36oU6dOae/evTp27Jh9ekpKis6fP+/EyrKPYJWBM2fOqGXLlipevLgk\naefOnRo4cCDXYMoC2wD1Tz/9VP/5z38cHvv3v//NntstzJ49W9K1AcTInutDfUpKitavX6+UlBQn\nVuReVq1apZUrV+qTTz5Rly5ddOzYMQb/Z5Gnp6eio6O1YcMGTZgwQTExMXz3biEkJEQnT57UmDFj\nHO7O4eXl5ba/uQSrG+zevVuDBg3SiBEj7JcL2LFjh3r37q1PP/1U1apVc3KFrm3QoEE6ePCgYmNj\nHS4NkJqaqhIlSjixMvdy9epVLVy4UDt37pSHh4caNGigl19+mTO1suDGW0+FhIQoJCREr7/+upMq\nci9FixZVvnz5VL58eR06dEhNmzbVhAkTnF2WW+jSpYuGDh2qFi1aqGzZsmrXrp3eeOMNZ5fl0vz8\n/FSvXj1Nnjw53WNXr151QkU5R7D6//buPqbG948D+Pt0VIf0Fa0OK8YmZJOp2Jp/NGMsU9ITWTEi\na0zHZIoOO+WpISaMg4lacjz0oIwZYzZNpGicSjP+aLQeJE/rnPz+sM6vU/T0Pbm6z/f92to69/3P\ne22nfe77+lzXp4vjx4/j8OHDZgXU+vXr4evri2PHjnGJqxfJycloaWlBWloaEhISTNflcjmPW+gH\njUaDtrY2BAUFob29HUVFRaitre3Wg0DdPXv2zOxzdXU1fvz4ISiN9Dg6OqKwsBBTpkzB1atX4ezs\njC9fvoiOJQmVlZXIzs42fb58+bLANNKgVqtx9OhRrFmzBjKZDD9//r/tWyaToaCgQGC6gWHzehcd\nPS6/ExkZiaysrL+cSLr0en238QSBgYGiY0lCWFiY2fJLe3s7IiIiuCTTB137I0eNGoWoqCi+be6j\n+vp63L59G5GRkTh8+DBKS0uxevVqLFy4UHS0IW/lypXIysrqcXoH/d79+/cxb9480TEsgm+sujAa\njb89HM9oNHKtvB9SU1NRVlaG5uZmTJo0CVVVVZg5cyYLqz5yc3PD+/fvMX78eABAY2MjlEql4FTS\nEB8f3+3Mr+fPnwtKIz0uLi6IjIwEAKhUKsFppGXUqFEIDQ3F9OnTzc4A4xl+vTt58iQLK2vl7e0N\nrVZrGu/QQavV8om3H0pLS6HT6XDw4EGEh4fjx48fOHLkiOhYkvH9+3dERkZi1qxZsLGxQXl5OVxc\nXExvY06ePCk44dBTXl4Oo9GI1NRU7Ny507SkYDQasX//fh6V0keFhYU4evQoPn/+bHadO1J7t2jR\nItERJMvd3R0ajQYzZswwK0ql+DdlYdXFxo0bsWXLFty8eRMeHh6wt7fHq1ev4OzsjEOHDomOJxnO\nzs6Qy+WYNGkS3rx5gwULFqClpUV0LMnoOtG9824Z+r2SkhKUlZWhoaEBp0+fNl2Xy+UICQkRmExa\ntFotMjIyeNL/AAQGBqK1tdV0BlhHCwT1buTIkaYpJ52xsLICDg4OOH36NJ48eYLq6mrIZDKEhoZK\ndsq2KC4uLjh//jx8fHxw4sQJtLe3cxhpH7x+/RrTpk374/3Og63JXMdb5sLCQp4S/i+4uLiwqBog\nrVaLrKwsGAwGODo6orGxEVOnTsWFCxdERxvyXF1dERcXJzqGRbB5nQbFly9f8OjRIyxcuBA5OTko\nLS1FREQEfH19RUcb0vbu3YvExMRuDdgduATYu4qKCrx48QJhYWFQqVTQ6/XYsWMH/P39RUeThIyM\nDHz//h1z586FnZ2d6TqL+t4FBQUhKysLaWlpiImJQW1tLa5du8Y2iD6wpsZ/vrGiQbF161bT0RQR\nERGIiIgQnEgawsPDsXbtWtTW1sLLywuJiYlsWu+nQ4cOITY2Fnfv3oW9vT0uXryI7du3s7Dqo5cv\nXwIAampqzK6zqO+ds7MzHBwcMHnyZFRVVcHf3x8ZGRmiY0mCNTX+s7CiQdHRW+Dm5iY6iqQcPHgQ\nAQEB8Pb2RlFREdLT07Fv3z7RsSSlvb0dfn5+UKvV8Pf3h1Kp5I7efmABNXAODg4oLi6Gh4cHrl+/\nDqVSidbWVtGxJEGKvVR/wsKKBkVjYyOWL18OJycnKBQK06w7zmvrWWtrK4KDgwH82kjBN339p1Ao\nkJubi8ePHyM+Ph43btwwewKm3+My9MDpdDqEhIQgKSkJt27dwuLFi/HgwQPs2bOHJ6/3UWBgIN6+\nfWs67f/du3ccaUPU2a5du0yzFqnv5HK56XeZTAZbW1uBaaRJrVZDp9NBo9HAyckJer0eu3fvFh1r\nyFu2bBmA7jtSqXd5eXkICQmBUqlEdHQ0AHBKQj8VFhYiJycHX79+ha+vLzZt2oQNGzZg6dKloqP1\nGweP0aBISUnBuHHjuv1QzzqPcwBgFY2cf5u7uzuWLFmCpqYmGAwGBAcHw93dXXSsIe/bt2/dxgER\n/S05OTk4c+YMRowYgdGjR+PixYuSnXTCN1Y0KDw8PFBQUNDtsDcOYu5ZbW2t2fDq+vp6BAUFcSm1\nH/Lz86HT6dDa2oo5c+YgPj4e69atM/u7Undnzpzp8T6XAv+s6/e2A7+3fSeXyzF8+HDT5zFjxkh2\n6DwLKxoUlZWVqKysNLvGfzC90+l0oiNIXm5uLrRaLdatW2d68o2NjWVh1QsWTgPn7u6O9PR00TEk\nbfLkydDpdDAYDKipqcGVK1fg4eEhOtaAsLAii8rMzERUVBTy8vJQUVEBLy8v0z2NRiMwmTRwufTf\ns7GxMXtLOnr0aMk++YpQUVGBzMxMswHqdXV1yM/PFx1tyLK1teV3d4A6Gv+3bduGs2fPYtiwYUhO\nTjb1WUkR/9uQRd25c8f0e1pamtm9qqqqvx2H/oOmTp2Ky5cvo62tDXq9HhqNhieJ98PevXvh7+8P\ng8GA0NBQKJVKqxmOO1g8PT1FR5CsvLw8AL9288bFxeHSpUvIzs6GSqXCyJEjBacbGBZWZFGdm6+7\nNmJ3/Uw0GD59+oSmpiYoFAqkpKTgn3/+QUJCguhYkmFra4uAgAD4+PjA0dERarUaJSUlomMNaUlJ\nSaIj0BDCpUAaNF13tHGHG/0NHz9+RHJyMmJjY0VHkSQ7Ozt8+vQJEyZMQGVlJWbPno1v376JjkVW\nyhob/1lYkUWxeKKhYOnSpZg4cSLs7e1N19ic3TcrVqxAYmIiDhw4gOjoaBQXF5v1ShJZkjU2/rOw\nIovq/PTRcVQA8Ovpo6GhQWQ0+o/YvHmz6AiS9fDhQ3h6emL+/Pm4d+8exo4dCzs7Ox6wSoPGGhv/\nZc3NzWx8IYupq6vr8b61fYGIrEVmZibu3r0LtVoNg8GAmJgYqFQqVFVVQS6XQ6VSiY5IVig1NdXq\netRYWBEREVauXIlz585BoVDg+PHjqKurQ2pqKn7+/Inw8HDk5uaKjkgkCdwVSEREZud/PX36FHPm\nzAHwq2+SvZNEfcceKyIigkwmw+fPn/H161fo9Xr4+fkBAD58+GA2HJyIesbCioiIEBUVhVWrVsFo\nNCI4OBiurq64c+cOTp06hbVr14qORyQZ7LEiIiIAv3byNjc3m2a0PXr0CAqFAj4+PoKTEUkHCysi\nIiIiC2HzOhEREZGFsLAiIiIishAWVkREREQWwsKKiIiIyEJYWBERERFZCAsrIiIiIgv5H1508lvM\nkJa1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17c36d668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "lst9.plot(kind = 'bar') \n",
    "plt.xticks\n",
    "plt.ylabel=('% of Species')\n",
    "plt.xlabel=('Species') \n",
    "plt.title('% of Species found in All Traps 2009');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "tot10 = len(df_2010)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [],
   "source": [
    "cul10 = (sum(df_2010['culex']) / tot10 * 100)\n",
    "err10 = (sum(df_2010['erraticus']) / tot10 * 100)\n",
    "pip10 = (sum(df_2010['pipiens']) / tot10 * 100)\n",
    "res10 = (sum(df_2010['restuans']) / tot10 * 100)\n",
    "sal10 = (sum(df_2010['salinarius']) / tot10 * 100)\n",
    "tar10 = (sum(df_2010['tarsalis']) / tot10 * 100)\n",
    "ter10 = (sum(df_2010['territans']) / tot10 * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x10 = [cul10, err10, pip10, res10, sal10, tar10, ter10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lst10 = pd.DataFrame(x10, index=['Culex', 'Erraticus', 'Pipiens', 'restuans', \n",
    "                              'Salinarius', 'Tarsalis', 'Territans'], columns=['col'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17c7caf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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D1aFDhwxDQh4eHvrggw/08ccfa9OmTYqMjFS5cuXUv39/devWLceXnMnKm2++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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17c7b0cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "lst10.plot(kind = 'bar') \n",
    "plt.xticks\n",
    "plt.ylabel=('% of Species')\n",
    "plt.xlabel=('Species') \n",
    "plt.title('% of Species found in All Traps 2010');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [],
   "source": [
    "tot11 = len(df_2011)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cul11 = (sum(df_2011['culex']) / tot11 * 100)\n",
    "err11 = (sum(df_2011['erraticus']) / tot11 * 100)\n",
    "pip11 = (sum(df_2011['pipiens']) / tot11 * 100)\n",
    "res11 = (sum(df_2011['restuans']) / tot11 * 100)\n",
    "sal11 = (sum(df_2011['salinarius']) / tot11 * 100)\n",
    "tar11 = (sum(df_2011['tarsalis']) / tot11 * 100)\n",
    "ter11 = (sum(df_2011['territans']) / tot11 * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x11 = [cul11, err11, pip11, res11, sal11, tar11, ter11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lst11 = pd.DataFrame(x11, index=['Culex', 'Erraticus', 'Pipiens', 'restuans', \n",
    "                              'Salinarius', 'Tarsalis', 'Territans'], columns=['col'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17ca8e0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WrNDhw4e1f/9+FS1aVK1atVJwcHCOJ0OXK1dOCxYs0BdffKGffvpJmzdvVsGC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\nWex38wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17c3a1400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "lst11.plot(kind = 'bar') \n",
    "plt.xticks\n",
    "plt.ylabel=('% of Species')\n",
    "plt.xlabel=('Species') \n",
    "plt.title('% of Species found in All Traps 2011');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tot12 = len(df_2012)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cul12 = (sum(df_2012['culex']) / tot12 * 100)\n",
    "err12 = (sum(df_2012['erraticus']) / tot12 * 100)\n",
    "pip12 = (sum(df_2012['pipiens']) / tot12 * 100)\n",
    "res12 = (sum(df_2012['restuans']) / tot12 * 100)\n",
    "sal12 = (sum(df_2012['salinarius']) / tot12 * 100)\n",
    "tar12 = (sum(df_2012['tarsalis']) / tot12 * 100)\n",
    "ter12 = (sum(df_2012['territans']) / tot12 * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x12 = [cul12, err12, pip12, res12, sal12, tar12, ter12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lst12 = pd.DataFrame(x12, index=['Culex', 'Erraticus', 'Pipiens', 'restuans', \n",
    "                              'Salinarius', 'Tarsalis', 'Territans'], columns=['col'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17cac3c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ePatPP/1UnTp1cjqfjt2UKVP09ttv69tvv5XNZtP999+fpUv+WgYPHqyQkBAt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\nRDbyD7T9ZHYbB3p5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17caad4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "lst12.plot(kind = 'bar') \n",
    "plt.xticks\n",
    "plt.ylabel=('% of Species')\n",
    "plt.xlabel=('Species') \n",
    "plt.title('% of Species found in All Traps 2012');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tot13 = len(df_2013)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cul13 = (sum(df_2013['culex']) / tot13 * 100)\n",
    "err13 = (sum(df_2013['erraticus']) / tot13 * 100)\n",
    "pip13 = (sum(df_2013['pipiens']) / tot13 * 100)\n",
    "res13 = (sum(df_2013['restuans']) / tot13 * 100)\n",
    "sal13 = (sum(df_2013['salinarius']) / tot13 * 100)\n",
    "tar13 = (sum(df_2013['tarsalis']) / tot13 * 100)\n",
    "ter13 = (sum(df_2013['territans']) / tot13 * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x13 = [cul13, err13, pip13, res13, sal13, tar13, ter13]\n",
    "lst13 = pd.DataFrame(x13, index=['Culex', 'Erraticus', 'Pipiens', 'restuans', \n",
    "                              'Salinarius', 'Tarsalis', 'Territans'], columns=['col'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17cd56f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Rf//7Xy1dulRffPGFwsLCHH5tBw8e1A8//CAvLy/bvms9ALH2ql/Pz89PRYoUUVJSklJS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\nREREZCP/AO4ROvVGji9aAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17cc0f908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "lst13.plot(kind = 'bar') \n",
    "plt.xticks\n",
    "plt.ylabel=('% of Species')\n",
    "plt.xlabel=('Species') \n",
    "plt.title('% of Species found in All Traps 2013');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tot14 = len(df_2014)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cul14 = (sum(df_2014['culex']) / tot14 * 100)\n",
    "err14 = (sum(df_2014['erraticus']) / tot14 * 100)\n",
    "pip14 = (sum(df_2014['pipiens']) / tot14 * 100)\n",
    "res14 = (sum(df_2014['restuans']) / tot14 * 100)\n",
    "sal14 = (sum(df_2014['salinarius']) / tot14 * 100)\n",
    "tar14 = (sum(df_2014['tarsalis']) / tot14 * 100)\n",
    "ter14 = (sum(df_2014['territans']) / tot14 * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x14 = [cul14, err14, pip14, res14, sal14, tar14, ter14]\n",
    "lst14 = pd.DataFrame(x14, index=['Culex', 'Erraticus', 'Pipiens', 'restuans', \n",
    "                              'Salinarius', 'Tarsalis', 'Territans'], columns=['col'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17cd6f208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JEurYsaN69eqVqfu9dOnS+uSTT7RkyRJt2rRJ0dHRKlSokPz9/fXEE0+oc+fO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17cd7bef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "lst14.plot(kind = 'bar') \n",
    "plt.xticks\n",
    "plt.ylabel=('% of Species')\n",
    "plt.xlabel=('Species') \n",
    "plt.title('% of Species found in All Traps 2014');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}