B.Kiros_data_setup-EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "np.random.seed(42)\n",
    "\n",
    "from matplotlib import rcParams\n",
    "rcParams.update({'figure.autolayout':True})\n",
    "\n",
    "plt.style.use([\"presentation\"])\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_data=pd.read_csv('./assets/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "working_data=pd.read_csv('./assets/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "NOAA=pd.read_csv('./assets/weather.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "spray=pd.read_csv('./assets/spray.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test=train_test_split(working_data, test_size=.20, random_state=523)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Station', 'Date', 'Tmax', 'Tmin', 'Tavg', 'DewPoint', 'WetBulb',\n",
       "       'Heat', 'Cool', 'PrecipTotal', 'StnPressure', 'SeaLevel', 'ResultSpeed',\n",
       "       'ResultDir', 'AvgSpeed', 'joinID'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NOAA.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Address</th>\n",
       "      <th>Species</th>\n",
       "      <th>Block</th>\n",
       "      <th>Street</th>\n",
       "      <th>Trap</th>\n",
       "      <th>AddressNumberAndStreet</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>AddressAccuracy</th>\n",
       "      <th>NumMosquitos</th>\n",
       "      <th>WnvPresent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>4100 North Oak Park Avenue, Chicago, IL 60634,...</td>\n",
       "      <td>CULEX PIPIENS/RESTUANS</td>\n",
       "      <td>41</td>\n",
       "      <td>N OAK PARK AVE</td>\n",
       "      <td>T002</td>\n",
       "      <td>4100  N OAK PARK AVE, Chicago, IL</td>\n",
       "      <td>41.95469</td>\n",
       "      <td>-87.800991</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>4100 North Oak Park Avenue, Chicago, IL 60634,...</td>\n",
       "      <td>CULEX RESTUANS</td>\n",
       "      <td>41</td>\n",
       "      <td>N OAK PARK AVE</td>\n",
       "      <td>T002</td>\n",
       "      <td>4100  N OAK PARK AVE, Chicago, IL</td>\n",
       "      <td>41.95469</td>\n",
       "      <td>-87.800991</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date                                            Address  \\\n",
       "0  2007-05-29  4100 North Oak Park Avenue, Chicago, IL 60634,...   \n",
       "1  2007-05-29  4100 North Oak Park Avenue, Chicago, IL 60634,...   \n",
       "\n",
       "                  Species  Block           Street  Trap  \\\n",
       "0  CULEX PIPIENS/RESTUANS     41   N OAK PARK AVE  T002   \n",
       "1          CULEX RESTUANS     41   N OAK PARK AVE  T002   \n",
       "\n",
       "              AddressNumberAndStreet  Latitude  Longitude  AddressAccuracy  \\\n",
       "0  4100  N OAK PARK AVE, Chicago, IL  41.95469 -87.800991                9   \n",
       "1  4100  N OAK PARK AVE, Chicago, IL  41.95469 -87.800991                9   \n",
       "\n",
       "   NumMosquitos  WnvPresent  \n",
       "0             1           0  \n",
       "1             1           0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at the Data\n",
    "working_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10506 entries, 0 to 10505\n",
      "Data columns (total 12 columns):\n",
      "Date                      10506 non-null object\n",
      "Address                   10506 non-null object\n",
      "Species                   10506 non-null object\n",
      "Block                     10506 non-null int64\n",
      "Street                    10506 non-null object\n",
      "Trap                      10506 non-null object\n",
      "AddressNumberAndStreet    10506 non-null object\n",
      "Latitude                  10506 non-null float64\n",
      "Longitude                 10506 non-null float64\n",
      "AddressAccuracy           10506 non-null int64\n",
      "NumMosquitos              10506 non-null int64\n",
      "WnvPresent                10506 non-null int64\n",
      "dtypes: float64(2), int64(4), object(6)\n",
      "memory usage: 985.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# data types and missing values\n",
    "working_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_data.isnull().sum().any()  # checking any missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Block</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>AddressAccuracy</th>\n",
       "      <th>NumMosquitos</th>\n",
       "      <th>WnvPresent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10506.000000</td>\n",
       "      <td>10506.000000</td>\n",
       "      <td>10506.000000</td>\n",
       "      <td>10506.000000</td>\n",
       "      <td>10506.000000</td>\n",
       "      <td>10506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>35.687797</td>\n",
       "      <td>41.841139</td>\n",
       "      <td>-87.699908</td>\n",
       "      <td>7.819532</td>\n",
       "      <td>12.853512</td>\n",
       "      <td>0.052446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.339468</td>\n",
       "      <td>0.112742</td>\n",
       "      <td>0.096514</td>\n",
       "      <td>1.452921</td>\n",
       "      <td>16.133816</td>\n",
       "      <td>0.222936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>41.644612</td>\n",
       "      <td>-87.930995</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>41.732984</td>\n",
       "      <td>-87.760070</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>41.846283</td>\n",
       "      <td>-87.694991</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>41.954690</td>\n",
       "      <td>-87.627796</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>98.000000</td>\n",
       "      <td>42.017430</td>\n",
       "      <td>-87.531635</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Block      Latitude     Longitude  AddressAccuracy  \\\n",
       "count  10506.000000  10506.000000  10506.000000     10506.000000   \n",
       "mean      35.687797     41.841139    -87.699908         7.819532   \n",
       "std       24.339468      0.112742      0.096514         1.452921   \n",
       "min       10.000000     41.644612    -87.930995         3.000000   \n",
       "25%       12.000000     41.732984    -87.760070         8.000000   \n",
       "50%       33.000000     41.846283    -87.694991         8.000000   \n",
       "75%       52.000000     41.954690    -87.627796         9.000000   \n",
       "max       98.000000     42.017430    -87.531635         9.000000   \n",
       "\n",
       "       NumMosquitos    WnvPresent  \n",
       "count  10506.000000  10506.000000  \n",
       "mean      12.853512      0.052446  \n",
       "std       16.133816      0.222936  \n",
       "min        1.000000      0.000000  \n",
       "25%        2.000000      0.000000  \n",
       "50%        5.000000      0.000000  \n",
       "75%       17.000000      0.000000  \n",
       "max       50.000000      1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking statistics for each columns\n",
    "working_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Address</th>\n",
       "      <th>Species</th>\n",
       "      <th>Block</th>\n",
       "      <th>Street</th>\n",
       "      <th>Trap</th>\n",
       "      <th>AddressNumberAndStreet</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>AddressAccuracy</th>\n",
       "      <th>NumMosquitos</th>\n",
       "      <th>WnvPresent</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10506</td>\n",
       "      <td>10506</td>\n",
       "      <td>10506</td>\n",
       "      <td>10506.000000</td>\n",
       "      <td>10506</td>\n",
       "      <td>10506</td>\n",
       "      <td>10506</td>\n",
       "      <td>10506.000000</td>\n",
       "      <td>10506.000000</td>\n",
       "      <td>10506.000000</td>\n",
       "      <td>10506.000000</td>\n",
       "      <td>10506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>95</td>\n",
       "      <td>138</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "      <td>136</td>\n",
       "      <td>138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2007-08-01</td>\n",
       "      <td>ORD Terminal 5, O'Hare International Airport, ...</td>\n",
       "      <td>CULEX PIPIENS/RESTUANS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W OHARE AIRPORT</td>\n",
       "      <td>T900</td>\n",
       "      <td>1000  W OHARE AIRPORT, Chicago, IL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>551</td>\n",
       "      <td>750</td>\n",
       "      <td>4752</td>\n",
       "      <td>NaN</td>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.687797</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.841139</td>\n",
       "      <td>-87.699908</td>\n",
       "      <td>7.819532</td>\n",
       "      <td>12.853512</td>\n",
       "      <td>0.052446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.339468</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.112742</td>\n",
       "      <td>0.096514</td>\n",
       "      <td>1.452921</td>\n",
       "      <td>16.133816</td>\n",
       "      <td>0.222936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.644612</td>\n",
       "      <td>-87.930995</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.732984</td>\n",
       "      <td>-87.760070</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.846283</td>\n",
       "      <td>-87.694991</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.954690</td>\n",
       "      <td>-87.627796</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.017430</td>\n",
       "      <td>-87.531635</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date                                            Address  \\\n",
       "count        10506                                              10506   \n",
       "unique          95                                                138   \n",
       "top     2007-08-01  ORD Terminal 5, O'Hare International Airport, ...   \n",
       "freq           551                                                750   \n",
       "mean           NaN                                                NaN   \n",
       "std            NaN                                                NaN   \n",
       "min            NaN                                                NaN   \n",
       "25%            NaN                                                NaN   \n",
       "50%            NaN                                                NaN   \n",
       "75%            NaN                                                NaN   \n",
       "max            NaN                                                NaN   \n",
       "\n",
       "                       Species         Block            Street   Trap  \\\n",
       "count                    10506  10506.000000             10506  10506   \n",
       "unique                       7           NaN               128    136   \n",
       "top     CULEX PIPIENS/RESTUANS           NaN   W OHARE AIRPORT   T900   \n",
       "freq                      4752           NaN               750    750   \n",
       "mean                       NaN     35.687797               NaN    NaN   \n",
       "std                        NaN     24.339468               NaN    NaN   \n",
       "min                        NaN     10.000000               NaN    NaN   \n",
       "25%                        NaN     12.000000               NaN    NaN   \n",
       "50%                        NaN     33.000000               NaN    NaN   \n",
       "75%                        NaN     52.000000               NaN    NaN   \n",
       "max                        NaN     98.000000               NaN    NaN   \n",
       "\n",
       "                    AddressNumberAndStreet      Latitude     Longitude  \\\n",
       "count                                10506  10506.000000  10506.000000   \n",
       "unique                                 138           NaN           NaN   \n",
       "top     1000  W OHARE AIRPORT, Chicago, IL           NaN           NaN   \n",
       "freq                                   750           NaN           NaN   \n",
       "mean                                   NaN     41.841139    -87.699908   \n",
       "std                                    NaN      0.112742      0.096514   \n",
       "min                                    NaN     41.644612    -87.930995   \n",
       "25%                                    NaN     41.732984    -87.760070   \n",
       "50%                                    NaN     41.846283    -87.694991   \n",
       "75%                                    NaN     41.954690    -87.627796   \n",
       "max                                    NaN     42.017430    -87.531635   \n",
       "\n",
       "        AddressAccuracy  NumMosquitos    WnvPresent  \n",
       "count      10506.000000  10506.000000  10506.000000  \n",
       "unique              NaN           NaN           NaN  \n",
       "top                 NaN           NaN           NaN  \n",
       "freq                NaN           NaN           NaN  \n",
       "mean           7.819532     12.853512      0.052446  \n",
       "std            1.452921     16.133816      0.222936  \n",
       "min            3.000000      1.000000      0.000000  \n",
       "25%            8.000000      2.000000      0.000000  \n",
       "50%            8.000000      5.000000      0.000000  \n",
       "75%            9.000000     17.000000      0.000000  \n",
       "max            9.000000     50.000000      1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_data.describe(include= 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10506.000000\n",
       "mean         0.052446\n",
       "std          0.222936\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.000000\n",
       "max          1.000000\n",
       "Name: WnvPresent, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_data['WnvPresent'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Longitude         -0.060345\n",
      "Block              0.004877\n",
      "AddressAccuracy    0.008064\n",
      "Latitude           0.028697\n",
      "NumMosquitos       0.196820\n",
      "WnvPresent         1.000000\n",
      "Name: WnvPresent, dtype: float64 \n",
      "\n",
      "Longitude         -0.060345\n",
      "Block              0.004877\n",
      "AddressAccuracy    0.008064\n",
      "Latitude           0.028697\n",
      "NumMosquitos       0.196820\n",
      "WnvPresent         1.000000\n",
      "Name: WnvPresent, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "correlations_working_data = working_data.corr()['WnvPresent'].sort_values()\n",
    "print(correlations_working_data.head(10),'\\n')\n",
    "print(correlations_working_data.tail(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "working_data['Date'] = pd.to_datetime(working_data['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Address</th>\n",
       "      <th>Species</th>\n",
       "      <th>Block</th>\n",
       "      <th>Street</th>\n",
       "      <th>Trap</th>\n",
       "      <th>AddressNumberAndStreet</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>AddressAccuracy</th>\n",
       "      <th>NumMosquitos</th>\n",
       "      <th>WnvPresent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10484</th>\n",
       "      <td>2013-09-26</td>\n",
       "      <td>ORD Terminal 5, O'Hare International Airport, ...</td>\n",
       "      <td>CULEX PIPIENS/RESTUANS</td>\n",
       "      <td>10</td>\n",
       "      <td>W OHARE AIRPORT</td>\n",
       "      <td>T900</td>\n",
       "      <td>1000  W OHARE AIRPORT, Chicago, IL</td>\n",
       "      <td>41.974689</td>\n",
       "      <td>-87.890615</td>\n",
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       "      <td>39</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10485</th>\n",
       "      <td>2013-09-26</td>\n",
       "      <td>ORD Terminal 5, O'Hare International Airport, ...</td>\n",
       "      <td>CULEX PIPIENS/RESTUANS</td>\n",
       "      <td>10</td>\n",
       "      <td>W OHARE AIRPORT</td>\n",
       "      <td>T900</td>\n",
       "      <td>1000  W OHARE AIRPORT, Chicago, IL</td>\n",
       "      <td>41.974689</td>\n",
       "      <td>-87.890615</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10486</th>\n",
       "      <td>2013-09-26</td>\n",
       "      <td>ORD Terminal 5, O'Hare International Airport, ...</td>\n",
       "      <td>CULEX PIPIENS</td>\n",
       "      <td>10</td>\n",
       "      <td>W OHARE AIRPORT</td>\n",
       "      <td>T900</td>\n",
       "      <td>1000  W OHARE AIRPORT, Chicago, IL</td>\n",
       "      <td>41.974689</td>\n",
       "      <td>-87.890615</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10487</th>\n",
       "      <td>2013-09-26</td>\n",
       "      <td>ORD Terminal 5, O'Hare International Airport, ...</td>\n",
       "      <td>CULEX PIPIENS</td>\n",
       "      <td>10</td>\n",
       "      <td>W OHARE AIRPORT</td>\n",
       "      <td>T900</td>\n",
       "      <td>1000  W OHARE AIRPORT, Chicago, IL</td>\n",
       "      <td>41.974689</td>\n",
       "      <td>-87.890615</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10488</th>\n",
       "      <td>2013-09-26</td>\n",
       "      <td>ORD Terminal 5, O'Hare International Airport, ...</td>\n",
       "      <td>CULEX PIPIENS</td>\n",
       "      <td>10</td>\n",
       "      <td>W OHARE AIRPORT</td>\n",
       "      <td>T900</td>\n",
       "      <td>1000  W OHARE AIRPORT, Chicago, IL</td>\n",
       "      <td>41.974689</td>\n",
       "      <td>-87.890615</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10489</th>\n",
       "      <td>2013-09-26</td>\n",
       "      <td>ORD Terminal 5, O'Hare International Airport, ...</td>\n",
       "      <td>CULEX PIPIENS</td>\n",
       "      <td>10</td>\n",
       "      <td>W OHARE AIRPORT</td>\n",
       "      <td>T900</td>\n",
       "      <td>1000  W OHARE AIRPORT, Chicago, IL</td>\n",
       "      <td>41.974689</td>\n",
       "      <td>-87.890615</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date                                            Address  \\\n",
       "10484 2013-09-26  ORD Terminal 5, O'Hare International Airport, ...   \n",
       "10485 2013-09-26  ORD Terminal 5, O'Hare International Airport, ...   \n",
       "10486 2013-09-26  ORD Terminal 5, O'Hare International Airport, ...   \n",
       "10487 2013-09-26  ORD Terminal 5, O'Hare International Airport, ...   \n",
       "10488 2013-09-26  ORD Terminal 5, O'Hare International Airport, ...   \n",
       "10489 2013-09-26  ORD Terminal 5, O'Hare International Airport, ...   \n",
       "\n",
       "                      Species  Block            Street  Trap  \\\n",
       "10484  CULEX PIPIENS/RESTUANS     10   W OHARE AIRPORT  T900   \n",
       "10485  CULEX PIPIENS/RESTUANS     10   W OHARE AIRPORT  T900   \n",
       "10486           CULEX PIPIENS     10   W OHARE AIRPORT  T900   \n",
       "10487           CULEX PIPIENS     10   W OHARE AIRPORT  T900   \n",
       "10488           CULEX PIPIENS     10   W OHARE AIRPORT  T900   \n",
       "10489           CULEX PIPIENS     10   W OHARE AIRPORT  T900   \n",
       "\n",
       "                   AddressNumberAndStreet   Latitude  Longitude  \\\n",
       "10484  1000  W OHARE AIRPORT, Chicago, IL  41.974689 -87.890615   \n",
       "10485  1000  W OHARE AIRPORT, Chicago, IL  41.974689 -87.890615   \n",
       "10486  1000  W OHARE AIRPORT, Chicago, IL  41.974689 -87.890615   \n",
       "10487  1000  W OHARE AIRPORT, Chicago, IL  41.974689 -87.890615   \n",
       "10488  1000  W OHARE AIRPORT, Chicago, IL  41.974689 -87.890615   \n",
       "10489  1000  W OHARE AIRPORT, Chicago, IL  41.974689 -87.890615   \n",
       "\n",
       "       AddressAccuracy  NumMosquitos  WnvPresent  \n",
       "10484                9            39           1  \n",
       "10485                9             4           0  \n",
       "10486                9            16           0  \n",
       "10487                9             9           0  \n",
       "10488                9            11           0  \n",
       "10489                9             1           0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask1 = working_data['Date'] == '2013-09-26'\n",
    "mask2 = working_data['Trap'] == 'T900'\n",
    "working_data[mask1 & mask2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Species</th>\n",
       "      <th>Trap</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>NumMosquitos</th>\n",
       "      <th>WnvPresent</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>CULEX PIPIENS</td>\n",
       "      <td>T096</td>\n",
       "      <td>41.731922</td>\n",
       "      <td>-87.677512</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>CULEX PIPIENS/RESTUANS</td>\n",
       "      <td>T002</td>\n",
       "      <td>41.954690</td>\n",
       "      <td>-87.800991</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>CULEX PIPIENS/RESTUANS</td>\n",
       "      <td>T015</td>\n",
       "      <td>41.974089</td>\n",
       "      <td>-87.824812</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>CULEX PIPIENS/RESTUANS</td>\n",
       "      <td>T048</td>\n",
       "      <td>41.867108</td>\n",
       "      <td>-87.654224</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>CULEX PIPIENS/RESTUANS</td>\n",
       "      <td>T050</td>\n",
       "      <td>41.919343</td>\n",
       "      <td>-87.694259</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date                 Species  Trap   Latitude  Longitude  \\\n",
       "0 2007-05-29           CULEX PIPIENS  T096  41.731922 -87.677512   \n",
       "1 2007-05-29  CULEX PIPIENS/RESTUANS  T002  41.954690 -87.800991   \n",
       "2 2007-05-29  CULEX PIPIENS/RESTUANS  T015  41.974089 -87.824812   \n",
       "3 2007-05-29  CULEX PIPIENS/RESTUANS  T048  41.867108 -87.654224   \n",
       "4 2007-05-29  CULEX PIPIENS/RESTUANS  T050  41.919343 -87.694259   \n",
       "\n",
       "   NumMosquitos  WnvPresent  y  \n",
       "0             1           0  0  \n",
       "1             1           0  0  \n",
       "2             1           0  0  \n",
       "3             1           0  0  \n",
       "4             1           0  0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eda = working_data.pivot_table(index=['Date','Species','Trap','Latitude','Longitude'], values=['NumMosquitos','WnvPresent'], aggfunc=np.sum)\n",
    "eda = eda.reset_index()\n",
    "eda['y'] = (eda['WnvPresent'] >= 1) * 1\n",
    "eda.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21362fa8dd8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kiraz\\AppData\\Local\\conda\\conda\\envs\\dsi\\lib\\site-packages\\matplotlib\\figure.py:2267: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eda['WnvPresent'].value_counts().sort_index().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     8018\n",
       "1      409\n",
       "2       31\n",
       "3        9\n",
       "4        2\n",
       "5        1\n",
       "6        1\n",
       "7        1\n",
       "8        1\n",
       "9        1\n",
       "10       1\n",
       "Name: WnvPresent, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eda['WnvPresent'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2136315dda0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kiraz\\AppData\\Local\\conda\\conda\\envs\\dsi\\lib\\site-packages\\matplotlib\\figure.py:2267: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eda['WnvPresent'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    8018\n",
       "1     457\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eda['y'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21363473be0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kiraz\\AppData\\Local\\conda\\conda\\envs\\dsi\\lib\\site-packages\\matplotlib\\figure.py:2267: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eda['y'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x213634c1a90>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kiraz\\AppData\\Local\\conda\\conda\\envs\\dsi\\lib\\site-packages\\matplotlib\\figure.py:2267: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eda.pivot_table(index='Species',values='y', aggfunc=np.sum).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Species', 'Trap', 'Latitude', 'Longitude', 'NumMosquitos',\n",
       "       'WnvPresent', 'y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eda.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "NOAA=pd.read_csv('./assets/weather.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Station</th>\n",
       "      <th>Date</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>Tavg</th>\n",
       "      <th>Depart</th>\n",
       "      <th>DewPoint</th>\n",
       "      <th>WetBulb</th>\n",
       "      <th>Heat</th>\n",
       "      <th>Cool</th>\n",
       "      <th>...</th>\n",
       "      <th>CodeSum</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Water1</th>\n",
       "      <th>SnowFall</th>\n",
       "      <th>PrecipTotal</th>\n",
       "      <th>StnPressure</th>\n",
       "      <th>SeaLevel</th>\n",
       "      <th>ResultSpeed</th>\n",
       "      <th>ResultDir</th>\n",
       "      <th>AvgSpeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2007-05-01</td>\n",
       "      <td>83</td>\n",
       "      <td>50</td>\n",
       "      <td>67</td>\n",
       "      <td>14</td>\n",
       "      <td>51</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.10</td>\n",
       "      <td>29.82</td>\n",
       "      <td>1.7</td>\n",
       "      <td>27</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2007-05-01</td>\n",
       "      <td>84</td>\n",
       "      <td>52</td>\n",
       "      <td>68</td>\n",
       "      <td>M</td>\n",
       "      <td>51</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.18</td>\n",
       "      <td>29.82</td>\n",
       "      <td>2.7</td>\n",
       "      <td>25</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Station        Date  Tmax  Tmin Tavg Depart  DewPoint WetBulb Heat Cool  \\\n",
       "0        1  2007-05-01    83    50   67     14        51      56    0    2   \n",
       "1        2  2007-05-01    84    52   68      M        51      57    0    3   \n",
       "\n",
       "     ...    CodeSum Depth Water1 SnowFall PrecipTotal StnPressure SeaLevel  \\\n",
       "0    ...                0      M      0.0        0.00       29.10    29.82   \n",
       "1    ...                M      M        M        0.00       29.18    29.82   \n",
       "\n",
       "  ResultSpeed ResultDir  AvgSpeed  \n",
       "0         1.7        27       9.2  \n",
       "1         2.7        25       9.6  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NOAA.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M      1472\n",
       "  T      12\n",
       "0.1       1\n",
       "Name: SnowFall, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NOAA['SnowFall'][NOAA['SnowFall']!='0.0'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    2944\n",
       "Name: Water1, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NOAA['Water1'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "NOAA = NOAA.drop(['Depart', 'Sunrise', 'Sunset', 'Depth', 'CodeSum', 'Water1' ,'SnowFall'] , axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>25</td>\n",
       "      <td>9.6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2007-05-02</td>\n",
       "      <td>59</td>\n",
       "      <td>42</td>\n",
       "      <td>51</td>\n",
       "      <td>42</td>\n",
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       "      <td>14</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>29.38</td>\n",
       "      <td>30.09</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Station        Date  Tmax  Tmin Tavg  DewPoint WetBulb Heat Cool  \\\n",
       "0        1  2007-05-01    83    50   67        51      56    0    2   \n",
       "1        2  2007-05-01    84    52   68        51      57    0    3   \n",
       "2        1  2007-05-02    59    42   51        42      47   14    0   \n",
       "\n",
       "  PrecipTotal StnPressure SeaLevel  ResultSpeed  ResultDir AvgSpeed  \n",
       "0        0.00       29.10    29.82          1.7         27      9.2  \n",
       "1        0.00       29.18    29.82          2.7         25      9.6  \n",
       "2        0.00       29.38    30.09         13.0          4     13.4  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NOAA.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Station          int64\n",
       "Date            object\n",
       "Tmax             int64\n",
       "Tmin             int64\n",
       "Tavg            object\n",
       "DewPoint         int64\n",
       "WetBulb         object\n",
       "Heat            object\n",
       "Cool            object\n",
       "PrecipTotal     object\n",
       "StnPressure     object\n",
       "SeaLevel        object\n",
       "ResultSpeed    float64\n",
       "ResultDir        int64\n",
       "AvgSpeed        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NOAA.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "NOAA = NOAA.replace('M', -9999)\n",
    "NOAA = NOAA.replace('  T', -9999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Station          int64\n",
       "Date            object\n",
       "Tmax             int64\n",
       "Tmin             int64\n",
       "Tavg            object\n",
       "DewPoint         int64\n",
       "WetBulb         object\n",
       "Heat            object\n",
       "Cool            object\n",
       "PrecipTotal     object\n",
       "StnPressure     object\n",
       "SeaLevel        object\n",
       "ResultSpeed    float64\n",
       "ResultDir        int64\n",
       "AvgSpeed        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NOAA.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Station   Date   Tmax   Tmin   Tavg DewPoint WetBulb   Heat   Cool  \\\n",
       "count     2944   2944   2944   2944   2944     2944    2944   2944   2944   \n",
       "unique       1      1      1      1      1        1       1      1      1   \n",
       "top      False  False  False  False  False    False   False  False  False   \n",
       "freq      2944   2944   2944   2944   2944     2944    2944   2944   2944   \n",
       "\n",
       "       PrecipTotal StnPressure SeaLevel ResultSpeed ResultDir AvgSpeed  \n",
       "count         2944        2944     2944        2944      2944     2944  \n",
       "unique           1           1        1           1         1        1  \n",
       "top          False       False    False       False     False    False  \n",
       "freq          2944        2944     2944        2944      2944     2944  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NOAA.isnull().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "indices =['Station','Date']\n",
    "otherindices = NOAA.columns.difference(indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "NOAA[otherindices] =NOAA[otherindices].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2007-05-02</td>\n",
       "      <td>59.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>14.0</td>\n",
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       "      <td>2007-05-021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Station        Date  Tmax  Tmin  Tavg  DewPoint  WetBulb  Heat  Cool  \\\n",
       "0        1  2007-05-01  83.0  50.0  67.0      51.0     56.0   0.0   2.0   \n",
       "1        2  2007-05-01  84.0  52.0  68.0      51.0     57.0   0.0   3.0   \n",
       "2        1  2007-05-02  59.0  42.0  51.0      42.0     47.0  14.0   0.0   \n",
       "\n",
       "   PrecipTotal  StnPressure  SeaLevel  ResultSpeed  ResultDir  AvgSpeed  \\\n",
       "0          0.0        29.10     29.82          1.7       27.0       9.2   \n",
       "1          0.0        29.18     29.82          2.7       25.0       9.6   \n",
       "2          0.0        29.38     30.09         13.0        4.0      13.4   \n",
       "\n",
       "        joinID  \n",
       "0  2007-05-011  \n",
       "1  2007-05-012  \n",
       "2  2007-05-021  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NOAA['joinID'] = NOAA['Date'].astype(str)+NOAA['Station'].astype(str)\n",
    "NOAA.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Station</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>Tavg</th>\n",
       "      <th>DewPoint</th>\n",
       "      <th>WetBulb</th>\n",
       "      <th>Heat</th>\n",
       "      <th>Cool</th>\n",
       "      <th>PrecipTotal</th>\n",
       "      <th>StnPressure</th>\n",
       "      <th>SeaLevel</th>\n",
       "      <th>ResultSpeed</th>\n",
       "      <th>ResultDir</th>\n",
       "      <th>AvgSpeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2944.000000</td>\n",
       "      <td>2944.000000</td>\n",
       "      <td>2944.000000</td>\n",
       "      <td>2944.000000</td>\n",
       "      <td>2944.000000</td>\n",
       "      <td>2944.000000</td>\n",
       "      <td>2944.000000</td>\n",
       "      <td>2944.000000</td>\n",
       "      <td>2944.000000</td>\n",
       "      <td>2944.000000</td>\n",
       "      <td>2944.00000</td>\n",
       "      <td>2944.000000</td>\n",
       "      <td>2944.000000</td>\n",
       "      <td>2944.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>76.166101</td>\n",
       "      <td>57.810462</td>\n",
       "      <td>29.628397</td>\n",
       "      <td>53.457880</td>\n",
       "      <td>45.644701</td>\n",
       "      <td>-33.966033</td>\n",
       "      <td>-31.734375</td>\n",
       "      <td>-1086.716586</td>\n",
       "      <td>15.659042</td>\n",
       "      <td>-0.69108</td>\n",
       "      <td>6.960666</td>\n",
       "      <td>17.494905</td>\n",
       "      <td>-1.617493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.500085</td>\n",
       "      <td>11.461970</td>\n",
       "      <td>10.381939</td>\n",
       "      <td>614.354965</td>\n",
       "      <td>10.675181</td>\n",
       "      <td>370.681434</td>\n",
       "      <td>610.398482</td>\n",
       "      <td>610.536662</td>\n",
       "      <td>3112.833114</td>\n",
       "      <td>369.459051</td>\n",
       "      <td>553.75485</td>\n",
       "      <td>3.587527</td>\n",
       "      <td>10.063609</td>\n",
       "      <td>319.370107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.00000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.190000</td>\n",
       "      <td>29.87000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.280000</td>\n",
       "      <td>29.97000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>8.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>29.390000</td>\n",
       "      <td>30.06000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>10.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>6.860000</td>\n",
       "      <td>29.860000</td>\n",
       "      <td>30.53000</td>\n",
       "      <td>24.100000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>26.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Station         Tmax         Tmin         Tavg     DewPoint  \\\n",
       "count  2944.000000  2944.000000  2944.000000  2944.000000  2944.000000   \n",
       "mean      1.500000    76.166101    57.810462    29.628397    53.457880   \n",
       "std       0.500085    11.461970    10.381939   614.354965    10.675181   \n",
       "min       1.000000    41.000000    29.000000 -9999.000000    22.000000   \n",
       "25%       1.000000    69.000000    50.000000    60.000000    46.000000   \n",
       "50%       1.500000    78.000000    59.000000    69.000000    54.000000   \n",
       "75%       2.000000    85.000000    66.000000    75.000000    62.000000   \n",
       "max       2.000000   104.000000    83.000000    94.000000    75.000000   \n",
       "\n",
       "           WetBulb         Heat         Cool  PrecipTotal  StnPressure  \\\n",
       "count  2944.000000  2944.000000  2944.000000  2944.000000  2944.000000   \n",
       "mean     45.644701   -33.966033   -31.734375 -1086.716586    15.659042   \n",
       "std     370.681434   610.398482   610.536662  3112.833114   369.459051   \n",
       "min   -9999.000000 -9999.000000 -9999.000000 -9999.000000 -9999.000000   \n",
       "25%      53.000000     0.000000     0.000000     0.000000    29.190000   \n",
       "50%      61.000000     0.000000     4.000000     0.000000    29.280000   \n",
       "75%      67.000000     5.000000    10.000000     0.060000    29.390000   \n",
       "max      78.000000    29.000000    29.000000     6.860000    29.860000   \n",
       "\n",
       "         SeaLevel  ResultSpeed    ResultDir     AvgSpeed  \n",
       "count  2944.00000  2944.000000  2944.000000  2944.000000  \n",
       "mean     -0.69108     6.960666    17.494905    -1.617493  \n",
       "std     553.75485     3.587527    10.063609   319.370107  \n",
       "min   -9999.00000     0.100000     1.000000 -9999.000000  \n",
       "25%      29.87000     4.300000     7.000000     6.300000  \n",
       "50%      29.97000     6.400000    19.000000     8.100000  \n",
       "75%      30.06000     9.200000    25.000000    10.400000  \n",
       "max      30.53000    24.100000    36.000000    26.300000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NOAA.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Location Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train set\n",
    "lat_lon = working_data[['Latitude','Longitude']].copy()\n",
    "working_data['Station'] = lat_lon.apply(mapStation, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Address</th>\n",
       "      <th>Species</th>\n",
       "      <th>Block</th>\n",
       "      <th>Street</th>\n",
       "      <th>Trap</th>\n",
       "      <th>AddressNumberAndStreet</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>AddressAccuracy</th>\n",
       "      <th>NumMosquitos</th>\n",
       "      <th>WnvPresent</th>\n",
       "      <th>Station</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>4100 North Oak Park Avenue, Chicago, IL 60634,...</td>\n",
       "      <td>CULEX PIPIENS/RESTUANS</td>\n",
       "      <td>41</td>\n",
       "      <td>N OAK PARK AVE</td>\n",
       "      <td>T002</td>\n",
       "      <td>4100  N OAK PARK AVE, Chicago, IL</td>\n",
       "      <td>41.954690</td>\n",
       "      <td>-87.800991</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>4100 North Oak Park Avenue, Chicago, IL 60634,...</td>\n",
       "      <td>CULEX RESTUANS</td>\n",
       "      <td>41</td>\n",
       "      <td>N OAK PARK AVE</td>\n",
       "      <td>T002</td>\n",
       "      <td>4100  N OAK PARK AVE, Chicago, IL</td>\n",
       "      <td>41.954690</td>\n",
       "      <td>-87.800991</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>6200 North Mandell Avenue, Chicago, IL 60646, USA</td>\n",
       "      <td>CULEX RESTUANS</td>\n",
       "      <td>62</td>\n",
       "      <td>N MANDELL AVE</td>\n",
       "      <td>T007</td>\n",
       "      <td>6200  N MANDELL AVE, Chicago, IL</td>\n",
       "      <td>41.994991</td>\n",
       "      <td>-87.769279</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date                                            Address  \\\n",
       "0 2007-05-29  4100 North Oak Park Avenue, Chicago, IL 60634,...   \n",
       "1 2007-05-29  4100 North Oak Park Avenue, Chicago, IL 60634,...   \n",
       "2 2007-05-29  6200 North Mandell Avenue, Chicago, IL 60646, USA   \n",
       "\n",
       "                  Species  Block           Street  Trap  \\\n",
       "0  CULEX PIPIENS/RESTUANS     41   N OAK PARK AVE  T002   \n",
       "1          CULEX RESTUANS     41   N OAK PARK AVE  T002   \n",
       "2          CULEX RESTUANS     62    N MANDELL AVE  T007   \n",
       "\n",
       "              AddressNumberAndStreet   Latitude  Longitude  AddressAccuracy  \\\n",
       "0  4100  N OAK PARK AVE, Chicago, IL  41.954690 -87.800991                9   \n",
       "1  4100  N OAK PARK AVE, Chicago, IL  41.954690 -87.800991                9   \n",
       "2   6200  N MANDELL AVE, Chicago, IL  41.994991 -87.769279                9   \n",
       "\n",
       "   NumMosquitos  WnvPresent  Station  \n",
       "0             1           0        1  \n",
       "1             1           0        1  \n",
       "2             1           0        1  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop Addresses, NumMosquitos\n",
    "working_data = working_data[['Date','Trap','Station','Species','WnvPresent']]\n",
    "working_data['joinID'] = working_data['Date'].astype(str)+working_data['Station'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T900     0.071388\n",
       "T115     0.051590\n",
       "T138     0.029888\n",
       "T002     0.017609\n",
       "T135     0.017419\n",
       "T054     0.015515\n",
       "T128     0.015229\n",
       "T151     0.014849\n",
       "T212     0.014468\n",
       "T090     0.014373\n",
       "T114     0.014087\n",
       "T158     0.013992\n",
       "T031     0.013897\n",
       "T048     0.013897\n",
       "T903     0.013326\n",
       "T008     0.013231\n",
       "T011     0.012659\n",
       "T209     0.012374\n",
       "T200     0.012279\n",
       "T073     0.012184\n",
       "T030     0.011993\n",
       "T003     0.011612\n",
       "T094     0.011517\n",
       "T028     0.011136\n",
       "T066     0.011041\n",
       "T074     0.010946\n",
       "T047     0.010851\n",
       "T221     0.010661\n",
       "T102     0.010661\n",
       "T009     0.010565\n",
       "           ...   \n",
       "T107     0.002094\n",
       "T156     0.001999\n",
       "T219     0.001999\n",
       "T154     0.001999\n",
       "T206     0.001999\n",
       "T162     0.001999\n",
       "T075     0.001904\n",
       "T070     0.001904\n",
       "T071     0.001808\n",
       "T142     0.001713\n",
       "T050     0.001618\n",
       "T034     0.001618\n",
       "T097     0.001618\n",
       "T044     0.001428\n",
       "T051     0.001428\n",
       "T060     0.001428\n",
       "T001     0.001428\n",
       "T157     0.001333\n",
       "T005     0.001333\n",
       "T149     0.001333\n",
       "T072     0.001237\n",
       "T004     0.001142\n",
       "T054C    0.000857\n",
       "T229     0.000761\n",
       "T078     0.000761\n",
       "T238     0.000666\n",
       "T094B    0.000476\n",
       "T076     0.000476\n",
       "T237     0.000286\n",
       "T040     0.000190\n",
       "Name: Trap, Length: 136, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_data['Trap'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>Station</th>\n",
       "      <th>Species</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2007-05-29</td>\n",
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       "      <td>2007-05-29</td>\n",
       "      <td>T002</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>T007</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "        Date  Trap  Station                 Species  WnvPresent       joinID\n",
       "0 2007-05-29  T002        1  CULEX PIPIENS/RESTUANS           0  2007-05-291\n",
       "1 2007-05-29  T002        1          CULEX RESTUANS           0  2007-05-291\n",
       "2 2007-05-29  T007        1          CULEX RESTUANS           0  2007-05-291"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_x</th>\n",
       "      <th>Trap</th>\n",
       "      <th>Station_x</th>\n",
       "      <th>Species</th>\n",
       "      <th>WnvPresent</th>\n",
       "      <th>joinID</th>\n",
       "      <th>Station_y</th>\n",
       "      <th>Date_y</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>...</th>\n",
       "      <th>DewPoint</th>\n",
       "      <th>WetBulb</th>\n",
       "      <th>Heat</th>\n",
       "      <th>Cool</th>\n",
       "      <th>PrecipTotal</th>\n",
       "      <th>StnPressure</th>\n",
       "      <th>SeaLevel</th>\n",
       "      <th>ResultSpeed</th>\n",
       "      <th>ResultDir</th>\n",
       "      <th>AvgSpeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>T002</td>\n",
       "      <td>1</td>\n",
       "      <td>CULEX PIPIENS/RESTUANS</td>\n",
       "      <td>0</td>\n",
       "      <td>2007-05-291</td>\n",
       "      <td>1</td>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>88.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.39</td>\n",
       "      <td>30.11</td>\n",
       "      <td>5.8</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>T002</td>\n",
       "      <td>1</td>\n",
       "      <td>CULEX RESTUANS</td>\n",
       "      <td>0</td>\n",
       "      <td>2007-05-291</td>\n",
       "      <td>1</td>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>88.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.39</td>\n",
       "      <td>30.11</td>\n",
       "      <td>5.8</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>T007</td>\n",
       "      <td>1</td>\n",
       "      <td>CULEX RESTUANS</td>\n",
       "      <td>0</td>\n",
       "      <td>2007-05-291</td>\n",
       "      <td>1</td>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>88.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.39</td>\n",
       "      <td>30.11</td>\n",
       "      <td>5.8</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Date_x  Trap  Station_x                 Species  WnvPresent  \\\n",
       "0 2007-05-29  T002          1  CULEX PIPIENS/RESTUANS           0   \n",
       "1 2007-05-29  T002          1          CULEX RESTUANS           0   \n",
       "2 2007-05-29  T007          1          CULEX RESTUANS           0   \n",
       "\n",
       "        joinID  Station_y      Date_y  Tmax  Tmin    ...     DewPoint  \\\n",
       "0  2007-05-291          1  2007-05-29  88.0  60.0    ...         58.0   \n",
       "1  2007-05-291          1  2007-05-29  88.0  60.0    ...         58.0   \n",
       "2  2007-05-291          1  2007-05-29  88.0  60.0    ...         58.0   \n",
       "\n",
       "   WetBulb  Heat  Cool  PrecipTotal  StnPressure  SeaLevel  ResultSpeed  \\\n",
       "0     65.0   0.0   9.0          0.0        29.39     30.11          5.8   \n",
       "1     65.0   0.0   9.0          0.0        29.39     30.11          5.8   \n",
       "2     65.0   0.0   9.0          0.0        29.39     30.11          5.8   \n",
       "\n",
       "   ResultDir  AvgSpeed  \n",
       "0       18.0       6.5  \n",
       "1       18.0       6.5  \n",
       "2       18.0       6.5  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_data = working_data.merge(NOAA, on='joinID', how='inner')\n",
    "working_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['Species','Tmax', 'Tmin', 'Tavg', 'DewPoint', 'WetBulb', 'Heat', 'Cool','PrecipTotal', 'StnPressure', 'SeaLevel', 'ResultSpeed', 'ResultDir','AvgSpeed']\n",
    "label = 'WnvPresent'\n",
    "X = working_data[features]\n",
    "X = X.replace(-9999, np.nan)\n",
    "X = X.fillna(X.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.get_dummies(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>58.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88.0</td>\n",
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      ],
      "text/plain": [
       "   Tmax  Tmin  Tavg  DewPoint  WetBulb  Heat  Cool  PrecipTotal  StnPressure  \\\n",
       "0  88.0  60.0  74.0      58.0     65.0   0.0   9.0          0.0        29.39   \n",
       "1  88.0  60.0  74.0      58.0     65.0   0.0   9.0          0.0        29.39   \n",
       "2  88.0  60.0  74.0      58.0     65.0   0.0   9.0          0.0        29.39   \n",
       "3  88.0  60.0  74.0      58.0     65.0   0.0   9.0          0.0        29.39   \n",
       "4  88.0  60.0  74.0      58.0     65.0   0.0   9.0          0.0        29.39   \n",
       "\n",
       "   SeaLevel  ResultSpeed  ResultDir  AvgSpeed  Species_CULEX ERRATICUS  \\\n",
       "0     30.11          5.8       18.0       6.5                        0   \n",
       "1     30.11          5.8       18.0       6.5                        0   \n",
       "2     30.11          5.8       18.0       6.5                        0   \n",
       "3     30.11          5.8       18.0       6.5                        0   \n",
       "4     30.11          5.8       18.0       6.5                        0   \n",
       "\n",
       "   Species_CULEX PIPIENS  Species_CULEX PIPIENS/RESTUANS  \\\n",
       "0                      0                               1   \n",
       "1                      0                               0   \n",
       "2                      0                               0   \n",
       "3                      0                               1   \n",
       "4                      0                               0   \n",
       "\n",
       "   Species_CULEX RESTUANS  Species_CULEX SALINARIUS  Species_CULEX TARSALIS  \\\n",
       "0                       0                         0                       0   \n",
       "1                       1                         0                       0   \n",
       "2                       1                         0                       0   \n",
       "3                       0                         0                       0   \n",
       "4                       1                         0                       0   \n",
       "\n",
       "   Species_CULEX TERRITANS  \n",
       "0                        0  \n",
       "1                        0  \n",
       "2                        0  \n",
       "3                        0  \n",
       "4                        0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Station_x</th>\n",
       "      <th>WnvPresent</th>\n",
       "      <th>Station_y</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Tmin</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
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       "      <td>10506.000000</td>\n",
       "      <td>10506.000000</td>\n",
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       "      <td>10506.000000</td>\n",
       "      <td>10506.000000</td>\n",
       "      <td>10506.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.784790</td>\n",
       "      <td>0.052446</td>\n",
       "      <td>1.784790</td>\n",
       "      <td>81.947649</td>\n",
       "      <td>64.165334</td>\n",
       "      <td>73.284123</td>\n",
       "      <td>59.637826</td>\n",
       "      <td>39.995622</td>\n",
       "      <td>0.850181</td>\n",
       "      <td>9.134304</td>\n",
       "      <td>-833.598443</td>\n",
       "      <td>4.479905</td>\n",
       "      <td>29.957041</td>\n",
       "      <td>5.911003</td>\n",
       "      <td>17.720160</td>\n",
       "      <td>7.459880</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.410988</td>\n",
       "      <td>0.222936</td>\n",
       "      <td>0.410988</td>\n",
       "      <td>8.288981</td>\n",
       "      <td>7.528488</td>\n",
       "      <td>7.486538</td>\n",
       "      <td>7.848718</td>\n",
       "      <td>500.098791</td>\n",
       "      <td>2.639829</td>\n",
       "      <td>5.791930</td>\n",
       "      <td>2764.464894</td>\n",
       "      <td>498.285006</td>\n",
       "      <td>0.117327</td>\n",
       "      <td>2.909527</td>\n",
       "      <td>9.107697</td>\n",
       "      <td>2.516407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>29.590000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.210000</td>\n",
       "      <td>29.870000</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.800000</td>\n",
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       "    <tr>\n",
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       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.310000</td>\n",
       "      <td>29.960000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>7.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>29.390000</td>\n",
       "      <td>30.050000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>9.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>3.970000</td>\n",
       "      <td>29.650000</td>\n",
       "      <td>30.330000</td>\n",
       "      <td>15.400000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>16.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Station_x    WnvPresent     Station_y          Tmax          Tmin  \\\n",
       "count  10506.000000  10506.000000  10506.000000  10506.000000  10506.000000   \n",
       "mean       1.784790      0.052446      1.784790     81.947649     64.165334   \n",
       "std        0.410988      0.222936      0.410988      8.288981      7.528488   \n",
       "min        1.000000      0.000000      1.000000     57.000000     41.000000   \n",
       "25%        2.000000      0.000000      2.000000     78.000000     60.000000   \n",
       "50%        2.000000      0.000000      2.000000     83.000000     66.000000   \n",
       "75%        2.000000      0.000000      2.000000     88.000000     70.000000   \n",
       "max        2.000000      1.000000      2.000000     97.000000     79.000000   \n",
       "\n",
       "               Tavg      DewPoint       WetBulb          Heat          Cool  \\\n",
       "count  10506.000000  10506.000000  10506.000000  10506.000000  10506.000000   \n",
       "mean      73.284123     59.637826     39.995622      0.850181      9.134304   \n",
       "std        7.486538      7.848718    500.098791      2.639829      5.791930   \n",
       "min       50.000000     38.000000  -9999.000000      0.000000      0.000000   \n",
       "25%       70.000000     54.000000     61.000000      0.000000      5.000000   \n",
       "50%       75.000000     60.000000     66.000000      0.000000     10.000000   \n",
       "75%       79.000000     66.000000     70.000000      0.000000     14.000000   \n",
       "max       87.000000     73.000000     76.000000     15.000000     22.000000   \n",
       "\n",
       "        PrecipTotal   StnPressure      SeaLevel   ResultSpeed     ResultDir  \\\n",
       "count  10506.000000  10506.000000  10506.000000  10506.000000  10506.000000   \n",
       "mean    -833.598443      4.479905     29.957041      5.911003     17.720160   \n",
       "std     2764.464894    498.285006      0.117327      2.909527      9.107697   \n",
       "min    -9999.000000  -9999.000000     29.590000      0.100000      1.000000   \n",
       "25%        0.000000     29.210000     29.870000      3.900000     10.000000   \n",
       "50%        0.000000     29.310000     29.960000      5.500000     19.000000   \n",
       "75%        0.080000     29.390000     30.050000      7.800000     24.000000   \n",
       "max        3.970000     29.650000     30.330000     15.400000     36.000000   \n",
       "\n",
       "           AvgSpeed  \n",
       "count  10506.000000  \n",
       "mean       7.459880  \n",
       "std        2.516407  \n",
       "min        2.100000  \n",
       "25%        5.800000  \n",
       "50%        7.100000  \n",
       "75%        9.200000  \n",
       "max       16.300000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distance(x, y):\n",
    "    '''\n",
    "       Here i am using the euclidean distance between two provided points\n",
    "    '''\n",
    "    dist = ((x[0] - y[0])**2 + (x[1] - y[1])**2)**.5\n",
    "    return dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_distances(x):\n",
    "    distances = []\n",
    "    for i, trap in enumerate(traps):\n",
    "        distances.append(distance(x, coordinates[i]))\n",
    "    \n",
    "    trap_distances = dict(zip(traps, distances))\n",
    "    closest_trap = min(trap_distances, key=trap_distances.get)\n",
    "    _distance = trap_distances[closest_trap]\n",
    "    return closest_trap, _distance"
   ]
  }
 ],
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