modeling_pca.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from IPython.display import Image\n",
    "import pandas as pd\n",
    "from db_scripts.get_server_data import data_dir, get_table_for_variable, fig_dir, get_db_table_as_df\n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn import tree\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.legend_handler import HandlerPathCollection\n",
    "from operator import add\n",
    "import matplotlib\n",
    "import pydotplus\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Read in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rain_daily_sum</th>\n",
       "      <th>rain_hourly_max</th>\n",
       "      <th>rain_15_min_max</th>\n",
       "      <th>rain_prev_3_days</th>\n",
       "      <th>gw_daily_avg</th>\n",
       "      <th>tide_daily_avg</th>\n",
       "      <th>wind_dir_daily_avg</th>\n",
       "      <th>wind_vel_daily_avg</th>\n",
       "      <th>wind_vel_hourly_max_avg</th>\n",
       "      <th>num_flooded</th>\n",
       "      <th>flooded</th>\n",
       "      <th>event_name</th>\n",
       "      <th>event_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <td>1.320000</td>\n",
       "      <td>3.200000e-01</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.345000e+00</td>\n",
       "      <td>2.441582</td>\n",
       "      <td>0.064875</td>\n",
       "      <td>0.380328</td>\n",
       "      <td>4.722118</td>\n",
       "      <td>5.075417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2013-04-29 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1642</th>\n",
       "      <td>0.003333</td>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.496667e+00</td>\n",
       "      <td>1.689739</td>\n",
       "      <td>0.559250</td>\n",
       "      <td>0.229711</td>\n",
       "      <td>6.531604</td>\n",
       "      <td>7.807500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-14 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1643</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.643991e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.366667e-01</td>\n",
       "      <td>1.578101</td>\n",
       "      <td>0.288250</td>\n",
       "      <td>0.312248</td>\n",
       "      <td>4.901597</td>\n",
       "      <td>5.331250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1644</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.643991e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.333333e-03</td>\n",
       "      <td>1.563858</td>\n",
       "      <td>0.140667</td>\n",
       "      <td>0.542563</td>\n",
       "      <td>4.962458</td>\n",
       "      <td>5.590833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-16 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1645</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.643991e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.209102e-15</td>\n",
       "      <td>1.583206</td>\n",
       "      <td>0.199542</td>\n",
       "      <td>0.623577</td>\n",
       "      <td>5.924451</td>\n",
       "      <td>6.553750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-17 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1646</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.643991e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.209102e-15</td>\n",
       "      <td>1.604340</td>\n",
       "      <td>0.103917</td>\n",
       "      <td>0.679467</td>\n",
       "      <td>6.142667</td>\n",
       "      <td>7.832500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-18 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1647</th>\n",
       "      <td>0.150000</td>\n",
       "      <td>2.200000e-01</td>\n",
       "      <td>0.13</td>\n",
       "      <td>9.933333e-01</td>\n",
       "      <td>1.780793</td>\n",
       "      <td>0.531583</td>\n",
       "      <td>0.364468</td>\n",
       "      <td>5.013368</td>\n",
       "      <td>5.954167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-21 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1648</th>\n",
       "      <td>0.060000</td>\n",
       "      <td>9.000000e-02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>2.100000e-01</td>\n",
       "      <td>1.767121</td>\n",
       "      <td>0.743375</td>\n",
       "      <td>0.233175</td>\n",
       "      <td>7.694493</td>\n",
       "      <td>7.985000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-22 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.730727e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.100000e-01</td>\n",
       "      <td>1.621247</td>\n",
       "      <td>0.637667</td>\n",
       "      <td>0.258224</td>\n",
       "      <td>6.901049</td>\n",
       "      <td>7.464583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-23 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1650</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.730727e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.000000e-02</td>\n",
       "      <td>1.567121</td>\n",
       "      <td>0.414625</td>\n",
       "      <td>0.377472</td>\n",
       "      <td>5.628417</td>\n",
       "      <td>6.165417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-24 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1651</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.730727e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.186551e-15</td>\n",
       "      <td>1.661789</td>\n",
       "      <td>0.169458</td>\n",
       "      <td>0.580179</td>\n",
       "      <td>6.965688</td>\n",
       "      <td>8.294167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-25 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1652</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.730727e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.186551e-15</td>\n",
       "      <td>1.683308</td>\n",
       "      <td>0.187375</td>\n",
       "      <td>0.741093</td>\n",
       "      <td>5.771521</td>\n",
       "      <td>6.954167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-26 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.730727e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.186551e-15</td>\n",
       "      <td>1.575350</td>\n",
       "      <td>0.353625</td>\n",
       "      <td>0.319989</td>\n",
       "      <td>6.759625</td>\n",
       "      <td>7.465417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-27 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1654</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.730727e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.186551e-15</td>\n",
       "      <td>1.440167</td>\n",
       "      <td>0.614000</td>\n",
       "      <td>0.244540</td>\n",
       "      <td>8.543556</td>\n",
       "      <td>8.536250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-28 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.730727e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.186551e-15</td>\n",
       "      <td>1.416200</td>\n",
       "      <td>0.388792</td>\n",
       "      <td>0.226343</td>\n",
       "      <td>6.054750</td>\n",
       "      <td>6.920833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-29 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1656</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.730727e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.186551e-15</td>\n",
       "      <td>1.473754</td>\n",
       "      <td>0.146167</td>\n",
       "      <td>0.457082</td>\n",
       "      <td>4.472604</td>\n",
       "      <td>5.474583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1641</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>4.400000e-01</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.500000e+00</td>\n",
       "      <td>1.781763</td>\n",
       "      <td>0.672500</td>\n",
       "      <td>0.596543</td>\n",
       "      <td>5.478465</td>\n",
       "      <td>7.102500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-13 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.730727e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.186551e-15</td>\n",
       "      <td>1.509017</td>\n",
       "      <td>0.040792</td>\n",
       "      <td>0.563806</td>\n",
       "      <td>7.047260</td>\n",
       "      <td>7.676250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-07-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640</th>\n",
       "      <td>1.160000</td>\n",
       "      <td>1.180000e+00</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.166667e+00</td>\n",
       "      <td>1.730518</td>\n",
       "      <td>0.718458</td>\n",
       "      <td>0.397179</td>\n",
       "      <td>3.868646</td>\n",
       "      <td>4.498333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-12 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1638</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.429684e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.000000e-02</td>\n",
       "      <td>1.697533</td>\n",
       "      <td>0.178292</td>\n",
       "      <td>0.597742</td>\n",
       "      <td>6.222194</td>\n",
       "      <td>7.876667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-10 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1623</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.458239e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.179612e-15</td>\n",
       "      <td>2.053797</td>\n",
       "      <td>0.088500</td>\n",
       "      <td>0.630168</td>\n",
       "      <td>7.083201</td>\n",
       "      <td>8.035417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-05-26 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1624</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.458239e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.179612e-15</td>\n",
       "      <td>2.051103</td>\n",
       "      <td>-0.160833</td>\n",
       "      <td>0.651331</td>\n",
       "      <td>7.403993</td>\n",
       "      <td>8.865833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-05-27 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1625</th>\n",
       "      <td>0.370000</td>\n",
       "      <td>2.800000e-01</td>\n",
       "      <td>0.12</td>\n",
       "      <td>3.700000e-01</td>\n",
       "      <td>2.017969</td>\n",
       "      <td>0.254292</td>\n",
       "      <td>0.766599</td>\n",
       "      <td>6.382278</td>\n",
       "      <td>7.821250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-05-28 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1626</th>\n",
       "      <td>0.003333</td>\n",
       "      <td>1.600000e-01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.733333e-01</td>\n",
       "      <td>1.945804</td>\n",
       "      <td>1.187250</td>\n",
       "      <td>0.228828</td>\n",
       "      <td>8.235701</td>\n",
       "      <td>7.967500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-05-29 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1627</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.499073e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.733333e-01</td>\n",
       "      <td>1.964514</td>\n",
       "      <td>0.826958</td>\n",
       "      <td>0.224319</td>\n",
       "      <td>5.286403</td>\n",
       "      <td>5.886667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-05-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1628</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.499073e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.333333e-03</td>\n",
       "      <td>1.923617</td>\n",
       "      <td>0.324292</td>\n",
       "      <td>0.218466</td>\n",
       "      <td>8.785715</td>\n",
       "      <td>9.750000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-05-31 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1629</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.499073e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.181347e-15</td>\n",
       "      <td>1.813992</td>\n",
       "      <td>0.443250</td>\n",
       "      <td>0.213269</td>\n",
       "      <td>6.415146</td>\n",
       "      <td>7.255417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1630</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.499073e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.181347e-15</td>\n",
       "      <td>1.820740</td>\n",
       "      <td>0.183708</td>\n",
       "      <td>0.517774</td>\n",
       "      <td>3.756806</td>\n",
       "      <td>4.914583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-02 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1631</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.499073e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.181347e-15</td>\n",
       "      <td>1.908067</td>\n",
       "      <td>0.083458</td>\n",
       "      <td>0.641743</td>\n",
       "      <td>5.110229</td>\n",
       "      <td>6.203750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-03 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1632</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.499073e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.181347e-15</td>\n",
       "      <td>1.939693</td>\n",
       "      <td>0.324458</td>\n",
       "      <td>0.632614</td>\n",
       "      <td>5.089514</td>\n",
       "      <td>5.774167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-04 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>0.190000</td>\n",
       "      <td>1.800000e-01</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.950000e-01</td>\n",
       "      <td>2.511099</td>\n",
       "      <td>0.269375</td>\n",
       "      <td>0.627778</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2012-03-16 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.598089e-16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.050000e-01</td>\n",
       "      <td>2.388691</td>\n",
       "      <td>-0.358917</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>6.350000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2012-03-23 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.483333</td>\n",
       "      <td>4.140000e+00</td>\n",
       "      <td>1.42</td>\n",
       "      <td>2.483333e+00</td>\n",
       "      <td>1.345788</td>\n",
       "      <td>0.471708</td>\n",
       "      <td>0.318783</td>\n",
       "      <td>5.318687</td>\n",
       "      <td>7.167917</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Thunderstorm</td>\n",
       "      <td>2015-07-11 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.053333</td>\n",
       "      <td>1.860000e+00</td>\n",
       "      <td>0.72</td>\n",
       "      <td>4.358333e+00</td>\n",
       "      <td>1.237133</td>\n",
       "      <td>1.257583</td>\n",
       "      <td>0.200489</td>\n",
       "      <td>10.637542</td>\n",
       "      <td>10.992917</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainy Monday</td>\n",
       "      <td>2014-09-08 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.146667</td>\n",
       "      <td>2.110000e+00</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.146667e+00</td>\n",
       "      <td>1.025551</td>\n",
       "      <td>0.199708</td>\n",
       "      <td>0.523066</td>\n",
       "      <td>4.494181</td>\n",
       "      <td>6.092500</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Thunderstorm</td>\n",
       "      <td>2014-09-04 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.890000</td>\n",
       "      <td>1.300000e+00</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.890000e+00</td>\n",
       "      <td>1.698442</td>\n",
       "      <td>0.223333</td>\n",
       "      <td>0.267021</td>\n",
       "      <td>6.413535</td>\n",
       "      <td>7.871250</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>unnamed</td>\n",
       "      <td>2014-07-24 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.286667</td>\n",
       "      <td>1.970000e+00</td>\n",
       "      <td>1.27</td>\n",
       "      <td>3.766667e+00</td>\n",
       "      <td>1.413842</td>\n",
       "      <td>-0.213583</td>\n",
       "      <td>0.594620</td>\n",
       "      <td>5.101632</td>\n",
       "      <td>7.675833</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7/10 Thunderstorms</td>\n",
       "      <td>2014-07-10 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.853333</td>\n",
       "      <td>2.230000e+00</td>\n",
       "      <td>0.76</td>\n",
       "      <td>3.883333e+00</td>\n",
       "      <td>2.367703</td>\n",
       "      <td>0.328958</td>\n",
       "      <td>0.596759</td>\n",
       "      <td>6.531076</td>\n",
       "      <td>8.279167</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Heavy Rain</td>\n",
       "      <td>2014-05-16 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.843333</td>\n",
       "      <td>1.130000e+00</td>\n",
       "      <td>0.54</td>\n",
       "      <td>8.433333e-01</td>\n",
       "      <td>1.637858</td>\n",
       "      <td>0.317563</td>\n",
       "      <td>0.437330</td>\n",
       "      <td>7.114194</td>\n",
       "      <td>8.583750</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Thunderstorms</td>\n",
       "      <td>2014-06-19 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.140000</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2.943333e+00</td>\n",
       "      <td>0.869149</td>\n",
       "      <td>1.949806</td>\n",
       "      <td>0.109199</td>\n",
       "      <td>19.630806</td>\n",
       "      <td>18.424167</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Heavy Rain</td>\n",
       "      <td>2013-10-09 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.800000e-01</td>\n",
       "      <td>0.21</td>\n",
       "      <td>3.350000e+00</td>\n",
       "      <td>1.341512</td>\n",
       "      <td>2.903833</td>\n",
       "      <td>0.413495</td>\n",
       "      <td>23.504701</td>\n",
       "      <td>22.147917</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Sandy</td>\n",
       "      <td>2012-10-28 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.820000</td>\n",
       "      <td>1.220000e+00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>8.075000e+00</td>\n",
       "      <td>1.033844</td>\n",
       "      <td>1.493500</td>\n",
       "      <td>0.431944</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Irene</td>\n",
       "      <td>2011-08-27 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>6.200000e-01</td>\n",
       "      <td>0.47</td>\n",
       "      <td>6.466667e-01</td>\n",
       "      <td>2.344772</td>\n",
       "      <td>0.143083</td>\n",
       "      <td>0.616601</td>\n",
       "      <td>4.353889</td>\n",
       "      <td>6.048333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>09/02/15</td>\n",
       "      <td>2015-09-02 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.480000</td>\n",
       "      <td>1.280000e+00</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.480000e+00</td>\n",
       "      <td>1.339840</td>\n",
       "      <td>-0.495208</td>\n",
       "      <td>0.623042</td>\n",
       "      <td>9.391160</td>\n",
       "      <td>12.430833</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Thunderstorms</td>\n",
       "      <td>2014-07-09 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.084202e-15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.375000e-02</td>\n",
       "      <td>2.448416</td>\n",
       "      <td>0.826125</td>\n",
       "      <td>0.173663</td>\n",
       "      <td>14.603188</td>\n",
       "      <td>15.877083</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>unnamed</td>\n",
       "      <td>2015-09-17 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.580000</td>\n",
       "      <td>2.320000e+00</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.710000e+00</td>\n",
       "      <td>3.091967</td>\n",
       "      <td>-0.218250</td>\n",
       "      <td>0.792084</td>\n",
       "      <td>5.157444</td>\n",
       "      <td>8.119583</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Thunderstorm</td>\n",
       "      <td>2016-07-19 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.547500</td>\n",
       "      <td>7.500000e-01</td>\n",
       "      <td>0.30</td>\n",
       "      <td>3.199167e+00</td>\n",
       "      <td>3.198635</td>\n",
       "      <td>2.403806</td>\n",
       "      <td>0.069444</td>\n",
       "      <td>12.475000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Joaquin</td>\n",
       "      <td>2015-09-29 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.310000</td>\n",
       "      <td>1.090000e+00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.310000e+00</td>\n",
       "      <td>4.156397</td>\n",
       "      <td>-0.525167</td>\n",
       "      <td>0.243971</td>\n",
       "      <td>7.875028</td>\n",
       "      <td>10.387083</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>01/15/2016</td>\n",
       "      <td>2016-01-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.955000</td>\n",
       "      <td>7.000000e-01</td>\n",
       "      <td>0.36</td>\n",
       "      <td>1.152500e+00</td>\n",
       "      <td>4.987574</td>\n",
       "      <td>0.535250</td>\n",
       "      <td>0.736047</td>\n",
       "      <td>15.667056</td>\n",
       "      <td>18.303333</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Jan Winter Weather</td>\n",
       "      <td>2016-01-20 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.622500</td>\n",
       "      <td>5.700000e-01</td>\n",
       "      <td>0.18</td>\n",
       "      <td>1.074167e+00</td>\n",
       "      <td>4.853232</td>\n",
       "      <td>0.443903</td>\n",
       "      <td>0.653054</td>\n",
       "      <td>12.403833</td>\n",
       "      <td>15.670833</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>unnamed</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.832500</td>\n",
       "      <td>6.400000e-01</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.070000e+00</td>\n",
       "      <td>4.438215</td>\n",
       "      <td>0.574625</td>\n",
       "      <td>0.451178</td>\n",
       "      <td>12.273861</td>\n",
       "      <td>14.144583</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>February 24th Storm</td>\n",
       "      <td>2016-02-24 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.050000</td>\n",
       "      <td>2.120000e+00</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.047500e+00</td>\n",
       "      <td>3.462591</td>\n",
       "      <td>0.436750</td>\n",
       "      <td>0.426266</td>\n",
       "      <td>9.801771</td>\n",
       "      <td>13.714583</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Severe Weather - 6/5</td>\n",
       "      <td>2016-06-03 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.005000</td>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.585000e+00</td>\n",
       "      <td>3.117073</td>\n",
       "      <td>-0.058333</td>\n",
       "      <td>0.267980</td>\n",
       "      <td>4.901000</td>\n",
       "      <td>7.486250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>unnamed</td>\n",
       "      <td>2016-07-20 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.405126e-15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.216909e-15</td>\n",
       "      <td>3.149425</td>\n",
       "      <td>0.104667</td>\n",
       "      <td>0.625186</td>\n",
       "      <td>9.376660</td>\n",
       "      <td>11.248750</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Bernie Training</td>\n",
       "      <td>2016-07-25 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.287500</td>\n",
       "      <td>2.100000e+00</td>\n",
       "      <td>0.84</td>\n",
       "      <td>3.307500e+00</td>\n",
       "      <td>3.064679</td>\n",
       "      <td>0.280083</td>\n",
       "      <td>0.536181</td>\n",
       "      <td>5.637431</td>\n",
       "      <td>7.968750</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Thunderstorm</td>\n",
       "      <td>2016-07-31 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3.230000</td>\n",
       "      <td>8.700000e-01</td>\n",
       "      <td>0.33</td>\n",
       "      <td>2.070000e+00</td>\n",
       "      <td>2.635461</td>\n",
       "      <td>1.874437</td>\n",
       "      <td>0.145250</td>\n",
       "      <td>25.102972</td>\n",
       "      <td>27.909583</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>HERMINE</td>\n",
       "      <td>2016-09-02 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2.865000</td>\n",
       "      <td>1.910000e+00</td>\n",
       "      <td>0.82</td>\n",
       "      <td>2.865000e+00</td>\n",
       "      <td>2.901939</td>\n",
       "      <td>0.429250</td>\n",
       "      <td>0.382585</td>\n",
       "      <td>6.435618</td>\n",
       "      <td>8.750417</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>HEAVY RAIN</td>\n",
       "      <td>2016-09-19 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7.612500</td>\n",
       "      <td>1.430000e+00</td>\n",
       "      <td>0.51</td>\n",
       "      <td>6.396500e+00</td>\n",
       "      <td>4.794867</td>\n",
       "      <td>1.701608</td>\n",
       "      <td>0.152997</td>\n",
       "      <td>22.095951</td>\n",
       "      <td>23.999583</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Hurricane Matthew</td>\n",
       "      <td>2016-10-05 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.575000</td>\n",
       "      <td>1.500000e-01</td>\n",
       "      <td>0.08</td>\n",
       "      <td>4.333333e-01</td>\n",
       "      <td>2.788770</td>\n",
       "      <td>1.703764</td>\n",
       "      <td>0.087311</td>\n",
       "      <td>9.681333</td>\n",
       "      <td>4.125000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Noreaster</td>\n",
       "      <td>2015-09-24 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.255000</td>\n",
       "      <td>1.590000e+00</td>\n",
       "      <td>0.67</td>\n",
       "      <td>8.240000e+00</td>\n",
       "      <td>1.447289</td>\n",
       "      <td>1.111472</td>\n",
       "      <td>0.810185</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Nicole</td>\n",
       "      <td>2010-09-30 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2471 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      rain_daily_sum  rain_hourly_max  rain_15_min_max  rain_prev_3_days  \\\n",
       "1235        1.320000     3.200000e-01             0.12      1.345000e+00   \n",
       "1642        0.003333     1.000000e-02             0.01      1.496667e+00   \n",
       "1643        0.000000     6.643991e-16             0.00      3.366667e-01   \n",
       "1644        0.000000     6.643991e-16             0.00      3.333333e-03   \n",
       "1645        0.000000     6.643991e-16             0.00     -1.209102e-15   \n",
       "1646        0.000000     6.643991e-16             0.00     -1.209102e-15   \n",
       "1647        0.150000     2.200000e-01             0.13      9.933333e-01   \n",
       "1648        0.060000     9.000000e-02             0.05      2.100000e-01   \n",
       "1649        0.000000     6.730727e-16             0.00      2.100000e-01   \n",
       "1650        0.000000     6.730727e-16             0.00      6.000000e-02   \n",
       "1651        0.000000     6.730727e-16             0.00     -1.186551e-15   \n",
       "1652        0.000000     6.730727e-16             0.00     -1.186551e-15   \n",
       "1653        0.000000     6.730727e-16             0.00     -1.186551e-15   \n",
       "1654        0.000000     6.730727e-16             0.00     -1.186551e-15   \n",
       "1655        0.000000     6.730727e-16             0.00     -1.186551e-15   \n",
       "1656        0.000000     6.730727e-16             0.00     -1.186551e-15   \n",
       "1641        0.333333     4.400000e-01             0.34      1.500000e+00   \n",
       "1657        0.000000     6.730727e-16             0.00     -1.186551e-15   \n",
       "1640        1.160000     1.180000e+00             0.97      1.166667e+00   \n",
       "1638        0.000000     5.429684e-16             0.00      4.000000e-02   \n",
       "1623        0.000000     4.458239e-16             0.00     -1.179612e-15   \n",
       "1624        0.000000     4.458239e-16             0.00     -1.179612e-15   \n",
       "1625        0.370000     2.800000e-01             0.12      3.700000e-01   \n",
       "1626        0.003333     1.600000e-01             0.01      3.733333e-01   \n",
       "1627        0.000000     5.499073e-16             0.00      3.733333e-01   \n",
       "1628        0.000000     5.499073e-16             0.00      3.333333e-03   \n",
       "1629        0.000000     5.499073e-16             0.00     -1.181347e-15   \n",
       "1630        0.000000     5.499073e-16             0.00     -1.181347e-15   \n",
       "1631        0.000000     5.499073e-16             0.00     -1.181347e-15   \n",
       "1632        0.000000     5.499073e-16             0.00     -1.181347e-15   \n",
       "...              ...              ...              ...               ...   \n",
       "828         0.190000     1.800000e-01             0.10      1.950000e-01   \n",
       "835         0.000000    -7.598089e-16             0.00      1.050000e-01   \n",
       "11          2.483333     4.140000e+00             1.42      2.483333e+00   \n",
       "10          3.053333     1.860000e+00             0.72      4.358333e+00   \n",
       "9           2.146667     2.110000e+00             0.77      2.146667e+00   \n",
       "8           1.890000     1.300000e+00             0.64      1.890000e+00   \n",
       "7           2.286667     1.970000e+00             1.27      3.766667e+00   \n",
       "4           3.853333     2.230000e+00             0.76      3.883333e+00   \n",
       "5           0.843333     1.130000e+00             0.54      8.433333e-01   \n",
       "3           3.140000     5.000000e-01             0.22      2.943333e+00   \n",
       "2           2.000000     5.800000e-01             0.21      3.350000e+00   \n",
       "1           7.820000     1.220000e+00             0.33      8.075000e+00   \n",
       "12          0.500000     6.200000e-01             0.47      6.466667e-01   \n",
       "6           1.480000     1.280000e+00             0.63      1.480000e+00   \n",
       "13          0.000000     1.084202e-15             0.00      1.375000e-02   \n",
       "21          1.580000     2.320000e+00             1.05      1.710000e+00   \n",
       "15          3.547500     7.500000e-01             0.30      3.199167e+00   \n",
       "16          1.310000     1.090000e+00             0.33      1.310000e+00   \n",
       "17          0.955000     7.000000e-01             0.36      1.152500e+00   \n",
       "18          1.622500     5.700000e-01             0.18      1.074167e+00   \n",
       "19          0.832500     6.400000e-01             0.28      1.070000e+00   \n",
       "20          2.050000     2.120000e+00             1.06      1.047500e+00   \n",
       "22          0.005000     1.000000e-02             0.01      1.585000e+00   \n",
       "23          0.000000     1.405126e-15             0.00     -1.216909e-15   \n",
       "24          1.287500     2.100000e+00             0.84      3.307500e+00   \n",
       "25          3.230000     8.700000e-01             0.33      2.070000e+00   \n",
       "26          2.865000     1.910000e+00             0.82      2.865000e+00   \n",
       "27          7.612500     1.430000e+00             0.51      6.396500e+00   \n",
       "14          0.575000     1.500000e-01             0.08      4.333333e-01   \n",
       "0          10.255000     1.590000e+00             0.67      8.240000e+00   \n",
       "\n",
       "      gw_daily_avg  tide_daily_avg  wind_dir_daily_avg  wind_vel_daily_avg  \\\n",
       "1235      2.441582        0.064875            0.380328            4.722118   \n",
       "1642      1.689739        0.559250            0.229711            6.531604   \n",
       "1643      1.578101        0.288250            0.312248            4.901597   \n",
       "1644      1.563858        0.140667            0.542563            4.962458   \n",
       "1645      1.583206        0.199542            0.623577            5.924451   \n",
       "1646      1.604340        0.103917            0.679467            6.142667   \n",
       "1647      1.780793        0.531583            0.364468            5.013368   \n",
       "1648      1.767121        0.743375            0.233175            7.694493   \n",
       "1649      1.621247        0.637667            0.258224            6.901049   \n",
       "1650      1.567121        0.414625            0.377472            5.628417   \n",
       "1651      1.661789        0.169458            0.580179            6.965688   \n",
       "1652      1.683308        0.187375            0.741093            5.771521   \n",
       "1653      1.575350        0.353625            0.319989            6.759625   \n",
       "1654      1.440167        0.614000            0.244540            8.543556   \n",
       "1655      1.416200        0.388792            0.226343            6.054750   \n",
       "1656      1.473754        0.146167            0.457082            4.472604   \n",
       "1641      1.781763        0.672500            0.596543            5.478465   \n",
       "1657      1.509017        0.040792            0.563806            7.047260   \n",
       "1640      1.730518        0.718458            0.397179            3.868646   \n",
       "1638      1.697533        0.178292            0.597742            6.222194   \n",
       "1623      2.053797        0.088500            0.630168            7.083201   \n",
       "1624      2.051103       -0.160833            0.651331            7.403993   \n",
       "1625      2.017969        0.254292            0.766599            6.382278   \n",
       "1626      1.945804        1.187250            0.228828            8.235701   \n",
       "1627      1.964514        0.826958            0.224319            5.286403   \n",
       "1628      1.923617        0.324292            0.218466            8.785715   \n",
       "1629      1.813992        0.443250            0.213269            6.415146   \n",
       "1630      1.820740        0.183708            0.517774            3.756806   \n",
       "1631      1.908067        0.083458            0.641743            5.110229   \n",
       "1632      1.939693        0.324458            0.632614            5.089514   \n",
       "...            ...             ...                 ...                 ...   \n",
       "828       2.511099        0.269375            0.627778            7.500000   \n",
       "835       2.388691       -0.358917            0.566667            6.350000   \n",
       "11        1.345788        0.471708            0.318783            5.318687   \n",
       "10        1.237133        1.257583            0.200489           10.637542   \n",
       "9         1.025551        0.199708            0.523066            4.494181   \n",
       "8         1.698442        0.223333            0.267021            6.413535   \n",
       "7         1.413842       -0.213583            0.594620            5.101632   \n",
       "4         2.367703        0.328958            0.596759            6.531076   \n",
       "5         1.637858        0.317563            0.437330            7.114194   \n",
       "3         0.869149        1.949806            0.109199           19.630806   \n",
       "2         1.341512        2.903833            0.413495           23.504701   \n",
       "1         1.033844        1.493500            0.431944           31.000000   \n",
       "12        2.344772        0.143083            0.616601            4.353889   \n",
       "6         1.339840       -0.495208            0.623042            9.391160   \n",
       "13        2.448416        0.826125            0.173663           14.603188   \n",
       "21        3.091967       -0.218250            0.792084            5.157444   \n",
       "15        3.198635        2.403806            0.069444           12.475000   \n",
       "16        4.156397       -0.525167            0.243971            7.875028   \n",
       "17        4.987574        0.535250            0.736047           15.667056   \n",
       "18        4.853232        0.443903            0.653054           12.403833   \n",
       "19        4.438215        0.574625            0.451178           12.273861   \n",
       "20        3.462591        0.436750            0.426266            9.801771   \n",
       "22        3.117073       -0.058333            0.267980            4.901000   \n",
       "23        3.149425        0.104667            0.625186            9.376660   \n",
       "24        3.064679        0.280083            0.536181            5.637431   \n",
       "25        2.635461        1.874437            0.145250           25.102972   \n",
       "26        2.901939        0.429250            0.382585            6.435618   \n",
       "27        4.794867        1.701608            0.152997           22.095951   \n",
       "14        2.788770        1.703764            0.087311            9.681333   \n",
       "0         1.447289        1.111472            0.810185           13.200000   \n",
       "\n",
       "      wind_vel_hourly_max_avg  num_flooded  flooded            event_name  \\\n",
       "1235                 5.075417          0.0        0                  None   \n",
       "1642                 7.807500          0.0        0                  None   \n",
       "1643                 5.331250          0.0        0                  None   \n",
       "1644                 5.590833          0.0        0                  None   \n",
       "1645                 6.553750          0.0        0                  None   \n",
       "1646                 7.832500          0.0        0                  None   \n",
       "1647                 5.954167          0.0        0                  None   \n",
       "1648                 7.985000          0.0        0                  None   \n",
       "1649                 7.464583          0.0        0                  None   \n",
       "1650                 6.165417          0.0        0                  None   \n",
       "1651                 8.294167          0.0        0                  None   \n",
       "1652                 6.954167          0.0        0                  None   \n",
       "1653                 7.465417          0.0        0                  None   \n",
       "1654                 8.536250          0.0        0                  None   \n",
       "1655                 6.920833          0.0        0                  None   \n",
       "1656                 5.474583          0.0        0                  None   \n",
       "1641                 7.102500          0.0        0                  None   \n",
       "1657                 7.676250          0.0        0                  None   \n",
       "1640                 4.498333          0.0        0                  None   \n",
       "1638                 7.876667          0.0        0                  None   \n",
       "1623                 8.035417          0.0        0                  None   \n",
       "1624                 8.865833          0.0        0                  None   \n",
       "1625                 7.821250          0.0        0                  None   \n",
       "1626                 7.967500          0.0        0                  None   \n",
       "1627                 5.886667          0.0        0                  None   \n",
       "1628                 9.750000          0.0        0                  None   \n",
       "1629                 7.255417          0.0        0                  None   \n",
       "1630                 4.914583          0.0        0                  None   \n",
       "1631                 6.203750          0.0        0                  None   \n",
       "1632                 5.774167          0.0        0                  None   \n",
       "...                       ...          ...      ...                   ...   \n",
       "828                       NaN          0.0        0                  None   \n",
       "835                       NaN          0.0        0                  None   \n",
       "11                   7.167917          3.0        1          Thunderstorm   \n",
       "10                  10.992917         21.0        1          Rainy Monday   \n",
       "9                    6.092500          2.0        1          Thunderstorm   \n",
       "8                    7.871250          8.0        1               unnamed   \n",
       "7                    7.675833         27.0        1    7/10 Thunderstorms   \n",
       "4                    8.279167         21.0        1            Heavy Rain   \n",
       "5                    8.583750          5.0        1         Thunderstorms   \n",
       "3                   18.424167          6.0        1            Heavy Rain   \n",
       "2                   22.147917         45.0        1                 Sandy   \n",
       "1                         NaN         32.0        1                 Irene   \n",
       "12                   6.048333          1.0        1              09/02/15   \n",
       "6                   12.430833          1.0        1         Thunderstorms   \n",
       "13                  15.877083          1.0        1               unnamed   \n",
       "21                   8.119583          8.0        1          Thunderstorm   \n",
       "15                   0.000000         54.0        1               Joaquin   \n",
       "16                  10.387083          1.0        1            01/15/2016   \n",
       "17                  18.303333          9.0        1    Jan Winter Weather   \n",
       "18                  15.670833          2.0        1               unnamed   \n",
       "19                  14.144583          1.0        1   February 24th Storm   \n",
       "20                  13.714583          3.0        1  Severe Weather - 6/5   \n",
       "22                   7.486250          1.0        1               unnamed   \n",
       "23                  11.248750          1.0        1       Bernie Training   \n",
       "24                   7.968750          1.0        1          Thunderstorm   \n",
       "25                  27.909583         35.0        1               HERMINE   \n",
       "26                   8.750417          4.0        1            HEAVY RAIN   \n",
       "27                  23.999583         17.0        1     Hurricane Matthew   \n",
       "14                   4.125000          7.0        1             Noreaster   \n",
       "0                         NaN         48.0        1                Nicole   \n",
       "\n",
       "               event_date  \n",
       "1235  2013-04-29 00:00:00  \n",
       "1642  2014-06-14 00:00:00  \n",
       "1643  2014-06-15 00:00:00  \n",
       "1644  2014-06-16 00:00:00  \n",
       "1645  2014-06-17 00:00:00  \n",
       "1646  2014-06-18 00:00:00  \n",
       "1647  2014-06-21 00:00:00  \n",
       "1648  2014-06-22 00:00:00  \n",
       "1649  2014-06-23 00:00:00  \n",
       "1650  2014-06-24 00:00:00  \n",
       "1651  2014-06-25 00:00:00  \n",
       "1652  2014-06-26 00:00:00  \n",
       "1653  2014-06-27 00:00:00  \n",
       "1654  2014-06-28 00:00:00  \n",
       "1655  2014-06-29 00:00:00  \n",
       "1656  2014-06-30 00:00:00  \n",
       "1641  2014-06-13 00:00:00  \n",
       "1657  2014-07-01 00:00:00  \n",
       "1640  2014-06-12 00:00:00  \n",
       "1638  2014-06-10 00:00:00  \n",
       "1623  2014-05-26 00:00:00  \n",
       "1624  2014-05-27 00:00:00  \n",
       "1625  2014-05-28 00:00:00  \n",
       "1626  2014-05-29 00:00:00  \n",
       "1627  2014-05-30 00:00:00  \n",
       "1628  2014-05-31 00:00:00  \n",
       "1629  2014-06-01 00:00:00  \n",
       "1630  2014-06-02 00:00:00  \n",
       "1631  2014-06-03 00:00:00  \n",
       "1632  2014-06-04 00:00:00  \n",
       "...                   ...  \n",
       "828   2012-03-16 00:00:00  \n",
       "835   2012-03-23 00:00:00  \n",
       "11    2015-07-11 00:00:00  \n",
       "10    2014-09-08 00:00:00  \n",
       "9     2014-09-04 00:00:00  \n",
       "8     2014-07-24 00:00:00  \n",
       "7     2014-07-10 00:00:00  \n",
       "4     2014-05-16 00:00:00  \n",
       "5     2014-06-19 00:00:00  \n",
       "3     2013-10-09 00:00:00  \n",
       "2     2012-10-28 00:00:00  \n",
       "1     2011-08-27 00:00:00  \n",
       "12    2015-09-02 00:00:00  \n",
       "6     2014-07-09 00:00:00  \n",
       "13    2015-09-17 00:00:00  \n",
       "21    2016-07-19 00:00:00  \n",
       "15    2015-09-29 00:00:00  \n",
       "16    2016-01-15 00:00:00  \n",
       "17    2016-01-20 00:00:00  \n",
       "18    2016-02-01 00:00:00  \n",
       "19    2016-02-24 00:00:00  \n",
       "20    2016-06-03 00:00:00  \n",
       "22    2016-07-20 00:00:00  \n",
       "23    2016-07-25 00:00:00  \n",
       "24    2016-07-31 00:00:00  \n",
       "25    2016-09-02 00:00:00  \n",
       "26    2016-09-19 00:00:00  \n",
       "27    2016-10-05 00:00:00  \n",
       "14    2015-09-24 00:00:00  \n",
       "0     2010-09-30 00:00:00  \n",
       "\n",
       "[2471 rows x 13 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = get_db_table_as_df('for_model')\n",
    "df.sort_values('flooded', inplace=True)\n",
    "df['wind_dir_daily_avg'] = df['wind_dir_daily_avg']/360.\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rain_daily_sum</th>\n",
       "      <th>rain_hourly_max</th>\n",
       "      <th>rain_15_min_max</th>\n",
       "      <th>rain_prev_3_days</th>\n",
       "      <th>gw_daily_avg</th>\n",
       "      <th>tide_daily_avg</th>\n",
       "      <th>wind_dir_daily_avg</th>\n",
       "      <th>wind_vel_daily_avg</th>\n",
       "      <th>wind_vel_hourly_max_avg</th>\n",
       "      <th>num_flooded</th>\n",
       "      <th>flooded</th>\n",
       "      <th>event_name</th>\n",
       "      <th>event_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.000</td>\n",
       "      <td>1.084202e-15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.375000e-02</td>\n",
       "      <td>2.448416</td>\n",
       "      <td>0.826125</td>\n",
       "      <td>0.173663</td>\n",
       "      <td>14.603188</td>\n",
       "      <td>15.877083</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>unnamed</td>\n",
       "      <td>2015-09-17 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.005</td>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.585000e+00</td>\n",
       "      <td>3.117073</td>\n",
       "      <td>-0.058333</td>\n",
       "      <td>0.267980</td>\n",
       "      <td>4.901000</td>\n",
       "      <td>7.486250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>unnamed</td>\n",
       "      <td>2016-07-20 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.000</td>\n",
       "      <td>1.405126e-15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.216909e-15</td>\n",
       "      <td>3.149425</td>\n",
       "      <td>0.104667</td>\n",
       "      <td>0.625186</td>\n",
       "      <td>9.376660</td>\n",
       "      <td>11.248750</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Bernie Training</td>\n",
       "      <td>2016-07-25 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rain_daily_sum  rain_hourly_max  rain_15_min_max  rain_prev_3_days  \\\n",
       "13           0.000     1.084202e-15             0.00      1.375000e-02   \n",
       "22           0.005     1.000000e-02             0.01      1.585000e+00   \n",
       "23           0.000     1.405126e-15             0.00     -1.216909e-15   \n",
       "\n",
       "    gw_daily_avg  tide_daily_avg  wind_dir_daily_avg  wind_vel_daily_avg  \\\n",
       "13      2.448416        0.826125            0.173663           14.603188   \n",
       "22      3.117073       -0.058333            0.267980            4.901000   \n",
       "23      3.149425        0.104667            0.625186            9.376660   \n",
       "\n",
       "    wind_vel_hourly_max_avg  num_flooded  flooded       event_name  \\\n",
       "13                15.877083          1.0        1          unnamed   \n",
       "22                 7.486250          1.0        1          unnamed   \n",
       "23                11.248750          1.0        1  Bernie Training   \n",
       "\n",
       "             event_date  \n",
       "13  2015-09-17 00:00:00  \n",
       "22  2016-07-20 00:00:00  \n",
       "23  2016-07-25 00:00:00  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.rain_daily_sum<0.1) & (df.flooded)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "I feel comfortable taking out 2.5 events (I am counting 'Bernie Training' as 0.5 of an event because it says 'Training') filtering on a daily rainfall of above 0.1 inches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2471\n",
      "517\n"
     ]
    }
   ],
   "source": [
    "print len(df)\n",
    "df = df[df.rain_daily_sum > 0.1]\n",
    "print len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "let's separate the features out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rain_daily_sum</th>\n",
       "      <th>rain_hourly_max</th>\n",
       "      <th>rain_15_min_max</th>\n",
       "      <th>rain_prev_3_days</th>\n",
       "      <th>gw_daily_avg</th>\n",
       "      <th>tide_daily_avg</th>\n",
       "      <th>wind_dir_daily_avg</th>\n",
       "      <th>wind_vel_daily_avg</th>\n",
       "      <th>wind_vel_hourly_max_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3.2300</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.33</td>\n",
       "      <td>2.070000</td>\n",
       "      <td>2.635461</td>\n",
       "      <td>1.874437</td>\n",
       "      <td>0.145250</td>\n",
       "      <td>25.102972</td>\n",
       "      <td>27.909583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2.8650</td>\n",
       "      <td>1.91</td>\n",
       "      <td>0.82</td>\n",
       "      <td>2.865000</td>\n",
       "      <td>2.901939</td>\n",
       "      <td>0.429250</td>\n",
       "      <td>0.382585</td>\n",
       "      <td>6.435618</td>\n",
       "      <td>8.750417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7.6125</td>\n",
       "      <td>1.43</td>\n",
       "      <td>0.51</td>\n",
       "      <td>6.396500</td>\n",
       "      <td>4.794867</td>\n",
       "      <td>1.701608</td>\n",
       "      <td>0.152997</td>\n",
       "      <td>22.095951</td>\n",
       "      <td>23.999583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.5750</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>2.788770</td>\n",
       "      <td>1.703764</td>\n",
       "      <td>0.087311</td>\n",
       "      <td>9.681333</td>\n",
       "      <td>4.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.2550</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.67</td>\n",
       "      <td>8.240000</td>\n",
       "      <td>1.447289</td>\n",
       "      <td>1.111472</td>\n",
       "      <td>0.810185</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rain_daily_sum  rain_hourly_max  rain_15_min_max  rain_prev_3_days  \\\n",
       "25          3.2300             0.87             0.33          2.070000   \n",
       "26          2.8650             1.91             0.82          2.865000   \n",
       "27          7.6125             1.43             0.51          6.396500   \n",
       "14          0.5750             0.15             0.08          0.433333   \n",
       "0          10.2550             1.59             0.67          8.240000   \n",
       "\n",
       "    gw_daily_avg  tide_daily_avg  wind_dir_daily_avg  wind_vel_daily_avg  \\\n",
       "25      2.635461        1.874437            0.145250           25.102972   \n",
       "26      2.901939        0.429250            0.382585            6.435618   \n",
       "27      4.794867        1.701608            0.152997           22.095951   \n",
       "14      2.788770        1.703764            0.087311            9.681333   \n",
       "0       1.447289        1.111472            0.810185           13.200000   \n",
       "\n",
       "    wind_vel_hourly_max_avg  \n",
       "25                27.909583  \n",
       "26                 8.750417  \n",
       "27                23.999583  \n",
       "14                 4.125000  \n",
       "0                       NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_df = df.iloc[:, :9]\n",
    "feat_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "When I tried to do PCA it threw an error saying that my data contained NAN. So here's what I found:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "174\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rain_daily_sum</th>\n",
       "      <th>rain_hourly_max</th>\n",
       "      <th>rain_15_min_max</th>\n",
       "      <th>rain_prev_3_days</th>\n",
       "      <th>gw_daily_avg</th>\n",
       "      <th>tide_daily_avg</th>\n",
       "      <th>wind_dir_daily_avg</th>\n",
       "      <th>wind_vel_daily_avg</th>\n",
       "      <th>wind_vel_hourly_max_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>0.755</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.760</td>\n",
       "      <td>2.187163</td>\n",
       "      <td>-0.280083</td>\n",
       "      <td>0.544444</td>\n",
       "      <td>9.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>0.260</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.015</td>\n",
       "      <td>2.630532</td>\n",
       "      <td>-0.628917</td>\n",
       "      <td>0.505556</td>\n",
       "      <td>3.60</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>0.150</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.165</td>\n",
       "      <td>2.514490</td>\n",
       "      <td>-0.599875</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>8.80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>0.140</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.290</td>\n",
       "      <td>2.430261</td>\n",
       "      <td>-0.560167</td>\n",
       "      <td>0.530556</td>\n",
       "      <td>2.90</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>0.895</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.895</td>\n",
       "      <td>1.219828</td>\n",
       "      <td>-0.128792</td>\n",
       "      <td>0.147222</td>\n",
       "      <td>10.95</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>0.810</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.705</td>\n",
       "      <td>1.641429</td>\n",
       "      <td>0.335333</td>\n",
       "      <td>0.822222</td>\n",
       "      <td>6.85</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>0.830</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.840</td>\n",
       "      <td>2.180177</td>\n",
       "      <td>0.307750</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>11.15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>0.305</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.320</td>\n",
       "      <td>2.183361</td>\n",
       "      <td>-0.350583</td>\n",
       "      <td>0.602778</td>\n",
       "      <td>11.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>0.215</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.530</td>\n",
       "      <td>2.331999</td>\n",
       "      <td>0.361542</td>\n",
       "      <td>0.358333</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>0.115</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.340</td>\n",
       "      <td>2.391025</td>\n",
       "      <td>1.176042</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>11.65</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>0.220</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.230</td>\n",
       "      <td>2.175842</td>\n",
       "      <td>0.383125</td>\n",
       "      <td>0.105556</td>\n",
       "      <td>12.10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>0.765</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.765</td>\n",
       "      <td>2.533144</td>\n",
       "      <td>0.697167</td>\n",
       "      <td>0.463889</td>\n",
       "      <td>10.30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>0.645</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.645</td>\n",
       "      <td>2.021217</td>\n",
       "      <td>0.013375</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>14.80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>0.935</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.935</td>\n",
       "      <td>2.088569</td>\n",
       "      <td>-0.057833</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>12.95</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>0.185</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.185</td>\n",
       "      <td>1.298293</td>\n",
       "      <td>0.169917</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>14.10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>0.210</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.245</td>\n",
       "      <td>1.549074</td>\n",
       "      <td>0.747375</td>\n",
       "      <td>0.761111</td>\n",
       "      <td>6.45</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>0.110</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.355</td>\n",
       "      <td>1.648978</td>\n",
       "      <td>0.804125</td>\n",
       "      <td>0.844444</td>\n",
       "      <td>5.55</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>0.230</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.240</td>\n",
       "      <td>1.051981</td>\n",
       "      <td>-0.322375</td>\n",
       "      <td>0.738889</td>\n",
       "      <td>9.85</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>0.140</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.140</td>\n",
       "      <td>1.140378</td>\n",
       "      <td>-0.487917</td>\n",
       "      <td>0.072222</td>\n",
       "      <td>7.95</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>0.380</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.982240</td>\n",
       "      <td>-0.045875</td>\n",
       "      <td>0.105556</td>\n",
       "      <td>6.05</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>0.440</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.820</td>\n",
       "      <td>1.230819</td>\n",
       "      <td>0.240792</td>\n",
       "      <td>0.538889</td>\n",
       "      <td>10.25</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>0.360</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.360</td>\n",
       "      <td>1.118228</td>\n",
       "      <td>-0.474500</td>\n",
       "      <td>0.591667</td>\n",
       "      <td>2.70</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>0.280</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.315</td>\n",
       "      <td>1.116748</td>\n",
       "      <td>1.139833</td>\n",
       "      <td>0.013889</td>\n",
       "      <td>16.20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>0.570</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.570</td>\n",
       "      <td>2.090843</td>\n",
       "      <td>-0.116167</td>\n",
       "      <td>0.244444</td>\n",
       "      <td>8.85</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>1.155</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.930</td>\n",
       "      <td>1.057460</td>\n",
       "      <td>0.055083</td>\n",
       "      <td>0.572222</td>\n",
       "      <td>6.80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>1.330</td>\n",
       "      <td>2.36</td>\n",
       "      <td>1.32</td>\n",
       "      <td>2.525</td>\n",
       "      <td>1.065741</td>\n",
       "      <td>-0.072375</td>\n",
       "      <td>0.608333</td>\n",
       "      <td>6.70</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>0.630</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.960</td>\n",
       "      <td>1.162971</td>\n",
       "      <td>0.409958</td>\n",
       "      <td>0.991667</td>\n",
       "      <td>5.90</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>0.345</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.345</td>\n",
       "      <td>1.223337</td>\n",
       "      <td>0.262208</td>\n",
       "      <td>0.036111</td>\n",
       "      <td>5.15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>0.735</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.992621</td>\n",
       "      <td>0.147375</td>\n",
       "      <td>0.475000</td>\n",
       "      <td>7.35</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>0.150</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.250</td>\n",
       "      <td>1.387099</td>\n",
       "      <td>0.487292</td>\n",
       "      <td>0.747222</td>\n",
       "      <td>7.85</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>1.205</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.205</td>\n",
       "      <td>1.852750</td>\n",
       "      <td>0.124042</td>\n",
       "      <td>0.622222</td>\n",
       "      <td>6.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>1.525</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.525</td>\n",
       "      <td>1.772551</td>\n",
       "      <td>0.009708</td>\n",
       "      <td>0.527778</td>\n",
       "      <td>7.15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>0.815</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2.340</td>\n",
       "      <td>1.846795</td>\n",
       "      <td>0.113750</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>5.05</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>0.125</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.175</td>\n",
       "      <td>1.738412</td>\n",
       "      <td>0.227750</td>\n",
       "      <td>0.441667</td>\n",
       "      <td>4.80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>0.220</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.220</td>\n",
       "      <td>1.883590</td>\n",
       "      <td>0.959583</td>\n",
       "      <td>0.022222</td>\n",
       "      <td>13.10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>0.120</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.120</td>\n",
       "      <td>1.815094</td>\n",
       "      <td>0.409000</td>\n",
       "      <td>0.116667</td>\n",
       "      <td>9.70</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>2.160</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.17</td>\n",
       "      <td>2.170</td>\n",
       "      <td>2.007231</td>\n",
       "      <td>0.925625</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>14.55</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>0.110</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.180</td>\n",
       "      <td>1.726963</td>\n",
       "      <td>-0.349667</td>\n",
       "      <td>0.136111</td>\n",
       "      <td>7.95</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>0.190</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.420</td>\n",
       "      <td>1.982281</td>\n",
       "      <td>0.353375</td>\n",
       "      <td>0.511111</td>\n",
       "      <td>3.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903</th>\n",
       "      <td>2.340</td>\n",
       "      <td>1.64</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2.340</td>\n",
       "      <td>1.985568</td>\n",
       "      <td>0.288667</td>\n",
       "      <td>0.063889</td>\n",
       "      <td>9.85</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>0.860</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.41</td>\n",
       "      <td>3.205</td>\n",
       "      <td>2.028662</td>\n",
       "      <td>0.447583</td>\n",
       "      <td>0.522222</td>\n",
       "      <td>8.15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>906</th>\n",
       "      <td>0.460</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.325</td>\n",
       "      <td>2.096232</td>\n",
       "      <td>0.896500</td>\n",
       "      <td>0.913889</td>\n",
       "      <td>7.65</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>0.195</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.655</td>\n",
       "      <td>2.174189</td>\n",
       "      <td>0.673417</td>\n",
       "      <td>0.811111</td>\n",
       "      <td>9.80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>0.610</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.640</td>\n",
       "      <td>2.138493</td>\n",
       "      <td>0.432958</td>\n",
       "      <td>0.091667</td>\n",
       "      <td>19.65</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>0.200</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.810</td>\n",
       "      <td>2.235079</td>\n",
       "      <td>0.749750</td>\n",
       "      <td>0.008333</td>\n",
       "      <td>13.20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807</th>\n",
       "      <td>0.115</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.135</td>\n",
       "      <td>2.634993</td>\n",
       "      <td>-0.059500</td>\n",
       "      <td>0.627778</td>\n",
       "      <td>16.80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>0.370</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.370</td>\n",
       "      <td>2.324414</td>\n",
       "      <td>-0.449958</td>\n",
       "      <td>0.572222</td>\n",
       "      <td>6.60</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>0.620</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.635</td>\n",
       "      <td>2.465621</td>\n",
       "      <td>0.838625</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>8.70</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>0.865</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.485</td>\n",
       "      <td>2.715179</td>\n",
       "      <td>0.673083</td>\n",
       "      <td>0.080556</td>\n",
       "      <td>5.95</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>0.105</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.105</td>\n",
       "      <td>1.908633</td>\n",
       "      <td>-1.078833</td>\n",
       "      <td>0.641667</td>\n",
       "      <td>11.10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785</th>\n",
       "      <td>0.250</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.355</td>\n",
       "      <td>1.954875</td>\n",
       "      <td>-0.279208</td>\n",
       "      <td>0.872222</td>\n",
       "      <td>6.60</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>0.305</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.560</td>\n",
       "      <td>1.874032</td>\n",
       "      <td>0.038333</td>\n",
       "      <td>0.663889</td>\n",
       "      <td>6.45</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>0.285</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.595</td>\n",
       "      <td>2.041269</td>\n",
       "      <td>0.490708</td>\n",
       "      <td>0.036111</td>\n",
       "      <td>12.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>0.290</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.290</td>\n",
       "      <td>1.987531</td>\n",
       "      <td>-0.096125</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>4.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>0.480</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.525</td>\n",
       "      <td>2.417789</td>\n",
       "      <td>-0.064667</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>5.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>0.400</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.400</td>\n",
       "      <td>2.799064</td>\n",
       "      <td>-0.756708</td>\n",
       "      <td>0.019444</td>\n",
       "      <td>10.15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>0.115</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.115</td>\n",
       "      <td>2.646613</td>\n",
       "      <td>-0.940708</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>11.85</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>0.190</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.195</td>\n",
       "      <td>2.511099</td>\n",
       "      <td>0.269375</td>\n",
       "      <td>0.627778</td>\n",
       "      <td>7.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.820</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.33</td>\n",
       "      <td>8.075</td>\n",
       "      <td>1.033844</td>\n",
       "      <td>1.493500</td>\n",
       "      <td>0.431944</td>\n",
       "      <td>31.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.255</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.67</td>\n",
       "      <td>8.240</td>\n",
       "      <td>1.447289</td>\n",
       "      <td>1.111472</td>\n",
       "      <td>0.810185</td>\n",
       "      <td>13.20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>174 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     rain_daily_sum  rain_hourly_max  rain_15_min_max  rain_prev_3_days  \\\n",
       "425           0.755             0.30             0.09             0.760   \n",
       "427           0.260             0.16             0.16             1.015   \n",
       "430           0.150             0.07             0.02             0.165   \n",
       "431           0.140             0.07             0.02             0.290   \n",
       "406           0.895             0.24             0.08             0.895   \n",
       "407           0.810             0.49             0.17             1.705   \n",
       "415           0.830             0.34             0.12             0.840   \n",
       "469           0.305             0.22             0.12             0.320   \n",
       "471           0.215             0.12             0.08             0.530   \n",
       "472           0.115             0.10             0.03             0.340   \n",
       "475           0.220             0.06             0.03             0.230   \n",
       "458           0.765             0.19             0.09             0.765   \n",
       "448           0.645             0.32             0.16             0.645   \n",
       "454           0.935             0.59             0.27             0.935   \n",
       "359           0.185             0.15             0.09             0.185   \n",
       "332           0.210             0.10             0.04             0.245   \n",
       "333           0.110             0.07             0.02             0.355   \n",
       "391           0.230             0.10             0.06             0.240   \n",
       "400           0.140             0.05             0.02             0.140   \n",
       "369           0.380             0.08             0.03             0.380   \n",
       "370           0.440             0.19             0.12             0.820   \n",
       "374           0.360             0.11             0.03             0.360   \n",
       "384           0.280             0.07             0.03             0.315   \n",
       "478           0.570             0.16             0.05             0.570   \n",
       "576           1.155             0.86             0.58             1.930   \n",
       "577           1.330             2.36             1.32             2.525   \n",
       "579           0.630             0.55             0.31             1.960   \n",
       "583           0.345             0.38             0.34             0.345   \n",
       "574           0.735             1.13             0.71             0.735   \n",
       "559           0.150             0.18             0.12             0.250   \n",
       "..              ...              ...              ...               ...   \n",
       "882           1.205             0.31             0.17             1.205   \n",
       "888           1.525             1.36             0.45             1.525   \n",
       "889           0.815             0.55             0.39             2.340   \n",
       "877           0.125             0.10             0.10             0.175   \n",
       "893           0.220             0.12             0.10             0.220   \n",
       "862           0.120             0.03             0.01             0.120   \n",
       "865           2.160             0.46             0.17             2.170   \n",
       "871           0.110             0.12             0.05             0.180   \n",
       "895           0.190             0.09             0.03             0.420   \n",
       "903           2.340             1.64             0.57             2.340   \n",
       "905           0.860             0.99             0.41             3.205   \n",
       "906           0.460             0.48             0.42             1.325   \n",
       "908           0.195             0.16             0.15             0.655   \n",
       "802           0.610             0.23             0.10             0.640   \n",
       "803           0.200             0.10             0.04             0.810   \n",
       "807           0.115             0.13             0.10             0.135   \n",
       "812           0.370             0.33             0.22             0.370   \n",
       "815           0.620             0.33             0.13             0.635   \n",
       "816           0.865             0.15             0.05             1.485   \n",
       "784           0.105             0.10             0.10             0.105   \n",
       "785           0.250             0.07             0.07             0.355   \n",
       "787           0.305             0.11             0.04             0.560   \n",
       "788           0.285             0.08             0.03             0.595   \n",
       "799           0.290             0.12             0.04             0.290   \n",
       "836           0.480             0.14             0.08             0.525   \n",
       "821           0.400             0.23             0.10             0.400   \n",
       "825           0.115             0.08             0.03             0.115   \n",
       "828           0.190             0.18             0.10             0.195   \n",
       "1             7.820             1.22             0.33             8.075   \n",
       "0            10.255             1.59             0.67             8.240   \n",
       "\n",
       "     gw_daily_avg  tide_daily_avg  wind_dir_daily_avg  wind_vel_daily_avg  \\\n",
       "425      2.187163       -0.280083            0.544444                9.40   \n",
       "427      2.630532       -0.628917            0.505556                3.60   \n",
       "430      2.514490       -0.599875            0.041667                8.80   \n",
       "431      2.430261       -0.560167            0.530556                2.90   \n",
       "406      1.219828       -0.128792            0.147222               10.95   \n",
       "407      1.641429        0.335333            0.822222                6.85   \n",
       "415      2.180177        0.307750            0.050000               11.15   \n",
       "469      2.183361       -0.350583            0.602778               11.50   \n",
       "471      2.331999        0.361542            0.358333                7.75   \n",
       "472      2.391025        1.176042            0.050000               11.65   \n",
       "475      2.175842        0.383125            0.105556               12.10   \n",
       "458      2.533144        0.697167            0.463889               10.30   \n",
       "448      2.021217        0.013375            0.600000               14.80   \n",
       "454      2.088569       -0.057833            0.533333               12.95   \n",
       "359      1.298293        0.169917            0.625000               14.10   \n",
       "332      1.549074        0.747375            0.761111                6.45   \n",
       "333      1.648978        0.804125            0.844444                5.55   \n",
       "391      1.051981       -0.322375            0.738889                9.85   \n",
       "400      1.140378       -0.487917            0.072222                7.95   \n",
       "369      0.982240       -0.045875            0.105556                6.05   \n",
       "370      1.230819        0.240792            0.538889               10.25   \n",
       "374      1.118228       -0.474500            0.591667                2.70   \n",
       "384      1.116748        1.139833            0.013889               16.20   \n",
       "478      2.090843       -0.116167            0.244444                8.85   \n",
       "576      1.057460        0.055083            0.572222                6.80   \n",
       "577      1.065741       -0.072375            0.608333                6.70   \n",
       "579      1.162971        0.409958            0.991667                5.90   \n",
       "583      1.223337        0.262208            0.036111                5.15   \n",
       "574      0.992621        0.147375            0.475000                7.35   \n",
       "559      1.387099        0.487292            0.747222                7.85   \n",
       "..            ...             ...                 ...                 ...   \n",
       "882      1.852750        0.124042            0.622222                6.50   \n",
       "888      1.772551        0.009708            0.527778                7.15   \n",
       "889      1.846795        0.113750            0.575000                5.05   \n",
       "877      1.738412        0.227750            0.441667                4.80   \n",
       "893      1.883590        0.959583            0.022222               13.10   \n",
       "862      1.815094        0.409000            0.116667                9.70   \n",
       "865      2.007231        0.925625            0.066667               14.55   \n",
       "871      1.726963       -0.349667            0.136111                7.95   \n",
       "895      1.982281        0.353375            0.511111                3.00   \n",
       "903      1.985568        0.288667            0.063889                9.85   \n",
       "905      2.028662        0.447583            0.522222                8.15   \n",
       "906      2.096232        0.896500            0.913889                7.65   \n",
       "908      2.174189        0.673417            0.811111                9.80   \n",
       "802      2.138493        0.432958            0.091667               19.65   \n",
       "803      2.235079        0.749750            0.008333               13.20   \n",
       "807      2.634993       -0.059500            0.627778               16.80   \n",
       "812      2.324414       -0.449958            0.572222                6.60   \n",
       "815      2.465621        0.838625            0.650000                8.70   \n",
       "816      2.715179        0.673083            0.080556                5.95   \n",
       "784      1.908633       -1.078833            0.641667               11.10   \n",
       "785      1.954875       -0.279208            0.872222                6.60   \n",
       "787      1.874032        0.038333            0.663889                6.45   \n",
       "788      2.041269        0.490708            0.036111               12.50   \n",
       "799      1.987531       -0.096125            0.300000                4.40   \n",
       "836      2.417789       -0.064667            0.525000                5.40   \n",
       "821      2.799064       -0.756708            0.019444               10.15   \n",
       "825      2.646613       -0.940708            0.600000               11.85   \n",
       "828      2.511099        0.269375            0.627778                7.50   \n",
       "1        1.033844        1.493500            0.431944               31.00   \n",
       "0        1.447289        1.111472            0.810185               13.20   \n",
       "\n",
       "     wind_vel_hourly_max_avg  \n",
       "425                      NaN  \n",
       "427                      NaN  \n",
       "430                      NaN  \n",
       "431                      NaN  \n",
       "406                      NaN  \n",
       "407                      NaN  \n",
       "415                      NaN  \n",
       "469                      NaN  \n",
       "471                      NaN  \n",
       "472                      NaN  \n",
       "475                      NaN  \n",
       "458                      NaN  \n",
       "448                      NaN  \n",
       "454                      NaN  \n",
       "359                      NaN  \n",
       "332                      NaN  \n",
       "333                      NaN  \n",
       "391                      NaN  \n",
       "400                      NaN  \n",
       "369                      NaN  \n",
       "370                      NaN  \n",
       "374                      NaN  \n",
       "384                      NaN  \n",
       "478                      NaN  \n",
       "576                      NaN  \n",
       "577                      NaN  \n",
       "579                      NaN  \n",
       "583                      NaN  \n",
       "574                      NaN  \n",
       "559                      NaN  \n",
       "..                       ...  \n",
       "882                      NaN  \n",
       "888                      NaN  \n",
       "889                      NaN  \n",
       "877                      NaN  \n",
       "893                      NaN  \n",
       "862                      NaN  \n",
       "865                      NaN  \n",
       "871                      NaN  \n",
       "895                      NaN  \n",
       "903                      NaN  \n",
       "905                      NaN  \n",
       "906                      NaN  \n",
       "908                      NaN  \n",
       "802                      NaN  \n",
       "803                      NaN  \n",
       "807                      NaN  \n",
       "812                      NaN  \n",
       "815                      NaN  \n",
       "816                      NaN  \n",
       "784                      NaN  \n",
       "785                      NaN  \n",
       "787                      NaN  \n",
       "788                      NaN  \n",
       "799                      NaN  \n",
       "836                      NaN  \n",
       "821                      NaN  \n",
       "825                      NaN  \n",
       "828                      NaN  \n",
       "1                        NaN  \n",
       "0                        NaN  \n",
       "\n",
       "[174 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = feat_df.isnull().any(axis=1)\n",
    "print sum(n)\n",
    "feat_df[n]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "There is a bunch of shallow well depth data missing as well as hourly wind max. Here's some more investigation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3845\n",
      "60120\n",
      "0.0639554224884\n"
     ]
    },
    {
     "data": {
      "image/png": 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nyclXZ21Cu/snuTqBBwB1a0mtxl6UY7br1spAfp5+PW0AOK9jruE6haZ1swAAeU1y4tZd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\nNNrh5zGDzDcyYe3uYrw6K17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nTjIMw4QMzpxkGIYJGay4GYZhQgYrboZhmJDBipthGCZk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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe009eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gw_df = get_table_for_variable('groundwater')\n",
    "gw_df['Value'].plot()\n",
    "print sum(gw_df['Value'].isnull())\n",
    "print len(gw_df)\n",
    "print 3845./60120"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "So approximately 6% of the gw data is NaN and from the figure a lot of that lookds like it happens at once like for a few months about 2/3 through 2010. I guess we just drop those ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c3af98>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RMmU6RdVoW6/oxmOE6d3mE0PaelIKUxFxPF+0f5wYAV2peElkr4bR93ea27/i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\na0LrEJYLYJ5vXtvJW6HTm+W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CREnGY/hXq4pdNSPs/OhIxgNL5lbDiQfMxKpvn1dxPRWF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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12047588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index(pd.to_datetime(df['event_date']))['wind_vel_hourly_max_avg'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the hourly wind max, the earliest date is in 2012. Let's just try without that feature and delete it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del df['wind_vel_hourly_max_avg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del df['wind_dir_daily_avg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rain_daily_sum</th>\n",
       "      <th>rain_hourly_max</th>\n",
       "      <th>rain_15_min_max</th>\n",
       "      <th>rain_prev_3_days</th>\n",
       "      <th>gw_daily_avg</th>\n",
       "      <th>tide_daily_avg</th>\n",
       "      <th>wind_vel_daily_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.064818</td>\n",
       "      <td>-0.014648</td>\n",
       "      <td>-0.034190</td>\n",
       "      <td>0.044518</td>\n",
       "      <td>0.032131</td>\n",
       "      <td>-0.049035</td>\n",
       "      <td>-0.128273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.050425</td>\n",
       "      <td>-0.025436</td>\n",
       "      <td>-0.031324</td>\n",
       "      <td>0.007103</td>\n",
       "      <td>-0.075680</td>\n",
       "      <td>0.055116</td>\n",
       "      <td>-0.118277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.032367</td>\n",
       "      <td>-0.001703</td>\n",
       "      <td>0.028848</td>\n",
       "      <td>0.061009</td>\n",
       "      <td>-0.075522</td>\n",
       "      <td>0.086563</td>\n",
       "      <td>-0.102315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.049058</td>\n",
       "      <td>0.078124</td>\n",
       "      <td>0.209364</td>\n",
       "      <td>0.025544</td>\n",
       "      <td>-0.083883</td>\n",
       "      <td>0.096819</td>\n",
       "      <td>-0.157565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.028755</td>\n",
       "      <td>-0.018963</td>\n",
       "      <td>-0.034190</td>\n",
       "      <td>-0.059215</td>\n",
       "      <td>-0.036984</td>\n",
       "      <td>-0.006765</td>\n",
       "      <td>-0.071296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.052395</td>\n",
       "      <td>-0.031908</td>\n",
       "      <td>-0.039920</td>\n",
       "      <td>-0.084749</td>\n",
       "      <td>-0.046578</td>\n",
       "      <td>0.052494</td>\n",
       "      <td>0.019316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.002161</td>\n",
       "      <td>-0.008176</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>-0.030489</td>\n",
       "      <td>-0.122436</td>\n",
       "      <td>0.135165</td>\n",
       "      <td>0.142031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.033680</td>\n",
       "      <td>-0.036223</td>\n",
       "      <td>-0.037055</td>\n",
       "      <td>-0.064534</td>\n",
       "      <td>-0.129616</td>\n",
       "      <td>-0.040397</td>\n",
       "      <td>-0.101753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.026403</td>\n",
       "      <td>0.023108</td>\n",
       "      <td>0.014521</td>\n",
       "      <td>0.034411</td>\n",
       "      <td>-0.109581</td>\n",
       "      <td>-0.015886</td>\n",
       "      <td>-0.137841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.040575</td>\n",
       "      <td>-0.036223</td>\n",
       "      <td>-0.048516</td>\n",
       "      <td>0.061009</td>\n",
       "      <td>-0.099352</td>\n",
       "      <td>0.001464</td>\n",
       "      <td>-0.128669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.037292</td>\n",
       "      <td>0.018793</td>\n",
       "      <td>0.034578</td>\n",
       "      <td>-0.041837</td>\n",
       "      <td>-0.090818</td>\n",
       "      <td>0.013413</td>\n",
       "      <td>-0.136560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.043202</td>\n",
       "      <td>0.006927</td>\n",
       "      <td>0.066097</td>\n",
       "      <td>0.127327</td>\n",
       "      <td>-0.110976</td>\n",
       "      <td>-0.037589</td>\n",
       "      <td>-0.101932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.044843</td>\n",
       "      <td>0.015557</td>\n",
       "      <td>0.068962</td>\n",
       "      <td>-0.076592</td>\n",
       "      <td>-0.084141</td>\n",
       "      <td>-0.114914</td>\n",
       "      <td>0.021673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.058579</td>\n",
       "      <td>0.306819</td>\n",
       "      <td>0.705066</td>\n",
       "      <td>0.035120</td>\n",
       "      <td>-0.091174</td>\n",
       "      <td>0.055190</td>\n",
       "      <td>-0.071043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.053380</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.034190</td>\n",
       "      <td>-0.085813</td>\n",
       "      <td>0.044819</td>\n",
       "      <td>0.057366</td>\n",
       "      <td>-0.136956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.002818</td>\n",
       "      <td>-0.023278</td>\n",
       "      <td>-0.042785</td>\n",
       "      <td>-0.031198</td>\n",
       "      <td>0.142474</td>\n",
       "      <td>-0.195066</td>\n",
       "      <td>-0.021056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.051082</td>\n",
       "      <td>-0.027593</td>\n",
       "      <td>-0.034190</td>\n",
       "      <td>-0.015949</td>\n",
       "      <td>0.184218</td>\n",
       "      <td>-0.068896</td>\n",
       "      <td>0.112379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.004788</td>\n",
       "      <td>-0.004939</td>\n",
       "      <td>-0.011267</td>\n",
       "      <td>-0.033326</td>\n",
       "      <td>0.088448</td>\n",
       "      <td>-0.058687</td>\n",
       "      <td>-0.074752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.050753</td>\n",
       "      <td>-0.028672</td>\n",
       "      <td>-0.048516</td>\n",
       "      <td>-0.082976</td>\n",
       "      <td>0.157727</td>\n",
       "      <td>0.008038</td>\n",
       "      <td>0.118431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.031054</td>\n",
       "      <td>-0.039459</td>\n",
       "      <td>-0.059977</td>\n",
       "      <td>-0.061697</td>\n",
       "      <td>0.061799</td>\n",
       "      <td>-0.042917</td>\n",
       "      <td>0.141111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-0.035322</td>\n",
       "      <td>-0.043774</td>\n",
       "      <td>-0.062843</td>\n",
       "      <td>-0.029425</td>\n",
       "      <td>0.075315</td>\n",
       "      <td>0.251850</td>\n",
       "      <td>0.317518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.028099</td>\n",
       "      <td>-0.039459</td>\n",
       "      <td>-0.059977</td>\n",
       "      <td>0.010650</td>\n",
       "      <td>0.116492</td>\n",
       "      <td>0.236954</td>\n",
       "      <td>0.285962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.006429</td>\n",
       "      <td>-0.037302</td>\n",
       "      <td>-0.059977</td>\n",
       "      <td>-0.024460</td>\n",
       "      <td>0.114220</td>\n",
       "      <td>0.032567</td>\n",
       "      <td>0.044790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.053983</td>\n",
       "      <td>0.005848</td>\n",
       "      <td>0.005925</td>\n",
       "      <td>0.030155</td>\n",
       "      <td>0.087739</td>\n",
       "      <td>-0.117034</td>\n",
       "      <td>0.131007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.046813</td>\n",
       "      <td>-0.036223</td>\n",
       "      <td>-0.051381</td>\n",
       "      <td>-0.078720</td>\n",
       "      <td>0.056935</td>\n",
       "      <td>-0.053833</td>\n",
       "      <td>-0.081857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-0.054693</td>\n",
       "      <td>-0.043774</td>\n",
       "      <td>-0.059977</td>\n",
       "      <td>-0.087231</td>\n",
       "      <td>0.152129</td>\n",
       "      <td>0.011981</td>\n",
       "      <td>0.041318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-0.018249</td>\n",
       "      <td>0.003691</td>\n",
       "      <td>-0.005536</td>\n",
       "      <td>-0.047866</td>\n",
       "      <td>0.074063</td>\n",
       "      <td>-0.030197</td>\n",
       "      <td>-0.078018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.012667</td>\n",
       "      <td>-0.006018</td>\n",
       "      <td>-0.016998</td>\n",
       "      <td>-0.041837</td>\n",
       "      <td>0.052100</td>\n",
       "      <td>0.031451</td>\n",
       "      <td>-0.052431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-0.032367</td>\n",
       "      <td>0.058707</td>\n",
       "      <td>0.189306</td>\n",
       "      <td>-0.058506</td>\n",
       "      <td>-0.126120</td>\n",
       "      <td>0.005221</td>\n",
       "      <td>-0.116924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-0.032367</td>\n",
       "      <td>-0.013569</td>\n",
       "      <td>-0.016998</td>\n",
       "      <td>-0.047157</td>\n",
       "      <td>-0.148886</td>\n",
       "      <td>-0.222096</td>\n",
       "      <td>0.145787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>-0.036635</td>\n",
       "      <td>-0.036223</td>\n",
       "      <td>-0.057112</td>\n",
       "      <td>-0.067726</td>\n",
       "      <td>-0.041950</td>\n",
       "      <td>-0.084964</td>\n",
       "      <td>-0.139328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>-0.017921</td>\n",
       "      <td>-0.034066</td>\n",
       "      <td>-0.045651</td>\n",
       "      <td>-0.042724</td>\n",
       "      <td>0.028249</td>\n",
       "      <td>-0.077944</td>\n",
       "      <td>-0.105008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>-0.025800</td>\n",
       "      <td>-0.024357</td>\n",
       "      <td>-0.039920</td>\n",
       "      <td>-0.056023</td>\n",
       "      <td>0.090457</td>\n",
       "      <td>-0.232380</td>\n",
       "      <td>0.058014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>-0.053872</td>\n",
       "      <td>-0.040538</td>\n",
       "      <td>-0.059977</td>\n",
       "      <td>-0.086345</td>\n",
       "      <td>0.065583</td>\n",
       "      <td>-0.273441</td>\n",
       "      <td>0.116358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>-0.046485</td>\n",
       "      <td>-0.029751</td>\n",
       "      <td>-0.039920</td>\n",
       "      <td>-0.077833</td>\n",
       "      <td>0.043473</td>\n",
       "      <td>-0.003399</td>\n",
       "      <td>-0.032935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>0.179403</td>\n",
       "      <td>0.397434</td>\n",
       "      <td>0.338303</td>\n",
       "      <td>0.165628</td>\n",
       "      <td>-0.146654</td>\n",
       "      <td>0.041754</td>\n",
       "      <td>-0.107799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>0.235547</td>\n",
       "      <td>0.151479</td>\n",
       "      <td>0.137730</td>\n",
       "      <td>0.365114</td>\n",
       "      <td>-0.164382</td>\n",
       "      <td>0.217130</td>\n",
       "      <td>0.074746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>0.146243</td>\n",
       "      <td>0.178448</td>\n",
       "      <td>0.152057</td>\n",
       "      <td>0.129809</td>\n",
       "      <td>-0.198903</td>\n",
       "      <td>-0.018946</td>\n",
       "      <td>-0.136096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>0.120961</td>\n",
       "      <td>0.091069</td>\n",
       "      <td>0.114808</td>\n",
       "      <td>0.102502</td>\n",
       "      <td>-0.089117</td>\n",
       "      <td>-0.013673</td>\n",
       "      <td>-0.070223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>0.160032</td>\n",
       "      <td>0.163345</td>\n",
       "      <td>0.295323</td>\n",
       "      <td>0.302165</td>\n",
       "      <td>-0.135551</td>\n",
       "      <td>-0.111176</td>\n",
       "      <td>-0.115248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>0.314346</td>\n",
       "      <td>0.191393</td>\n",
       "      <td>0.149192</td>\n",
       "      <td>0.314577</td>\n",
       "      <td>0.020078</td>\n",
       "      <td>0.009898</td>\n",
       "      <td>-0.066189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>0.017867</td>\n",
       "      <td>0.072730</td>\n",
       "      <td>0.086154</td>\n",
       "      <td>-0.008856</td>\n",
       "      <td>-0.099001</td>\n",
       "      <td>0.007355</td>\n",
       "      <td>-0.046176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>0.244084</td>\n",
       "      <td>0.004769</td>\n",
       "      <td>-0.005536</td>\n",
       "      <td>0.214568</td>\n",
       "      <td>-0.224421</td>\n",
       "      <td>0.371607</td>\n",
       "      <td>0.383398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>0.131796</td>\n",
       "      <td>0.013399</td>\n",
       "      <td>-0.008402</td>\n",
       "      <td>0.257835</td>\n",
       "      <td>-0.147352</td>\n",
       "      <td>0.584508</td>\n",
       "      <td>0.516351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>0.705054</td>\n",
       "      <td>0.082439</td>\n",
       "      <td>0.025982</td>\n",
       "      <td>0.760539</td>\n",
       "      <td>-0.197550</td>\n",
       "      <td>0.269777</td>\n",
       "      <td>0.773593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>-0.015951</td>\n",
       "      <td>0.017714</td>\n",
       "      <td>0.066097</td>\n",
       "      <td>-0.029780</td>\n",
       "      <td>0.016336</td>\n",
       "      <td>-0.031582</td>\n",
       "      <td>-0.140911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>0.080577</td>\n",
       "      <td>0.088912</td>\n",
       "      <td>0.111942</td>\n",
       "      <td>0.058881</td>\n",
       "      <td>-0.147625</td>\n",
       "      <td>-0.174023</td>\n",
       "      <td>0.031970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>0.090427</td>\n",
       "      <td>0.201102</td>\n",
       "      <td>0.232286</td>\n",
       "      <td>0.083351</td>\n",
       "      <td>0.138246</td>\n",
       "      <td>-0.112217</td>\n",
       "      <td>-0.113333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>0.284222</td>\n",
       "      <td>0.031738</td>\n",
       "      <td>0.017386</td>\n",
       "      <td>0.241787</td>\n",
       "      <td>0.155649</td>\n",
       "      <td>0.472921</td>\n",
       "      <td>0.137808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>0.063833</td>\n",
       "      <td>0.068415</td>\n",
       "      <td>0.025982</td>\n",
       "      <td>0.040794</td>\n",
       "      <td>0.311914</td>\n",
       "      <td>-0.180709</td>\n",
       "      <td>-0.020064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>0.028866</td>\n",
       "      <td>0.026344</td>\n",
       "      <td>0.034578</td>\n",
       "      <td>0.024037</td>\n",
       "      <td>0.447526</td>\n",
       "      <td>0.055934</td>\n",
       "      <td>0.247361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>0.094613</td>\n",
       "      <td>0.012321</td>\n",
       "      <td>-0.016998</td>\n",
       "      <td>0.015703</td>\n",
       "      <td>0.425607</td>\n",
       "      <td>0.035549</td>\n",
       "      <td>0.135366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>0.016800</td>\n",
       "      <td>0.019872</td>\n",
       "      <td>0.011656</td>\n",
       "      <td>0.015260</td>\n",
       "      <td>0.357894</td>\n",
       "      <td>0.064721</td>\n",
       "      <td>0.130905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>0.136721</td>\n",
       "      <td>0.179527</td>\n",
       "      <td>0.235151</td>\n",
       "      <td>0.012866</td>\n",
       "      <td>0.198715</td>\n",
       "      <td>0.033953</td>\n",
       "      <td>0.046062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>0.061616</td>\n",
       "      <td>0.177369</td>\n",
       "      <td>0.172114</td>\n",
       "      <td>0.253313</td>\n",
       "      <td>0.133794</td>\n",
       "      <td>-0.001009</td>\n",
       "      <td>-0.096859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>0.252949</td>\n",
       "      <td>0.044683</td>\n",
       "      <td>0.025982</td>\n",
       "      <td>0.121652</td>\n",
       "      <td>0.063764</td>\n",
       "      <td>0.354787</td>\n",
       "      <td>0.571205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>0.216997</td>\n",
       "      <td>0.156873</td>\n",
       "      <td>0.166384</td>\n",
       "      <td>0.206234</td>\n",
       "      <td>0.107241</td>\n",
       "      <td>0.032279</td>\n",
       "      <td>-0.069465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>0.684616</td>\n",
       "      <td>0.105093</td>\n",
       "      <td>0.077558</td>\n",
       "      <td>0.581959</td>\n",
       "      <td>0.416084</td>\n",
       "      <td>0.316219</td>\n",
       "      <td>0.468003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>-0.008563</td>\n",
       "      <td>-0.032987</td>\n",
       "      <td>-0.045651</td>\n",
       "      <td>-0.052477</td>\n",
       "      <td>0.088777</td>\n",
       "      <td>0.316700</td>\n",
       "      <td>0.041929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0.944896</td>\n",
       "      <td>0.122353</td>\n",
       "      <td>0.123404</td>\n",
       "      <td>0.778093</td>\n",
       "      <td>-0.130094</td>\n",
       "      <td>0.184524</td>\n",
       "      <td>0.162691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>501 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     rain_daily_sum  rain_hourly_max  rain_15_min_max  rain_prev_3_days  \\\n",
       "0          0.064818        -0.014648        -0.034190          0.044518   \n",
       "1         -0.050425        -0.025436        -0.031324          0.007103   \n",
       "2         -0.032367        -0.001703         0.028848          0.061009   \n",
       "3          0.049058         0.078124         0.209364          0.025544   \n",
       "4         -0.028755        -0.018963        -0.034190         -0.059215   \n",
       "5         -0.052395        -0.031908        -0.039920         -0.084749   \n",
       "6         -0.002161        -0.008176         0.000194         -0.030489   \n",
       "7         -0.033680        -0.036223        -0.037055         -0.064534   \n",
       "8          0.026403         0.023108         0.014521          0.034411   \n",
       "9         -0.040575        -0.036223        -0.048516          0.061009   \n",
       "10        -0.037292         0.018793         0.034578         -0.041837   \n",
       "11        -0.043202         0.006927         0.066097          0.127327   \n",
       "12        -0.044843         0.015557         0.068962         -0.076592   \n",
       "13         0.058579         0.306819         0.705066          0.035120   \n",
       "14        -0.053380        -0.035144        -0.034190         -0.085813   \n",
       "15        -0.002818        -0.023278        -0.042785         -0.031198   \n",
       "16        -0.051082        -0.027593        -0.034190         -0.015949   \n",
       "17        -0.004788        -0.004939        -0.011267         -0.033326   \n",
       "18        -0.050753        -0.028672        -0.048516         -0.082976   \n",
       "19        -0.031054        -0.039459        -0.059977         -0.061697   \n",
       "20        -0.035322        -0.043774        -0.062843         -0.029425   \n",
       "21        -0.028099        -0.039459        -0.059977          0.010650   \n",
       "22        -0.006429        -0.037302        -0.059977         -0.024460   \n",
       "23         0.053983         0.005848         0.005925          0.030155   \n",
       "24        -0.046813        -0.036223        -0.051381         -0.078720   \n",
       "25        -0.054693        -0.043774        -0.059977         -0.087231   \n",
       "26        -0.018249         0.003691        -0.005536         -0.047866   \n",
       "27        -0.012667        -0.006018        -0.016998         -0.041837   \n",
       "28        -0.032367         0.058707         0.189306         -0.058506   \n",
       "29        -0.032367        -0.013569        -0.016998         -0.047157   \n",
       "..              ...              ...              ...               ...   \n",
       "471       -0.036635        -0.036223        -0.057112         -0.067726   \n",
       "472       -0.017921        -0.034066        -0.045651         -0.042724   \n",
       "473       -0.025800        -0.024357        -0.039920         -0.056023   \n",
       "474       -0.053872        -0.040538        -0.059977         -0.086345   \n",
       "475       -0.046485        -0.029751        -0.039920         -0.077833   \n",
       "476        0.179403         0.397434         0.338303          0.165628   \n",
       "477        0.235547         0.151479         0.137730          0.365114   \n",
       "478        0.146243         0.178448         0.152057          0.129809   \n",
       "479        0.120961         0.091069         0.114808          0.102502   \n",
       "480        0.160032         0.163345         0.295323          0.302165   \n",
       "481        0.314346         0.191393         0.149192          0.314577   \n",
       "482        0.017867         0.072730         0.086154         -0.008856   \n",
       "483        0.244084         0.004769        -0.005536          0.214568   \n",
       "484        0.131796         0.013399        -0.008402          0.257835   \n",
       "485        0.705054         0.082439         0.025982          0.760539   \n",
       "486       -0.015951         0.017714         0.066097         -0.029780   \n",
       "487        0.080577         0.088912         0.111942          0.058881   \n",
       "488        0.090427         0.201102         0.232286          0.083351   \n",
       "489        0.284222         0.031738         0.017386          0.241787   \n",
       "490        0.063833         0.068415         0.025982          0.040794   \n",
       "491        0.028866         0.026344         0.034578          0.024037   \n",
       "492        0.094613         0.012321        -0.016998          0.015703   \n",
       "493        0.016800         0.019872         0.011656          0.015260   \n",
       "494        0.136721         0.179527         0.235151          0.012866   \n",
       "495        0.061616         0.177369         0.172114          0.253313   \n",
       "496        0.252949         0.044683         0.025982          0.121652   \n",
       "497        0.216997         0.156873         0.166384          0.206234   \n",
       "498        0.684616         0.105093         0.077558          0.581959   \n",
       "499       -0.008563        -0.032987        -0.045651         -0.052477   \n",
       "500        0.944896         0.122353         0.123404          0.778093   \n",
       "\n",
       "     gw_daily_avg  tide_daily_avg  wind_vel_daily_avg  \n",
       "0        0.032131       -0.049035           -0.128273  \n",
       "1       -0.075680        0.055116           -0.118277  \n",
       "2       -0.075522        0.086563           -0.102315  \n",
       "3       -0.083883        0.096819           -0.157565  \n",
       "4       -0.036984       -0.006765           -0.071296  \n",
       "5       -0.046578        0.052494            0.019316  \n",
       "6       -0.122436        0.135165            0.142031  \n",
       "7       -0.129616       -0.040397           -0.101753  \n",
       "8       -0.109581       -0.015886           -0.137841  \n",
       "9       -0.099352        0.001464           -0.128669  \n",
       "10      -0.090818        0.013413           -0.136560  \n",
       "11      -0.110976       -0.037589           -0.101932  \n",
       "12      -0.084141       -0.114914            0.021673  \n",
       "13      -0.091174        0.055190           -0.071043  \n",
       "14       0.044819        0.057366           -0.136956  \n",
       "15       0.142474       -0.195066           -0.021056  \n",
       "16       0.184218       -0.068896            0.112379  \n",
       "17       0.088448       -0.058687           -0.074752  \n",
       "18       0.157727        0.008038            0.118431  \n",
       "19       0.061799       -0.042917            0.141111  \n",
       "20       0.075315        0.251850            0.317518  \n",
       "21       0.116492        0.236954            0.285962  \n",
       "22       0.114220        0.032567            0.044790  \n",
       "23       0.087739       -0.117034            0.131007  \n",
       "24       0.056935       -0.053833           -0.081857  \n",
       "25       0.152129        0.011981            0.041318  \n",
       "26       0.074063       -0.030197           -0.078018  \n",
       "27       0.052100        0.031451           -0.052431  \n",
       "28      -0.126120        0.005221           -0.116924  \n",
       "29      -0.148886       -0.222096            0.145787  \n",
       "..            ...             ...                 ...  \n",
       "471     -0.041950       -0.084964           -0.139328  \n",
       "472      0.028249       -0.077944           -0.105008  \n",
       "473      0.090457       -0.232380            0.058014  \n",
       "474      0.065583       -0.273441            0.116358  \n",
       "475      0.043473       -0.003399           -0.032935  \n",
       "476     -0.146654        0.041754           -0.107799  \n",
       "477     -0.164382        0.217130            0.074746  \n",
       "478     -0.198903       -0.018946           -0.136096  \n",
       "479     -0.089117       -0.013673           -0.070223  \n",
       "480     -0.135551       -0.111176           -0.115248  \n",
       "481      0.020078        0.009898           -0.066189  \n",
       "482     -0.099001        0.007355           -0.046176  \n",
       "483     -0.224421        0.371607            0.383398  \n",
       "484     -0.147352        0.584508            0.516351  \n",
       "485     -0.197550        0.269777            0.773593  \n",
       "486      0.016336       -0.031582           -0.140911  \n",
       "487     -0.147625       -0.174023            0.031970  \n",
       "488      0.138246       -0.112217           -0.113333  \n",
       "489      0.155649        0.472921            0.137808  \n",
       "490      0.311914       -0.180709           -0.020064  \n",
       "491      0.447526        0.055934            0.247361  \n",
       "492      0.425607        0.035549            0.135366  \n",
       "493      0.357894        0.064721            0.130905  \n",
       "494      0.198715        0.033953            0.046062  \n",
       "495      0.133794       -0.001009           -0.096859  \n",
       "496      0.063764        0.354787            0.571205  \n",
       "497      0.107241        0.032279           -0.069465  \n",
       "498      0.416084        0.316219            0.468003  \n",
       "499      0.088777        0.316700            0.041929  \n",
       "500     -0.130094        0.184524            0.162691  \n",
       "\n",
       "[501 rows x 7 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filt = df[df['gw_daily_avg'].isnull()!= True]\n",
    "df_filt.reset_index(inplace=True, drop=True)\n",
    "feat_df = df_filt.iloc[:, :7]\n",
    "feat_df_norm = (feat_df - feat_df.mean()) / (feat_df.max() - feat_df.min())\n",
    "feat_df_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rain_daily_sum</th>\n",
       "      <th>rain_hourly_max</th>\n",
       "      <th>rain_15_min_max</th>\n",
       "      <th>rain_prev_3_days</th>\n",
       "      <th>gw_daily_avg</th>\n",
       "      <th>tide_daily_avg</th>\n",
       "      <th>wind_vel_daily_avg</th>\n",
       "      <th>num_flooded</th>\n",
       "      <th>flooded</th>\n",
       "      <th>event_name</th>\n",
       "      <th>event_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.320000</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.345000</td>\n",
       "      <td>2.441582</td>\n",
       "      <td>0.064875</td>\n",
       "      <td>4.722118</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2013-04-29 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.993333</td>\n",
       "      <td>1.780793</td>\n",
       "      <td>0.531583</td>\n",
       "      <td>5.013368</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-21 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.781763</td>\n",
       "      <td>0.672500</td>\n",
       "      <td>5.478465</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-13 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.160000</td>\n",
       "      <td>1.18</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>1.730518</td>\n",
       "      <td>0.718458</td>\n",
       "      <td>3.868646</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-12 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>2.017969</td>\n",
       "      <td>0.254292</td>\n",
       "      <td>6.382278</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-05-28 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>1.959165</td>\n",
       "      <td>0.519833</td>\n",
       "      <td>9.022451</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-06-05 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>1.494224</td>\n",
       "      <td>0.890292</td>\n",
       "      <td>12.598028</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-07-04 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.450218</td>\n",
       "      <td>0.103583</td>\n",
       "      <td>5.494833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-08-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.930000</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.573014</td>\n",
       "      <td>0.213417</td>\n",
       "      <td>4.443340</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-08-02 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.635707</td>\n",
       "      <td>0.291167</td>\n",
       "      <td>4.710569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-08-03 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.283333</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>1.688012</td>\n",
       "      <td>0.344708</td>\n",
       "      <td>4.480661</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-08-05 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.223333</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2.123333</td>\n",
       "      <td>1.564464</td>\n",
       "      <td>0.116167</td>\n",
       "      <td>5.489625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-07-26 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.206667</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.206667</td>\n",
       "      <td>1.728936</td>\n",
       "      <td>-0.230333</td>\n",
       "      <td>9.091146</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-07-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.256667</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1.256667</td>\n",
       "      <td>1.685832</td>\n",
       "      <td>0.531917</td>\n",
       "      <td>6.389653</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-08-12 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>2.519349</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>4.469125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-05-22 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>3.117883</td>\n",
       "      <td>-0.589500</td>\n",
       "      <td>7.846125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-03-29 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.143333</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.776667</td>\n",
       "      <td>3.373736</td>\n",
       "      <td>-0.024125</td>\n",
       "      <td>11.734056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-03-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.613333</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.613333</td>\n",
       "      <td>2.786750</td>\n",
       "      <td>0.021625</td>\n",
       "      <td>6.281569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-04-07 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.146667</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.146667</td>\n",
       "      <td>3.211371</td>\n",
       "      <td>0.320625</td>\n",
       "      <td>11.910382</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-03-12 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.346667</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.346667</td>\n",
       "      <td>2.623415</td>\n",
       "      <td>0.092292</td>\n",
       "      <td>12.571243</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-03-16 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.303333</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>2.706257</td>\n",
       "      <td>1.413167</td>\n",
       "      <td>17.711250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-03-17 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.376667</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.026667</td>\n",
       "      <td>2.958639</td>\n",
       "      <td>1.346417</td>\n",
       "      <td>16.791792</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-03-18 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.596667</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.696667</td>\n",
       "      <td>2.944711</td>\n",
       "      <td>0.430542</td>\n",
       "      <td>9.764688</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-03-25 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.210000</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.210000</td>\n",
       "      <td>2.782407</td>\n",
       "      <td>-0.239833</td>\n",
       "      <td>12.276826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-04-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.186667</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.186667</td>\n",
       "      <td>2.593604</td>\n",
       "      <td>0.043375</td>\n",
       "      <td>6.074569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-05-05 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.106667</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.106667</td>\n",
       "      <td>3.177058</td>\n",
       "      <td>0.338292</td>\n",
       "      <td>9.663542</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-04-19 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.476667</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.476667</td>\n",
       "      <td>2.698583</td>\n",
       "      <td>0.149292</td>\n",
       "      <td>6.186424</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-04-25 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>2.563971</td>\n",
       "      <td>0.425542</td>\n",
       "      <td>6.931958</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-04-28 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.376667</td>\n",
       "      <td>1.471644</td>\n",
       "      <td>0.308000</td>\n",
       "      <td>5.052812</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-08-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.483333</td>\n",
       "      <td>1.332111</td>\n",
       "      <td>-0.710625</td>\n",
       "      <td>12.707465</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2014-11-24 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>1.987531</td>\n",
       "      <td>-0.096125</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2012-02-16 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>2.417789</td>\n",
       "      <td>-0.064667</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2012-03-24 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>2.799064</td>\n",
       "      <td>-0.756708</td>\n",
       "      <td>10.150000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2012-03-09 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>0.115000</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.115000</td>\n",
       "      <td>2.646613</td>\n",
       "      <td>-0.940708</td>\n",
       "      <td>11.850000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2012-03-13 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.195000</td>\n",
       "      <td>2.511099</td>\n",
       "      <td>0.269375</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2012-03-16 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>2.483333</td>\n",
       "      <td>4.14</td>\n",
       "      <td>1.42</td>\n",
       "      <td>2.483333</td>\n",
       "      <td>1.345788</td>\n",
       "      <td>0.471708</td>\n",
       "      <td>5.318687</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Thunderstorm</td>\n",
       "      <td>2015-07-11 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>3.053333</td>\n",
       "      <td>1.86</td>\n",
       "      <td>0.72</td>\n",
       "      <td>4.358333</td>\n",
       "      <td>1.237133</td>\n",
       "      <td>1.257583</td>\n",
       "      <td>10.637542</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Rainy Monday</td>\n",
       "      <td>2014-09-08 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>2.146667</td>\n",
       "      <td>2.11</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.146667</td>\n",
       "      <td>1.025551</td>\n",
       "      <td>0.199708</td>\n",
       "      <td>4.494181</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Thunderstorm</td>\n",
       "      <td>2014-09-04 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>1.890000</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.890000</td>\n",
       "      <td>1.698442</td>\n",
       "      <td>0.223333</td>\n",
       "      <td>6.413535</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>unnamed</td>\n",
       "      <td>2014-07-24 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>2.286667</td>\n",
       "      <td>1.97</td>\n",
       "      <td>1.27</td>\n",
       "      <td>3.766667</td>\n",
       "      <td>1.413842</td>\n",
       "      <td>-0.213583</td>\n",
       "      <td>5.101632</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7/10 Thunderstorms</td>\n",
       "      <td>2014-07-10 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>3.853333</td>\n",
       "      <td>2.23</td>\n",
       "      <td>0.76</td>\n",
       "      <td>3.883333</td>\n",
       "      <td>2.367703</td>\n",
       "      <td>0.328958</td>\n",
       "      <td>6.531076</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Heavy Rain</td>\n",
       "      <td>2014-05-16 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>0.843333</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.843333</td>\n",
       "      <td>1.637858</td>\n",
       "      <td>0.317563</td>\n",
       "      <td>7.114194</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Thunderstorms</td>\n",
       "      <td>2014-06-19 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>3.140000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2.943333</td>\n",
       "      <td>0.869149</td>\n",
       "      <td>1.949806</td>\n",
       "      <td>19.630806</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Heavy Rain</td>\n",
       "      <td>2013-10-09 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.21</td>\n",
       "      <td>3.350000</td>\n",
       "      <td>1.341512</td>\n",
       "      <td>2.903833</td>\n",
       "      <td>23.504701</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Sandy</td>\n",
       "      <td>2012-10-28 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>7.820000</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.33</td>\n",
       "      <td>8.075000</td>\n",
       "      <td>1.033844</td>\n",
       "      <td>1.493500</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Irene</td>\n",
       "      <td>2011-08-27 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.646667</td>\n",
       "      <td>2.344772</td>\n",
       "      <td>0.143083</td>\n",
       "      <td>4.353889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>09/02/15</td>\n",
       "      <td>2015-09-02 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>1.480000</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.480000</td>\n",
       "      <td>1.339840</td>\n",
       "      <td>-0.495208</td>\n",
       "      <td>9.391160</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Thunderstorms</td>\n",
       "      <td>2014-07-09 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>1.580000</td>\n",
       "      <td>2.32</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.710000</td>\n",
       "      <td>3.091967</td>\n",
       "      <td>-0.218250</td>\n",
       "      <td>5.157444</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Thunderstorm</td>\n",
       "      <td>2016-07-19 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>3.547500</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.30</td>\n",
       "      <td>3.199167</td>\n",
       "      <td>3.198635</td>\n",
       "      <td>2.403806</td>\n",
       "      <td>12.475000</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Joaquin</td>\n",
       "      <td>2015-09-29 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>1.310000</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.310000</td>\n",
       "      <td>4.156397</td>\n",
       "      <td>-0.525167</td>\n",
       "      <td>7.875028</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>01/15/2016</td>\n",
       "      <td>2016-01-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>0.955000</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.36</td>\n",
       "      <td>1.152500</td>\n",
       "      <td>4.987574</td>\n",
       "      <td>0.535250</td>\n",
       "      <td>15.667056</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Jan Winter Weather</td>\n",
       "      <td>2016-01-20 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>1.622500</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.18</td>\n",
       "      <td>1.074167</td>\n",
       "      <td>4.853232</td>\n",
       "      <td>0.443903</td>\n",
       "      <td>12.403833</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>unnamed</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>0.832500</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.070000</td>\n",
       "      <td>4.438215</td>\n",
       "      <td>0.574625</td>\n",
       "      <td>12.273861</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>February 24th Storm</td>\n",
       "      <td>2016-02-24 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>2.050000</td>\n",
       "      <td>2.12</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.047500</td>\n",
       "      <td>3.462591</td>\n",
       "      <td>0.436750</td>\n",
       "      <td>9.801771</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Severe Weather - 6/5</td>\n",
       "      <td>2016-06-03 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>1.287500</td>\n",
       "      <td>2.10</td>\n",
       "      <td>0.84</td>\n",
       "      <td>3.307500</td>\n",
       "      <td>3.064679</td>\n",
       "      <td>0.280083</td>\n",
       "      <td>5.637431</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Thunderstorm</td>\n",
       "      <td>2016-07-31 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>3.230000</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.33</td>\n",
       "      <td>2.070000</td>\n",
       "      <td>2.635461</td>\n",
       "      <td>1.874437</td>\n",
       "      <td>25.102972</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>HERMINE</td>\n",
       "      <td>2016-09-02 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>2.865000</td>\n",
       "      <td>1.91</td>\n",
       "      <td>0.82</td>\n",
       "      <td>2.865000</td>\n",
       "      <td>2.901939</td>\n",
       "      <td>0.429250</td>\n",
       "      <td>6.435618</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>HEAVY RAIN</td>\n",
       "      <td>2016-09-19 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>7.612500</td>\n",
       "      <td>1.43</td>\n",
       "      <td>0.51</td>\n",
       "      <td>6.396500</td>\n",
       "      <td>4.794867</td>\n",
       "      <td>1.701608</td>\n",
       "      <td>22.095951</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Hurricane Matthew</td>\n",
       "      <td>2016-10-05 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>0.575000</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>2.788770</td>\n",
       "      <td>1.703764</td>\n",
       "      <td>9.681333</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Noreaster</td>\n",
       "      <td>2015-09-24 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>10.255000</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.67</td>\n",
       "      <td>8.240000</td>\n",
       "      <td>1.447289</td>\n",
       "      <td>1.111472</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Nicole</td>\n",
       "      <td>2010-09-30 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>501 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     rain_daily_sum  rain_hourly_max  rain_15_min_max  rain_prev_3_days  \\\n",
       "0          1.320000             0.32             0.12          1.345000   \n",
       "1          0.150000             0.22             0.13          0.993333   \n",
       "2          0.333333             0.44             0.34          1.500000   \n",
       "3          1.160000             1.18             0.97          1.166667   \n",
       "4          0.370000             0.28             0.12          0.370000   \n",
       "5          0.130000             0.16             0.10          0.130000   \n",
       "6          0.640000             0.38             0.24          0.640000   \n",
       "7          0.320000             0.12             0.11          0.320000   \n",
       "8          0.930000             0.67             0.29          1.250000   \n",
       "9          0.250000             0.12             0.07          1.500000   \n",
       "10         0.283333             0.63             0.36          0.533333   \n",
       "11         0.223333             0.52             0.47          2.123333   \n",
       "12         0.206667             0.60             0.48          0.206667   \n",
       "13         1.256667             3.30             2.70          1.256667   \n",
       "14         0.120000             0.13             0.12          0.120000   \n",
       "15         0.633333             0.24             0.09          0.633333   \n",
       "16         0.143333             0.20             0.12          0.776667   \n",
       "17         0.613333             0.41             0.20          0.613333   \n",
       "18         0.146667             0.19             0.07          0.146667   \n",
       "19         0.346667             0.09             0.03          0.346667   \n",
       "20         0.303333             0.05             0.02          0.650000   \n",
       "21         0.376667             0.09             0.03          1.026667   \n",
       "22         0.596667             0.11             0.03          0.696667   \n",
       "23         1.210000             0.51             0.26          1.210000   \n",
       "24         0.186667             0.12             0.06          0.186667   \n",
       "25         0.106667             0.05             0.03          0.106667   \n",
       "26         0.476667             0.49             0.22          0.476667   \n",
       "27         0.533333             0.40             0.18          0.533333   \n",
       "28         0.333333             1.00             0.90          0.376667   \n",
       "29         0.333333             0.33             0.18          0.483333   \n",
       "..              ...              ...              ...               ...   \n",
       "471        0.290000             0.12             0.04          0.290000   \n",
       "472        0.480000             0.14             0.08          0.525000   \n",
       "473        0.400000             0.23             0.10          0.400000   \n",
       "474        0.115000             0.08             0.03          0.115000   \n",
       "475        0.190000             0.18             0.10          0.195000   \n",
       "476        2.483333             4.14             1.42          2.483333   \n",
       "477        3.053333             1.86             0.72          4.358333   \n",
       "478        2.146667             2.11             0.77          2.146667   \n",
       "479        1.890000             1.30             0.64          1.890000   \n",
       "480        2.286667             1.97             1.27          3.766667   \n",
       "481        3.853333             2.23             0.76          3.883333   \n",
       "482        0.843333             1.13             0.54          0.843333   \n",
       "483        3.140000             0.50             0.22          2.943333   \n",
       "484        2.000000             0.58             0.21          3.350000   \n",
       "485        7.820000             1.22             0.33          8.075000   \n",
       "486        0.500000             0.62             0.47          0.646667   \n",
       "487        1.480000             1.28             0.63          1.480000   \n",
       "488        1.580000             2.32             1.05          1.710000   \n",
       "489        3.547500             0.75             0.30          3.199167   \n",
       "490        1.310000             1.09             0.33          1.310000   \n",
       "491        0.955000             0.70             0.36          1.152500   \n",
       "492        1.622500             0.57             0.18          1.074167   \n",
       "493        0.832500             0.64             0.28          1.070000   \n",
       "494        2.050000             2.12             1.06          1.047500   \n",
       "495        1.287500             2.10             0.84          3.307500   \n",
       "496        3.230000             0.87             0.33          2.070000   \n",
       "497        2.865000             1.91             0.82          2.865000   \n",
       "498        7.612500             1.43             0.51          6.396500   \n",
       "499        0.575000             0.15             0.08          0.433333   \n",
       "500       10.255000             1.59             0.67          8.240000   \n",
       "\n",
       "     gw_daily_avg  tide_daily_avg  wind_vel_daily_avg  num_flooded  flooded  \\\n",
       "0        2.441582        0.064875            4.722118          0.0        0   \n",
       "1        1.780793        0.531583            5.013368          0.0        0   \n",
       "2        1.781763        0.672500            5.478465          0.0        0   \n",
       "3        1.730518        0.718458            3.868646          0.0        0   \n",
       "4        2.017969        0.254292            6.382278          0.0        0   \n",
       "5        1.959165        0.519833            9.022451          0.0        0   \n",
       "6        1.494224        0.890292           12.598028          0.0        0   \n",
       "7        1.450218        0.103583            5.494833          0.0        0   \n",
       "8        1.573014        0.213417            4.443340          0.0        0   \n",
       "9        1.635707        0.291167            4.710569          0.0        0   \n",
       "10       1.688012        0.344708            4.480661          0.0        0   \n",
       "11       1.564464        0.116167            5.489625          0.0        0   \n",
       "12       1.728936       -0.230333            9.091146          0.0        0   \n",
       "13       1.685832        0.531917            6.389653          0.0        0   \n",
       "14       2.519349        0.541667            4.469125          0.0        0   \n",
       "15       3.117883       -0.589500            7.846125          0.0        0   \n",
       "16       3.373736       -0.024125           11.734056          0.0        0   \n",
       "17       2.786750        0.021625            6.281569          0.0        0   \n",
       "18       3.211371        0.320625           11.910382          0.0        0   \n",
       "19       2.623415        0.092292           12.571243          0.0        0   \n",
       "20       2.706257        1.413167           17.711250          0.0        0   \n",
       "21       2.958639        1.346417           16.791792          0.0        0   \n",
       "22       2.944711        0.430542            9.764688          0.0        0   \n",
       "23       2.782407       -0.239833           12.276826          0.0        0   \n",
       "24       2.593604        0.043375            6.074569          0.0        0   \n",
       "25       3.177058        0.338292            9.663542          0.0        0   \n",
       "26       2.698583        0.149292            6.186424          0.0        0   \n",
       "27       2.563971        0.425542            6.931958          0.0        0   \n",
       "28       1.471644        0.308000            5.052812          0.0        0   \n",
       "29       1.332111       -0.710625           12.707465          0.0        0   \n",
       "..            ...             ...                 ...          ...      ...   \n",
       "471      1.987531       -0.096125            4.400000          0.0        0   \n",
       "472      2.417789       -0.064667            5.400000          0.0        0   \n",
       "473      2.799064       -0.756708           10.150000          0.0        0   \n",
       "474      2.646613       -0.940708           11.850000          0.0        0   \n",
       "475      2.511099        0.269375            7.500000          0.0        0   \n",
       "476      1.345788        0.471708            5.318687          3.0        1   \n",
       "477      1.237133        1.257583           10.637542         21.0        1   \n",
       "478      1.025551        0.199708            4.494181          2.0        1   \n",
       "479      1.698442        0.223333            6.413535          8.0        1   \n",
       "480      1.413842       -0.213583            5.101632         27.0        1   \n",
       "481      2.367703        0.328958            6.531076         21.0        1   \n",
       "482      1.637858        0.317563            7.114194          5.0        1   \n",
       "483      0.869149        1.949806           19.630806          6.0        1   \n",
       "484      1.341512        2.903833           23.504701         45.0        1   \n",
       "485      1.033844        1.493500           31.000000         32.0        1   \n",
       "486      2.344772        0.143083            4.353889          1.0        1   \n",
       "487      1.339840       -0.495208            9.391160          1.0        1   \n",
       "488      3.091967       -0.218250            5.157444          8.0        1   \n",
       "489      3.198635        2.403806           12.475000         54.0        1   \n",
       "490      4.156397       -0.525167            7.875028          1.0        1   \n",
       "491      4.987574        0.535250           15.667056          9.0        1   \n",
       "492      4.853232        0.443903           12.403833          2.0        1   \n",
       "493      4.438215        0.574625           12.273861          1.0        1   \n",
       "494      3.462591        0.436750            9.801771          3.0        1   \n",
       "495      3.064679        0.280083            5.637431          1.0        1   \n",
       "496      2.635461        1.874437           25.102972         35.0        1   \n",
       "497      2.901939        0.429250            6.435618          4.0        1   \n",
       "498      4.794867        1.701608           22.095951         17.0        1   \n",
       "499      2.788770        1.703764            9.681333          7.0        1   \n",
       "500      1.447289        1.111472           13.200000         48.0        1   \n",
       "\n",
       "               event_name           event_date  \n",
       "0                    None  2013-04-29 00:00:00  \n",
       "1                    None  2014-06-21 00:00:00  \n",
       "2                    None  2014-06-13 00:00:00  \n",
       "3                    None  2014-06-12 00:00:00  \n",
       "4                    None  2014-05-28 00:00:00  \n",
       "5                    None  2014-06-05 00:00:00  \n",
       "6                    None  2014-07-04 00:00:00  \n",
       "7                    None  2014-08-01 00:00:00  \n",
       "8                    None  2014-08-02 00:00:00  \n",
       "9                    None  2014-08-03 00:00:00  \n",
       "10                   None  2014-08-05 00:00:00  \n",
       "11                   None  2014-07-26 00:00:00  \n",
       "12                   None  2014-07-15 00:00:00  \n",
       "13                   None  2014-08-12 00:00:00  \n",
       "14                   None  2014-05-22 00:00:00  \n",
       "15                   None  2014-03-29 00:00:00  \n",
       "16                   None  2014-03-30 00:00:00  \n",
       "17                   None  2014-04-07 00:00:00  \n",
       "18                   None  2014-03-12 00:00:00  \n",
       "19                   None  2014-03-16 00:00:00  \n",
       "20                   None  2014-03-17 00:00:00  \n",
       "21                   None  2014-03-18 00:00:00  \n",
       "22                   None  2014-03-25 00:00:00  \n",
       "23                   None  2014-04-15 00:00:00  \n",
       "24                   None  2014-05-05 00:00:00  \n",
       "25                   None  2014-04-19 00:00:00  \n",
       "26                   None  2014-04-25 00:00:00  \n",
       "27                   None  2014-04-28 00:00:00  \n",
       "28                   None  2014-08-15 00:00:00  \n",
       "29                   None  2014-11-24 00:00:00  \n",
       "..                    ...                  ...  \n",
       "471                  None  2012-02-16 00:00:00  \n",
       "472                  None  2012-03-24 00:00:00  \n",
       "473                  None  2012-03-09 00:00:00  \n",
       "474                  None  2012-03-13 00:00:00  \n",
       "475                  None  2012-03-16 00:00:00  \n",
       "476          Thunderstorm  2015-07-11 00:00:00  \n",
       "477          Rainy Monday  2014-09-08 00:00:00  \n",
       "478          Thunderstorm  2014-09-04 00:00:00  \n",
       "479               unnamed  2014-07-24 00:00:00  \n",
       "480    7/10 Thunderstorms  2014-07-10 00:00:00  \n",
       "481            Heavy Rain  2014-05-16 00:00:00  \n",
       "482         Thunderstorms  2014-06-19 00:00:00  \n",
       "483            Heavy Rain  2013-10-09 00:00:00  \n",
       "484                 Sandy  2012-10-28 00:00:00  \n",
       "485                 Irene  2011-08-27 00:00:00  \n",
       "486              09/02/15  2015-09-02 00:00:00  \n",
       "487         Thunderstorms  2014-07-09 00:00:00  \n",
       "488          Thunderstorm  2016-07-19 00:00:00  \n",
       "489               Joaquin  2015-09-29 00:00:00  \n",
       "490            01/15/2016  2016-01-15 00:00:00  \n",
       "491    Jan Winter Weather  2016-01-20 00:00:00  \n",
       "492               unnamed  2016-02-01 00:00:00  \n",
       "493   February 24th Storm  2016-02-24 00:00:00  \n",
       "494  Severe Weather - 6/5  2016-06-03 00:00:00  \n",
       "495          Thunderstorm  2016-07-31 00:00:00  \n",
       "496               HERMINE  2016-09-02 00:00:00  \n",
       "497            HEAVY RAIN  2016-09-19 00:00:00  \n",
       "498     Hurricane Matthew  2016-10-05 00:00:00  \n",
       "499             Noreaster  2015-09-24 00:00:00  \n",
       "500                Nicole  2010-09-30 00:00:00  \n",
       "\n",
       "[501 rows x 11 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "pca = PCA()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.32173283,  0.29104509,  0.20360802,  0.10561506,  0.05775536,\n",
       "        0.01666304,  0.0035806 ])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pca.fit(feat_df_norm)\n",
    "p.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.612"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ".321+.291"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "476"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_vis = pca.fit_transform(feat_df)\n",
    "df_pca = pd.DataFrame(p_vis)\n",
    "df_filt[df_filt.flooded == 1].index.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SCoVCBIPB4u+VUlit0Vc7uu+++5g4cSK33HILl112GT/++CMPPPAA33//PUuWLMFisWAY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\nMAwjFpjCMgzDMGKBKSzDMAwjFvw/XPFRkQk8g+EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1376e128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %matplotlib notebook \n",
    "fig = plt.figure()\n",
    "\n",
    "#uncomment if you want 3d plot\n",
    "# ax = fig.add_subplot(111, projection='3d')\n",
    "# nfl = ax.scatter(df_pca.loc[:476,0], df_pca.loc[:476, 1], df_pca.loc[:476, 2], c='#4286f4', label='Non-flooded', s=30)\n",
    "# fl = ax.scatter(df_pca.loc[476:,0], df_pca.loc[476:, 1], df_pca.loc[476:, 2], c='#ed2f1a', s=30+df_filt.loc[476:, 'num_flooded']*6, label='Flooded')\n",
    "# ax.set_zlabel('Principal Component 3',  fontsize=16)\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "nfl = ax.scatter(df_pca.loc[:476,0], df_pca.loc[:476, 1], c='#4286f4', label='Non-flooded', s=30)\n",
    "fl = ax.scatter(df_pca.loc[476:,0], df_pca.loc[476:, 1], c='#ed2f1a', s=30+df_filt.loc[476:, 'num_flooded']*6, label='Flooded')\n",
    "\n",
    "ax.set_ylabel('Principal Component 2', fontsize=16)\n",
    "ax.set_xlabel('Principal Component 1',  fontsize=16)\n",
    "# ax.set_xticks(())\n",
    "# ax.set_yticks(())\n",
    "ax.tick_params(labelsize=16)\n",
    "legend = ax.legend(fontsize=16, handler_map={fl: HandlerPathCollection(sizes=[100]), nfl: HandlerPathCollection(sizes=[100])})\n",
    "fig.tight_layout()\n",
    "fig.savefig('{}pc_wind.png'.format(fig_dir), dpi=300)\n",
    "# %matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "I'm interested in the non-flooding points that are far away from the rest of the group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([20, 21, 34, 57, 126, 293, 323, 333, 340, 360, 461, 463, 483, 484,\n",
       "            485, 496, 498],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_pc1 = df_pca[df_pca[0]>8].index\n",
    "high_pc1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def plot_features(record_indicies, df):\n",
    "    ax = df.iloc[record_indicies, range(8)].plot.bar(fontsize=14, rot=15, legend=False, figsize=(7, 5.5))\n",
    "    patches, labels = ax.get_legend_handles_labels()\n",
    "    label_units = ([' (in)', ' (in)', ' (in)', ' (in)', ' (ft)', ' (ft)', '(deg)', '(mph)'])\n",
    "    labels = map(add, labels, label_units)\n",
    "    ax.legend(patches, labels, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., fontsize=14)\n",
    "    print df.loc[record_indicies]\n",
    "    ax.set_xticklabels(\n",
    "        df.loc[record_indicies, 'event_name']+\"\\n\"+ \"(\" +  df.loc[record_indicies, 'num_flooded'].astype(str)+\")\"\n",
    "    )\n",
    "    ax.set_xlabel('Event name \\n (# locations flooded)', fontsize=14)\n",
    "    fig  = plt.gcf()\n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "rain_df = get_table_for_variable('rainfall')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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o9FxEfLjBcZmZdYRcLWFJRwE/AWaRVrz4LWnBzjVIC2yamVkf5O2O+BowISKOA94EfhoR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\nLcqsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1162bc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax2 = rain_df['2014-08-18'].groupby('SiteID').sum()['Value'].plot.bar(figsize=(5,4), fontsize=14, title='2014-08-18')\n",
    "ax2.set_ylabel('Daily rainfall (in)')\n",
    "ax2.yaxis.label.set_size(14)\n",
    "ax2.xaxis.label.set_size(14)\n",
    "ax2.title.set_size(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     rain_daily_sum  rain_hourly_max  rain_15_min_max  rain_prev_3_days  \\\n",
      "48         7.510000             9.30             3.50          8.826667   \n",
      "500       10.255000             1.59             0.67          8.240000   \n",
      "163        4.335000             1.47             0.60          9.502500   \n",
      "485        7.820000             1.22             0.33          8.075000   \n",
      "498        7.612500             1.43             0.51          6.396500   \n",
      "481        3.853333             2.23             0.76          3.883333   \n",
      "\n",
      "     gw_daily_avg  tide_daily_avg  wind_vel_daily_avg  num_flooded  flooded  \\\n",
      "48       1.487029        0.284375            3.571062          0.0        0   \n",
      "500      1.447289        1.111472           13.200000         48.0        1   \n",
      "163      3.432301        0.956542           14.254889          0.0        0   \n",
      "485      1.033844        1.493500           31.000000         32.0        1   \n",
      "498      4.794867        1.701608           22.095951         17.0        1   \n",
      "481      2.367703        0.328958            6.531076         21.0        1   \n",
      "\n",
      "            event_name           event_date  \n",
      "48                None  2014-08-18 00:00:00  \n",
      "500             Nicole  2010-09-30 00:00:00  \n",
      "163               None  2016-09-21 00:00:00  \n",
      "485              Irene  2011-08-27 00:00:00  \n",
      "498  Hurricane Matthew  2016-10-05 00:00:00  \n",
      "481         Heavy Rain  2014-05-16 00:00:00  \n"
     ]
    },
    {
     "data": {
      "image/png": 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U9T9CyJdq7VYAYAIAJ1UNFEU1E0JSAWAMAET3SqQIIYQQeqEJBALJzp0776q3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21a4fb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "high_pc2 = df_pca.sort_values(1, ascending=False).index[:6]\n",
    "plot_features(high_pc2, df_filt)\n",
    "# df_filt.loc[high_pc2, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rain_daily_sum</th>\n",
       "      <th>rain_hourly_max</th>\n",
       "      <th>rain_15_min_max</th>\n",
       "      <th>rain_prev_3_days</th>\n",
       "      <th>gw_daily_avg</th>\n",
       "      <th>tide_daily_avg</th>\n",
       "      <th>wind_vel_daily_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.320000</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.345000</td>\n",
       "      <td>2.441582</td>\n",
       "      <td>0.064875</td>\n",
       "      <td>4.722118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.993333</td>\n",
       "      <td>1.780793</td>\n",
       "      <td>0.531583</td>\n",
       "      <td>5.013368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.781763</td>\n",
       "      <td>0.672500</td>\n",
       "      <td>5.478465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.160000</td>\n",
       "      <td>1.18</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>1.730518</td>\n",
       "      <td>0.718458</td>\n",
       "      <td>3.868646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>2.017969</td>\n",
       "      <td>0.254292</td>\n",
       "      <td>6.382278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rain_daily_sum  rain_hourly_max  rain_15_min_max  rain_prev_3_days  \\\n",
       "0        1.320000             0.32             0.12          1.345000   \n",
       "1        0.150000             0.22             0.13          0.993333   \n",
       "2        0.333333             0.44             0.34          1.500000   \n",
       "3        1.160000             1.18             0.97          1.166667   \n",
       "4        0.370000             0.28             0.12          0.370000   \n",
       "\n",
       "   gw_daily_avg  tide_daily_avg  wind_vel_daily_avg  \n",
       "0      2.441582        0.064875            4.722118  \n",
       "1      1.780793        0.531583            5.013368  \n",
       "2      1.781763        0.672500            5.478465  \n",
       "3      1.730518        0.718458            3.868646  \n",
       "4      2.017969        0.254292            6.382278  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_pca[8] = df_filt['flooded']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-3.650310</td>\n",
       "      <td>0.868829</td>\n",
       "      <td>0.402703</td>\n",
       "      <td>-0.235043</td>\n",
       "      <td>-0.371763</td>\n",
       "      <td>0.511301</td>\n",
       "      <td>-0.016937</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-3.450532</td>\n",
       "      <td>-0.044589</td>\n",
       "      <td>-0.307173</td>\n",
       "      <td>-0.668219</td>\n",
       "      <td>0.080860</td>\n",
       "      <td>-0.175094</td>\n",
       "      <td>-0.028744</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.945922</td>\n",
       "      <td>0.506854</td>\n",
       "      <td>-0.301886</td>\n",
       "      <td>-0.704097</td>\n",
       "      <td>0.171045</td>\n",
       "      <td>-0.378246</td>\n",
       "      <td>0.069011</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-4.531490</td>\n",
       "      <td>1.209790</td>\n",
       "      <td>-0.356520</td>\n",
       "      <td>0.065220</td>\n",
       "      <td>0.797327</td>\n",
       "      <td>0.143797</td>\n",
       "      <td>0.328832</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.120103</td>\n",
       "      <td>-0.482808</td>\n",
       "      <td>-0.174204</td>\n",
       "      <td>-0.087516</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.185796</td>\n",
       "      <td>-0.048513</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  8\n",
       "0 -3.650310  0.868829  0.402703 -0.235043 -0.371763  0.511301 -0.016937  0\n",
       "1 -3.450532 -0.044589 -0.307173 -0.668219  0.080860 -0.175094 -0.028744  0\n",
       "2 -2.945922  0.506854 -0.301886 -0.704097  0.171045 -0.378246  0.069011  0\n",
       "3 -4.531490  1.209790 -0.356520  0.065220  0.797327  0.143797  0.328832  0\n",
       "4 -2.120103 -0.482808 -0.174204 -0.087516  0.071170  0.185796 -0.048513  0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = tree.DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clf = clf.fit(df_filt.iloc[:, :7], df_filt['flooded'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('flood.dot', 'w') as f:\n",
    "     f = tree.export_graphviz(clf, out_file=f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rain_daily_sum</th>\n",
       "      <th>rain_hourly_max</th>\n",
       "      <th>rain_15_min_max</th>\n",
       "      <th>rain_prev_3_days</th>\n",
       "      <th>gw_daily_avg</th>\n",
       "      <th>tide_daily_avg</th>\n",
       "      <th>wind_vel_daily_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.320000</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.345000</td>\n",
       "      <td>2.441582</td>\n",
       "      <td>0.064875</td>\n",
       "      <td>4.722118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.993333</td>\n",
       "      <td>1.780793</td>\n",
       "      <td>0.531583</td>\n",
       "      <td>5.013368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.781763</td>\n",
       "      <td>0.672500</td>\n",
       "      <td>5.478465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.160000</td>\n",
       "      <td>1.18</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>1.730518</td>\n",
       "      <td>0.718458</td>\n",
       "      <td>3.868646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>2.017969</td>\n",
       "      <td>0.254292</td>\n",
       "      <td>6.382278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rain_daily_sum  rain_hourly_max  rain_15_min_max  rain_prev_3_days  \\\n",
       "0        1.320000             0.32             0.12          1.345000   \n",
       "1        0.150000             0.22             0.13          0.993333   \n",
       "2        0.333333             0.44             0.34          1.500000   \n",
       "3        1.160000             1.18             0.97          1.166667   \n",
       "4        0.370000             0.28             0.12          0.370000   \n",
       "\n",
       "   gw_daily_avg  tide_daily_avg  wind_vel_daily_avg  \n",
       "0      2.441582        0.064875            4.722118  \n",
       "1      1.780793        0.531583            5.013368  \n",
       "2      1.781763        0.672500            5.478465  \n",
       "3      1.730518        0.718458            3.868646  \n",
       "4      2.017969        0.254292            6.382278  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filt.iloc[:, :7].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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hKqqqrVGhsREVEdx+QaIiIiIiIiqnumeb6Hv84ex/+mmkNNtfq2M2ByTe2Jqa7IfVmI+Yf+Qnhy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ZGR6CiFqmvXrpg9ezYmTZoEHx8f0XGIiBRCWFgY5s6di99//x316tUTHUfmscCGiKjgsLiG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\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A5x1/oUsrHXgNMpO7sAbg+RFRdbx8GInQ7+QvPK6KSa+hSt8HUV3FzjVEREQkuMo6uWz4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\nHgjydjba35bVWvZfLIdc5i4u+QQ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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = tree.export_graphviz(clf, out_file=None, \n",
    "                         feature_names=df_filt.iloc[:, :7].columns,  \n",
    "                         filled=True, rounded=True,  \n",
    "                         special_characters=True,\n",
    "                               )  \n",
    "graph = pydotplus.graph_from_dot_data(dot_data)  \n",
    "Image(graph.create_png()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}