Autocorrelation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas.compat import lmap\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def autocorrelation_plot(series, n_samples=None, ax=None, **kwds):\n",
    "    \"\"\"Autocorrelation plot for time series.\n",
    "\n",
    "    Parameters:\n",
    "    -----------\n",
    "    series: Time series\n",
    "    ax: Matplotlib axis object, optional\n",
    "    kwds : keywords\n",
    "        Options to pass to matplotlib plotting method\n",
    "\n",
    "    Returns:\n",
    "    -----------\n",
    "    ax: Matplotlib axis object\n",
    "    \"\"\"\n",
    "    import matplotlib.pyplot as plt\n",
    "    n = len(series)\n",
    "    data = np.asarray(series)\n",
    "    if ax is None:\n",
    "        ax = plt.gca(xlim=(1, n_samples), ylim=(-1.0, 1.0))\n",
    "    mean = np.mean(data)\n",
    "    c0 = np.sum((data - mean) ** 2) / float(n)\n",
    "\n",
    "    def r(h):\n",
    "        return ((data[:n - h] - mean) *\n",
    "                (data[h:] - mean)).sum() / float(n) / c0\n",
    "    x = (np.arange(n) + 1).astype(int)\n",
    "    y = lmap(r, x)\n",
    "    z95 = 1.959963984540054\n",
    "    z99 = 2.5758293035489004\n",
    "    ax.axhline(y=z99 / np.sqrt(n), linestyle='--', color='grey')\n",
    "    ax.axhline(y=z95 / np.sqrt(n), color='grey')\n",
    "    ax.axhline(y=0.0, color='black')\n",
    "    ax.axhline(y=-z95 / np.sqrt(n), color='grey')\n",
    "    ax.axhline(y=-z99 / np.sqrt(n), linestyle='--', color='grey')\n",
    "    ax.set_xlabel(\"Lag\")\n",
    "    ax.set_ylabel(\"Autocorrelation\")\n",
    "    if n_samples:\n",
    "        ax.plot(x[:n_samples], y[:n_samples], **kwds)\n",
    "    else:\n",
    "        ax.plot(x, y, **kwds)\n",
    "    if 'label' in kwds:\n",
    "        ax.legend()\n",
    "    ax.grid()\n",
    "    return ax\n",
    "def autocorr(x):\n",
    "    n = x.size\n",
    "    norm = (x - np.mean(x))\n",
    "    result = np.correlate(norm, norm, mode='same')\n",
    "    acorr = result[n//2 + 1:] / (x.var() * np.arange(n-1, n//2, -1))\n",
    "    lag = np.abs(acorr).argmax() + 1\n",
    "    r = acorr[lag-1]        \n",
    "    if np.abs(r) > 0.5:\n",
    "      print('Appears to be autocorrelated with r = {}, lag = {}'. format(r, lag))\n",
    "    else: \n",
    "      print('Appears to be not autocorrelated')\n",
    "    return r, lag\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "droughts=pd.read_csv('droughts.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "water_usage=pd.read_csv('water_usage.csv')\n",
    "water_usage=water_usage[water_usage['year']==2010]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "droughts['date']=pd.to_datetime(droughts['valid_end'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "droughts['year']=droughts.date.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "merge=droughts.merge(water_usage,left_on=['fips'],right_on=['fips'],how='inner')\n",
    "merge['state']=merge['state_x']\n",
    "merge['county']=merge['county_x']\n",
    "merge['year']=merge['year_x']\n",
    "del(merge['state_y'])\n",
    "del(merge['county_y'])\n",
    "del(merge['state_x'])\n",
    "del(merge['county_x'])\n",
    "del(merge['year_x'])\n",
    "del(merge['year_y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "672144"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge=merge[(merge['year']>2010)&(merge['year']<2015)]\n",
    "len(merge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fips',\n",
       " 'none',\n",
       " 'd0',\n",
       " 'd1',\n",
       " 'd2',\n",
       " 'd3',\n",
       " 'd4',\n",
       " 'valid_start',\n",
       " 'valid_end',\n",
       " 'date',\n",
       " 'state_fips',\n",
       " 'county_fips',\n",
       " 'population',\n",
       " 'pub_sup_1',\n",
       " 'pub_sup_2',\n",
       " 'pub_sup_3',\n",
       " 'pub_sup_4',\n",
       " 'pub_sup_5',\n",
       " 'pub_sup_6',\n",
       " 'pub_sup_7',\n",
       " 'pub_sup_8',\n",
       " 'pub_sup_9',\n",
       " 'pub_sup_10',\n",
       " 'pub_sup_11',\n",
       " 'pub_sup_12',\n",
       " 'dom_sup_1',\n",
       " 'dom_sup_2',\n",
       " 'dom_sup_3',\n",
       " 'dom_sup_4',\n",
       " 'dom_sup_5',\n",
       " 'dom_sup_6',\n",
       " 'dom_sup_7',\n",
       " 'dom_sup_8',\n",
       " 'ind_1',\n",
       " 'ind_2',\n",
       " 'ind_3',\n",
       " 'ind_4',\n",
       " 'ind_5',\n",
       " 'ind_6',\n",
       " 'ind_7',\n",
       " 'ind_8',\n",
       " 'ind_9',\n",
       " 'irrigation_1',\n",
       " 'irrigation_2',\n",
       " 'irrigation_3',\n",
       " 'irrigation_4',\n",
       " 'irrigation_5',\n",
       " 'irrigation_6',\n",
       " 'irrigation_7',\n",
       " 'crop_1',\n",
       " 'crop_2',\n",
       " 'crop_3',\n",
       " 'crop_4',\n",
       " 'crop_5',\n",
       " 'crop_6',\n",
       " 'crop_7',\n",
       " 'golf_1',\n",
       " 'golf_2',\n",
       " 'golf_3',\n",
       " 'golf_4',\n",
       " 'golf_5',\n",
       " 'golf_6',\n",
       " 'golf_7',\n",
       " 'livestock_1',\n",
       " 'livestock_2',\n",
       " 'livestock_3',\n",
       " 'aqua_1',\n",
       " 'aqua_2',\n",
       " 'aqua_3',\n",
       " 'aqua_4',\n",
       " 'aqua_5',\n",
       " 'aqua_6',\n",
       " 'aqua_7',\n",
       " 'aqua_8',\n",
       " 'aqua_9',\n",
       " 'mining_1',\n",
       " 'mining_2',\n",
       " 'mining_3',\n",
       " 'mining_4',\n",
       " 'mining_5',\n",
       " 'mining_6',\n",
       " 'mining_7',\n",
       " 'mining_8',\n",
       " 'mining_9',\n",
       " 'thermoelectric_1',\n",
       " 'thermoelectric_2',\n",
       " 'thermoelectric_3',\n",
       " 'thermoelectric_4',\n",
       " 'thermoelectric_5',\n",
       " 'thermoelectric_6',\n",
       " 'thermoelectric_7',\n",
       " 'thermoelectric_8',\n",
       " 'thermoelectric_9',\n",
       " 'thermoelectric_10',\n",
       " 'thermoelectric_11',\n",
       " 'thermoelectric_12',\n",
       " 'thermoelectric_13',\n",
       " 'thermoelectric_14',\n",
       " 'thermoelectric_15',\n",
       " 'thermoelectric_16',\n",
       " 'thermoelectric_17',\n",
       " 'thermoelectric_18',\n",
       " 'thermoelectric_19',\n",
       " 'thermoelectric_20',\n",
       " 'thermoelectric_21',\n",
       " 'thermoelectric_22',\n",
       " 'thermoelectric_23',\n",
       " 'thermoelectric_24',\n",
       " 'thermoelectric_25',\n",
       " 'thermoelectric_26',\n",
       " 'thermoelectric_27',\n",
       " 'thermoelectric_28',\n",
       " 'thermoelectric_29',\n",
       " 'thermoelectric_30',\n",
       " 'gro_wat_1',\n",
       " 'gro_wat_2',\n",
       " 'gro_wat_3',\n",
       " 'surf_wat_1',\n",
       " 'surf_wat_2',\n",
       " 'surf_wat_3',\n",
       " 'total_withdrawal_1',\n",
       " 'total_withdrawal_2',\n",
       " 'total_withdrawal_3',\n",
       " 'state',\n",
       " 'county',\n",
       " 'year']"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_list=list(merge.columns.values)\n",
    "col_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "49\n",
      "50\n",
      "51\n",
      "52\n"
     ]
    }
   ],
   "source": [
    "#a=droughts[droughts['none']==100]\n",
    "#aa=droughts.drop(a.index)\n",
    "#california\n",
    "#len(aa)\n",
    "\n",
    "length=52\n",
    "auto_gpby_list=[]\n",
    "for i in range(1,length+1):\n",
    "    print(i)\n",
    "    def df_autocorr(df):\n",
    "        lag=i\n",
    "        axis=0\n",
    "        \"\"\"Compute full-sample column-wise autocorrelation for a DataFrame.\"\"\"\n",
    "        #return df.apply(lambda col: col.autocorr(lag), axis=axis)\n",
    "        return df.apply(lambda col: col.autocorr(lag))\n",
    "\n",
    "    auto_gpby_list.append(droughts.groupby('fips')[['none','d0','d1','d2','d3','d4']].apply(df_autocorr))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>none</th>\n",
       "      <th>d0</th>\n",
       "      <th>d1</th>\n",
       "      <th>d2</th>\n",
       "      <th>d3</th>\n",
       "      <th>d4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fips</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>0.900615</td>\n",
       "      <td>0.792416</td>\n",
       "      <td>0.811994</td>\n",
       "      <td>0.787235</td>\n",
       "      <td>0.846301</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>0.889535</td>\n",
       "      <td>0.754276</td>\n",
       "      <td>0.854555</td>\n",
       "      <td>0.808659</td>\n",
       "      <td>0.785882</td>\n",
       "      <td>0.768372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>0.924384</td>\n",
       "      <td>0.816283</td>\n",
       "      <td>0.753073</td>\n",
       "      <td>0.905808</td>\n",
       "      <td>0.942767</td>\n",
       "      <td>0.912636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007</th>\n",
       "      <td>0.910035</td>\n",
       "      <td>0.740478</td>\n",
       "      <td>0.818719</td>\n",
       "      <td>0.814942</td>\n",
       "      <td>0.714539</td>\n",
       "      <td>0.766186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>0.905325</td>\n",
       "      <td>0.711176</td>\n",
       "      <td>0.799625</td>\n",
       "      <td>0.871034</td>\n",
       "      <td>0.715845</td>\n",
       "      <td>0.922439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          none        d0        d1        d2        d3        d4\n",
       "fips                                                            \n",
       "1001  0.900615  0.792416  0.811994  0.787235  0.846301       NaN\n",
       "1003  0.889535  0.754276  0.854555  0.808659  0.785882  0.768372\n",
       "1005  0.924384  0.816283  0.753073  0.905808  0.942767  0.912636\n",
       "1007  0.910035  0.740478  0.818719  0.814942  0.714539  0.766186\n",
       "1009  0.905325  0.711176  0.799625  0.871034  0.715845  0.922439"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_gpby_list[0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>none</th>\n",
       "      <th>d0</th>\n",
       "      <th>d1</th>\n",
       "      <th>d2</th>\n",
       "      <th>d3</th>\n",
       "      <th>d4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fips</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>0.797171</td>\n",
       "      <td>0.590577</td>\n",
       "      <td>0.637484</td>\n",
       "      <td>0.561229</td>\n",
       "      <td>0.660757</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>0.781615</td>\n",
       "      <td>0.526017</td>\n",
       "      <td>0.717902</td>\n",
       "      <td>0.679866</td>\n",
       "      <td>0.597362</td>\n",
       "      <td>0.488254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>0.856076</td>\n",
       "      <td>0.643758</td>\n",
       "      <td>0.575193</td>\n",
       "      <td>0.844304</td>\n",
       "      <td>0.903185</td>\n",
       "      <td>0.855867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007</th>\n",
       "      <td>0.819830</td>\n",
       "      <td>0.600350</td>\n",
       "      <td>0.696897</td>\n",
       "      <td>0.661724</td>\n",
       "      <td>0.536975</td>\n",
       "      <td>0.525511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>0.810181</td>\n",
       "      <td>0.456772</td>\n",
       "      <td>0.608465</td>\n",
       "      <td>0.726973</td>\n",
       "      <td>0.398987</td>\n",
       "      <td>0.823262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          none        d0        d1        d2        d3        d4\n",
       "fips                                                            \n",
       "1001  0.797171  0.590577  0.637484  0.561229  0.660757       NaN\n",
       "1003  0.781615  0.526017  0.717902  0.679866  0.597362  0.488254\n",
       "1005  0.856076  0.643758  0.575193  0.844304  0.903185  0.855867\n",
       "1007  0.819830  0.600350  0.696897  0.661724  0.536975  0.525511\n",
       "1009  0.810181  0.456772  0.608465  0.726973  0.398987  0.823262"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_gpby_list[1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,10):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "resp_list=['none','d0','d1','d2','d3','d4']\n",
    "avg_auto_list=[]\n",
    "for lag in range(length):\n",
    "    tmp={}\n",
    "    for col in resp_list:\n",
    "        #print 'average autocorrelation of '+col+': '+  str(gpby[each].mean(axis=0))\n",
    "        tmp[col]=auto_gpby_list[lag][col].mean(axis=0)\n",
    "        \n",
    "    avg_auto_list.append(tmp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_auto_df=pd.DataFrame(avg_auto_list)\n",
    "average_auto_df.index=average_auto_df.index+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#df.plot(color=[color_dict.get(x, '#333333') for x in df.columns])\n",
    "plt.style.use('ggplot')\n",
    "ax=average_auto_df.plot(figsize=(14,10))\n",
    "ax.set_ylabel(\"Autocorrelation\")\n",
    "ax.set_xlabel(\"Number of Weeks Lag\")\n",
    "plt.title('Autocorrelation Plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fips</th>\n",
       "      <th>county</th>\n",
       "      <th>state</th>\n",
       "      <th>none</th>\n",
       "      <th>d0</th>\n",
       "      <th>d1</th>\n",
       "      <th>d2</th>\n",
       "      <th>d3</th>\n",
       "      <th>d4</th>\n",
       "      <th>valid_start</th>\n",
       "      <th>valid_end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-12-26</td>\n",
       "      <td>2018-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-12-19</td>\n",
       "      <td>2017-12-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-12-12</td>\n",
       "      <td>2017-12-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-12-05</td>\n",
       "      <td>2017-12-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-28</td>\n",
       "      <td>2017-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-21</td>\n",
       "      <td>2017-11-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-14</td>\n",
       "      <td>2017-11-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-07</td>\n",
       "      <td>2017-11-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-10-31</td>\n",
       "      <td>2017-11-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-10-24</td>\n",
       "      <td>2017-10-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-10-17</td>\n",
       "      <td>2017-10-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-10-10</td>\n",
       "      <td>2017-10-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-10-03</td>\n",
       "      <td>2017-10-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-09-26</td>\n",
       "      <td>2017-10-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-09-19</td>\n",
       "      <td>2017-09-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-09-12</td>\n",
       "      <td>2017-09-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-09-05</td>\n",
       "      <td>2017-09-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-29</td>\n",
       "      <td>2017-09-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>2017-08-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>2017-08-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-08</td>\n",
       "      <td>2017-08-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>2017-08-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-07-25</td>\n",
       "      <td>2017-07-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-07-18</td>\n",
       "      <td>2017-07-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-07-11</td>\n",
       "      <td>2017-07-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-07-04</td>\n",
       "      <td>2017-07-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>2017-07-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-06-20</td>\n",
       "      <td>2017-06-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-06-13</td>\n",
       "      <td>2017-06-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-06-06</td>\n",
       "      <td>2017-06-12</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>388</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-07-20</td>\n",
       "      <td>2010-07-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-07-13</td>\n",
       "      <td>2010-07-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-07-06</td>\n",
       "      <td>2010-07-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-06-29</td>\n",
       "      <td>2010-07-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-06-22</td>\n",
       "      <td>2010-06-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-06-15</td>\n",
       "      <td>2010-06-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-06-08</td>\n",
       "      <td>2010-06-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-06-01</td>\n",
       "      <td>2010-06-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-05-25</td>\n",
       "      <td>2010-05-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-05-18</td>\n",
       "      <td>2010-05-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-05-11</td>\n",
       "      <td>2010-05-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-05-04</td>\n",
       "      <td>2010-05-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-04-27</td>\n",
       "      <td>2010-05-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-04-20</td>\n",
       "      <td>2010-04-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-04-13</td>\n",
       "      <td>2010-04-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-04-06</td>\n",
       "      <td>2010-04-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-03-30</td>\n",
       "      <td>2010-04-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-03-23</td>\n",
       "      <td>2010-03-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-03-16</td>\n",
       "      <td>2010-03-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-03-09</td>\n",
       "      <td>2010-03-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-03-02</td>\n",
       "      <td>2010-03-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-02-23</td>\n",
       "      <td>2010-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-02-16</td>\n",
       "      <td>2010-02-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-02-09</td>\n",
       "      <td>2010-02-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-02-02</td>\n",
       "      <td>2010-02-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-01-26</td>\n",
       "      <td>2010-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-01-19</td>\n",
       "      <td>2010-01-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-01-12</td>\n",
       "      <td>2010-01-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>2010-01-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>2013</td>\n",
       "      <td>Aleutians East Borough</td>\n",
       "      <td>AK</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009-12-29</td>\n",
       "      <td>2010-01-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     fips                  county state   none   d0   d1   d2   d3   d4  \\\n",
       "0    2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "1    2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2    2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "3    2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4    2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "5    2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "6    2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "7    2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "8    2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "9    2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "10   2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "11   2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "12   2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "13   2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "14   2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "15   2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "16   2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "17   2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "18   2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "19   2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "20   2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "21   2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "22   2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "23   2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "24   2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "25   2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "26   2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "27   2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "28   2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "29   2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "..    ...                     ...   ...    ...  ...  ...  ...  ...  ...   \n",
       "388  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "389  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "390  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "391  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "392  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "393  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "394  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "395  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "396  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "397  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "398  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "399  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "400  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "401  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "402  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "403  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "404  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "405  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "406  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "407  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "408  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "409  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "410  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "411  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "412  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "413  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "414  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "415  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "416  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "417  2013  Aleutians East Borough    AK  100.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "    valid_start   valid_end  \n",
       "0    2017-12-26  2018-01-01  \n",
       "1    2017-12-19  2017-12-25  \n",
       "2    2017-12-12  2017-12-18  \n",
       "3    2017-12-05  2017-12-11  \n",
       "4    2017-11-28  2017-12-04  \n",
       "5    2017-11-21  2017-11-27  \n",
       "6    2017-11-14  2017-11-20  \n",
       "7    2017-11-07  2017-11-13  \n",
       "8    2017-10-31  2017-11-06  \n",
       "9    2017-10-24  2017-10-30  \n",
       "10   2017-10-17  2017-10-23  \n",
       "11   2017-10-10  2017-10-16  \n",
       "12   2017-10-03  2017-10-09  \n",
       "13   2017-09-26  2017-10-02  \n",
       "14   2017-09-19  2017-09-25  \n",
       "15   2017-09-12  2017-09-18  \n",
       "16   2017-09-05  2017-09-11  \n",
       "17   2017-08-29  2017-09-04  \n",
       "18   2017-08-22  2017-08-28  \n",
       "19   2017-08-15  2017-08-21  \n",
       "20   2017-08-08  2017-08-14  \n",
       "21   2017-08-01  2017-08-07  \n",
       "22   2017-07-25  2017-07-31  \n",
       "23   2017-07-18  2017-07-24  \n",
       "24   2017-07-11  2017-07-17  \n",
       "25   2017-07-04  2017-07-10  \n",
       "26   2017-06-27  2017-07-03  \n",
       "27   2017-06-20  2017-06-26  \n",
       "28   2017-06-13  2017-06-19  \n",
       "29   2017-06-06  2017-06-12  \n",
       "..          ...         ...  \n",
       "388  2010-07-20  2010-07-26  \n",
       "389  2010-07-13  2010-07-19  \n",
       "390  2010-07-06  2010-07-12  \n",
       "391  2010-06-29  2010-07-05  \n",
       "392  2010-06-22  2010-06-28  \n",
       "393  2010-06-15  2010-06-21  \n",
       "394  2010-06-08  2010-06-14  \n",
       "395  2010-06-01  2010-06-07  \n",
       "396  2010-05-25  2010-05-31  \n",
       "397  2010-05-18  2010-05-24  \n",
       "398  2010-05-11  2010-05-17  \n",
       "399  2010-05-04  2010-05-10  \n",
       "400  2010-04-27  2010-05-03  \n",
       "401  2010-04-20  2010-04-26  \n",
       "402  2010-04-13  2010-04-19  \n",
       "403  2010-04-06  2010-04-12  \n",
       "404  2010-03-30  2010-04-05  \n",
       "405  2010-03-23  2010-03-29  \n",
       "406  2010-03-16  2010-03-22  \n",
       "407  2010-03-09  2010-03-15  \n",
       "408  2010-03-02  2010-03-08  \n",
       "409  2010-02-23  2010-03-01  \n",
       "410  2010-02-16  2010-02-22  \n",
       "411  2010-02-09  2010-02-15  \n",
       "412  2010-02-02  2010-02-08  \n",
       "413  2010-01-26  2010-02-01  \n",
       "414  2010-01-19  2010-01-25  \n",
       "415  2010-01-12  2010-01-18  \n",
       "416  2010-01-05  2010-01-11  \n",
       "417  2009-12-29  2010-01-04  \n",
       "\n",
       "[418 rows x 11 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alaska=droughts[droughts['fips']==2013]\n",
    "\n",
    "alaska"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:31: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10cd624d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1,figsize=(8, 6))\n",
    "plt.style.use('ggplot')\n",
    "autocorrelation_plot(alaska['none'],n_samples=14)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}