France Analyses.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from datetime import datetime\n",
    "pd.set_option('display.max_columns', 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "carac = pd.read_csv(\"caracteristics.csv\", encoding=\"latin-1\")\n",
    "holi = pd.read_csv(\"holidays.csv\", encoding=\"latin-1\")\n",
    "places = pd.read_csv(\"places.csv\", encoding=\"latin-1\")\n",
    "users = pd.read_csv(\"users.csv\", encoding=\"latin-1\")\n",
    "veh = pd.read_csv(\"vehicles.csv\", encoding=\"latin-1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Treating features with some null values and droping useless features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "places = places.drop([\"v1\", \"v2\", \"voie\", \"nbv\", \"pr\", \"pr1\"], axis=1)\n",
    "places.env1[places.env1 == 99] = 1\n",
    "places.env1[places.env1 == 3] = 0\n",
    "for n in places.columns:\n",
    "    places[n] = places[n].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# categories between 1 and 14\n",
    "veh.catv = veh.catv.apply(lambda x: 14 if x > 14 else x)\n",
    "veh = veh.drop([\"senc\", \"occutc\", \"obs\", \"obsm\", \"choc\", \"manv\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "carac.an = 2000 + carac.an\n",
    "carac['date'] = pd.to_datetime([f'{row.an}-{row.mois}-{row.jour}'\n",
    "                             for _, row in carac.iterrows()] )\n",
    "\n",
    "carac.atm = carac.atm.fillna(1)\n",
    "carac.col = carac.col.fillna(6)\n",
    "carac.com = carac.com.fillna(55)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "carac.drop(['adr', 'gps', 'lat', 'long'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "users['secu'] = users['secu'].fillna(53)\n",
    "users['secu1']= users['secu'].apply(lambda x: x % 10)\n",
    "users['locp'] = users['locp'].fillna(0)\n",
    "users['actp'] = users['actp'].fillna(0)\n",
    "users['etatp'] = users['etatp'].fillna(0)\n",
    "users['trajet'] = users['trajet'].fillna(3)\n",
    "users['an_nais'] = users['an_nais'].fillna(1973)\n",
    "users = users.drop(\"place\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(carac, users, how=\"outer\", on=\"Num_Acc\")\n",
    "df = pd.merge(df, places, how=\"outer\", on=\"Num_Acc\")\n",
    "df = pd.merge(df, veh, how=\"inner\", on=[\"Num_Acc\", 'num_veh'])\n",
    "# filtering for realistic people\n",
    "df = df[df.an_nais > 1918]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date'] = pd.to_datetime([f'{row.an}-{row.mois}-{row.jour}'\n",
    "                             for _, row in df.iterrows()] )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"age\"] = df.an - df.an_nais\n",
    "df['secu2'] = df['secu'].apply(lambda x: int(x/10) if x>=10 else x)\n",
    "\n",
    "# Grouping class 2(dead) and 3(Hospitalized) as serious\n",
    "df['grav1'] = ((df.grav == 2) | (df.grav == 3)).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"df.csv\", sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,15))\n",
    "for i in range(2):\n",
    "    print(i)\n",
    "    ax = plt.subplot(2, 1, i + 1)\n",
    "    sns.distplot(df[df.grav1 == i].age, bins=20)\n",
    "    if i:\n",
    "        plt.title(\"Serious\")\n",
    "    else:\n",
    "        plt.title(\"Not Serious\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1914f358>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.distplot(df.age[df.grav==2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Between serious (S) and not serious (NS) the distribution is almost the same, but for S we can see a higher frequency in age higher than 60's and for those had died it increases, it probably has some influence about the fragile in elder people."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sex analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1873467"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sexe.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.671306\n",
       "2    0.328694\n",
       "Name: sexe, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sexe.value_counts()/df.sexe.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0,'0%'),\n",
       " Text(0,0,'10%'),\n",
       " Text(0,0,'20%'),\n",
       " Text(0,0,'30%'),\n",
       " Text(0,0,'40%'),\n",
       " Text(0,0,'50%'),\n",
       " Text(0,0,'60%'),\n",
       " Text(0,0,'70%')]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ut3bu3Nnps3cAQOBZtm3bPT2E1L2X83WHT/W6l3Th8DJ23IjEgB6PUzTdt2P/FwE7VrCEw3070LInJl/7UzQAgPBF4AHAUAQeAAxF4AHAUAQeAAxF4AHAUAQeAAxF4AHAUAQeAAxF4AHAUAQeAAxF4AHAUAQeAAxF4AHAUAQeAAxF4AHAUAQeAAxF4AHAUAQeAAxF4AHAUAQeAAxF4AHAUAQeAAxF4AHAUAQeAAx1RYH/+OOPlZ+fL0lqaGhQbm6u8vLy5Ha75fP55PP5NHfuXGVnZ6umpkaS1NjYqKVLlwZvcgBAhzoN/Msvv6zFixfL6/VKkkpKSuRyuVRRUSHbtlVVVaX6+nolJiZqw4YN2rRpkySprKxMc+bMCe70AIB2dRr4IUOGqLS01P/1gQMHNGrUKElSWlqadu3aJafTKY/HI4/HI6fTqbq6Og0dOlTx8fHBmxwA0KHIzq6Qnp6uzz//3P+1bduyLEuSFB0drebmZiUlJWngwIEqLi5WQUGBVq9ercLCQrndbsXFxcnlcikiouPHkgEDnIqMdHRti8OnFBsT1bXbhrFQ3zkhITYsjhnqArlzqN9nvhMucwZSMO7bnQb+//vPULe2tqpfv36SpPnz50uSKisrNWHCBG3evFlZWVnas2ePamtrlZqa2uFxm5rarnaUSzS3nO3W7cNNbExUyO984kRzQI+XkBAb8GOGukDvHOr3GSk87tvB0NWfc0cPDFf9t2huv/127d69W5JUXV2tlJQU/2Ver1fbtm1TRkaGPB6PHA6HLMtSW1v34g0AuHpXHfhFixaptLRUOTk5On/+vNLT0/2XlZeXKz8/X5ZlKTMzU263Wzt37uz02TsAIPAs27btnh5C6t7L+brDp3rdS7pweBk7bkRiQI/HKZru27H/i4AdK1jC4b4daNkTk0PjFA0AIDwQeAAwFIEHAEMReAAwFIEHAEMReAAwFIEHAEMReAAwFIEHAEMReAAwFIEHAEMReAAwFIEHAEMReAAwFIEHAEMReAAwFIEHAEMReAAwFIEHAEMReAAwFIEHAEMReAAwFIEHAEMReAAwFIEHAEN1KfA+n09FRUXKyclRfn6+GhoatGXLFk2bNk3PPPOM/3qPPfaYWlpaAjUrAOAqRHblRtu3b9e5c+f0pz/9Sfv379dzzz2n5uZmvf7665o3b56+/fZb7du3TyNHjlRMTEygZwYAXIEuPYOvq6vTmDFjJEkjRozQp59+qqioKHm9Xl24cEERERF64403NG3atIAOCwC4cpZt2/bV3ujpp5/WpEmTNHbsWEnSuHHj9Pzzz+uVV17R+PHjde7cOSUmJurQoUM6duyYZsyYoWHDhgV8eABA+7r0DD4mJkatra3+r30+n1JSUvTSSy/pZz/7merq6jRkyBAdP35cBQUFevHFFwM2MADgynQp8D/+8Y9VXV0tSdq/f79uvfVW/2Xr1q3Tww8/rLNnzyoiIkKWZamtrS0w0wIArliX/ifrxIkTVVNTowceeEC2bau4uFiS9Pnnn+vMmTO67bbb5PP5dOzYMc2ePVsulyugQwMAOtelc/AAgNDHG50AwFAEHgAMReABwFBhGfiPP/5Y+fn5l33//fffV2ZmpnJycrR58+YemCw42tv3nXfeUXZ2th544AEVFRXJ5/P1wHTB0d7O31myZImef/75azhR8LW38yeffKK8vDzl5uZq4cKF8nq9PTBdcLS389tvv60pU6YoMzNTFRUVPTBZ4J0/f16FhYXKy8tTVlaWqqqqLrk8KP2yw8z69evt++67z87Ozr7k++fOnbPvuece+/Tp07bX67WnTp1qHz9+vIemDJz29vV4PPaECRPstrY227Zt+9FHH7W3b9/eEyMGXHs7f+e1116zp02bZq9cufIaTxY87e3s8/nsjIwM++jRo7Zt2/bmzZvtzz77rCdGDLiOfs6pqal2U1OT7fV6/f9dh7utW7faS5cutW3btr/55ht77Nix/suC1a+wewY/ZMgQlZaWXvb9zz77TEOGDFFcXJz69OmjkSNHau/evT0wYWC1t2+fPn30+uuv6/rrr5ckXbhwQX379r3W4wVFeztL0r59+/Txxx8rJyfnGk8VXO3tfOTIEfXv31/l5eV66KGHdPr0aWPeFd7Rz/mHP/yhmpubde7cOdm2LcuyrvF0gXfvvfeqoKDA/7XD4fD/c7D6FXaBT09PV2Tk5X99v6WlRbGxsf6vo6Ojjfgky/b2jYiIUHx8vCRp48aNamtrU2pq6rUeLyja2/n48eNas2aNioqKemCq4Gpv56amJu3bt095eXn6/e9/r3/84x+qra3tgQkDr72dJWn48OHKzMzUz3/+c40bN079+vW7xtMFXnR0tGJiYtTS0qKFCxde8v6gYPUr7ALfnv//8Qmtra2X/Aszkc/n0/Lly1VTU6PS0lIjnuV05L333lNTU5Nmz56t9evX65133tGbb77Z02MFVf/+/XXzzTfrBz/4ga677jqNGTNGn376aU+PFVSHDh3Sjh07VFVVpffff1/ffPON/vrXv/b0WAFx7NgxTZ8+XZMnT9b999/v/36w+mVM4G+55RY1NDTo9OnTOnfunPbu3as777yzp8cKqqKiInm9XpWVlflP1Zhs+vTpevPNN7Vx40bNnj1b9913n6ZOndrTYwXV4MGD1draqoaGBknS3r17NXz48B6eKrhiY2MVFRWlvn37yuFw6IYbbtCZM2d6eqxuO3nypGbOnKnCwkJlZWVdclmw+tWljyoIJZWVlWpra1NOTo6eeOIJzZo1S7ZtKzMzU9/73vd6eryA+27fO+64Q1u3blVKSopmzJgh6X8DOHHixB6eMPD+82fcW/znzsuWLdNjjz0m27Z15513aty4cT09XlD85845OTnKy8vTddddpyFDhmjKlCk9PV63rV27VmfOnFFZWZnKysokSdnZ2fJ4PEHrFx9VAACGMuYUDQDgUgQeAAxF4AHAUAQeAAxF4AHAUAQeAAxF4AHAUAQevdJXX32lhx56SFOnTlVWVpb279+vTz75RLm5uZoyZYpmzpypxsZGtbS0aPz48f7Pf5k1a5ZeffVVSdL69es1ZcoUZWRkaMWKFeItJQg1vNEJvdKaNWsUFRWlX/3qV6qurta//vUvVVZWau3atRo0aJB27typV155RX/4wx9UW1urZ555RtOnT9cHH3ygDRs2qLq6Wlu3btXvfvc7WZalwsJCpaWlafLkyT29GuAX9h9VAHTF3XffrQULFqi+vl5jx47V2LFjVVZWpkceecR/ne8+ze/uu+/WXXfdpVWrVvk/9Kq2tlaffPKJ/7Nwzp49q0GDBl37RYAOEHj0SiNHjtS7776rHTt26C9/+Yu2bNmim266SW+99ZYk6eLFizp58qQkybZtHTlyRNdff72OHDmiG2+8URcvXtSMGTP0y1/+UpJ05syZSz7fGwgFnINHr7RixQr/r4UrKirSoUOH9O233/p/ycIbb7yhxx9/XJJUUVEhp9OpsrIyLVmyRK2trbrrrrv01ltvqbW1VRcuXNC8efP0t7/9rSdXAi7DOXj0SseOHdNjjz2m1tZWORwOLVy4UHFxcVq2bJm8Xq9iYmK0fPlyWZal3NxcbdmyRd///vf129/+Vj6fT88884zKysr07rvv6uLFixozZoyeeuop4z+TH+GFwAOAoThFAwCGIvAAYCgCDwCGIvAAYCgCDwCGIvAAYCgCDwCG+h/1xK/g5jdDzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot(df.sexe, bins=3, kde=False)\n",
    "# manipulate\n",
    "vals = ax.get_yticks()\n",
    "ax.set_yticklabels(['{:.0%}'.format(x/2000000) for x in vals])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 67% of  people envolved in accidents are mens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a197f4b38>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df.sexe, hue = df.grav1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accidents frequency along the year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Number of Accidents x Month')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OO2QAAACGEcgAAAAMI5ABAAAYRiADAAAwjEAGAABgGIEMAADAMAIZAACAYQQyAAAAwwhkAAAAhvFN/QAAoFrzxV8EeLLXj6v8HCVhhwwAAMCwMgWyAwcOKDExUZKUlZWlbt26KTExUYmJiXrnnXckSUuWLNGAAQMUHx+vgwcPSpKOHz+uwYMHKyEhQWlpafJ4POWuBQAA8HelXrJctWqVtm3bprvuukuS9Pnnn2v48OEaMWKEtyYrK0t79+7Vpk2blJ2draSkJG3evFnz5s1TcnKyOnfurNTUVG3fvl1NmjQpc22vXr2qbuQAAADVRKmBLCIiQosXL9akSZMkSYcPH9bRo0e1fft2NW/eXFOnTtW+ffsUGxsrm82mJk2ayO1268KFC8rKylKnTp0kSd27d9euXbsUGRlZ5loCGUwrz30LTkewcl3Xyn2Oh6J+WO7nAAD8S6mBLC4uTqdOnfI+bt++vZ588knde++9Wr58uZYuXSqn06n69et7a0JCQpSbmyvLsmSz2Qq1uVyuMteWJiysngID7WUfbS3UsKHTdBdqNKcjuErrJd+sUUX6VRG+HouvxlVVauLvp8k++2K9fTW+so6lMmP2p/cWXzH5+i73pyx79eql0NBQ73+np6fr4YcfVl5enrcmLy9PTqdTAQEBhdpCQ0PlcDjKXFuaixevlLf7tUrDhk7l5JQebFG88ux4VXSHzBdrVJF+VYQvx1LR+a5Oatrvp+n3FF+st6/GV5axVPY17k/vLb5S1XNWUuAr96csR44c6b0RPyMjQ+3atVPHjh21c+dOeTwenTlzRh6PR+Hh4Wrbtq327NkjSdqxY4eio6PLVQsAAFAblHuHbMaMGUpPT1dQUJAaNGig9PR0ORwORUdHa9CgQfJ4PEpNTZUkpaSkaPr06Vq0aJFatGihuLg42e32MtcCAADUBjbLsizTnaiomrbd72umLy/4A3+5qd8XX6oo+XYs/nDJsqZ9oMP0e4ovXse+WpOyjKWyr3F/em/xhSd7/bhmXbIEAADAnUUgAwAAMIy/ZYk7zp8uKwAA4AvskAEAABjGDhkA+LE7uWNd0k3m7FoDlUMgKwMuwQEAgKrEJUsAAADDCGQAAACGEcgAAAAMI5ABAAAYRiADAAAwjEAGAABgGIEMAADAMAIZAACAYQQyAAAAwwhkAAAAhhHIAAAADCOQAQAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGEEMgAAAMMIZAAAAIYRyAAAAAwjkAEAABhGIAMAADCMQAYAAGAYgQwAAMCwwLIUHThwQAsXLtTq1at15MgRpaeny263q06dOlqwYIEaNGig2bNna//+/QoJCZEkLVu2TPn5+ZowYYKuXbumRo0aad68ebrrrru0ceNGrV+/XoGBgRozZox69OihCxcuFFkLAADg70oNZKtWrdK2bdu84WjOnDmaPn262rRpo/Xr12vVqlWaMmWKsrKy9Nprryk8PNz73NmzZ6tv3756/PHHtXLlSm3YsEF9+vTR6tWrtXnzZl2/fl0JCQnq2rWrli1bdlvtsGHDqmzg1c1Hmafv+DGdjmDluq4Vanso6od3/DwAAKBySr1kGRERocWLF3sfL1q0SG3atJEkud1u1a1bVx6PR8ePH1dqaqri4+P15ptvSpL27dunbt26SZK6d++u3bt36+DBg/rpT3+qOnXqyOl0KiIiQl988UWRtQAAALVBqTtkcXFxOnXqlPdxo0aNJEn79+/XmjVrtHbtWl25ckVPPfWUhg8fLrfbraFDh+ree++Vy+WS0+mUJIWEhCg3N7dQ2812l8tVZG1pwsLqKTDQXr4RV4DTEVzl56gqt/a9YUNnMZVVd86q4ItxSOUfS0XG7i9rIvl+LDX5d1OqmWtf3PFq4liKUt3eWyozZn9ZE1/y1foXpUz3kN3qnXfe0fLly7Vy5UqFh4d7Q9jNy5pdunTRF198IYfDoby8PAUHBysvL0+hoaHetpvy8vLkdDqLrC3NxYtXKtL9crv1sl9NUdQly5yc0oNuZflivnwxDql8YylqvsvCX9ZE8u1YKjrf1UlNW/uS5rymjaU41em9pbKvcX9ZE1+q6jkrKfCV+1OWb731ltasWaPVq1erWbNmkqRjx44pISFBbrdb+fn52r9/v9q1a6eOHTvq448/liTt2LFD9913n9q3b699+/bp+vXrys3N1ddff61WrVoVWQsAAFAblGuHzO12a86cOWrcuLGSkpIkSffff7/Gjx+vfv36aeDAgQoKClL//v3VsmVLjRkzRikpKdq4caPCwsL04osvql69ekpMTFRCQoIsy9Kzzz6runXrFlkLAABQG5QpkDVt2lQbN26UJO3du7fImlGjRmnUqFGF2ho0aKDf/va3t9UOHDhQAwcOLFMtAACAv+OLYQEAAAwjkAEAABhGIAMAADCMQAYAAGAYgQwAAMAwAhkAAIBhBDIAAADDCGQAAACGEcgAAAAMI5ABAAAYRiADAAAwjEAGAABgGIEMAADAMAIZAACAYQQyAAAAwwhkAAAAhhHIAAAADCOQAQAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGEEMgAAAMMIZAAAAIYRyAAAAAwjkAEAABhGIAMAADCMQAYAAGBYmQLZgQMHlJiYKEk6fvy4Bg8erISEBKWlpcnj8UiSlixZogEDBig+Pl4HDx68Y7UAAAD+rtRAtmrVKk2bNk3Xr1+XJM2bN0/Jyclat26dLMvS9u3blZWVpb1792rTpk1atGiRZs6ceUdqAQAAaoNSA1lERIQWL17sfZyVlaVOnTpJkrp3767du3dr3759io2Nlc1mU5MmTeR2u3XhwoVK1wIAANQGpQayuLg4BQYGeh9bliWbzSZJCgkJUW5urlwulxwOh7fmZntlawEAAGqDwNJLCgsI+HuGy8vLU2hoqBwOh/Ly8gq1O53OSteWJiysngID7eUdQrk5HcFVfo6qcmvfGzZ0+vycVcEX45DKP5aKjN1f1kTy/Vhq8u+mVDPXvrjj1cSxFKW6vbdUZsz+sia+5Kv1L0q5A1nbtm21Z88ede7cWTt27FCXLl0UERGhF154QSNHjtTZs2fl8XgUHh5e6drSXLx4pUKDLq9c1zWfnOdOczqCb+t7Tk7V7zz6Yr58MQ6pfGMpar7Lwl/WRPLtWCo639VJTVv7kua8po2lONXpvaWyr3F/WRNfquo5KynwlTuQpaSkaPr06Vq0aJFatGihuLg42e12RUdHa9CgQfJ4PEpNTb0jtQAAALVBmQJZ06ZNtXHjRklSZGSk1qxZc1tNUlKSkpKSCrXdiVoAAAB/xxfDAgAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGEEMgAAAMMIZAAAAIYRyAAAAAwjkAEAABhGIAMAADCMQAYAAGAYgQwAAMAwAhkAAIBhBDIAAADDCGQAAACGEcgAAAAMI5ABAAAYRiADAAAwjEAGAABgGIEMAADAMAIZAACAYQQyAAAAwwhkAAAAhhHIAAAADCOQAQAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGGBFXnSli1btHXrVknS9evXdeTIEb344ot6/vnn1bhxY0lSUlKSoqOjNWPGDH355ZeqU6eOZs+erebNmyszM1Nz5syR3W5XbGysxo0bJ4/HU2QtAACAv6tQIHv88cf1+OOPS5JmzpypJ554QllZWZo4caLi4uK8de+9955u3LihDRs2KDMzU/Pnz9fy5cuVlpamxYsXq1mzZnrmmWeUlZWl06dPF1kLAADg7yp1yfLQoUP661//qkGDBikrK0ubN29WQkKC5s+fr4KCAu3bt0/dunWTJEVFRenw4cNyuVy6ceOGIiIiZLPZFBsbq4yMjCJrAQAAaoMK7ZDd9Oqrr2rs2LGSpK5du+pf//Vf1bRpU6WlpWn9+vVyuVxyOBzeervdfltbSEiITp48WWRtQUGBAgOL72JYWD0FBtorM4QycTqCq/wcVeXWvjds6PT5OauCL8YhlX8sFRm7v6yJ5Pux1OTfTalmrn1xx6uJYylKdXtvqcyY/WVNfMlX61+UCgeyy5cv65tvvlGXLl0kSU888YRCQ0MlSQ8//LD++Mc/yul0Ki8vz/scj8cjh8NRqC0vL0+hoaG6du3abbUlhTFJunjxSkW7Xy65rms+Oc+d5nQE39b3nJzcKj+vL+bLF+OQyjeWoua7LPxlTSTfjqWi812d1LS1L2nOa9pYilOd3lsq+xr3lzXxpaqes5ICX4UvWX766ad64IEHJEmWZemRRx7R2bNnJUkZGRlq166dOnbsqB07dkiSMjMz1apVKzkcDgUFBenEiROyLEs7d+5UdHR0kbUAAAC1QYV3yI4ePaqmTZtKkmw2m2bPnq1x48YpODhY99xzjwYOHCi73a5du3YpPj5elmVp7ty5kr7/IMCECRPkdrsVGxurDh066Cc/+UmRtQAAAP6uwoHs6aefLvQ4NjZWsbGxt9XNmjXrtraoqCht3LixUFtAQECRtQAAAP6OL4YFAAAwjEAGAABgGIEMAADAMAIZAACAYQQyAAAAwwhkAAAAhhHIAAAADCOQAQAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGEEMgAAAMMIZAAAAIYRyAAAAAwjkAEAABhGIAMAADCMQAYAAGAYgQwAAMAwAhkAAIBhBDIAAADDCGQAAACGEcgAAAAMI5ABAAAYRiADAAAwjEAGAABgGIEMAADAMAIZAACAYQQyAAAAwwIr+sRHH31UTqdTktS0aVMNGjRIc+bMkd1uV2xsrMaNGyePx6MZM2boyy+/VJ06dTR79mw1b95cmZmZZa4FAADwdxUKZNevX5ckrV692tvWv39/LV68WM2aNdMzzzyjrKwsnT59Wjdu3NCGDRuUmZmp+fPna/ny5UpLSytzLQAAgL+rUCD74osvdPXqVY0YMUIFBQVKSkrSjRs3FBERIUmKjY1VRkaGcnJy1K1bN0lSVFSUDh8+LJfLVeZaAACA2qBCgSw4OFgjR47Uk08+qWPHjmnUqFEKDQ31/jwkJEQnT56Uy+WSw+Hwttvt9tvaSqotKChQYGDxXQwLq6fAQHtFhlAuTkdwlZ+jqtza94YNnT4/Z1XwxTik8o+lImP3lzWRfD+Wmvy7KdXMtS/ueDVxLEWpbu8tlRmzv6yJL/lq/YtSoUAWGRmp5s2by2azKTIyUk6nU3/729+8P8/Ly1NoaKiuXbumvLw8b7vH45HD4SjUVlJtSWFMki5evFKR7pdbruuaT85zpzkdwbf1PScnt8rP64v58sU4pPKNpaj5Lgt/WRPJt2Op6HxXJzVt7Uua85o2luJUp/eWyr7G/WVNfKmq56ykwFehT1m++eabmj9/viTp3Llzunr1qurVq6cTJ07Isizt3LlT0dHR6tixo3bs2CFJyszMVKtWreRwOBQUFFSmWgAAgNqgQjtkAwYM0JQpUzR48GDZbDbNnTtXAQEBmjBhgtxut2JjY9WhQwf95Cc/0a5duxQfHy/LsjR37lxJ0syZM8tcCwAA4O8qFMjq1KmjF1988bb2jRs3FnocEBCgWbNm3VYXFRVV5loAAAB/xxfDAgAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGEEMgAAAMMIZAAAAIYRyAAAAAwjkAEAABhGIAMAADCMQAYAAGAYgQwAAMAwAhkAAIBhBDIAAADDCGQAAACGEcgAAAAMI5ABAAAYRiADAAAwjEAGAABgGIEMAADAMAIZAACAYQQyAAAAwwhkAAAAhhHIAAAADCOQAQAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGGBFXlSfn6+pk6dqtOnT+vGjRsaM2aMfvCDH2j06NH60Y9+JEkaPHiwfvazn2nJkiX66KOPFBgYqKlTp6p9+/Y6fvy4Jk+eLJvNppYtWyotLU0BAQFF1gIAAPi7CgWybdu2qX79+nrhhRd08eJFPfbYYxo7dqyGDx+uESNGeOuysrK0d+9ebdq0SdnZ2UpKStLmzZs1b948JScnq3PnzkpNTdX27dvVpEmTImsBAAD8XYUCWe/evRUXF+d9bLfbdfjwYR09elTbt2/QGWPsAAAJXUlEQVRX8+bNNXXqVO3bt0+xsbGy2Wxq0qSJ3G63Lly4oKysLHXq1EmS1L17d+3atUuRkZFF1oaHh9+ZkQIAAFRTFQpkISEhkiSXy6Xx48crOTlZN27c0JNPPql7771Xy5cv19KlS+V0OlW/fv1Cz8vNzZVlWbLZbIXaXC5XkbUlBbKwsHoKDLRXZAjl4nQEV/k5qsqtfW/Y0Onzc1YFX4xDKv9YKjJ2f1kTyfdjqcm/m1LNXPvijlcTx1KU6vbeUpkx+8ua+JKv1r8oFQpkkpSdna2xY8cqISFB/fr10+XLlxUaGipJ6tWrl9LT0/Xwww8rLy/P+5y8vDw5nU4FBAQUagsNDZXD4SiytiQXL16paPfLJdd1zSfnudOcjuDb+p6Tk1vl5/XFfPliHFL5xlLUfJeFv6yJ5NuxVHS+q5OatvYlzXlNG0txqtN7S2Vf4/6yJr5U1XNWUuCr0Kcsz58/rxEjRmjixIkaMGCAJGnkyJE6ePCgJCkjI0Pt2rVTx44dtXPnTnk8Hp05c0Yej0fh4eFq27at9uzZI0nasWOHoqOji60FAADwdxXaIVuxYoUuX76sZcuWadmyZZKkyZMna+7cuQoKClKDBg2Unp4uh8Oh6OhoDRo0SB6PR6mpqZKklJQUTZ8+XYsWLVKLFi0UFxcnu91eZC0AAIC/q1AgmzZtmqZNm3Zb+/r1629rS0pKUlJSUqG2yMhIrVmzpky1AAAA/o4vhgUAADCMQAYAAGAYgQwAAMAwAhkAAIBhBDIAAADDCGQAAACGEcgAAAAMI5ABAAAYRiADAAAwjEAGAABgGIEMAADAMAIZAACAYQQyAAAAwwhkAAAAhhHIAAAADCOQAQAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGEEMgAAAMMIZAAAAIYRyAAAAAwjkAEAABhGIAMAADCMQAYAAGAYgQwAAMAwAhkAAIBhgaY78I88Ho9mzJihL7/8UnXq1NHs2bPVvHlz090CAACoUtVqh+yDDz7QjRs3tGHDBv3617/W/PnzTXcJAACgylWrQLZv3z5169ZNkhQVFaXDhw8b7hEAAEDVq1aBzOVyyeFweB/b7XYVFBQY7BEAAEDVs1mWZZnuxE3z5s1Thw4d9LOf/UyS1L17d+3YscNwrwAAAKpWtdoh69ixozeAZWZmqlWrVoZ7BAAAUPWq1Q7ZzU9ZfvXVV7IsS3PnztU999xjulsAAABVqloFMgAAgNqoWl2yBAAAqI0IZAAAAIYRyAAAAAwjkPmZ/Px8TZw4UQkJCRowYIC2b99uuku1wnfffacHH3xQX3/9temu1AqvvvqqBg0apMcff1ybNm0y3R2/lp+fr1//+teKj49XQkICr/EqdODAASUmJkqSjh8/rsGDByshIUFpaWnyeDyGe+ef/nHOjxw5ooSEBCUmJmrkyJE6f/68T/tCIPMz27ZtU/369bVu3TqtWrVK6enpprvk9/Lz85Wamqrg4GDTXakV9uzZo88++0z/9V//pdWrV+vs2bOmu+TXPv74YxUUFGj9+vUaO3asXn75ZdNd8kurVq3StGnTdP36dUnffy9ncnKy1q1bJ8uy+J/rKnDrnM+ZM0fTp0/X6tWr1atXL61atcqn/SGQ+ZnevXvrl7/8pfex3W432JvaYcGCBYqPj1ejRo1Md6VW2Llzp1q1aqWxY8dq9OjReuihh0x3ya9FRkbK7XbL4/HI5XIpMDDQdJf8UkREhBYvXux9nJWVpU6dOkn6/kvSd+/ebaprfuvWOV+0aJHatGkjSXK73apbt65P+8Nvlp8JCQmR9P2foRo/frySk5MN98i/bdmyReHh4erWrZtWrlxpuju1wsWLF3XmzBmtWLFCp06d0pgxY/Tuu+/KZrOZ7ppfqlevnk6fPq1///d/18WLF7VixQrTXfJLcXFxOnXqlPexZVne13RISIhyc3NNdc1v3TrnN/+nev/+/VqzZo3Wrl3r0/6wQ+aHsrOzNXToUPXv31/9+vUz3R2/tnnzZu3evVuJiYk6cuSIUlJSlJOTY7pbfq1+/fqKjY1VnTp11KJFC9WtW1cXLlww3S2/9bvf/U6xsbH64x//qLfeekuTJ0/2XuJB1QkI+Ps/z3l5eQoNDTXYm9rjnXfeUVpamlauXKnw8HCfnptA5mfOnz+vESNGaOLEiRowYIDp7vi9tWvXas2aNVq9erXatGmjBQsWqGHDhqa75dfuu+8+/fnPf5ZlWTp37pyuXr2q+vXrm+6W3woNDZXT6ZQk/dM//ZMKCgrkdrsN98r/tW3bVnv27JEk7dixQ9HR0YZ75P/eeust7/t5s2bNfH5+Lln6mRUrVujy5ctatmyZli1bJun7Gxe54Rz+okePHvr00081YMAAWZal1NRU7pWsQsOGDdPUqVOVkJCg/Px8Pfvss6pXr57pbvm9lJQUTZ8+XYsWLVKLFi0UFxdnukt+ze12a86cOWrcuLGSkpIkSffff7/Gjx/vsz7wp5MAAAAM45IlAACAYQQyAAAAwwhkAAAAhhHIAAAADCOQAQAAGEYgA4AyGDVqlM6dO2e6GwD8FF97AQAAYBhfDAug1tizZ49WrFihoKAgnTp1Sj179lS9evX0wQcfSJJWrlypQ4cO6eWXX5bH41GzZs00a9YsNWjQQD179tQbb7whl8ul1NRUFRQUqG7dupo3b55+9KMfmR0YgBqPS5YAapUDBw5o5syZ2rx5s9auXavw8HBt2bJFrVu31vr165WamqqlS5fqf/7nf9SxY0fNmjWr0PNff/11DR8+XFu2bNHAgQOVmZlpaCQA/AmBDECt0qpVKzVu3Fh33XWXwsLCFBMTI0lq0qSJPvzwQ7Vv315NmzaVJA0aNEiffPJJoec/+OCDSk9P19SpU+V0OtWvXz+fjwGA/yGQAahVgoKCCj3+x7+DeesttZZlqaCgoFBb7969tXXrVrVv316/+93vlJaWVnWdBVBrEMgA4P+1b99eBw4c0KlTpyRJGzZsUOfOnQvVJCcn69ChQ4qPj9cvf/lLff755ya6CsDPcFM/APy/Bg0aaNasWRo3bpzy8/PVpEkTzZkzp1DN6NGj9dxzz2np0qUKCgrSjBkzzHQWgF/hay8AAAAM45IlAACAYQQyAAAAwwhkAAAAhhHIAAAADCOQAQAAGEYgAwAAMIxABgAAYBiBDAAAwLD/A2eDVqY9+H0/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.distplot(df.mois.dropna(), bins=20, kde=False)\n",
    "plt.title(\"Number of Accidents x Month\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Number of Accidents x Month')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.subplot(2,1,1)\n",
    "sns.countplot(df.mois.dropna())\n",
    "plt.subplot(2,1,2)\n",
    "sns.countplot(df.mois.dropna(), hue = df.grav1)\n",
    "plt.title(\"Number of Accidents x Month\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### In the first graph, we can see that in middle of the and in final-beginning of the year has more ocurrence of accidents, matching with vacation period. In second graph was divided by grav category ( S and NS ), We can notice a negative correlation between S and NS in July, summer vacation period, eventhough NS decrease S increase."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What was the condition in the accident?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2d0780b8>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.distplot(df.surf.dropna(), bins=10, kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a30efd1d0>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(df.surf, hue=df.grav1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Great majority was in normal surface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.countplot(df.mois,hue=df.atm)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df.atm,hue=df.grav1)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Majority of accidents are in normal conditions of climate, but there is a variation in light rain and cloudy weather "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "nao_grave = df[(df.grav1==0)].secu1.value_counts()/df[(df.grav1==0)].secu1.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "grave = df[(df.grav1==1)].secu1.value_counts()/df[(df.grav1==1)].secu1.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    2.238224\n",
       "1.0    0.887578\n",
       "2.0    3.305970\n",
       "3.0    1.006532\n",
       "Name: secu1, dtype: float64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grave/nao_grave"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pessoas que nao usam cinto tem 3.3x mais chances de se envolverem em um acidente grave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2db8e9e8>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(df.an, hue=df.grav)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 675,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_pessoas = df[(df.grav1 == 1) & (df.an>=2010) & (df.an<=2013)].groupby([\"Num_Acc\"])['Num_Acc'].count()\n",
    "\n",
    "n_pessoas = pd.DataFrame(n_pessoas)\n",
    "\n",
    "n_pessoas.columns = [\"n\"]\n",
    "\n",
    "n_pessoas = n_pessoas.reset_index()\n",
    "\n",
    "n_pessoas_grave = n_pessoas.groupby(\"n\").count()\n",
    "\n",
    "n_pessoas1 = df[(df.grav1 == 0) & (df.an>=2010) & (df.an<=2013)].groupby([\"Num_Acc\"])['Num_Acc'].count()\n",
    "\n",
    "n_pessoas1 = pd.DataFrame(n_pessoas1)\n",
    "\n",
    "n_pessoas1.columns = [\"n\"]\n",
    "\n",
    "n_pessoas1 = n_pessoas1.reset_index()\n",
    "\n",
    "n_pessoas_nao_grave = n_pessoas1.groupby(\"n\").count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 676,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_grave=(n_pessoas_grave.sort_values(by=\"Num_Acc\", ascending=False).head(3)/n_pessoas_grave.sum() *100)#.T.plot(kind='bar', stacked=True)\n",
    "p_grave.columns = [\"n\"]\n",
    "p_nao_grave = (n_pessoas_nao_grave.sort_values(by=\"Num_Acc\", ascending=False).head(3)/n_pessoas_nao_grave.sum()*100)\n",
    "p_nao_grave.columns = [\"n\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 677,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           n\n",
      "n           \n",
      "1  85.934707\n",
      "2  10.576732\n",
      "3   2.298622            n\n",
      "n           \n",
      "2  45.061628\n",
      "1  37.001842\n",
      "3  11.687546\n"
     ]
    }
   ],
   "source": [
    "print(p_grave, p_nao_grave)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 669,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_pessoas = df[df.grav1 == 1].groupby([\"Num_Acc\"])['Num_Acc'].count()\n",
    "\n",
    "n_pessoas = pd.DataFrame(n_pessoas)\n",
    "\n",
    "n_pessoas.columns = [\"n\"]\n",
    "\n",
    "n_pessoas = n_pessoas.reset_index()\n",
    "\n",
    "n_pessoas_grave = n_pessoas.groupby(\"n\").count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 670,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_grave=(n_pessoas_grave.sort_values(by=\"Num_Acc\", ascending=False).head(3)/n_pessoas_grave.sum() *100)#.T.plot(kind='bar', stacked=True)\n",
    "p_grave.columns = [\"n\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 671,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_pessoas1 = df[df.grav1 == 0].groupby([\"Num_Acc\"])['Num_Acc'].count()\n",
    "\n",
    "n_pessoas1 = pd.DataFrame(n_pessoas1)\n",
    "\n",
    "n_pessoas1.columns = [\"n\"]\n",
    "\n",
    "n_pessoas1 = n_pessoas1.reset_index()\n",
    "\n",
    "n_pessoas_nao_grave = n_pessoas1.groupby(\"n\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 680,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_nao_grave = (n_pessoas_nao_grave.sort_values(by=\"Num_Acc\", ascending=False).head(3)/n_pessoas_nao_grave.sum()*100)\n",
    "p_nao_grave.columns = [\"n\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 679,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           n\n",
      "n           \n",
      "1  85.934707\n",
      "2  10.576732\n",
      "3   2.298622                n\n",
      "n               \n",
      "2  276962.162162\n",
      "1  227424.324324\n",
      "3   71835.135135\n"
     ]
    }
   ],
   "source": [
    "print(p_grave, p_nao_grave)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 615,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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b65tvvlH//v115MgRDRgwQG63W7feeqtmzZpV5jgzZ87U0qVL9cgjj6i4uFh5eXm64YYbNGjQIPXt2/c3HTcgIECBgYGe04jnVs8hISEaNmyYYmNj5XA45O/vr+eee04Oh6PKz3XbOExZr8/wm2RlZSk9Pb3UOVAAqCmcNgEAC7HyBgALsfIGAAvV+BuWp0+fVnZ2tpo0aVLmO84AgNJcLpdyc3MVGBhY4hLPc2o83tnZ2YqOjq7pwwDAZWnp0qWeH2w6X43H+9xPmy1dulQ33HBDTR8OAC4LOTk5io6OLvV7ZM6p8XifO1Vyww03XNbXXAJATSjvdDNvWAKAhYg3AFiIeAOAhYg3AFiIeAOAhYg3AFiIeAOAhYg3AFiIP8ZgoR29+tX2FIAydVq7urancMUg3gBQSVlZWXrmmWd0880364svvlBxcbHS0tLUtm3bSz4XTpsAwG/w6aefKjY2Vm+88Yb69u2rZ555plbmQbwB4De48cYb1apVK0lS69atlZeXVyvzIN4A8Buc/7u1HQ5HqT+IfakQbwCwEPEGAAsRbwCopPbt22v9+vXlfnwpEW8AsBDxBgALEW8AsBDxBgALEW8AsBC/2wTAFSfiybXVOt6//9GrWserDFbeAHCJfPLJJ4qJiamWsVh5A8Al8OKLL2rdunWqX79+tYzHyhsALoFbbrlF8+bNq7bxiDcAXAJhYWHy8am+kx3EGwAsRLwBwEK8YQngilMbl/ZVN1beAHCJ3HTTTVq5cmW1jEW8AcBCxBsALES8AcBCxBsALMTVJhaaO+i62p4CUKZOtT2BK0iF8XY6nUpISNDhw4fl5eWl9PR0+fj4KCEhQQ6HQ3feeadSUlLk5cUiHoAdBq4YUa3jrYx8vlrHq4wK471161YVFxcrIyNDO3bs0Jw5c+R0OhUXF6f27dsrOTlZGzduVI8ePS7FfAHAOk6nUxMmTNDhw4dVVFSkESNGqHv37lUas8Ll8u233y6XyyW3262CggL5+Pho7969CgoKkiR17txZmZmZVZoEAFzO1q1bp0aNGmnZsmV68cUXlZ6eXuUxK1x5N2jQQIcPH9Z9992nEydOaMGCBdq1a5ccDockyc/PT/n5+VWeCABcrsLDwxUWFub52Nvbu8pjVhjvV155RSEhIXryySd15MgRDRkyRE6n03N7YWGhGjZsWOWJAMDlys/PT5JUUFCgMWPGKC4urspjVnjapGHDhrrqqqskSVdffbWKi4vVunVrZWVlSZK2bdumdu3aVXkiAHA5O3LkiAYPHqxevXopIiKiyuNVuPIeOnSoJkyYoEGDBsnpdOrxxx9XYGCgkpKSNHv2bAUEBJR4OQAAKOmHH35QbGyskpOTFRwcXC1jVhhvPz8/zZ07t9T2JUuWVMsEAOBSu9SX9i1YsEAnT57U/PnzNX/+fEln/yyar6/vRY/JD+kAQA1LTExUYmJitY7JT9YAgIWINwBYiHgDgIWINwBYiHgDgIW42gTAFWdHr37VOl6ntaurdbzKIN4AUMNcLpcSExP11VdfydvbW1OnTtUtt9xSpTE5bQIANWzz5s2SpIyMDI0ZM0ZTp06t8pisvAGghv35z39W165dJUnff/+9rr322iqPSbwB4BLw8fFRfHy83n33XT377LNVHo/TJgBwiUyfPl0bNmxQUlKSfvrppyqNRbwBoIa98cYbWrhwoSSpfv36cjgcVf6DDJw2AXDFudSX9vXs2VNPPfWUoqOjVVxcrAkTJqhevXpVGpN4A0ANa9CgQZm/WrsqOG0CABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABaq1F+PX7hwoTZt2iSn06kHH3xQQUFBSkhIkMPh0J133qmUlBR5efF9AAAulQqLm5WVpY8++kjLly/X4sWLlZOTo6lTpyouLk7Lli2TMUYbN268FHMFAPyswni///77at68uUaNGqVHH31UXbt21d69exUUFCRJ6ty5szIzM2t8ogCAX1R42uTEiRP6/vvvtWDBAn333XcaMWKEjDFyOBySJD8/P+Xn59f4RAEAv6gw3o0aNVJAQIDq1q2rgIAA1atXTzk5OZ7bCwsL1bBhwxqdJACgpApPm7Rt21bbt2+XMUZHjx7VqVOnFBwcrKysLEnStm3b1K5duxqfKADgFxWuvLt166Zdu3apf//+MsYoOTlZN910k5KSkjR79mwFBAQoLCzsUswVAPCzSl0qOH78+FLblixZUu2TAQBUDhdnA4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFKhXv//3vf+rSpYv++9//6ptvvtGDDz6oQYMGKSUlRW63u6bnCAD4lQrj7XQ6lZycLF9fX0nS1KlTFRcXp2XLlskYo40bN9b4JAEAJVUY7+nTpysqKkrXXXedJGnv3r0KCgqSJHXu3FmZmZk1O0MAQCkXjPfrr7+uxo0bKzQ01LPNGCOHwyFJ8vPzU35+fs3OEABQis+Fbly9erUcDod27typ/fv3Kz4+XsePH/fcXlhYqIYNG9b4JAEAJV0w3kuXLvX8OyYmRqmpqZo5c6aysrLUvn17bdu2TR06dKjxSQIASvrNlwrGx8dr3rx5ioyMlNPpVFhYWE3MCwBwARdceZ9v8eLFnn8vWbKkRiYDAKgcfkgHACxU6ZU3/u849f/Ca3sKQNkia3sCVw5W3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABbyudCNTqdTEyZM0OHDh1VUVKQRI0bojjvuUEJCghwOh+68806lpKTIy4vvAQBwKV0w3uvWrVOjRo00c+ZMnThxQn369FHLli0VFxen9u3bKzk5WRs3blSPHj0u1XwBAKrgtEl4eLgee+wxz8fe3t7au3evgoKCJEmdO3dWZmZmzc4QAFDKBePt5+cnf39/FRQUaMyYMYqLi5MxRg6Hw3N7fn7+JZkoAOAXFZ6sPnLkiAYPHqxevXopIiKixPntwsJCNWzYsEYnCAAo7YLx/uGHHxQbG6tx48apf//+kqTWrVsrKytLkrRt2za1a9eu5mcJACjhgvFesGCBTp48qfnz5ysmJkYxMTGKi4vTvHnzFBkZKafTqbCwsEs1VwDAzy54tUliYqISExNLbV+yZEmNTQgAUDEu0AYACxFvALAQ8QYACxFvALAQ8QYACxFvALAQ8QYACxFvALAQ8QYACxFvALAQ8QYACxFvALAQ8QYACxFvALAQ8QYACxFvALAQ8QYACxFvALAQ8QYACxFvALAQ8QYACxFvALAQ8QYACxFvALAQ8QYACxFvALAQ8QYACxFvALAQ8QYACxFvALAQ8QYACxFvALAQ8QYACxFvALAQ8QYACxFvALCQz8V8ktvtVmpqqj777DPVrVtXkyZN0q233lrdcwMAlOOiVt7vvfeeioqKtGLFCj355JOaNm1adc8LAHABF7Xy/vDDDxUaGipJatOmjbKzs8vd1+VySZJycnIu5lAog/On47U9BaBM3333XW1P4bJxrpnnGvprFxXvgoIC+fv7ez729vZWcXGxfHxKD5ebmytJio6OvphDAbBI9028Cq9uubm5ZZ6Wvqh4+/v7q7Cw0POx2+0uM9ySFBgYqKVLl6pJkyby9va+mMMBwBXH5XIpNzdXgYGBZd5+UfG+5557tHnzZt1///36+OOP1bx583L39fX1Vbt27S7mMABwRbvQhSAOY4z5rQOeu9rk888/lzFGU6ZMUbNmzao0SQBA5V1UvAEAtYsf0gEACxFvALAQ8QYACxFvALAQ8QYAC13Udd7A5eD111/X1q1bdfr0aX377bcaNmyY+vbtW9vTAiqFlTeuaAUFBVq4cKGef/55vfDCC7U9HaDSiDeuaC1btpQkNW3aVEVFRbU8G6DyiDeuaA6Ho7anAFwU4g0AFuLH4wHAQqy8AcBCxBsALES8AcBCxBsALES8AcBCxBsALES8AcBC/x+NgO/bOFjRaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "p_nao_grave.sort_index().T.plot(kind='bar', stacked=True)\n",
    "plt.xticks(rotation=0)\n",
    "plt.title(\"N pessoas no veículo - Acidentes não Graves \")\n",
    "plt.savefig(\"N pessoas no veículo - Acidentes não Graves.jpg\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 616,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "p_grave.T.plot(kind='bar', stacked=True)\n",
    "plt.xticks(rotation=0)\n",
    "plt.title(\"N pessoas no veículo - Acidentes Graves \")\n",
    "plt.savefig(\"N pessoas no veículo - Acidentes Graves.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_col= ['secu1','secu','col','catr','agg','plan','catu','situ','dep','atm','grav1','grav']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 617,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2880x2160 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from string import ascii_letters\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(40,30))\n",
    "\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "# Compute the correlation matrix\n",
    "corr = df[x_col].corr()\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
    "\n",
    "plt.savefig(\"heatmap.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {},
   "outputs": [],
   "source": [
    "dep = pd.read_csv(\"departement.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"dep2\"] = df.dep/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "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>1</th>\n",
       "      <th>01</th>\n",
       "      <th>Ain</th>\n",
       "      <th>AIN</th>\n",
       "      <th>ain</th>\n",
       "      <th>A500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>02</td>\n",
       "      <td>Aisne</td>\n",
       "      <td>AISNE</td>\n",
       "      <td>aisne</td>\n",
       "      <td>A250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>03</td>\n",
       "      <td>Allier</td>\n",
       "      <td>ALLIER</td>\n",
       "      <td>allier</td>\n",
       "      <td>A460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>05</td>\n",
       "      <td>Hautes-Alpes</td>\n",
       "      <td>HAUTES-ALPES</td>\n",
       "      <td>hautes-alpes</td>\n",
       "      <td>H32412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>04</td>\n",
       "      <td>Alpes-de-Haute-Provence</td>\n",
       "      <td>ALPES-DE-HAUTE-PROVENCE</td>\n",
       "      <td>alpes-de-haute-provence</td>\n",
       "      <td>A412316152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>06</td>\n",
       "      <td>Alpes-Maritimes</td>\n",
       "      <td>ALPES-MARITIMES</td>\n",
       "      <td>alpes-maritimes</td>\n",
       "      <td>A41256352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1  01                      Ain                      AIN  \\\n",
       "0  2  02                    Aisne                    AISNE   \n",
       "1  3  03                   Allier                   ALLIER   \n",
       "2  5  05             Hautes-Alpes             HAUTES-ALPES   \n",
       "3  4  04  Alpes-de-Haute-Provence  ALPES-DE-HAUTE-PROVENCE   \n",
       "4  6  06          Alpes-Maritimes          ALPES-MARITIMES   \n",
       "\n",
       "                       ain        A500  \n",
       "0                    aisne        A250  \n",
       "1                   allier        A460  \n",
       "2             hautes-alpes      H32412  \n",
       "3  alpes-de-haute-provence  A412316152  \n",
       "4          alpes-maritimes   A41256352  "
      ]
     },
     "execution_count": 538,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dep.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {},
   "outputs": [],
   "source": [
    "dep.columns = ['1', 'dep2', 'Ain', 'AIN', 'ain', 'A500']\n",
    "dep = dep[['dep2', 'Ain', 'AIN', 'ain', 'A500']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {},
   "outputs": [],
   "source": [
    "dep = dep[dep.dep2 != \"2a\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {},
   "outputs": [],
   "source": [
    "dep.dep2 = dep.dep2.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(df, dep, how=\"outer\" , on=\"dep2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Num_Acc', 'an', 'mois', 'jour', 'hrmn', 'lum', 'agg', 'int', 'atm',\n",
       "       'col', 'com', 'dep', 'date', 'catu', 'grav', 'sexe', 'trajet', 'secu',\n",
       "       'locp', 'actp', 'etatp', 'an_nais', 'num_veh', 'secu1', 'catr', 'circ',\n",
       "       'vosp', 'prof', 'plan', 'lartpc', 'larrout', 'surf', 'infra', 'situ',\n",
       "       'env1', 'catv', 'idade', 'secu2', 'grav1', 'dep2', 'Ain', 'AIN', 'ain',\n",
       "       'A500'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 588,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "839821"
      ]
     },
     "execution_count": 552,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Num_Acc.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 594,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa017d550>"
      ]
     },
     "execution_count": 594,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('mois').count()['an'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 579,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_date = df.set_index('date')\n",
    "temp = df_date.groupby(pd.Grouper(freq='M')).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num_Acc</th>\n",
       "      <th>an</th>\n",
       "      <th>mois</th>\n",
       "      <th>jour</th>\n",
       "      <th>hrmn</th>\n",
       "      <th>lum</th>\n",
       "      <th>agg</th>\n",
       "      <th>int</th>\n",
       "      <th>atm</th>\n",
       "      <th>col</th>\n",
       "      <th>com</th>\n",
       "      <th>dep</th>\n",
       "      <th>catu</th>\n",
       "      <th>grav</th>\n",
       "      <th>sexe</th>\n",
       "      <th>trajet</th>\n",
       "      <th>secu</th>\n",
       "      <th>locp</th>\n",
       "      <th>actp</th>\n",
       "      <th>etatp</th>\n",
       "      <th>an_nais</th>\n",
       "      <th>num_veh</th>\n",
       "      <th>secu1</th>\n",
       "      <th>catr</th>\n",
       "      <th>circ</th>\n",
       "      <th>vosp</th>\n",
       "      <th>prof</th>\n",
       "      <th>plan</th>\n",
       "      <th>lartpc</th>\n",
       "      <th>larrout</th>\n",
       "      <th>surf</th>\n",
       "      <th>infra</th>\n",
       "      <th>situ</th>\n",
       "      <th>env1</th>\n",
       "      <th>catv</th>\n",
       "      <th>idade</th>\n",
       "      <th>secu2</th>\n",
       "      <th>grav1</th>\n",
       "      <th>dep2</th>\n",
       "      <th>Ain</th>\n",
       "      <th>AIN</th>\n",
       "      <th>ain</th>\n",
       "      <th>A500</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2005-01-31</th>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>16351</td>\n",
       "      <td>15595</td>\n",
       "      <td>15595</td>\n",
       "      <td>15595</td>\n",
       "      <td>15595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-28</th>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12737</td>\n",
       "      <td>12136</td>\n",
       "      <td>12136</td>\n",
       "      <td>12136</td>\n",
       "      <td>12136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-31</th>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>14730</td>\n",
       "      <td>13966</td>\n",
       "      <td>13966</td>\n",
       "      <td>13966</td>\n",
       "      <td>13966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-30</th>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14850</td>\n",
       "      <td>14138</td>\n",
       "      <td>14138</td>\n",
       "      <td>14138</td>\n",
       "      <td>14138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-05-31</th>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>17731</td>\n",
       "      <td>16835</td>\n",
       "      <td>16835</td>\n",
       "      <td>16835</td>\n",
       "      <td>16835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-06-30</th>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>18775</td>\n",
       "      <td>17996</td>\n",
       "      <td>17996</td>\n",
       "      <td>17996</td>\n",
       "      <td>17996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-07-31</th>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>17667</td>\n",
       "      <td>16935</td>\n",
       "      <td>16935</td>\n",
       "      <td>16935</td>\n",
       "      <td>16935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-08-31</th>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>15099</td>\n",
       "      <td>14310</td>\n",
       "      <td>14310</td>\n",
       "      <td>14310</td>\n",
       "      <td>14310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-09-30</th>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>17724</td>\n",
       "      <td>16995</td>\n",
       "      <td>16995</td>\n",
       "      <td>16995</td>\n",
       "      <td>16995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-10-31</th>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>18009</td>\n",
       "      <td>17239</td>\n",
       "      <td>17239</td>\n",
       "      <td>17239</td>\n",
       "      <td>17239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-11-30</th>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16963</td>\n",
       "      <td>16090</td>\n",
       "      <td>16090</td>\n",
       "      <td>16090</td>\n",
       "      <td>16090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31</th>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>16185</td>\n",
       "      <td>15304</td>\n",
       "      <td>15304</td>\n",
       "      <td>15304</td>\n",
       "      <td>15304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-31</th>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>14066</td>\n",
       "      <td>13342</td>\n",
       "      <td>13342</td>\n",
       "      <td>13342</td>\n",
       "      <td>13342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-02-28</th>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11872</td>\n",
       "      <td>11220</td>\n",
       "      <td>11220</td>\n",
       "      <td>11220</td>\n",
       "      <td>11220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-03-31</th>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13919</td>\n",
       "      <td>13140</td>\n",
       "      <td>13140</td>\n",
       "      <td>13140</td>\n",
       "      <td>13140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-30</th>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>14713</td>\n",
       "      <td>13923</td>\n",
       "      <td>13923</td>\n",
       "      <td>13923</td>\n",
       "      <td>13923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-05-31</th>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>15567</td>\n",
       "      <td>14720</td>\n",
       "      <td>14720</td>\n",
       "      <td>14720</td>\n",
       "      <td>14720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-06-30</th>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>17545</td>\n",
       "      <td>16750</td>\n",
       "      <td>16750</td>\n",
       "      <td>16750</td>\n",
       "      <td>16750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07-31</th>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>17124</td>\n",
       "      <td>16219</td>\n",
       "      <td>16219</td>\n",
       "      <td>16219</td>\n",
       "      <td>16219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-08-31</th>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>14043</td>\n",
       "      <td>13274</td>\n",
       "      <td>13274</td>\n",
       "      <td>13274</td>\n",
       "      <td>13274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-09-30</th>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>16713</td>\n",
       "      <td>15847</td>\n",
       "      <td>15847</td>\n",
       "      <td>15847</td>\n",
       "      <td>15847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-31</th>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>17472</td>\n",
       "      <td>16674</td>\n",
       "      <td>16674</td>\n",
       "      <td>16674</td>\n",
       "      <td>16674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-11-30</th>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16844</td>\n",
       "      <td>16097</td>\n",
       "      <td>16097</td>\n",
       "      <td>16097</td>\n",
       "      <td>16097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-31</th>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>16673</td>\n",
       "      <td>15838</td>\n",
       "      <td>15838</td>\n",
       "      <td>15838</td>\n",
       "      <td>15838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-31</th>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>14337</td>\n",
       "      <td>13674</td>\n",
       "      <td>13674</td>\n",
       "      <td>13674</td>\n",
       "      <td>13674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-02-28</th>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>13496</td>\n",
       "      <td>12752</td>\n",
       "      <td>12752</td>\n",
       "      <td>12752</td>\n",
       "      <td>12752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-03-31</th>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>15144</td>\n",
       "      <td>14402</td>\n",
       "      <td>14402</td>\n",
       "      <td>14402</td>\n",
       "      <td>14402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-04-30</th>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15911</td>\n",
       "      <td>15185</td>\n",
       "      <td>15185</td>\n",
       "      <td>15185</td>\n",
       "      <td>15185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-05-31</th>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>15707</td>\n",
       "      <td>14929</td>\n",
       "      <td>14929</td>\n",
       "      <td>14929</td>\n",
       "      <td>14929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-06-30</th>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>16918</td>\n",
       "      <td>15963</td>\n",
       "      <td>15963</td>\n",
       "      <td>15963</td>\n",
       "      <td>15963</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",
       "      <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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-31</th>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10954</td>\n",
       "      <td>10515</td>\n",
       "      <td>10515</td>\n",
       "      <td>10515</td>\n",
       "      <td>10515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31</th>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9653</td>\n",
       "      <td>9202</td>\n",
       "      <td>9202</td>\n",
       "      <td>9202</td>\n",
       "      <td>9202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-30</th>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11890</td>\n",
       "      <td>11392</td>\n",
       "      <td>11392</td>\n",
       "      <td>11392</td>\n",
       "      <td>11392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-31</th>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12577</td>\n",
       "      <td>12130</td>\n",
       "      <td>12130</td>\n",
       "      <td>12130</td>\n",
       "      <td>12130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-30</th>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>11383</td>\n",
       "      <td>10910</td>\n",
       "      <td>10910</td>\n",
       "      <td>10910</td>\n",
       "      <td>10910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>11020</td>\n",
       "      <td>10454</td>\n",
       "      <td>10454</td>\n",
       "      <td>10454</td>\n",
       "      <td>10454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9669</td>\n",
       "      <td>9204</td>\n",
       "      <td>9204</td>\n",
       "      <td>9204</td>\n",
       "      <td>9204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-28</th>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8729</td>\n",
       "      <td>8195</td>\n",
       "      <td>8195</td>\n",
       "      <td>8195</td>\n",
       "      <td>8195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9742</td>\n",
       "      <td>9212</td>\n",
       "      <td>9212</td>\n",
       "      <td>9212</td>\n",
       "      <td>9212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30</th>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10718</td>\n",
       "      <td>10133</td>\n",
       "      <td>10133</td>\n",
       "      <td>10133</td>\n",
       "      <td>10133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-31</th>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>11007</td>\n",
       "      <td>10403</td>\n",
       "      <td>10403</td>\n",
       "      <td>10403</td>\n",
       "      <td>10403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>12553</td>\n",
       "      <td>11975</td>\n",
       "      <td>11975</td>\n",
       "      <td>11975</td>\n",
       "      <td>11975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11840</td>\n",
       "      <td>11337</td>\n",
       "      <td>11337</td>\n",
       "      <td>11337</td>\n",
       "      <td>11337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-31</th>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>10149</td>\n",
       "      <td>9632</td>\n",
       "      <td>9632</td>\n",
       "      <td>9632</td>\n",
       "      <td>9632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-30</th>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11315</td>\n",
       "      <td>11315</td>\n",
       "      <td>11315</td>\n",
       "      <td>11315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-31</th>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11749</td>\n",
       "      <td>11178</td>\n",
       "      <td>11178</td>\n",
       "      <td>11178</td>\n",
       "      <td>11178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-30</th>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>11224</td>\n",
       "      <td>10741</td>\n",
       "      <td>10741</td>\n",
       "      <td>10741</td>\n",
       "      <td>10741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>11150</td>\n",
       "      <td>10602</td>\n",
       "      <td>10602</td>\n",
       "      <td>10602</td>\n",
       "      <td>10602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-31</th>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10704</td>\n",
       "      <td>10248</td>\n",
       "      <td>10248</td>\n",
       "      <td>10248</td>\n",
       "      <td>10248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-29</th>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>9026</td>\n",
       "      <td>8584</td>\n",
       "      <td>8584</td>\n",
       "      <td>8584</td>\n",
       "      <td>8584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-31</th>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>10242</td>\n",
       "      <td>9684</td>\n",
       "      <td>9684</td>\n",
       "      <td>9684</td>\n",
       "      <td>9684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-30</th>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9980</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-31</th>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>11356</td>\n",
       "      <td>10771</td>\n",
       "      <td>10771</td>\n",
       "      <td>10771</td>\n",
       "      <td>10771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>12038</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-31</th>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>12083</td>\n",
       "      <td>11481</td>\n",
       "      <td>11481</td>\n",
       "      <td>11481</td>\n",
       "      <td>11481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-31</th>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "      <td>9431</td>\n",
       "      <td>9431</td>\n",
       "      <td>9431</td>\n",
       "      <td>9431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-30</th>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11979</td>\n",
       "      <td>11432</td>\n",
       "      <td>11432</td>\n",
       "      <td>11432</td>\n",
       "      <td>11432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-31</th>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>12489</td>\n",
       "      <td>11986</td>\n",
       "      <td>11986</td>\n",
       "      <td>11986</td>\n",
       "      <td>11986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-30</th>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11989</td>\n",
       "      <td>11479</td>\n",
       "      <td>11479</td>\n",
       "      <td>11479</td>\n",
       "      <td>11479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>11525</td>\n",
       "      <td>10899</td>\n",
       "      <td>10899</td>\n",
       "      <td>10899</td>\n",
       "      <td>10899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Num_Acc     an   mois   jour   hrmn    lum    agg    int    atm  \\\n",
       "date                                                                          \n",
       "2005-01-31    16351  16351  16351  16351  16351  16351  16351  16351  16351   \n",
       "2005-02-28    12737  12737  12737  12737  12737  12737  12737  12737  12737   \n",
       "2005-03-31    14730  14730  14730  14730  14730  14730  14730  14730  14730   \n",
       "2005-04-30    14850  14850  14850  14850  14850  14850  14850  14850  14850   \n",
       "2005-05-31    17731  17731  17731  17731  17731  17731  17731  17731  17731   \n",
       "2005-06-30    18775  18775  18775  18775  18775  18775  18775  18775  18775   \n",
       "2005-07-31    17667  17667  17667  17667  17667  17667  17667  17667  17667   \n",
       "2005-08-31    15099  15099  15099  15099  15099  15099  15099  15099  15099   \n",
       "2005-09-30    17724  17724  17724  17724  17724  17724  17724  17724  17724   \n",
       "2005-10-31    18009  18009  18009  18009  18009  18009  18009  18009  18009   \n",
       "2005-11-30    16963  16963  16963  16963  16963  16963  16963  16963  16963   \n",
       "2005-12-31    16185  16185  16185  16185  16185  16185  16185  16185  16185   \n",
       "2006-01-31    14066  14066  14066  14066  14066  14066  14066  14066  14066   \n",
       "2006-02-28    11872  11872  11872  11872  11872  11872  11872  11872  11872   \n",
       "2006-03-31    13919  13919  13919  13919  13919  13919  13919  13919  13919   \n",
       "2006-04-30    14713  14713  14713  14713  14713  14713  14713  14713  14713   \n",
       "2006-05-31    15567  15567  15567  15567  15567  15567  15567  15567  15567   \n",
       "2006-06-30    17545  17545  17545  17545  17545  17545  17545  17545  17545   \n",
       "2006-07-31    17124  17124  17124  17124  17124  17124  17124  17124  17124   \n",
       "2006-08-31    14043  14043  14043  14043  14043  14043  14043  14043  14043   \n",
       "2006-09-30    16713  16713  16713  16713  16713  16713  16713  16713  16713   \n",
       "2006-10-31    17472  17472  17472  17472  17472  17472  17472  17472  17472   \n",
       "2006-11-30    16844  16844  16844  16844  16844  16844  16844  16844  16844   \n",
       "2006-12-31    16673  16673  16673  16673  16673  16673  16673  16673  16673   \n",
       "2007-01-31    14337  14337  14337  14337  14337  14337  14337  14337  14337   \n",
       "2007-02-28    13496  13496  13496  13496  13496  13496  13496  13496  13496   \n",
       "2007-03-31    15144  15144  15144  15144  15144  15144  15144  15144  15144   \n",
       "2007-04-30    15911  15911  15911  15911  15911  15911  15911  15911  15911   \n",
       "2007-05-31    15707  15707  15707  15707  15707  15707  15707  15707  15707   \n",
       "2007-06-30    16918  16918  16918  16918  16918  16918  16918  16918  16918   \n",
       "...             ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "2014-07-31    10954  10954  10954  10954  10954  10954  10954  10954  10954   \n",
       "2014-08-31     9653   9653   9653   9653   9653   9653   9653   9653   9653   \n",
       "2014-09-30    11890  11890  11890  11890  11890  11890  11890  11890  11890   \n",
       "2014-10-31    12577  12577  12577  12577  12577  12577  12577  12577  12577   \n",
       "2014-11-30    11383  11383  11383  11383  11383  11383  11383  11383  11383   \n",
       "2014-12-31    11020  11020  11020  11020  11020  11020  11020  11020  11020   \n",
       "2015-01-31     9669   9669   9669   9669   9669   9669   9669   9669   9669   \n",
       "2015-02-28     8729   8729   8729   8729   8729   8729   8729   8729   8729   \n",
       "2015-03-31     9742   9742   9742   9742   9742   9742   9742   9742   9742   \n",
       "2015-04-30    10718  10718  10718  10718  10718  10718  10718  10718  10718   \n",
       "2015-05-31    11007  11007  11007  11007  11007  11007  11007  11007  11007   \n",
       "2015-06-30    12553  12553  12553  12553  12553  12553  12553  12553  12553   \n",
       "2015-07-31    11840  11840  11840  11840  11840  11840  11840  11840  11840   \n",
       "2015-08-31    10149  10149  10149  10149  10149  10149  10149  10149  10149   \n",
       "2015-09-30    11824  11824  11824  11824  11824  11824  11824  11824  11824   \n",
       "2015-10-31    11749  11749  11749  11749  11749  11749  11749  11749  11749   \n",
       "2015-11-30    11224  11224  11224  11224  11224  11224  11224  11224  11224   \n",
       "2015-12-31    11150  11150  11150  11150  11150  11150  11150  11150  11150   \n",
       "2016-01-31    10704  10704  10704  10704  10704  10704  10704  10704  10704   \n",
       "2016-02-29     9026   9026   9026   9026   9026   9026   9026   9026   9026   \n",
       "2016-03-31    10242  10242  10242  10242  10242  10242  10242  10242  10242   \n",
       "2016-04-30     9980   9980   9980   9980   9980   9980   9980   9980   9980   \n",
       "2016-05-31    11356  11356  11356  11356  11356  11356  11356  11356  11356   \n",
       "2016-06-30    12038  12038  12038  12038  12038  12038  12038  12038  12038   \n",
       "2016-07-31    12083  12083  12083  12083  12083  12083  12083  12083  12083   \n",
       "2016-08-31     9999   9999   9999   9999   9999   9999   9999   9999   9999   \n",
       "2016-09-30    11979  11979  11979  11979  11979  11979  11979  11979  11979   \n",
       "2016-10-31    12489  12489  12489  12489  12489  12489  12489  12489  12489   \n",
       "2016-11-30    11989  11989  11989  11989  11989  11989  11989  11989  11989   \n",
       "2016-12-31    11525  11525  11525  11525  11525  11525  11525  11525  11525   \n",
       "\n",
       "              col    com    dep   catu   grav   sexe  trajet   secu   locp  \\\n",
       "date                                                                         \n",
       "2005-01-31  16351  16351  16351  16351  16351  16351   16351  16351  16351   \n",
       "2005-02-28  12737  12737  12737  12737  12737  12737   12737  12737  12737   \n",
       "2005-03-31  14730  14730  14730  14730  14730  14730   14730  14730  14730   \n",
       "2005-04-30  14850  14850  14850  14850  14850  14850   14850  14850  14850   \n",
       "2005-05-31  17731  17731  17731  17731  17731  17731   17731  17731  17731   \n",
       "2005-06-30  18775  18775  18775  18775  18775  18775   18775  18775  18775   \n",
       "2005-07-31  17667  17667  17667  17667  17667  17667   17667  17667  17667   \n",
       "2005-08-31  15099  15099  15099  15099  15099  15099   15099  15099  15099   \n",
       "2005-09-30  17724  17724  17724  17724  17724  17724   17724  17724  17724   \n",
       "2005-10-31  18009  18009  18009  18009  18009  18009   18009  18009  18009   \n",
       "2005-11-30  16963  16963  16963  16963  16963  16963   16963  16963  16963   \n",
       "2005-12-31  16185  16185  16185  16185  16185  16185   16185  16185  16185   \n",
       "2006-01-31  14066  14066  14066  14066  14066  14066   14066  14066  14066   \n",
       "2006-02-28  11872  11872  11872  11872  11872  11872   11872  11872  11872   \n",
       "2006-03-31  13919  13919  13919  13919  13919  13919   13919  13919  13919   \n",
       "2006-04-30  14713  14713  14713  14713  14713  14713   14713  14713  14713   \n",
       "2006-05-31  15567  15567  15567  15567  15567  15567   15567  15567  15567   \n",
       "2006-06-30  17545  17545  17545  17545  17545  17545   17545  17545  17545   \n",
       "2006-07-31  17124  17124  17124  17124  17124  17124   17124  17124  17124   \n",
       "2006-08-31  14043  14043  14043  14043  14043  14043   14043  14043  14043   \n",
       "2006-09-30  16713  16713  16713  16713  16713  16713   16713  16713  16713   \n",
       "2006-10-31  17472  17472  17472  17472  17472  17472   17472  17472  17472   \n",
       "2006-11-30  16844  16844  16844  16844  16844  16844   16844  16844  16844   \n",
       "2006-12-31  16673  16673  16673  16673  16673  16673   16673  16673  16673   \n",
       "2007-01-31  14337  14337  14337  14337  14337  14337   14337  14337  14337   \n",
       "2007-02-28  13496  13496  13496  13496  13496  13496   13496  13496  13496   \n",
       "2007-03-31  15144  15144  15144  15144  15144  15144   15144  15144  15144   \n",
       "2007-04-30  15911  15911  15911  15911  15911  15911   15911  15911  15911   \n",
       "2007-05-31  15707  15707  15707  15707  15707  15707   15707  15707  15707   \n",
       "2007-06-30  16918  16918  16918  16918  16918  16918   16918  16918  16918   \n",
       "...           ...    ...    ...    ...    ...    ...     ...    ...    ...   \n",
       "2014-07-31  10954  10954  10954  10954  10954  10954   10954  10954  10954   \n",
       "2014-08-31   9653   9653   9653   9653   9653   9653    9653   9653   9653   \n",
       "2014-09-30  11890  11890  11890  11890  11890  11890   11890  11890  11890   \n",
       "2014-10-31  12577  12577  12577  12577  12577  12577   12577  12577  12577   \n",
       "2014-11-30  11383  11383  11383  11383  11383  11383   11383  11383  11383   \n",
       "2014-12-31  11020  11020  11020  11020  11020  11020   11020  11020  11020   \n",
       "2015-01-31   9669   9669   9669   9669   9669   9669    9669   9669   9669   \n",
       "2015-02-28   8729   8729   8729   8729   8729   8729    8729   8729   8729   \n",
       "2015-03-31   9742   9742   9742   9742   9742   9742    9742   9742   9742   \n",
       "2015-04-30  10718  10718  10718  10718  10718  10718   10718  10718  10718   \n",
       "2015-05-31  11007  11007  11007  11007  11007  11007   11007  11007  11007   \n",
       "2015-06-30  12553  12553  12553  12553  12553  12553   12553  12553  12553   \n",
       "2015-07-31  11840  11840  11840  11840  11840  11840   11840  11840  11840   \n",
       "2015-08-31  10149  10149  10149  10149  10149  10149   10149  10149  10149   \n",
       "2015-09-30  11824  11824  11824  11824  11824  11824   11824  11824  11824   \n",
       "2015-10-31  11749  11749  11749  11749  11749  11749   11749  11749  11749   \n",
       "2015-11-30  11224  11224  11224  11224  11224  11224   11224  11224  11224   \n",
       "2015-12-31  11150  11150  11150  11150  11150  11150   11150  11150  11150   \n",
       "2016-01-31  10704  10704  10704  10704  10704  10704   10704  10704  10704   \n",
       "2016-02-29   9026   9026   9026   9026   9026   9026    9026   9026   9026   \n",
       "2016-03-31  10242  10242  10242  10242  10242  10242   10242  10242  10242   \n",
       "2016-04-30   9980   9980   9980   9980   9980   9980    9980   9980   9980   \n",
       "2016-05-31  11356  11356  11356  11356  11356  11356   11356  11356  11356   \n",
       "2016-06-30  12038  12038  12038  12038  12038  12038   12038  12038  12038   \n",
       "2016-07-31  12083  12083  12083  12083  12083  12083   12083  12083  12083   \n",
       "2016-08-31   9999   9999   9999   9999   9999   9999    9999   9999   9999   \n",
       "2016-09-30  11979  11979  11979  11979  11979  11979   11979  11979  11979   \n",
       "2016-10-31  12489  12489  12489  12489  12489  12489   12489  12489  12489   \n",
       "2016-11-30  11989  11989  11989  11989  11989  11989   11989  11989  11989   \n",
       "2016-12-31  11525  11525  11525  11525  11525  11525   11525  11525  11525   \n",
       "\n",
       "             actp  etatp  an_nais  num_veh  secu1   catr   circ   vosp   prof  \\\n",
       "date                                                                            \n",
       "2005-01-31  16351  16351    16351    16351  16351  16351  16351  16351  16351   \n",
       "2005-02-28  12737  12737    12737    12737  12737  12737  12737  12737  12737   \n",
       "2005-03-31  14730  14730    14730    14730  14730  14730  14730  14730  14730   \n",
       "2005-04-30  14850  14850    14850    14850  14850  14850  14850  14850  14850   \n",
       "2005-05-31  17731  17731    17731    17731  17731  17731  17731  17731  17731   \n",
       "2005-06-30  18775  18775    18775    18775  18775  18775  18775  18775  18775   \n",
       "2005-07-31  17667  17667    17667    17667  17667  17667  17667  17667  17667   \n",
       "2005-08-31  15099  15099    15099    15099  15099  15099  15099  15099  15099   \n",
       "2005-09-30  17724  17724    17724    17724  17724  17724  17724  17724  17724   \n",
       "2005-10-31  18009  18009    18009    18009  18009  18009  18009  18009  18009   \n",
       "2005-11-30  16963  16963    16963    16963  16963  16963  16963  16963  16963   \n",
       "2005-12-31  16185  16185    16185    16185  16185  16185  16185  16185  16185   \n",
       "2006-01-31  14066  14066    14066    14066  14066  14066  14066  14066  14066   \n",
       "2006-02-28  11872  11872    11872    11872  11872  11872  11872  11872  11872   \n",
       "2006-03-31  13919  13919    13919    13919  13919  13919  13919  13919  13919   \n",
       "2006-04-30  14713  14713    14713    14713  14713  14713  14713  14713  14713   \n",
       "2006-05-31  15567  15567    15567    15567  15567  15567  15567  15567  15567   \n",
       "2006-06-30  17545  17545    17545    17545  17545  17545  17545  17545  17545   \n",
       "2006-07-31  17124  17124    17124    17124  17124  17124  17124  17124  17124   \n",
       "2006-08-31  14043  14043    14043    14043  14043  14043  14043  14043  14043   \n",
       "2006-09-30  16713  16713    16713    16713  16713  16713  16713  16713  16713   \n",
       "2006-10-31  17472  17472    17472    17472  17472  17472  17472  17472  17472   \n",
       "2006-11-30  16844  16844    16844    16844  16844  16844  16844  16844  16844   \n",
       "2006-12-31  16673  16673    16673    16673  16673  16673  16673  16673  16673   \n",
       "2007-01-31  14337  14337    14337    14337  14337  14337  14337  14337  14337   \n",
       "2007-02-28  13496  13496    13496    13496  13496  13496  13496  13496  13496   \n",
       "2007-03-31  15144  15144    15144    15144  15144  15144  15144  15144  15144   \n",
       "2007-04-30  15911  15911    15911    15911  15911  15911  15911  15911  15911   \n",
       "2007-05-31  15707  15707    15707    15707  15707  15707  15707  15707  15707   \n",
       "2007-06-30  16918  16918    16918    16918  16918  16918  16918  16918  16918   \n",
       "...           ...    ...      ...      ...    ...    ...    ...    ...    ...   \n",
       "2014-07-31  10954  10954    10954    10954  10954  10954  10954  10954  10954   \n",
       "2014-08-31   9653   9653     9653     9653   9653   9653   9653   9653   9653   \n",
       "2014-09-30  11890  11890    11890    11890  11890  11890  11890  11890  11890   \n",
       "2014-10-31  12577  12577    12577    12577  12577  12577  12577  12577  12577   \n",
       "2014-11-30  11383  11383    11383    11383  11383  11383  11383  11383  11383   \n",
       "2014-12-31  11020  11020    11020    11020  11020  11020  11020  11020  11020   \n",
       "2015-01-31   9669   9669     9669     9669   9669   9669   9669   9669   9669   \n",
       "2015-02-28   8729   8729     8729     8729   8729   8729   8729   8729   8729   \n",
       "2015-03-31   9742   9742     9742     9742   9742   9742   9742   9742   9742   \n",
       "2015-04-30  10718  10718    10718    10718  10718  10718  10718  10718  10718   \n",
       "2015-05-31  11007  11007    11007    11007  11007  11007  11007  11007  11007   \n",
       "2015-06-30  12553  12553    12553    12553  12553  12553  12553  12553  12553   \n",
       "2015-07-31  11840  11840    11840    11840  11840  11840  11840  11840  11840   \n",
       "2015-08-31  10149  10149    10149    10149  10149  10149  10149  10149  10149   \n",
       "2015-09-30  11824  11824    11824    11824  11824  11824  11824  11824  11824   \n",
       "2015-10-31  11749  11749    11749    11749  11749  11749  11749  11749  11749   \n",
       "2015-11-30  11224  11224    11224    11224  11224  11224  11224  11224  11224   \n",
       "2015-12-31  11150  11150    11150    11150  11150  11150  11150  11150  11150   \n",
       "2016-01-31  10704  10704    10704    10704  10704  10704  10704  10704  10704   \n",
       "2016-02-29   9026   9026     9026     9026   9026   9026   9026   9026   9026   \n",
       "2016-03-31  10242  10242    10242    10242  10242  10242  10242  10242  10242   \n",
       "2016-04-30   9980   9980     9980     9980   9980   9980   9980   9980   9980   \n",
       "2016-05-31  11356  11356    11356    11356  11356  11356  11356  11356  11356   \n",
       "2016-06-30  12038  12038    12038    12038  12038  12038  12038  12038  12038   \n",
       "2016-07-31  12083  12083    12083    12083  12083  12083  12083  12083  12083   \n",
       "2016-08-31   9999   9999     9999     9999   9999   9999   9999   9999   9999   \n",
       "2016-09-30  11979  11979    11979    11979  11979  11979  11979  11979  11979   \n",
       "2016-10-31  12489  12489    12489    12489  12489  12489  12489  12489  12489   \n",
       "2016-11-30  11989  11989    11989    11989  11989  11989  11989  11989  11989   \n",
       "2016-12-31  11525  11525    11525    11525  11525  11525  11525  11525  11525   \n",
       "\n",
       "             plan  lartpc  larrout   surf  infra   situ   env1   catv  idade  \\\n",
       "date                                                                           \n",
       "2005-01-31  16351   16351    16351  16351  16351  16351  16351  16351  16351   \n",
       "2005-02-28  12737   12737    12737  12737  12737  12737  12737  12737  12737   \n",
       "2005-03-31  14730   14730    14730  14730  14730  14730  14730  14730  14730   \n",
       "2005-04-30  14850   14850    14850  14850  14850  14850  14850  14850  14850   \n",
       "2005-05-31  17731   17731    17731  17731  17731  17731  17731  17731  17731   \n",
       "2005-06-30  18775   18775    18775  18775  18775  18775  18775  18775  18775   \n",
       "2005-07-31  17667   17667    17667  17667  17667  17667  17667  17667  17667   \n",
       "2005-08-31  15099   15099    15099  15099  15099  15099  15099  15099  15099   \n",
       "2005-09-30  17724   17724    17724  17724  17724  17724  17724  17724  17724   \n",
       "2005-10-31  18009   18009    18009  18009  18009  18009  18009  18009  18009   \n",
       "2005-11-30  16963   16963    16963  16963  16963  16963  16963  16963  16963   \n",
       "2005-12-31  16185   16185    16185  16185  16185  16185  16185  16185  16185   \n",
       "2006-01-31  14066   14066    14066  14066  14066  14066  14066  14066  14066   \n",
       "2006-02-28  11872   11872    11872  11872  11872  11872  11872  11872  11872   \n",
       "2006-03-31  13919   13919    13919  13919  13919  13919  13919  13919  13919   \n",
       "2006-04-30  14713   14713    14713  14713  14713  14713  14713  14713  14713   \n",
       "2006-05-31  15567   15567    15567  15567  15567  15567  15567  15567  15567   \n",
       "2006-06-30  17545   17545    17545  17545  17545  17545  17545  17545  17545   \n",
       "2006-07-31  17124   17124    17124  17124  17124  17124  17124  17124  17124   \n",
       "2006-08-31  14043   14043    14043  14043  14043  14043  14043  14043  14043   \n",
       "2006-09-30  16713   16713    16713  16713  16713  16713  16713  16713  16713   \n",
       "2006-10-31  17472   17472    17472  17472  17472  17472  17472  17472  17472   \n",
       "2006-11-30  16844   16844    16844  16844  16844  16844  16844  16844  16844   \n",
       "2006-12-31  16673   16673    16673  16673  16673  16673  16673  16673  16673   \n",
       "2007-01-31  14337   14337    14337  14337  14337  14337  14337  14337  14337   \n",
       "2007-02-28  13496   13496    13496  13496  13496  13496  13496  13496  13496   \n",
       "2007-03-31  15144   15144    15144  15144  15144  15144  15144  15144  15144   \n",
       "2007-04-30  15911   15911    15911  15911  15911  15911  15911  15911  15911   \n",
       "2007-05-31  15707   15707    15707  15707  15707  15707  15707  15707  15707   \n",
       "2007-06-30  16918   16918    16918  16918  16918  16918  16918  16918  16918   \n",
       "...           ...     ...      ...    ...    ...    ...    ...    ...    ...   \n",
       "2014-07-31  10954   10954    10954  10954  10954  10954  10954  10954  10954   \n",
       "2014-08-31   9653    9653     9653   9653   9653   9653   9653   9653   9653   \n",
       "2014-09-30  11890   11890    11890  11890  11890  11890  11890  11890  11890   \n",
       "2014-10-31  12577   12577    12577  12577  12577  12577  12577  12577  12577   \n",
       "2014-11-30  11383   11383    11383  11383  11383  11383  11383  11383  11383   \n",
       "2014-12-31  11020   11020    11020  11020  11020  11020  11020  11020  11020   \n",
       "2015-01-31   9669    9669     9669   9669   9669   9669   9669   9669   9669   \n",
       "2015-02-28   8729    8729     8729   8729   8729   8729   8729   8729   8729   \n",
       "2015-03-31   9742    9742     9742   9742   9742   9742   9742   9742   9742   \n",
       "2015-04-30  10718   10718    10718  10718  10718  10718  10718  10718  10718   \n",
       "2015-05-31  11007   11007    11007  11007  11007  11007  11007  11007  11007   \n",
       "2015-06-30  12553   12553    12553  12553  12553  12553  12553  12553  12553   \n",
       "2015-07-31  11840   11840    11840  11840  11840  11840  11840  11840  11840   \n",
       "2015-08-31  10149   10149    10149  10149  10149  10149  10149  10149  10149   \n",
       "2015-09-30  11824   11824    11824  11824  11824  11824  11824  11824  11824   \n",
       "2015-10-31  11749   11749    11749  11749  11749  11749  11749  11749  11749   \n",
       "2015-11-30  11224   11224    11224  11224  11224  11224  11224  11224  11224   \n",
       "2015-12-31  11150   11150    11150  11150  11150  11150  11150  11150  11150   \n",
       "2016-01-31  10704   10704    10704  10704  10704  10704  10704  10704  10704   \n",
       "2016-02-29   9026    9026     9026   9026   9026   9026   9026   9026   9026   \n",
       "2016-03-31  10242   10242    10242  10242  10242  10242  10242  10242  10242   \n",
       "2016-04-30   9980    9980     9980   9980   9980   9980   9980   9980   9980   \n",
       "2016-05-31  11356   11356    11356  11356  11356  11356  11356  11356  11356   \n",
       "2016-06-30  12038   12038    12038  12038  12038  12038  12038  12038  12038   \n",
       "2016-07-31  12083   12083    12083  12083  12083  12083  12083  12083  12083   \n",
       "2016-08-31   9999    9999     9999   9999   9999   9999   9999   9999   9999   \n",
       "2016-09-30  11979   11979    11979  11979  11979  11979  11979  11979  11979   \n",
       "2016-10-31  12489   12489    12489  12489  12489  12489  12489  12489  12489   \n",
       "2016-11-30  11989   11989    11989  11989  11989  11989  11989  11989  11989   \n",
       "2016-12-31  11525   11525    11525  11525  11525  11525  11525  11525  11525   \n",
       "\n",
       "            secu2  grav1   dep2    Ain    AIN    ain   A500  \n",
       "date                                                         \n",
       "2005-01-31  16351  16351  16351  15595  15595  15595  15595  \n",
       "2005-02-28  12737  12737  12737  12136  12136  12136  12136  \n",
       "2005-03-31  14730  14730  14730  13966  13966  13966  13966  \n",
       "2005-04-30  14850  14850  14850  14138  14138  14138  14138  \n",
       "2005-05-31  17731  17731  17731  16835  16835  16835  16835  \n",
       "2005-06-30  18775  18775  18775  17996  17996  17996  17996  \n",
       "2005-07-31  17667  17667  17667  16935  16935  16935  16935  \n",
       "2005-08-31  15099  15099  15099  14310  14310  14310  14310  \n",
       "2005-09-30  17724  17724  17724  16995  16995  16995  16995  \n",
       "2005-10-31  18009  18009  18009  17239  17239  17239  17239  \n",
       "2005-11-30  16963  16963  16963  16090  16090  16090  16090  \n",
       "2005-12-31  16185  16185  16185  15304  15304  15304  15304  \n",
       "2006-01-31  14066  14066  14066  13342  13342  13342  13342  \n",
       "2006-02-28  11872  11872  11872  11220  11220  11220  11220  \n",
       "2006-03-31  13919  13919  13919  13140  13140  13140  13140  \n",
       "2006-04-30  14713  14713  14713  13923  13923  13923  13923  \n",
       "2006-05-31  15567  15567  15567  14720  14720  14720  14720  \n",
       "2006-06-30  17545  17545  17545  16750  16750  16750  16750  \n",
       "2006-07-31  17124  17124  17124  16219  16219  16219  16219  \n",
       "2006-08-31  14043  14043  14043  13274  13274  13274  13274  \n",
       "2006-09-30  16713  16713  16713  15847  15847  15847  15847  \n",
       "2006-10-31  17472  17472  17472  16674  16674  16674  16674  \n",
       "2006-11-30  16844  16844  16844  16097  16097  16097  16097  \n",
       "2006-12-31  16673  16673  16673  15838  15838  15838  15838  \n",
       "2007-01-31  14337  14337  14337  13674  13674  13674  13674  \n",
       "2007-02-28  13496  13496  13496  12752  12752  12752  12752  \n",
       "2007-03-31  15144  15144  15144  14402  14402  14402  14402  \n",
       "2007-04-30  15911  15911  15911  15185  15185  15185  15185  \n",
       "2007-05-31  15707  15707  15707  14929  14929  14929  14929  \n",
       "2007-06-30  16918  16918  16918  15963  15963  15963  15963  \n",
       "...           ...    ...    ...    ...    ...    ...    ...  \n",
       "2014-07-31  10954  10954  10954  10515  10515  10515  10515  \n",
       "2014-08-31   9653   9653   9653   9202   9202   9202   9202  \n",
       "2014-09-30  11890  11890  11890  11392  11392  11392  11392  \n",
       "2014-10-31  12577  12577  12577  12130  12130  12130  12130  \n",
       "2014-11-30  11383  11383  11383  10910  10910  10910  10910  \n",
       "2014-12-31  11020  11020  11020  10454  10454  10454  10454  \n",
       "2015-01-31   9669   9669   9669   9204   9204   9204   9204  \n",
       "2015-02-28   8729   8729   8729   8195   8195   8195   8195  \n",
       "2015-03-31   9742   9742   9742   9212   9212   9212   9212  \n",
       "2015-04-30  10718  10718  10718  10133  10133  10133  10133  \n",
       "2015-05-31  11007  11007  11007  10403  10403  10403  10403  \n",
       "2015-06-30  12553  12553  12553  11975  11975  11975  11975  \n",
       "2015-07-31  11840  11840  11840  11337  11337  11337  11337  \n",
       "2015-08-31  10149  10149  10149   9632   9632   9632   9632  \n",
       "2015-09-30  11824  11824  11824  11315  11315  11315  11315  \n",
       "2015-10-31  11749  11749  11749  11178  11178  11178  11178  \n",
       "2015-11-30  11224  11224  11224  10741  10741  10741  10741  \n",
       "2015-12-31  11150  11150  11150  10602  10602  10602  10602  \n",
       "2016-01-31  10704  10704  10704  10248  10248  10248  10248  \n",
       "2016-02-29   9026   9026   9026   8584   8584   8584   8584  \n",
       "2016-03-31  10242  10242  10242   9684   9684   9684   9684  \n",
       "2016-04-30   9980   9980   9980   9515   9515   9515   9515  \n",
       "2016-05-31  11356  11356  11356  10771  10771  10771  10771  \n",
       "2016-06-30  12038  12038  12038  11467  11467  11467  11467  \n",
       "2016-07-31  12083  12083  12083  11481  11481  11481  11481  \n",
       "2016-08-31   9999   9999   9999   9431   9431   9431   9431  \n",
       "2016-09-30  11979  11979  11979  11432  11432  11432  11432  \n",
       "2016-10-31  12489  12489  12489  11986  11986  11986  11986  \n",
       "2016-11-30  11989  11989  11989  11479  11479  11479  11479  \n",
       "2016-12-31  11525  11525  11525  10899  10899  10899  10899  \n",
       "\n",
       "[144 rows x 43 columns]"
      ]
     },
     "execution_count": 580,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 619,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa4e82978>"
      ]
     },
     "execution_count": 619,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n=0\n",
    "(df[df.grav1 == n].ain.value_counts().head(5)/df[df.grav1 == n].Num_Acc.nunique() *100).plot(kind=\"barh\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 691,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: grav1, dtype: float64)"
      ]
     },
     "execution_count": 691,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n=0\n",
    "df[df['agg']==n].grav1.value_counts()/df[df['agg']==n].grav1.count()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 693,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    50.209756\n",
       "2.0    49.790244\n",
       "Name: agg, dtype: float64"
      ]
     },
     "execution_count": 693,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n=1\n",
    "df[df.grav1 == n]['agg'].value_counts()/df[df.grav1 == n]['agg'].count() *100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n=1\n",
    "df[df.grav1 == n]['agg'].value_counts()/df[df.grav1 == n]['agg'].count() *100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 686,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    0.266817\n",
       "1.0    0.271116\n",
       "2.0    0.549593\n",
       "3.0    0.566480\n",
       "4.0    0.616744\n",
       "Name: plan, dtype: float64"
      ]
     },
     "execution_count": 686,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = 1\n",
    "grave = (df[df.grav1 == n].plan.value_counts()/df.plan.value_counts()*100)\n",
    "\n",
    "n = 0\n",
    "n_grave = (df[df.grav1 == n].plan.value_counts()/df.plan.value_counts()*100)\n",
    " \n",
    "grave/n_grave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 687,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    21.328953\n",
       "2.0    35.466909\n",
       "3.0    36.162616\n",
       "0.0    21.061992\n",
       "4.0    38.147279\n",
       "Name: plan, dtype: float64"
      ]
     },
     "execution_count": 687,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 682,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    16.507363\n",
       "2.0     2.671357\n",
       "3.0     2.600473\n",
       "0.0     1.396929\n",
       "4.0     0.476977\n",
       "Name: plan, dtype: float64"
      ]
     },
     "execution_count": 682,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 647,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    79.750184\n",
       "0.0     6.857559\n",
       "2.0     6.366485\n",
       "3.0     6.012791\n",
       "4.0     1.012980\n",
       "Name: plan, dtype: float64"
      ]
     },
     "execution_count": 647,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_grave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 662,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae342b240>"
      ]
     },
     "execution_count": 662,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df.grav1==1].an.value_counts().sort_index().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Num_Acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 664,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59430"
      ]
     },
     "execution_count": 664,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.an == 2016].Num_Acc.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "x_col= ['col','catr','agg','plan','catu','situ','dep','atm']\n",
    "df_dummies = pd.DataFrame()\n",
    "for coluna in x_col:\n",
    "    temp = pd.get_dummies(df[coluna], prefix=coluna)\n",
    "    df_dummies = pd.concat([df_dummies, temp], axis=1)\n",
    "\n",
    "df_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}