US BabyNames Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mary,F,7065\r",
      "\r\n",
      "Anna,F,2604\r",
      "\r\n",
      "Emma,F,2003\r",
      "\r\n",
      "Elizabeth,F,1939\r",
      "\r\n",
      "Minnie,F,1746\r",
      "\r\n",
      "Margaret,F,1578\r",
      "\r\n",
      "Ida,F,1472\r",
      "\r\n",
      "Alice,F,1414\r",
      "\r\n",
      "Bertha,F,1320\r",
      "\r\n",
      "Sarah,F,1288\r",
      "\r\n",
      "Annie,F,1258\r",
      "\r\n",
      "Clara,F,1226\r",
      "\r\n",
      "Ella,F,1156\r",
      "\r\n",
      "Florence,F,1063\r",
      "\r\n",
      "Cora,F,1045\r",
      "\r\n",
      "Martha,F,1040\r",
      "\r\n",
      "Laura,F,1012\r",
      "\r\n",
      "Nellie,F,995\r",
      "\r\n",
      "Grace,F,982\r",
      "\r\n",
      "Carrie,F,949\r",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!head -20 names/yob1880.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "names1880 = pd.read_csv('names/yob1880.txt', names=['name', 'sex', 'births'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "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>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Margaret</td>\n",
       "      <td>F</td>\n",
       "      <td>1578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ida</td>\n",
       "      <td>F</td>\n",
       "      <td>1472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alice</td>\n",
       "      <td>F</td>\n",
       "      <td>1414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bertha</td>\n",
       "      <td>F</td>\n",
       "      <td>1320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>1288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Annie</td>\n",
       "      <td>F</td>\n",
       "      <td>1258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Clara</td>\n",
       "      <td>F</td>\n",
       "      <td>1226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ella</td>\n",
       "      <td>F</td>\n",
       "      <td>1156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Florence</td>\n",
       "      <td>F</td>\n",
       "      <td>1063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cora</td>\n",
       "      <td>F</td>\n",
       "      <td>1045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Martha</td>\n",
       "      <td>F</td>\n",
       "      <td>1040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Laura</td>\n",
       "      <td>F</td>\n",
       "      <td>1012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nellie</td>\n",
       "      <td>F</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Grace</td>\n",
       "      <td>F</td>\n",
       "      <td>982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Carrie</td>\n",
       "      <td>F</td>\n",
       "      <td>949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maude</td>\n",
       "      <td>F</td>\n",
       "      <td>858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Mabel</td>\n",
       "      <td>F</td>\n",
       "      <td>808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bessie</td>\n",
       "      <td>F</td>\n",
       "      <td>796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Jennie</td>\n",
       "      <td>F</td>\n",
       "      <td>793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Gertrude</td>\n",
       "      <td>F</td>\n",
       "      <td>787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Julia</td>\n",
       "      <td>F</td>\n",
       "      <td>783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Hattie</td>\n",
       "      <td>F</td>\n",
       "      <td>769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Edith</td>\n",
       "      <td>F</td>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Mattie</td>\n",
       "      <td>F</td>\n",
       "      <td>704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Rose</td>\n",
       "      <td>F</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>Philo</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>Phineas</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>Presley</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>Ransom</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>Reece</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>Rene</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>Roswell</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>Rowland</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>Sampson</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>Samual</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>Santos</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>Schuyler</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>Sheppard</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>Spurgeon</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>Starling</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>Sylvanus</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>Theadore</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>Theophile</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>Tilmon</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>Tommy</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>Vann</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>Wes</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>Winston</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>Wood</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>Woodie</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>Worthy</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>Wright</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>York</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>Zachariah</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           name sex  births\n",
       "0          Mary   F    7065\n",
       "1          Anna   F    2604\n",
       "2          Emma   F    2003\n",
       "3     Elizabeth   F    1939\n",
       "4        Minnie   F    1746\n",
       "5      Margaret   F    1578\n",
       "6           Ida   F    1472\n",
       "7         Alice   F    1414\n",
       "8        Bertha   F    1320\n",
       "9         Sarah   F    1288\n",
       "10        Annie   F    1258\n",
       "11        Clara   F    1226\n",
       "12         Ella   F    1156\n",
       "13     Florence   F    1063\n",
       "14         Cora   F    1045\n",
       "15       Martha   F    1040\n",
       "16        Laura   F    1012\n",
       "17       Nellie   F     995\n",
       "18        Grace   F     982\n",
       "19       Carrie   F     949\n",
       "20        Maude   F     858\n",
       "21        Mabel   F     808\n",
       "22       Bessie   F     796\n",
       "23       Jennie   F     793\n",
       "24     Gertrude   F     787\n",
       "25        Julia   F     783\n",
       "26       Hattie   F     769\n",
       "27        Edith   F     768\n",
       "28       Mattie   F     704\n",
       "29         Rose   F     700\n",
       "...         ...  ..     ...\n",
       "1970      Philo   M       5\n",
       "1971    Phineas   M       5\n",
       "1972    Presley   M       5\n",
       "1973     Ransom   M       5\n",
       "1974      Reece   M       5\n",
       "1975       Rene   M       5\n",
       "1976    Roswell   M       5\n",
       "1977    Rowland   M       5\n",
       "1978    Sampson   M       5\n",
       "1979     Samual   M       5\n",
       "1980     Santos   M       5\n",
       "1981   Schuyler   M       5\n",
       "1982   Sheppard   M       5\n",
       "1983   Spurgeon   M       5\n",
       "1984   Starling   M       5\n",
       "1985   Sylvanus   M       5\n",
       "1986   Theadore   M       5\n",
       "1987  Theophile   M       5\n",
       "1988     Tilmon   M       5\n",
       "1989      Tommy   M       5\n",
       "1990    Unknown   M       5\n",
       "1991       Vann   M       5\n",
       "1992        Wes   M       5\n",
       "1993    Winston   M       5\n",
       "1994       Wood   M       5\n",
       "1995     Woodie   M       5\n",
       "1996     Worthy   M       5\n",
       "1997     Wright   M       5\n",
       "1998       York   M       5\n",
       "1999  Zachariah   M       5\n",
       "\n",
       "[2000 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "F     90993\n",
       "M    110491\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880.groupby('sex')['births'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = range(1880,2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pieces = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['name', 'sex', 'births']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for year in years:\n",
    "    path = 'names/yob%d.txt'% year\n",
    "    frame = pd.read_csv(path, names=columns)\n",
    "    frame ['year'] = year\n",
    "    pieces.append(frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = pd.concat(pieces, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Margaret</td>\n",
       "      <td>F</td>\n",
       "      <td>1578</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ida</td>\n",
       "      <td>F</td>\n",
       "      <td>1472</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alice</td>\n",
       "      <td>F</td>\n",
       "      <td>1414</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bertha</td>\n",
       "      <td>F</td>\n",
       "      <td>1320</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>1288</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Annie</td>\n",
       "      <td>F</td>\n",
       "      <td>1258</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Clara</td>\n",
       "      <td>F</td>\n",
       "      <td>1226</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ella</td>\n",
       "      <td>F</td>\n",
       "      <td>1156</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Florence</td>\n",
       "      <td>F</td>\n",
       "      <td>1063</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cora</td>\n",
       "      <td>F</td>\n",
       "      <td>1045</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Martha</td>\n",
       "      <td>F</td>\n",
       "      <td>1040</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Laura</td>\n",
       "      <td>F</td>\n",
       "      <td>1012</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nellie</td>\n",
       "      <td>F</td>\n",
       "      <td>995</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Grace</td>\n",
       "      <td>F</td>\n",
       "      <td>982</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Carrie</td>\n",
       "      <td>F</td>\n",
       "      <td>949</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maude</td>\n",
       "      <td>F</td>\n",
       "      <td>858</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Mabel</td>\n",
       "      <td>F</td>\n",
       "      <td>808</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bessie</td>\n",
       "      <td>F</td>\n",
       "      <td>796</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Jennie</td>\n",
       "      <td>F</td>\n",
       "      <td>793</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Gertrude</td>\n",
       "      <td>F</td>\n",
       "      <td>787</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Julia</td>\n",
       "      <td>F</td>\n",
       "      <td>783</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Hattie</td>\n",
       "      <td>F</td>\n",
       "      <td>769</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Edith</td>\n",
       "      <td>F</td>\n",
       "      <td>768</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Mattie</td>\n",
       "      <td>F</td>\n",
       "      <td>704</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Rose</td>\n",
       "      <td>F</td>\n",
       "      <td>700</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924635</th>\n",
       "      <td>Zayer</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924636</th>\n",
       "      <td>Zayir</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924637</th>\n",
       "      <td>Zechari</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924638</th>\n",
       "      <td>Zecharia</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924639</th>\n",
       "      <td>Zee</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924640</th>\n",
       "      <td>Zeid</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924641</th>\n",
       "      <td>Zennith</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924642</th>\n",
       "      <td>Zeo</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924643</th>\n",
       "      <td>Zerrick</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924644</th>\n",
       "      <td>Zevon</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924645</th>\n",
       "      <td>Zhen</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924646</th>\n",
       "      <td>Zhiyuan</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924647</th>\n",
       "      <td>Zhyir</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924648</th>\n",
       "      <td>Zikora</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924649</th>\n",
       "      <td>Zionn</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924650</th>\n",
       "      <td>Ziyang</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924651</th>\n",
       "      <td>Zkari</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924652</th>\n",
       "      <td>Zohaan</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924653</th>\n",
       "      <td>Zoren</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924654</th>\n",
       "      <td>Zorion</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924655</th>\n",
       "      <td>Zubeyr</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924656</th>\n",
       "      <td>Zy</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924657</th>\n",
       "      <td>Zyel</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924658</th>\n",
       "      <td>Zyheem</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924659</th>\n",
       "      <td>Zyhier</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924660</th>\n",
       "      <td>Zykai</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924661</th>\n",
       "      <td>Zykeem</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924662</th>\n",
       "      <td>Zylin</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924663</th>\n",
       "      <td>Zylis</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924664</th>\n",
       "      <td>Zyrie</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1924665 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              name sex  births  year\n",
       "0             Mary   F    7065  1880\n",
       "1             Anna   F    2604  1880\n",
       "2             Emma   F    2003  1880\n",
       "3        Elizabeth   F    1939  1880\n",
       "4           Minnie   F    1746  1880\n",
       "5         Margaret   F    1578  1880\n",
       "6              Ida   F    1472  1880\n",
       "7            Alice   F    1414  1880\n",
       "8           Bertha   F    1320  1880\n",
       "9            Sarah   F    1288  1880\n",
       "10           Annie   F    1258  1880\n",
       "11           Clara   F    1226  1880\n",
       "12            Ella   F    1156  1880\n",
       "13        Florence   F    1063  1880\n",
       "14            Cora   F    1045  1880\n",
       "15          Martha   F    1040  1880\n",
       "16           Laura   F    1012  1880\n",
       "17          Nellie   F     995  1880\n",
       "18           Grace   F     982  1880\n",
       "19          Carrie   F     949  1880\n",
       "20           Maude   F     858  1880\n",
       "21           Mabel   F     808  1880\n",
       "22          Bessie   F     796  1880\n",
       "23          Jennie   F     793  1880\n",
       "24        Gertrude   F     787  1880\n",
       "25           Julia   F     783  1880\n",
       "26          Hattie   F     769  1880\n",
       "27           Edith   F     768  1880\n",
       "28          Mattie   F     704  1880\n",
       "29            Rose   F     700  1880\n",
       "...            ...  ..     ...   ...\n",
       "1924635      Zayer   M       5  2017\n",
       "1924636      Zayir   M       5  2017\n",
       "1924637    Zechari   M       5  2017\n",
       "1924638   Zecharia   M       5  2017\n",
       "1924639        Zee   M       5  2017\n",
       "1924640       Zeid   M       5  2017\n",
       "1924641    Zennith   M       5  2017\n",
       "1924642        Zeo   M       5  2017\n",
       "1924643    Zerrick   M       5  2017\n",
       "1924644      Zevon   M       5  2017\n",
       "1924645       Zhen   M       5  2017\n",
       "1924646    Zhiyuan   M       5  2017\n",
       "1924647      Zhyir   M       5  2017\n",
       "1924648     Zikora   M       5  2017\n",
       "1924649      Zionn   M       5  2017\n",
       "1924650     Ziyang   M       5  2017\n",
       "1924651      Zkari   M       5  2017\n",
       "1924652     Zohaan   M       5  2017\n",
       "1924653      Zoren   M       5  2017\n",
       "1924654     Zorion   M       5  2017\n",
       "1924655     Zubeyr   M       5  2017\n",
       "1924656         Zy   M       5  2017\n",
       "1924657       Zyel   M       5  2017\n",
       "1924658     Zyheem   M       5  2017\n",
       "1924659     Zyhier   M       5  2017\n",
       "1924660      Zykai   M       5  2017\n",
       "1924661     Zykeem   M       5  2017\n",
       "1924662      Zylin   M       5  2017\n",
       "1924663      Zylis   M       5  2017\n",
       "1924664      Zyrie   M       5  2017\n",
       "\n",
       "[1924665 rows x 4 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_births = names.pivot_table('births', 'year', 'sex', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>1750321</td>\n",
       "      <td>1886989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>1781072</td>\n",
       "      <td>1915239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1778883</td>\n",
       "      <td>1909804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1763916</td>\n",
       "      <td>1889052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1711811</td>\n",
       "      <td>1834490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex         F        M\n",
       "year                  \n",
       "2013  1750321  1886989\n",
       "2014  1781072  1915239\n",
       "2015  1778883  1909804\n",
       "2016  1763916  1889052\n",
       "2017  1711811  1834490"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f246cedca50>"
      ]
     },
     "execution_count": 25,
     "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": [
    "total_births.plot(title='Total births by sex and year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_prop(group):\n",
    "    #Integer division floors\n",
    "    births = group.births.astype(float)\n",
    "    group['prop'] = births / births.sum()\n",
    "    return group\n",
    "names = names.groupby(['year', 'sex']).apply(add_prop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Margaret</td>\n",
       "      <td>F</td>\n",
       "      <td>1578</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.017342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ida</td>\n",
       "      <td>F</td>\n",
       "      <td>1472</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.016177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alice</td>\n",
       "      <td>F</td>\n",
       "      <td>1414</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.015540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bertha</td>\n",
       "      <td>F</td>\n",
       "      <td>1320</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>1288</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Annie</td>\n",
       "      <td>F</td>\n",
       "      <td>1258</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.013825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Clara</td>\n",
       "      <td>F</td>\n",
       "      <td>1226</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.013474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ella</td>\n",
       "      <td>F</td>\n",
       "      <td>1156</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.012704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Florence</td>\n",
       "      <td>F</td>\n",
       "      <td>1063</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cora</td>\n",
       "      <td>F</td>\n",
       "      <td>1045</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Martha</td>\n",
       "      <td>F</td>\n",
       "      <td>1040</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Laura</td>\n",
       "      <td>F</td>\n",
       "      <td>1012</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nellie</td>\n",
       "      <td>F</td>\n",
       "      <td>995</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.010935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Grace</td>\n",
       "      <td>F</td>\n",
       "      <td>982</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.010792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Carrie</td>\n",
       "      <td>F</td>\n",
       "      <td>949</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.010429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maude</td>\n",
       "      <td>F</td>\n",
       "      <td>858</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.009429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Mabel</td>\n",
       "      <td>F</td>\n",
       "      <td>808</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bessie</td>\n",
       "      <td>F</td>\n",
       "      <td>796</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Jennie</td>\n",
       "      <td>F</td>\n",
       "      <td>793</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Gertrude</td>\n",
       "      <td>F</td>\n",
       "      <td>787</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Julia</td>\n",
       "      <td>F</td>\n",
       "      <td>783</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Hattie</td>\n",
       "      <td>F</td>\n",
       "      <td>769</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Edith</td>\n",
       "      <td>F</td>\n",
       "      <td>768</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Mattie</td>\n",
       "      <td>F</td>\n",
       "      <td>704</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.007737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Rose</td>\n",
       "      <td>F</td>\n",
       "      <td>700</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.007693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924635</th>\n",
       "      <td>Zayer</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924636</th>\n",
       "      <td>Zayir</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924637</th>\n",
       "      <td>Zechari</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924638</th>\n",
       "      <td>Zecharia</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924639</th>\n",
       "      <td>Zee</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924640</th>\n",
       "      <td>Zeid</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924641</th>\n",
       "      <td>Zennith</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924642</th>\n",
       "      <td>Zeo</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924643</th>\n",
       "      <td>Zerrick</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924644</th>\n",
       "      <td>Zevon</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924645</th>\n",
       "      <td>Zhen</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924646</th>\n",
       "      <td>Zhiyuan</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924647</th>\n",
       "      <td>Zhyir</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924648</th>\n",
       "      <td>Zikora</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924649</th>\n",
       "      <td>Zionn</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924650</th>\n",
       "      <td>Ziyang</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924651</th>\n",
       "      <td>Zkari</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924652</th>\n",
       "      <td>Zohaan</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924653</th>\n",
       "      <td>Zoren</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924654</th>\n",
       "      <td>Zorion</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924655</th>\n",
       "      <td>Zubeyr</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924656</th>\n",
       "      <td>Zy</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924657</th>\n",
       "      <td>Zyel</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924658</th>\n",
       "      <td>Zyheem</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924659</th>\n",
       "      <td>Zyhier</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924660</th>\n",
       "      <td>Zykai</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924661</th>\n",
       "      <td>Zykeem</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924662</th>\n",
       "      <td>Zylin</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924663</th>\n",
       "      <td>Zylis</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924664</th>\n",
       "      <td>Zyrie</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1924665 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              name sex  births  year      prop\n",
       "0             Mary   F    7065  1880  0.077643\n",
       "1             Anna   F    2604  1880  0.028618\n",
       "2             Emma   F    2003  1880  0.022013\n",
       "3        Elizabeth   F    1939  1880  0.021309\n",
       "4           Minnie   F    1746  1880  0.019188\n",
       "5         Margaret   F    1578  1880  0.017342\n",
       "6              Ida   F    1472  1880  0.016177\n",
       "7            Alice   F    1414  1880  0.015540\n",
       "8           Bertha   F    1320  1880  0.014507\n",
       "9            Sarah   F    1288  1880  0.014155\n",
       "10           Annie   F    1258  1880  0.013825\n",
       "11           Clara   F    1226  1880  0.013474\n",
       "12            Ella   F    1156  1880  0.012704\n",
       "13        Florence   F    1063  1880  0.011682\n",
       "14            Cora   F    1045  1880  0.011484\n",
       "15          Martha   F    1040  1880  0.011429\n",
       "16           Laura   F    1012  1880  0.011122\n",
       "17          Nellie   F     995  1880  0.010935\n",
       "18           Grace   F     982  1880  0.010792\n",
       "19          Carrie   F     949  1880  0.010429\n",
       "20           Maude   F     858  1880  0.009429\n",
       "21           Mabel   F     808  1880  0.008880\n",
       "22          Bessie   F     796  1880  0.008748\n",
       "23          Jennie   F     793  1880  0.008715\n",
       "24        Gertrude   F     787  1880  0.008649\n",
       "25           Julia   F     783  1880  0.008605\n",
       "26          Hattie   F     769  1880  0.008451\n",
       "27           Edith   F     768  1880  0.008440\n",
       "28          Mattie   F     704  1880  0.007737\n",
       "29            Rose   F     700  1880  0.007693\n",
       "...            ...  ..     ...   ...       ...\n",
       "1924635      Zayer   M       5  2017  0.000003\n",
       "1924636      Zayir   M       5  2017  0.000003\n",
       "1924637    Zechari   M       5  2017  0.000003\n",
       "1924638   Zecharia   M       5  2017  0.000003\n",
       "1924639        Zee   M       5  2017  0.000003\n",
       "1924640       Zeid   M       5  2017  0.000003\n",
       "1924641    Zennith   M       5  2017  0.000003\n",
       "1924642        Zeo   M       5  2017  0.000003\n",
       "1924643    Zerrick   M       5  2017  0.000003\n",
       "1924644      Zevon   M       5  2017  0.000003\n",
       "1924645       Zhen   M       5  2017  0.000003\n",
       "1924646    Zhiyuan   M       5  2017  0.000003\n",
       "1924647      Zhyir   M       5  2017  0.000003\n",
       "1924648     Zikora   M       5  2017  0.000003\n",
       "1924649      Zionn   M       5  2017  0.000003\n",
       "1924650     Ziyang   M       5  2017  0.000003\n",
       "1924651      Zkari   M       5  2017  0.000003\n",
       "1924652     Zohaan   M       5  2017  0.000003\n",
       "1924653      Zoren   M       5  2017  0.000003\n",
       "1924654     Zorion   M       5  2017  0.000003\n",
       "1924655     Zubeyr   M       5  2017  0.000003\n",
       "1924656         Zy   M       5  2017  0.000003\n",
       "1924657       Zyel   M       5  2017  0.000003\n",
       "1924658     Zyheem   M       5  2017  0.000003\n",
       "1924659     Zyhier   M       5  2017  0.000003\n",
       "1924660      Zykai   M       5  2017  0.000003\n",
       "1924661     Zykeem   M       5  2017  0.000003\n",
       "1924662      Zylin   M       5  2017  0.000003\n",
       "1924663      Zylis   M       5  2017  0.000003\n",
       "1924664      Zyrie   M       5  2017  0.000003\n",
       "\n",
       "[1924665 rows x 5 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(names.groupby(['year', 'sex']).prop.sum(), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_top1000(group):\n",
    "    return group.sort_values(by='births', ascending=False)[:1000]\n",
    "grouped = names.groupby(['year', 'sex'])\n",
    "top1000 = grouped.apply(get_top1000)\n",
    "top1000.index = np.arange(len(top1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Margaret</td>\n",
       "      <td>F</td>\n",
       "      <td>1578</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.017342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ida</td>\n",
       "      <td>F</td>\n",
       "      <td>1472</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.016177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alice</td>\n",
       "      <td>F</td>\n",
       "      <td>1414</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.015540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bertha</td>\n",
       "      <td>F</td>\n",
       "      <td>1320</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>1288</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Annie</td>\n",
       "      <td>F</td>\n",
       "      <td>1258</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.013825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Clara</td>\n",
       "      <td>F</td>\n",
       "      <td>1226</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.013474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ella</td>\n",
       "      <td>F</td>\n",
       "      <td>1156</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.012704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Florence</td>\n",
       "      <td>F</td>\n",
       "      <td>1063</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cora</td>\n",
       "      <td>F</td>\n",
       "      <td>1045</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Martha</td>\n",
       "      <td>F</td>\n",
       "      <td>1040</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Laura</td>\n",
       "      <td>F</td>\n",
       "      <td>1012</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nellie</td>\n",
       "      <td>F</td>\n",
       "      <td>995</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.010935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Grace</td>\n",
       "      <td>F</td>\n",
       "      <td>982</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.010792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Carrie</td>\n",
       "      <td>F</td>\n",
       "      <td>949</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.010429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maude</td>\n",
       "      <td>F</td>\n",
       "      <td>858</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.009429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Mabel</td>\n",
       "      <td>F</td>\n",
       "      <td>808</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bessie</td>\n",
       "      <td>F</td>\n",
       "      <td>796</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Jennie</td>\n",
       "      <td>F</td>\n",
       "      <td>793</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Gertrude</td>\n",
       "      <td>F</td>\n",
       "      <td>787</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Julia</td>\n",
       "      <td>F</td>\n",
       "      <td>783</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Hattie</td>\n",
       "      <td>F</td>\n",
       "      <td>769</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Edith</td>\n",
       "      <td>F</td>\n",
       "      <td>768</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Mattie</td>\n",
       "      <td>F</td>\n",
       "      <td>704</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.007737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Rose</td>\n",
       "      <td>F</td>\n",
       "      <td>700</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.007693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275847</th>\n",
       "      <td>Simeon</td>\n",
       "      <td>M</td>\n",
       "      <td>211</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275848</th>\n",
       "      <td>Yadiel</td>\n",
       "      <td>M</td>\n",
       "      <td>211</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275849</th>\n",
       "      <td>Ira</td>\n",
       "      <td>M</td>\n",
       "      <td>211</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275850</th>\n",
       "      <td>Ledger</td>\n",
       "      <td>M</td>\n",
       "      <td>211</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275851</th>\n",
       "      <td>Avi</td>\n",
       "      <td>M</td>\n",
       "      <td>211</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275852</th>\n",
       "      <td>Maddux</td>\n",
       "      <td>M</td>\n",
       "      <td>210</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275853</th>\n",
       "      <td>Seamus</td>\n",
       "      <td>M</td>\n",
       "      <td>210</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275854</th>\n",
       "      <td>Jad</td>\n",
       "      <td>M</td>\n",
       "      <td>209</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275855</th>\n",
       "      <td>Jeremias</td>\n",
       "      <td>M</td>\n",
       "      <td>209</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275856</th>\n",
       "      <td>Kylen</td>\n",
       "      <td>M</td>\n",
       "      <td>209</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275857</th>\n",
       "      <td>Rashad</td>\n",
       "      <td>M</td>\n",
       "      <td>208</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275858</th>\n",
       "      <td>Santos</td>\n",
       "      <td>M</td>\n",
       "      <td>208</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275859</th>\n",
       "      <td>Dominique</td>\n",
       "      <td>M</td>\n",
       "      <td>207</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275860</th>\n",
       "      <td>Gianluca</td>\n",
       "      <td>M</td>\n",
       "      <td>207</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275861</th>\n",
       "      <td>Jovanni</td>\n",
       "      <td>M</td>\n",
       "      <td>207</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275862</th>\n",
       "      <td>Craig</td>\n",
       "      <td>M</td>\n",
       "      <td>207</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275863</th>\n",
       "      <td>Cedric</td>\n",
       "      <td>M</td>\n",
       "      <td>207</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275864</th>\n",
       "      <td>Bishop</td>\n",
       "      <td>M</td>\n",
       "      <td>205</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275865</th>\n",
       "      <td>Brenden</td>\n",
       "      <td>M</td>\n",
       "      <td>204</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275866</th>\n",
       "      <td>Anton</td>\n",
       "      <td>M</td>\n",
       "      <td>203</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275867</th>\n",
       "      <td>Camron</td>\n",
       "      <td>M</td>\n",
       "      <td>203</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275868</th>\n",
       "      <td>Giancarlo</td>\n",
       "      <td>M</td>\n",
       "      <td>203</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275869</th>\n",
       "      <td>Lyle</td>\n",
       "      <td>M</td>\n",
       "      <td>203</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275870</th>\n",
       "      <td>Alaric</td>\n",
       "      <td>M</td>\n",
       "      <td>202</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275871</th>\n",
       "      <td>Decker</td>\n",
       "      <td>M</td>\n",
       "      <td>202</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275872</th>\n",
       "      <td>Eliezer</td>\n",
       "      <td>M</td>\n",
       "      <td>202</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275873</th>\n",
       "      <td>Ramiro</td>\n",
       "      <td>M</td>\n",
       "      <td>202</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275874</th>\n",
       "      <td>Yisroel</td>\n",
       "      <td>M</td>\n",
       "      <td>202</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275875</th>\n",
       "      <td>Marquis</td>\n",
       "      <td>M</td>\n",
       "      <td>201</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275876</th>\n",
       "      <td>Turner</td>\n",
       "      <td>M</td>\n",
       "      <td>201</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>275877 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             name sex  births  year      prop\n",
       "0            Mary   F    7065  1880  0.077643\n",
       "1            Anna   F    2604  1880  0.028618\n",
       "2            Emma   F    2003  1880  0.022013\n",
       "3       Elizabeth   F    1939  1880  0.021309\n",
       "4          Minnie   F    1746  1880  0.019188\n",
       "5        Margaret   F    1578  1880  0.017342\n",
       "6             Ida   F    1472  1880  0.016177\n",
       "7           Alice   F    1414  1880  0.015540\n",
       "8          Bertha   F    1320  1880  0.014507\n",
       "9           Sarah   F    1288  1880  0.014155\n",
       "10          Annie   F    1258  1880  0.013825\n",
       "11          Clara   F    1226  1880  0.013474\n",
       "12           Ella   F    1156  1880  0.012704\n",
       "13       Florence   F    1063  1880  0.011682\n",
       "14           Cora   F    1045  1880  0.011484\n",
       "15         Martha   F    1040  1880  0.011429\n",
       "16          Laura   F    1012  1880  0.011122\n",
       "17         Nellie   F     995  1880  0.010935\n",
       "18          Grace   F     982  1880  0.010792\n",
       "19         Carrie   F     949  1880  0.010429\n",
       "20          Maude   F     858  1880  0.009429\n",
       "21          Mabel   F     808  1880  0.008880\n",
       "22         Bessie   F     796  1880  0.008748\n",
       "23         Jennie   F     793  1880  0.008715\n",
       "24       Gertrude   F     787  1880  0.008649\n",
       "25          Julia   F     783  1880  0.008605\n",
       "26         Hattie   F     769  1880  0.008451\n",
       "27          Edith   F     768  1880  0.008440\n",
       "28         Mattie   F     704  1880  0.007737\n",
       "29           Rose   F     700  1880  0.007693\n",
       "...           ...  ..     ...   ...       ...\n",
       "275847     Simeon   M     211  2017  0.000115\n",
       "275848     Yadiel   M     211  2017  0.000115\n",
       "275849        Ira   M     211  2017  0.000115\n",
       "275850     Ledger   M     211  2017  0.000115\n",
       "275851        Avi   M     211  2017  0.000115\n",
       "275852     Maddux   M     210  2017  0.000114\n",
       "275853     Seamus   M     210  2017  0.000114\n",
       "275854        Jad   M     209  2017  0.000114\n",
       "275855   Jeremias   M     209  2017  0.000114\n",
       "275856      Kylen   M     209  2017  0.000114\n",
       "275857     Rashad   M     208  2017  0.000113\n",
       "275858     Santos   M     208  2017  0.000113\n",
       "275859  Dominique   M     207  2017  0.000113\n",
       "275860   Gianluca   M     207  2017  0.000113\n",
       "275861    Jovanni   M     207  2017  0.000113\n",
       "275862      Craig   M     207  2017  0.000113\n",
       "275863     Cedric   M     207  2017  0.000113\n",
       "275864     Bishop   M     205  2017  0.000112\n",
       "275865    Brenden   M     204  2017  0.000111\n",
       "275866      Anton   M     203  2017  0.000111\n",
       "275867     Camron   M     203  2017  0.000111\n",
       "275868  Giancarlo   M     203  2017  0.000111\n",
       "275869       Lyle   M     203  2017  0.000111\n",
       "275870     Alaric   M     202  2017  0.000110\n",
       "275871     Decker   M     202  2017  0.000110\n",
       "275872    Eliezer   M     202  2017  0.000110\n",
       "275873     Ramiro   M     202  2017  0.000110\n",
       "275874    Yisroel   M     202  2017  0.000110\n",
       "275875    Marquis   M     201  2017  0.000110\n",
       "275876     Turner   M     201  2017  0.000110\n",
       "\n",
       "[275877 rows x 5 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "boys = top1000[top1000.sex == 'M']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "girls = top1000[top1000.sex == 'F']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_births = top1000.pivot_table('births', 'year', 'name', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 138 entries, 1880 to 2017\n",
      "Columns: 7134 entries, Aaden to Zyaire\n",
      "dtypes: float64(7134)\n",
      "memory usage: 7.5 MB\n"
     ]
    }
   ],
   "source": [
    "total_births.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset = total_births[['John', 'Harry', 'Mary', 'Marilyn']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f245e5f5890>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f245e49a390>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f245e4d8410>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f245e42b490>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rfZ0LbPP2bTWzaiDVa5/b7tDt99m2T/vxh3Q2IiJHkbU7a5i3qYLbzhlOaEjnI7/5KTHc+cVj+N7pQ3hm7hae+Ggzlz8yj1G5CdQ3t7GxtI6pw9L5yfSRDErv+GagAWmxvPqtk/jeC0v468wN/pHp4RlMHZbByYPT2Lm7kfdW7+LdVTu57711OAchBoMz4vjC4DQK0mJ5bWkJ331hCb97Zy3fmDKQr4zLIyr80NfTXrezhksfmktdcyvnjs7hson5jO+f/LnR8JY2H995/mPeX1vKr74ymi8dl8f6XbU88P5GymubSI2LPOQaRI42XRlR7i57pnR0JDLy03/HoaGhtLa2fm7bvu2HS1emfyTjHzkeAFQBfwPO6aDrnjPs6H92d4D2joYpOrxaZnYDcANAv34aVRCRo9uri4sJDzUuLsw/qP2SYiK46dQhfP0LA/n7x8U8NmsTYSHGY9cUcurwzE73T4gK55GvFrJ9dyPZCVGEtAv0KbERjMhO4NunDKa8tomiygaGZMYRE/Hpj5ubThnMjFU7uf+/6/m/vy/nwfc38sCV4xmZ8/npKJ3ZWFrL5Y/MIyTEuGBsLq8vLeGVxUUMyYhj2uhszKChuY265lbW7qhl/uYK7jxv5N6R6eljcvjLfzfw1vIdXLmfKTQi0rNMnjyZBx98kKuvvpqKigo++OADfvOb33TLPOmD0ZXpH6cDm5xzpQBm9ipwEpBkZmHeaHUeUOL1LwLygSIzCwMSgYp27Xu032d/7Z/hnHsIeAigsLBw/29TRESOAjPX7KKwfwopsRGHtH9UeCiXTezHZRMPfpAiJMTITYo+YJ/UuMgOR39DQoyzjsnizJGZfLCujFtfXspX/voRv7loDNPH5HS5hq3l9Vz+8Dx8PscLN5zAkMx4fjJ9JP9aVsLz87fxx3f9D7+JCg8hNiKMmMhQ7jhvJNdM+vRpacOz4hmUHsvrS0sUqkV6uNbWViIjI/nSl77EnDlzOPbYYzEzfv3rX5OVlRX0UG0HGkIHMLPjgceACfinfzwBLAQmA6+0u1FxmXPufjP7NjDaOfdN70bFLzvnLjazY4Dn8M+jzgHeBYbgH8FeC5wGFAMLgMudcysOVFdhYaFbuHDhIZ62iEjvtr26gRN/+R63nzOcb0wZFOxyArKrppEbn1nMoi2VfHPKIG45a1in01mKqxq45ME51Da18vz1JzAi+/Oj3PXNrUSGhXZ6rHtnrOW+99Yx9/bTyNzPDZYiAqtWrWLEiBFBe/2lS5dy/fXXM3/+/G57jY7O0cwWOecKO9u3K3Oq55nZy8BioBX4GP9o8RvAC2Z2l9f2qLfLo8DT3o2IFfhX/MA5t8JbOWSld5xvO+favGJvAt4GQoHHOgvUIiJHu/fXlAIwdVhGkCsJXEZ8FM9ffwJ3vr6CB97fwCfFVQzNjKe4soHiKv9Ha5sjJTaClNgI0uIiWLW9ht2NLTz39Y4DNfCZKScHct6x2fzx3XW8+cl2rm03ii0iPccDDzzAfffdxx/+8Idgl7JfnY5U91QaqRaRo9k3n17E0qIqPrrt1D712ODn5m3lp6+vINSbXpKbHE1uUjQRYSFU1DVTUddMeW0zPue4+8ujGdcv+bC87tl/+IDYyDBeufGkw3I8kb4o2CPVR0K3jlSLiEjP0tLmY/b6MqYfm92nAjXA5cf34+LCPEJD7Iie23nH5vCbt9dQXNXQ6VxxEZGO6DHlIiK9zKItldQ0tTJlaO+f+tGRsNCQI/5mYfqYbADeWNbhffIi4umtMxy6ItBzU6gWEellZq4pJSzEmDQ4Ndil9Bn9U2MZk5fIv5ZtD3YpIj1WVFQU5eXlfTJYO+coLy8nKurQb1bW9A8RkV5m5ppdFBYkd/r0QDk408dkc/ebq9lSXkf/1NhglyPS4+Tl5VFUVERpaWmwS+kWUVFRe5/GeCgUqkVEepEd1Y2s3lHDbecMD3Ypfc65Y3L45VureXrOFn48fWSwyxHpccLDwxkwQCvk7I+mf4iI9CLvr90FwNRh6UGupO/JTYrmovF5PDlnMxtLa4Ndjoj0MgrVIiI9QGubD5+v83mKM9eUkpUQxbDM+CNQ1dHnf88aRmRYKL94Y1WwSxGRXkahWkQkyJYVVXHK72Zyxr3vM29j+X77tbT5mLWujKnD0vvcUno9RUZ8FDefOph3V+9i5ppdwS5HRHoRhWoRkSBxzvHM3C1c+Nc5tLU5Glt8XPLQXH748jKq6ps/13+xt5Sepn50r2smFVCQGsPP/7WSljZfsMsRkV5CoVpEJAjqm1v5/otL+PE/lnPioFTe+M4XmPE/k/nG5IG8vLiI03//Pi8u2Mr6XbU0trQBMHOtfym9kwanBbn6vi0yLJQfnzuSDaV1PD1nS7DLEZFeQqt/iIgcYRV1zVz60BzW7arlf84Yyk2nDCYkxD+d4/ZpI/ji2Bx+9Oon/PCVT/bukxEfSX1zG+P6J5OgpfS63WkjMvjCkDT+8J+1XHBcLimxEcEuSUR6OIVqEZEj7PHZm1i3q5Ynrp3IlKGfn8pxTE4ir35rEku2VbK1op6iiga2VdZTUtXIV0/sH4SKjz5mxv+bPpKz//ghv3prNfd8ZbTmsYvIASlUi4gcQY0tbTw7byunj8jsMFDvERpijO+fwvj+KUewOmlvSGY8X//CAB58fyMxkaH85NyRe3+jICKyL4VqEZEj6J9Liqmoa+Zrk/QAhd7gh2cNp63N8cisTZTXNvPbi44lIky3I4nI5ylUi4gcIc45Hp21iRHZCZwwUCPQvUFIiPF/544gLT6Se95aTWV9Mw9cOZ7YSP34FJHP6tLbbTNLMrOXzWy1ma0ysxPNLMXMZpjZOu9zstfXzOw+M1tvZsvMbFy741zt9V9nZle3ax9vZp94+9xnmrgmIn3Q7PXlrN1Zy3UnD9D83F7EzPjmlEH8+itjmL2+jMsenss/lxSzdmeNltwTkb26+lb7j8C/nXMXmlkEEAP8CHjXOXePmd0G3Ab8EDgHGOJ9HA/8FTjezFKAO4BCwAGLzOw151yl1+cGYC7wJnA28NZhOkcRkR7h0VkbSYuL4Lxjs4NdihyCiyfkkxwbwfdfXMJ3X1gCQERoCIMz4jhleDrfnDKIeK3MInLU6jRUm1kCMBm4BsA51ww0m9n5wFSv25PATPyh+nzgKeecA+Z6o9zZXt8ZzrkK77gzgLPNbCaQ4Jyb47U/BVyAQrWI9CEbSmv575pSvnf6ECLDQoNdjhyiM0ZmsvgnZ7CxrJZV23ezensNy0uq+ct/N/DigiJuPXsYF47L0w2NIkehroxUDwRKgcfN7FhgEfBdINM5tx3AObfdzDK8/rnAtnb7F3ltB2ov6qBdRHqo5cXVzFyzizNGZjEsKz7Y5fQKj8/eRERoCFeeoCXxeruIsBCGZyUwPCsBjvO3Ld1WxU9fX8GtLy/jmblb+NG0EYzMSSA+MkxTfUSOEl0J1WHAOOBm59w8M/sj/qke+9PR/x7uENo/f2CzG/BPE6Ffv34HqllEuqDN55izoZySqgbGFyQzMC12vwGgtc3HjJU7eXz2ZuZvrgDgdzPWMm10Nt85dYjC9QFU1TfzyqJizh+bQ1pcZLDLkW5wbH4Sr9x4Ev9YUsw9b63m0ofmAv6lEROiwkiKieCGyQO5bKJ+don0VV0J1UVAkXNunvf9y/hD9U4zy/ZGqbOBXe3657fbPw8o8dqn7tM+02vP66D/5zjnHgIeAigsLOwweIvIgTnn+HhbFa8tKeGNT7ZTWtO0d1tGfCQnDExlQkEyISFGdUML1Q0t7G5o4YO1ZRRXNZCXHM2Pzx3BmSOzeHHhVp6YvZk3lm3n3NHZXDupgOP6JROqX31/xuOzN9PQ0sbXTtYyen2ZmfGl4/I4c2QWb6/YQXltM9UNLVQ1NLN4i38k+9ThGWQmRAW7VBHpBuaf+txJJ7MPga8759aY2Z1ArLepvN2NiinOuVvN7FzgJmAa/hsV73POTfRuVFyEf9QbYDEw3jlXYWYLgJuBefhvVPyTc+7NA9VUWFjoFi5ceLDnK9KnfbS+jIyESAZndDxqvLmsjhueXsjanbVEhIVw6rAMzh+bw5DMOBZsrmTOhnLmbixnV7ugHREaQkJ0OEMz47j6pAJOH5H5mdBcWdfMI7M28sTszdQ1t5EYHc4XhqQxdVgGJwxMITE6nMiwUMJD7aj7NXhjSxs/+9dKnpu3lTNGZvLwVwuDXZIEydbyek793UwunpDP3V8aHexyROQgmNki51yn/4F3NVSPBR4BIoCNwLX4l+N7CegHbAUu8gKyAX/Gv4JHPXCtc26hd5yv4V81BOAXzrnHvfZC4AkgGv8Nije7TgpTqBb5rPfXlnLN4/OJDAvhV18Zw/ljP3trwtJtVXztiQX4nOP2aSM4e1QWCR2sVOCco6S6kbAQ8wJxSJfCcHVDCx+sLeV976P9CDiAGUSGhTBlaDo/Pnck+SkxgZ1wD7d2Zw03P/cxa3bW8I3JA/nBmcP00JCj3P/753KenbeVGd+fzMD0uGCXIyJddFhDdU+kUC3yqa3l9Zz351lkJ0aREB3O/E0VfG3SAG6fNpzw0BBmrtnFt55dTEpsBE99bWK3/0D3+Ryrduzm461VNLa00dTqo6mljcr6Fl5eVESbc3xz8kBunDqY6Ii+txLGC/O3cufrK4iNCOP3l4w94OPI5ehRWtPElN/8l1OGZ/CXy8d1voOI9AhdDdV6JJRIL1ff3MoNT/vfYD541XhykqL5xRureGz2JlaUVHPOqCzuemMVQzPjeeLaCWQcgfmcISHGMTmJHJOT+Llt3zplEL98czX3vbeeVxYXc/u04ZwzKrvPzMP+w3/W8of/rGPS4FTuvXjsEbne0jukx0fy9ZMHcN976/nm5GpG533+34eI9F4aqRbpxZxzfOeFJfxrWQlPXDvxMyOif/+4iNte+YSmVh+TBqfywJXje9SDKeZtLOeO11awekcN/VNjuPakAi4qzO/Vj3/eE6gvHJ/Hr74yps+8UZDDp6axhcm//i+jchN5+rrjg12OiHSBpn+IHAUe/mAjv3hzFbeePYxvTR38ue0rS3bzwbpSrp1U0CMfONLa5uPtFTt5dNZGFm+tIj4qjEsn5JOVGE1FXRMVdc2U1zaTnxLDbef4p7L0VPfOWMsf31Wgls498uFG7npjFc9+/XgmDU4Ldjki0gmFapE+rLKumcdmb+Iv/13P2aOy+Mvl43r9yhofb63k0VmbeGv5Dtp8jtAQIzkmgqSYcNbvquXLx+Xy24uO7ZFPqtsTqC8an8c9CtTSicaWNk773fukxkXwz29P6vX/dkX6Os2pFumDymqbeOTDTTw9x7983bmjs/nVhWP6xA/l4/ol8+fLk6mub8HhSIgK3xug//TuOn43Yy0psRH837kjgnq+a3bUsGr7braU17OlvI4NZXUs3VbFRd4IdU8M/dKzRIWH8t3Th3Dry8v4YF2ZbmQV6SMUqkV6gcaWNu57dx2Pzd5EU6uP6WNyuOmUwX3yKYaJMZ+f933TqYMpr2vmkVmbSI2L5Mapg45oTc45PlxXxl9nbmDOxvK97dmJUfRLieH7pw/l5lMHK1BLl10wNpffvr2GRz7cqFAt0kcoVIv0cIu3VnLL35ayobSOC8bmcPNpQxh0lK1xa2b8v+kjqaxv5lf/Xk1KbDiXTPA/7rm1zUd9Sxsx4aGEHeY51y1tPt5ZsZO/vr+e5cW7yUyI5MfnjmDqsHTykmOICu9589Sld4gIC+Hqkwr4zdtrWL1jN8OzEoJdkogESHOqRXqoxpY27p2xloc/3EhWQhT3fGUMk4/yEa3mVh/XP7WQD9aVkhgdTn1zG82tPsA/anz3l0dzyrCMgF6jvLaJmWtKeW/NLj5YW0pNYysD0mL55pSBXHBcbo+84VN6p6r6Zk785XucOyab3150bLDLEZH90JxqkV6soq6Zix+cw/pdtVw2MZ8fTRvRo5bDC5aIsBD+euU4/vzeeuqaWomOCCMmIpSo8BD+trCIax9fwFfG5fGT6SNIion43P57nha5orialdt3s6G0jrqmVuqbW2lobqOmqZVNZXU4519T+JxRWZwxMotTh2fo5kM57JJiIrioMI8sn5jpAAAgAElEQVTn52/l1rOGaU1zkV5OI9UiPdD9M9fz63+v4bFrCjl1eGawy+kVmlrb+PN767l/5gb/DY3TRhAdEcqmsjo2l9WxsayOtTtrqKpvAfyPTc9PjiEhOoyY8DBiIkOJiQhlWGYCpw7P4JicBM2Rlm63uayOU343k5tOGcwPzhwW7HJEpAMaqRbppZxzvLRgGxMHpChQH4TIsFB+cOYwzjomi1teXsb3Xlyyd1tqbAQD0mI5Z1QWI3MSGZmdwIjseGIi9F+gBFdBWixnjMjkmblb+NbUwURHaHqRSG+lnygiPcy8TRVsLq/n5lOHBLuUXmlUbiKv3TSJ2evLSImNoCAtlgRNnZEe7OtfGMg7K3fyyuIirjyhf7DLEZFD1HMfTyZylHppwTbiI8OYNjo72KX0WuGhIUwdlsGYvCQFaunxJhQkMyYvkcdmbcLn651TMkVEoVqkR6luaOHN5dv54tgc/RpY5ChhZnz9CwPZWFbHOyt3BrscETlECtUiPchrS0tobPFxyYT8YJciIkfQOaOyGJgeyw9fWcbqHbuDXY6IHIIuh2ozCzWzj83sX973A8xsnpmtM7MXzSzCa4/0vl/vbS9od4zbvfY1ZnZWu/azvbb1Znbb4Ts9kd7lpQXbGJ4Vz+jcxGCXIiJHUHhoCE9eO5Go8BCuenQ+W8rrgl2SiBykgxmp/i6wqt33vwLudc4NASqB67z264BK59xg4F6vH2Y2ErgUOAY4G7jfC+qhwF+Ac4CRwGVeX5GjyoqSaj4prubSCfmYaSk3kaNNfkoMz1x3PK1tPq54ZB47qhuDXZKIHIQuhWozywPOBR7xvjfgVOBlr8uTwAXe1+d73+NtP83rfz7wgnOuyTm3CVgPTPQ+1jvnNjrnmoEXvL4iR5WXFmwjIiyEC47LDXYpIhIkQzLjefJrE6mqb+GqR+dRUdcc7JJEpIu6OlL9B+BWwOd9nwpUOedave+LgD1JIBfYBuBtr/b6723fZ5/9tX+Omd1gZgvNbGFpaWkXSxfp+Rpb2vj7x8WcdUxWh08CFJGjx5i8JB65upCtFfVc8uAc/vFxMY0tbcEuS0Q60WmoNrPpwC7n3KL2zR10dZ1sO9j2zzc695BzrtA5V5ienn6AqkV6l7dX7GB3YyuX6gZFEQFOGJjKQ18tpKXNx/deXMKJv3yXX7yxko2ltcEuTUT2oysPf5kEfNHMpgFRQAL+keskMwvzRqPzgBKvfxGQDxSZWRiQCFS0a9+j/T77axc5Kjw3byv5KdGcODA12KWISA8xZWg67/1gKh9tKOe5+Vt4fPZmHv5wExcX5vGT6SOJ1xrsIj1KpyPVzrnbnXN5zrkC/DcavuecuwL4L3Ch1+1q4J/e16953+Ntf88557z2S73VQQYAQ4D5wAJgiLeaSIT3Gq8dlrMT6QVWlFQzb1MFV53Qn5AQ3aAoIp8KCTFOHpLG/VeM56PbT+WGyQN5eVERZ//hQ+ZsKA92eSLSTiCPKf8h8IKZ3QV8DDzqtT8KPG1m6/GPUF8K4JxbYWYvASuBVuDbzrk2ADO7CXgbCAUec86tCKAukV7lidmbiQ4P5ZLCfsEuRUR6sIz4KH40bQRnHZPJD15aymUPz+VrkwZw69nDiAo/9IdFtbb5mLW+jLLaZmIiQokODyU6IpS4yDBykqJJjgnXikQiXWD+QeTep7Cw0C1cuDDYZYgEpLy2iRPveY+Lxufxiy+NDnY5ItJL1De3cs9bq3lqzhYy4iM5bUQmpw3PYNLgtC4/jXX9rhr+trCIVz8uprSmab/9osNDyU2OJjcpmsTocKLDQ4kKDyEqIpTshCi+ODaXlFjdYC19l5ktcs4VdtYvkJFqEQnQ8/O30tzq45qTCoJdioj0IjERYfzs/FGcfUwWz8zbwutLS3h+/lYiw0KYUJBCaIixu7GF3Q0t1DS24nMQFxlKbGQYsRFh1Le0srx4N6EhxinDMri4MI/hWQk0tLRR39xKQ3MbuxtbKK5qpKSqgeLKBkqqG9haUU9DcxsNLf6P5lYfd7+5mnNGZ3HF8f2ZUJCsUW05ailUiwRJS5uPp+du4QtD0hiSGR/sckSkFzppcBonDU6judXHgs0VvLtqF3M3lhMeaiREh5OTGE18VBhmRl1TK3VNrdQ2tRITHsaPpg3nguNyyYiPOuTXX7OjhufmbeHVxcX8c0kJgzPiOH5ACgPSYhmUHsfA9FjykmMI1f0ichTQ9A+RIHltaQnfef5jHrumkFOHZwa7HBGRQ1bf3Mq/lm7nlcVFrN5RQ3VDy95tCVFhTBmWwWnDM5g6LF1r8Uuvo+kfIj3c47M3UZAaw9ShGcEuRUQkIDERYVw8IZ+LJ+TjnKOirpmNZXVsLK1l4eZK/rtmF68vLSHEoLB/CtNGZ3HumBzS4yODXbrIYaORapEgWLKtigv+Mps7zhvJtZMGBLscEZFu5fM5lhZV8d7qXbyzYidrdtYQYjBpcBrnHZvDacMzSI1TwJaeSSPVIj3YE7M3ERcZxoXj84JdiohItwsJMY7rl8xx/ZL5wZnDWLOjhteWFvPa0hJufXkZAJkJkQzPSmBEdgJDM+NIjA4nJiKMmIhQYiNDaWzxUVXfQlVDM5X1LbS2+RiRncCo3ETiIhVnJPj0t1DkCFuzo4Y3PtnOlSf01xPRROSoNCwrnluyhvO/Zw5jybYqFmyuYNX2GlZt383s9WW0+rr+W3QzGJQex5i8RKYMTef0EZnEKmRLEOhvncgRVN3QwjefWURSTAQ3ThkU7HJERILK7NMR7D2aW31sq6z3VivxL/FX19xGRGgIyTHhJMVEkBzjH5BYUbKbpUVVfFJUzftrSnl1cTGRYSGcOjyD6WNyOHV4RpfX7RYJlEK1yBHi8zl+8NIStlXU8/wNJ5CRcOjLWImI9FURYSEMSo/rUt+MhChOGe6/2dvncyzcUskby0p445MdvLV8B3GRYXxlXC5XndifwRlaulS6l25UFDlC7nt3Hb+fsZY7zxvJNbo5UUSk27T5HPM2lfO3hUW8sWw7zW0+ThqUylUn9Oe0EZlEhIUEu0TpRbp6o6JCtcgR8N81u/jaEwu4YGwuv7/4WD1xTETkCCmrbeKlhdt4du5WiqsaSIgK48xjsjh3TDaTBqUpYEunFKpFeoit5fVM/9OH5CbH8OqNJ2l+n4hIELT5HB+sLeVfy7bzzsod1DS2khAVxtRhGYzrl8TYfsmMzE5QyJbP0ZJ6Ij3Evf9Zi8/Bg1eOV6AWEQmS0BDjlOEZnDI8g6bWUcxaV8Yby7Yze0MZry0tAfzzuUflJDBxQConDExhQkGKVhKRLtPfFJFu1NjSxoyVO5k2Oot+qTHBLkdERIDIsFBOG5HJaSMyAdhe3cDHW6tYsq2KRVsqeXTWRh54fwOhIcaYvEQmDUpjyrB0jstPIixUI9nSsU5DtZnlA08BWYAPeMg590czSwFeBAqAzcDFzrlK808W/SMwDagHrnHOLfaOdTXwY+/QdznnnvTaxwNPANHAm8B3XW+dlyLSzofryqhtauXcMTnBLkVERPYjOzGa7NHRTBudDUB9cyuLt1QxZ2MZczaU89f3N/Dn/64nPiqMkwenMXloOsf1S2JIRjyhIbpHRvy6MlLdCvzAObfYzOKBRWY2A7gGeNc5d4+Z3QbcBvwQOAcY4n0cD/wVON4L4XcAhYDzjvOac67S63MDMBd/qD4beOvwnaZIcLyxrISkmHBOGpQa7FJERKSLYiLCOHlIGicPSQP8zxj4aH0Z768tZeaaUt5avsPrF8qonETG5CUyJj+JY/MS6ZcSo5vRj1Kdhmrn3HZgu/d1jZmtAnKB84GpXrcngZn4Q/X5wFPeSPNcM0sys2yv7wznXAWAF8zPNrOZQIJzbo7X/hRwAQrV0svtmfoxfUwO4fp1oYhIr5UYHc45o7M5Z3Q2zjk2ltWxrKiKpduqWVpUxVNzt9A8axMASTHhjM5NZGROAqmxESRFR5DkPbQmKSacpOhwEmPCiQzTPTZ9zUHNqTazAuA4YB6Q6QVunHPbzSzD65YLbGu3W5HXdqD2og7aRXq199eWUtfcxrljsoNdioiIHCZmxqD0OAalx/Gl4/IAaGnzsWZHDcuKqllW5J+b/eiHmw74uPWYiFDykqM5fUQm54zKZlRugka4e7kuh2oziwNeAb7nnNt9gD/4jja4Q2jvqIYb8E8ToV+/fp2VLBJUb36yneSYcE7U1A8RkT4tPDSEUbmJjMpN5PLj/fnEOUddcxtV9c1U1bf4Pxr8X1c3tFBV38zK7bt58ION3D9zA7lJ0Zw9KosvHZfLqNzEIJ+RHIouhWozC8cfqJ91zr3qNe80s2xvlDob2OW1FwH57XbPA0q89qn7tM/02vM66P85zrmHgIfAv051V2oXCYbGljb+s3In5x2rqR8iIkcjMyMuMoy4yDDykvffr7Kumf+s2sm/l+/g6TlbeHTWJkZmJ3BxYR7nj80lOTbiyBUtAenK6h8GPAqscs79vt2m14CrgXu8z/9s136Tmb2A/0bFai94vw3cbWZ7/mqdCdzunKswsxozOwH/tJKvAn86DOcmEjQz12jqh4iIdC45NoKLCvO5qDCf6voWXltWwksLtnHn6yu5+83VHNcvicTocOKjwomPCiMpJpzhWQkcm59IVkKUpoz0IF0ZqZ4EXAV8YmZLvLYf4Q/TL5nZdcBW4CJv25v4l9Nbj39JvWsBvPD8c2CB1+9ne25aBG7k0yX13kI3KUov98aeqR8DNfVDRES6JjEmnKtO6M9VJ/RnZclu/rZoG8uLq9laUU9NYyu7G1qoaWrd2z89PpJj8/zTToZnJTAiO5785BhCtMxfUOgx5SKHWWNLG+N+PoPzx+byyy+PDnY5IiLShzS2tLFy+26WbatiWZF/9ZGNZXXsiXMxEaEMy4rfG7KHZyUwLCuexOjw4Bbei+kx5SJBMnPNLuqb2zh3tKZ+iIjI4RUVHsq4fsmM6/fpRO365lbW7axl9Y7drNpew+odu3lr+Xaen791b5/cpGiGZ8X7A3d2AqNyEhiQFqvpI4eRQrXIYfavZdtJiY3ghIEpwS5FRESOAjERYRybn8Sx+Ul725xz7NzdxOodu1m9o4bV2/2f319bunepv4LUGM4YmcmZx2Qxrl+yng4ZIIVqkcOkrLaJB2Zu4O0VO7ioMJ8wrfohIiJBYmZkJUaRlRjF1GEZe9ubW31sKK1l4ZZK/rNyJ098tJmHP9xEamwEpw7P4IyRmXxhSDrREXo4zcHSnGqRAFXVN/Pwhxt5fPZmGlva+PK4PH40bQQpWgZJRER6uJrGFt5fW8qMlTt5b/UuahpbiQoP4eTB6UwZmsbA9DgK0mLJTog6am+A1JxqkW5W09jCY7M288iHG6ltbmX6mBy+d/oQBqXHBbs0ERGRLomPCmf6mBymj8mhpc3H/E0VzFi5k3dW7OA/q3bu7RcRFkL/lBgK0mIpSN3zOZZ+KTFkJUbpmQxopFrkoNU3t/LkR1t48IMNVNW3cNYxmXz/jKEMz0oIdmkiIiKHhXOO7dWNbC6rY3N5PZvL69hUVseWcv/3za2+vX3NID0ukuykaDLjIwH/o9ub23y0tDp8zmEGhoFBeKiREhtJelwkafERpMdFkh4fSVpcJBnxkaTERvSoKZQaqe4Gj8/eRF1TKxMHpDImL5Go8E/nG7X5HOt21bBsWzXhYcawzAQGZcQSGfZpH+ccpbVNFFc2kBgdTv/U2IBuCqhpbCEuMqzP3LlbXd/CL95cSf/UWG6cMiiov2ZqafNRWddMRX0zFbXNlNc1U1HXzI7djfxt4TbKapuZOiyd/zljKGPykjo/oIiISC9iZuQkRZOTFM1Jgz+7zedz7NjtD9zbKuspqWpke3UDJVWNbCmvx8w/sh0eGkJ4qBFmITgHDodz0NDcxieVVZTWNFHX3NbBa0NaXCQDUmMZmB7LgDT/x+i8RLITo4/QFTh4CtUHYc6Gct5Z6f9VSERYCGPzkxieFc/anTV8UlT9ub8YYSHGgLRYspOi2VHdwLaKBhpaPu0TERbCoPQ4hmbGkZkQRXV9C1UNzVTWt1DT2Mqg9FiOH5DCxAGpDMnwTylYVlzNu6t2MmPlTlbvqCE+KoyR2QmMyE5gZE4C2YlRhJoREmKEhhhRYaEMyYz7zBsA8P+DWLilkr9/XMTy4t0c1y+JkwalceLAVBJjOl/L0udzhzX0Liuq4lvPLqa4qgHnYOHmCu69ZCxJMd03L9k5x7aKBj4prmZ5STXLi6spqmygvLaJ3Y2t+93vpEGpPHjVUMb31+oeIiJy9AkJ+TRwB6q+uZWymmZKaxsprWmitLaZ0pomtlc1sLm8jhkrd1Je1wzA908fyndPHxLwa3YXTf84SFX1zSzYXMn8TeXM31TBul21DMmI49j8JMZ6y9m0+Ryrd9SwZsdu1uyoYcfuRnISo8lPiaFfSgy5SdFU1jezblcta3fWsG5nLaW1TSRFh5McE0FiTDixEaGs2u7fFyA5Jpyw0BBKa5oIMSgsSGHSoDRKaxtZWeJfJqe+g3d74P81y8icRMb189e4YVctf19SzLaKBmIiQhmVk8gnxdU0tLQRYjAqN5GEqHBqm1qp8z4aWtpoaXM0t/lobfPhcxARGkJsZCixkWHERYaRFhfJoPRYBmfEMSg9jrzkGIqrGli/q4a1O2tZt6uGhKhwzhiZyekjMkmOjcA5xzNzt/Dzf60iLS6CP18xjpUlu/np6yvISozigSvHc0xOIuAP8qt31DB3Yzk+50iJjSA5NoLU2AhCzNhWUc9W76O4qoE2n8PMCDUIDTFafY66plZqGlupbWqlqr6FWu/JVOGhxtDMeAakxZLa7rgpsf5fQ6XGRZAcE7H3z0FERESOjOr6FjaV15EaG0F+SswRf/2uTv9QqO7BnHMUVTYwb1MF8zaW09zm45RhGUwdlv65EVyfz7G5vI7yumbafA6fz9HmHLWNrSwtqmbx1kqWFVXR2OIjxGDS4DS+PC6Xs47JIiYijOZWH0u2VTF7fRnzNpXT3OrbG5ZjIsKIiQj1/xonzIgIDSE0xGhs8VHX5A+otU2t7NrdyIbSur1Btb34yDAGZcSxa3cjJdWNhIYYEwtSiIkI5d3VuzhlWDq/v3gsyd6KGYu3VvKtZxZTWd/MN6YMYnNZHR9tKKOstrnT65YcE05ecgzhoUabdx3afBAaAvGR4cRFhREfGUZCdDhDM+MZlet/2lT7qToiIiIioFAtHWhp87FmRw3p8ZFkJkR1y2vsWWx+Q2ktRZX15CRFMyQjnsyESMwM5xyfFFfzzoqdvL1iB5vL6/j+GUP55uTPz6EurWni5ucXM3djBWlxkZw8OJWTh6QzaXAqMeFh/vnO3lznNp+PvOQY+qXGkBClR7GKiIjI4aFQLb1Ca5vvgNMp2nyOnbsbyU6M6jM3ZIqIiEjvodU/pFfobH5yqHczhIiIiEhPpjuuREREREQCpFAtIiIiIhKgXjun2sxKgS1BeOl+wNYgvO7RRNe4++kadz9d4+6l69v9dI27n65x9zsc17i/cy69s069NlQHi5mVduXCyqHTNe5+usbdT9e4e+n6dj9d4+6na9z9juQ11vSPg1cV7AKOArrG3U/XuPvpGncvXd/up2vc/XSNu98Ru8YK1QevOtgFHAV0jbufrnH30zXuXrq+3U/XuPvpGne/I3aNFaoP3kPBLuAooGvc/XSNu5+ucffS9e1+usbdT9e4+x2xa6w51SIiIiIiAdJItYiIiIhIgBSqRUREREQCpFAtIiIiIhIghWoRERERkQApVIuIiIiIBEihWkREREQkQArVIiIiIiIBUqgWEREREQmQQrWIiIiISIAUqkVEREREAqRQLSIiIiISIIVqEREREZEAKVSLiIiIiARIoVpEREREJEAK1SIiIiIiAVKoFhEREREJUFhnHczsMWA6sMs5N8prSwFeBAqAzcDFzrlKMzPgj8A0oB64xjm32NvnauDH3mHvcs496bWPB54AooE3ge8651xndaWlpbmCgoKunqeIiIiIyEFbtGhRmXMuvbN+1ll+NbPJQC3wVLtQ/Wugwjl3j5ndBiQ7535oZtOAm/GH6uOBPzrnjvdC+EKgEHDAImC8F8TnA98F5uIP1fc5597qrPDCwkK3cOHCzrqJiIiIiBwyM1vknCvsrF+n0z+ccx8AFfs0nw886X39JHBBu/annN9cIMnMsoGzgBnOuQrnXCUwAzjb25bgnJvjjU4/1e5YIiIiIiK9wqHOqc50zm0H8D5neO25wLZ2/Yq8tgO1F3XQ3iEzu8HMFprZwtLS0kMsXaQP27UK6sqCXYWIiMhR53DfqGgdtLlDaO+Qc+4h51yhc64wPb3TqS0iR5fG3fDIGfDKdcGuRERE5KjT6Y2K+7HTzLKdc9u9KRy7vPYiIL9dvzygxGufuk/7TK89r4P+InKwlr4AzTWwcSZsWwD5E7q23/JXoXw9TL4FrKP3uSIicrRoaWmhqKiIxsbGYJdyxEVFRZGXl0d4ePgh7X+oofo14GrgHu/zP9u132RmL+C/UbHaC95vA3ebWbLX70zgdudchZnVmNkJwDzgq8CfDrEmkaOXzwfzH4Ks0VBdDB/8Bq546cD7NNXCm7fA0uf83yfkwHFXdn+tIiLSYxUVFREfH09BQQF2FA20OOcoLy+nqKiIAQMGHNIxOp3+YWbPA3OAYWZWZGbX4Q/TZ5jZOuAM73vwr96xEVgPPAx8yyu0Avg5sMD7+JnXBnAj8Ii3zwag05U/RGQfm2ZC+To48WY48Vuw7m3YvnT//bcvg4emwNLnYfKt0H8S/Pt2qC7a/z4iItLnNTY2kpqaelQFagAzIzU1NaAR+k5Hqp1zl+1n02kd9HXAt/dznMeAxzpoXwiM6qwOETmAeQ9BbDoccwG0NsLsP8EHv4VLnv583/kPw9s/gphUuPp1GPAFqNgEf50Er90MV76qaSAiIkexoy1Q7xHoeeuJiiK9XeVmWPtvGH8NhEVCVCIc/w1Y9Zp/NZA9nIP/3Alv/i8MPAW+OdsfqAFSBsCZP4MN78Gix4NwEiIiIn5xcXGf+f6JJ57gpptuClI1XadQLdLbLXgELATGX/tp2wk3QkQcfPg7//c+H7x1K8y619/vshcgNvWzxxn/NRgwBd7+sT+oA7Q0wpLn4eHT4E/j4T8/hR2f+AO6iIhID9Ta2nrA77vLod6oKCI9QXM9LH4aRpwHie2WeI9JgQnXwUd/8s+Znv0HWPIsnHgTnHlXx9M7QkLg/L/A/SfCP74F+cfD4iehvhzShkFiPsz+I8z6PaQOgdEXwaTvQHj0kTtfERE5qr3++uvcddddNDc3k5qayrPPPktmZiZ33nknJSUlbN68mbS0NM4880zeeOMNGhsbqaurIzc3lwsvvJDzzz8fgCuuuIJLLrmEL37xi4etNoVqkd7sk79BYxVMvOHz2068CeY9CI+e4e8z9XaY8sMDz5dOyoez7/bPrd46B4ZNg4nX+0ewzfwPlln1mn8Zvpl3w45lcPFTEBLafecoIiLB8dZt/t9OHk5Zo+Gcew7YpaHh/7N33+FRFmsfx7+TRkKAQOgQunSQYgQFEZAiCIKKKKio6BEVsGHXYzmW9yiKiPWIKGADK4iVpoJKDagg0kIRQodAKCGQMu8fs0CQQEKS5dkkv891PdfuzlP23kdM7p3cM3OQ5s2bH32dmJh4NPm94IILmDdvHsYYxowZw/Dhwxkxwv1VdtGiRfzyyy9EREQwbtw45s6dy5IlS4iOjmbWrFmMHDmS3r17k5SUxJw5cxg/fnyW759bSqpFCipr3aDDik2gRpsT95eo4Eo95r/peqfb3JGz67YY4AYxVmoKpasfvy+yHMTe5LZ5b8L3D8G0f0O3/+b984iIiAARERH8/vvvR1+PGzeOuLg4wE35d/XVV7NlyxYOHz583PR3vXr1IiLi2F9Pu3TpQnR0NADt27dnyJAhbN++nS+++II+ffoQEpK/abCSapGCaulnsG0pXDrq5L3PXZ+GlgOgYuOcX9cYaNAj++POux32bIB5b7jk+7zbc/4eIiIS+LLpUfbCHXfcwbBhw+jVqxc//fQTTz755NF9kZGRxx37z9cDBgzgww8/ZOLEibz77gkT0uWZkmqRgsZamPsaTHsMqsbC2Vef/Njg0NNLqE9X12cgaaOb4zoqxtV2i4iI+ElSUhJVq7oxRKdbvnHjjTfSqlUrKlWqROPG+f+7UbN/iBQk6Wnwzb2u5KLhpXDj194OFAwKhivehphY+PxfsHGBd7GIiEih9+STT9K3b1/atWtHuXLlTuvcihUr0rBhQwYOHJj9wblgbAGdGis2NtYeqa8RKfSshUN7XeK6ehq0uRM6/8fN2BEIDux0AyL374B+H0Lt9l5HJCIiubB8+XIaNmzodRh+kZycTNOmTVm8eDFRUVFZHpPV5zfGLLLWxmZ3fZV/iASi3ethTGdISYKMNLAZrt0EQ8+RbqBgIIksBzd+Ax/0cdsVo6HJFV5HJSIiAsCMGTO46aabGDZs2EkT6rxSUi0SiOJnwIEd0Pp2CIuEoBBXalHrQqh+ntfRZa1UFRj4LUy4Bj67yRf/rV5HJSIiQufOndmwYYNf30NJtUgg2jAPSlZ2U9Wdal7pQBNRBgZ84cpUvnsA9m1x82OHFPM6MhEREb8KkIJMETnOhnmuR7ogJdRHhEZA3/Fwzo1uWfSRjeGn52D/dq8jExGRHCio4+3yKq+fW0m1SKDZs9FNU1f9fK8jyb3gEOj5MgyYDFVawk//dcn15MGwa43X0YmIyEmEh4eza9euIpdYW2vZtWsX4eHhub6Gyj9EAs3G+e4xUGunc8oYqNPRbTvjYf7/4PePYPlXbiBj/e5eRygiIv8QExNDQkICO3bs8DqUMy48PJyYmJhcn0Xg930AACAASURBVK+kWiTQbJgLYSWhgh8XbTnTyp0FPV6EtnfBx9fChH6u1vrCBwJnWkARESE0NPS4pb8l53L928wYU98Y83umba8x5m5jzJPGmE2Z2i/JdM7Dxph4Y8xKY8zFmdq7+drijTEP5fVDiRRoG+ZBtXNdCUVhU7oa3DQVmvV3JSEfX+umDRQRESngcp1UW2tXWmubW2ubA+cAycAk3+6RR/ZZa78FMMY0AvoBjYFuwBvGmGBjTDDwOtAdaAT09x0rUvQc3APblhXseurshEbAZW9C9+FuIZsxnWHfVq+jEhERyZP8+rtrJ2CNtfbvUxzTG5horT1krV0HxAOtfFu8tXattfYwMNF3rEjRk7AQsAW/njo7xrg5rAdMhqRN8P7lkJzodVQiIiK5ll9JdT9gQqbXQ40xS4wx7xpjyvjaqgIbMx2T4Gs7WbtI0bNhrlvopeo5XkdyZtRqB/0/gl3x8OGVcGif1xGJiIjkSp6TamNMGNAL+NTX9CZQB2gObAFGHDk0i9PtKdqzeq9Bxpg4Y0xcURyVKkXAhnlQuZlbRbGoqN0B+o6Dzb/DhP6QetDjgERERE5ffvRUdwcWW2u3AVhrt1lr0621GcDbuPIOcD3Q1TKdFwNsPkX7Cay1o621sdba2PLly+dD6CIBJO0QbFpUuOupT6ZBD7j8f7D+F/j0RkhP9ToiERGR05IfSXV/MpV+GGMqZ9p3OfCn7/kUoJ8xppgxphZQF1gALATqGmNq+Xq9+/mOFSlatvwBaSlQrbXXkXjj7KvctHurvnelIKqxFhGRAiRPc3YZY4oDXYBbMzUPN8Y0x5VwrD+yz1q7zBjzCfAXkAYMsdam+64zFJgKBAPvWmuX5SUukQJpw1z3WNgHKZ7Kuf+C4GLwzTAY3QH6fQiVmnodlYiISLZMQV2GMjY21sbFxXkdhkj+mdAfdqyEOxd7HYn3EuLg4+vcFIO9X4OmV3odkYiIFFHGmEXW2tjsjtNSZiKBICPDDVIsivXUWYmJhUGz3KDNz2+GH57xOiIREZFTUlItEgh2rYaDiUW79OOfSlaEG76CZtfA7Bdc77WIiEiAUlItEgiO1lOrp/o4IWFwyQsQWQGmPQYFtFxNREQKPyXVIoFg/S9QvByUreN1JIGnWAno+DBsmAMrv/M6msC3d4srJxIRkTNKSbWI11L2wopvoMElbvluOVGL66FsXZjxBKSneR1N4Fr5PYxsDJNvU6++iMgZpqRaxGt/fg6pydDyRq8jCVzBIdD5Sdi5Cn573+toAtOG+W7hnPAoWPIxLBzjdUQiIkWKkmoRry1+Dyo0hqotvY4ksDXoAdXOg5/+C4cPeB1NYNm+HD66CkpVgSHzoW5X+P5hDe4UETmDlFSLeGnrUti8GFper9KP7BgDXZ+G/dtg7uteRxM4khLggz4QUgwGfAElKsDlb0GpyvDJ9XBgp9cRiogUCUqqRby0+H23guDZV3kdScFQrRU07AW/joK9m72OxnvJifD+FXBoH1z3OZSp6dqLR8NV77uE+vObISPd0zBFRIoCJdUiXklNcbWvDXu6JEhypvOTLkl8u5NbMKeo2rUG3ukKu9dDv49OXM69SnPoMQLW/gQ/PO1FhCIiRYqSahGvrPgaUva40g/JubJ14OZprtxhXA9XClLUZrpY/yuM6QTJu+D6yVCrXdbHtRwA59wIv4yE6U8UvfskInIGKakW8cri8VC6BtS80OtICp7KZ8Ots6BeN5j6CHwyAFKSvI4q96yFxHU5+wy/fwTv9YbI8nDLTKjR5tTH93gJYm+CX1+GKUM1JaGIiJ+EeB2ASJGUuBbWzYaL/g1B+m6bK+FRcPUHrqd6+uOQ2ANungphkV5Hlj1rYc0Prnxl0yK3peyBYqWg3TBofRuERhx/zt7N8OsrMP9NqNUerhoPEWWyf6+gYJdYR1aAWc9B8m648p0Try8iInmipFrEC799CCYIml/rdSQFmzHQZiiUq+emlPv6HjfzRaDPpPLzi/DDM2CCoUIjaNQbKjeD1dNgxpOwYIz7wtX0SljzIywaC6u+B5sBsTdD9+chODTn72eMW5WyeFn47gE3W8ilr0C5s/z2EUVEihpjC2iNXWxsrI2L0xysUgClp8HLTVwSdc3HXkdTeMwaDj8+C5e8CK1u8Tqak1vzg5uxo0kf6PXKiT3r636Gaf+GLb9DaHG3MFBkeWhxHbS8AaJr5e39//wcJt0G6YfdvN/Nr4HGl0N4qbxdV0SkkDLGLLLWxmZ7nJJqkTNs8Xsw5Q645hOod7HX0RQeGRkwoZ9LWgd+B9XO9TqiEyUlwFsXulKMW2aevFQlIwOWfeF6rut3h/o9ICQs/+LYtxX+mAi/f+hWqQyJgNrtoUJDKN/AbeXqQVjx/HtPEZEC6owk1caY9cA+IB1Is9bGGmOigY+BmsB64Cpr7W5jjAFGAZcAycCN1trFvuvcAPzbd9lnrLXjs3tvJdVSIKWmwKst3cp3N08P/DKFgubgbhjdAdIOw62zoUR5ryM6Ju0QjO0OO1bBoB+hXF2vI3K13ZsWueT677mwKx4yUt2+4DBo/yBccI+ryxYRKaJymlTnR011R2tt5iW7HgJmWmufM8Y85Hv9INAdqOvbWgNvAq19SfgTQCxggUXGmCnW2t35EJtIYFk4BvZugsv/p4TaHyLKuEVP3ukCnw2EC++HEhXdKoMRZby951MfcQnsVe8FRkIN7n7ExLoNID3VDaLdsQL+/MLNb716mqtTz2vZiYhIIeePgYq9gQ6+5+OBn3BJdW/gPeu6xucZY0obYyr7jp1urU0EMMZMB7oBE/wQm4h3UvbCzyOgzkVQS9Po+U3ls6HnSJh8O6z/+Vh7UOixBPvIY1Q1998iJta/vbG/feC+ULW5ww1KDFTBoVC+vtsa9YYln8I398L/LoBu/4UWA/RlUETkJPKaVFtgmjHGAm9Za0cDFa21WwCstVuMMRV8x1YFNmY6N8HXdrJ2kcJl7utwMBE6Pe51JIVf82ug5gWwZ4OrH96/HfZvO/aYlOB6jQ/sgB+fgfDS7stO3S7usWSl/InDWrek+ownXPLe6cn8ue6ZcnZfqHG+G9g45Q43iPKyN05v5hERkSIir0l1W2vtZl/iPN0Ys+IUx2bVvWFP0X7iBYwZBAwCqF69+unGKuKdAzth7muu969KC6+jKRpKV3fbqSQnwtofYfUMiJ/hBgcClKvvkuDa7Y/990pPhYw0t0R62TrZJ5bpafDtfW46vMaXw2X/g+ACOItpVAxcP8X9leXHZ9xsJFe+61a0FBGRo/L0E95au9n3uN0YMwloBWwzxlT29VJXBrb7Dk8AqmU6PQbY7Gvv8I/2n07yfqOB0eAGKuYldpEz6ucRLhnp+O/sj5Uzp3i0m9quSR8348bWJbBulluY5/cPYeHbJzmvLDS+Apr1g6rnnFgSkbIXPr0R1sx0A/0uerxgL/ITFATt73cL7nx3P0y8xi28owVkRESOyvXsH8aYSCDIWrvP93w68BTQCdiVaaBitLX2AWNMD2AobvaP1sAr1tpWvoGKi4CWvksvBs45UmN9Mpr9QwqMPRvdjB9nXwW9X/c6GsmptMOuRGTHcggK8W2hrrd69TRY+S2kpUB0HajVzrWnpri2bctc6UnPl+CcG73+JPlr8Xsw5U5XXtN/IhQr4XVEIiJ+dSZm/6gITHIz5RECfGSt/d4YsxD4xBhzM7AB6Os7/ltcQh2Pm1JvIIC1NtEY8zSw0HfcU9kl1CIFyoK3XG1t+4e8jkROR0iYqyeucf6J+5r3d73Ry6fAko9h+VcQEu5KIkIiXE12jxFwVqczH7e/tbzefcZJt8L7l7kl0Cuf7XVUIiKe0+IvIv72TlfAwM1TvY5EJP/8NQW+HAKH9kL9S9z0hVVbZn+eiEgBk9Oe6gJc5CdSAKSnwpY/XN2tSGHSqBfcvRQ6Pgp/z4G3O8KHfWHjwuzPFREphJRUi/jT9r9cja168KQwiigN7R9wyXWnxyEhDt7pDO9d5hJtEZEiREm1iD9tWuQe1VMthVl4KWh3r0uuuzwF2/50S7KP6+lmUimgZYYiIqdDSbWIP21a5KZfK1PT60hE/K9YCWh7F9y1BC7+L+xcBeMvhXe7QfxMJdciUqgpqRbxp02Ls57HWKQwCysO5w+Gu/6A7i9A0kb44AoY0xlWTXVzgouIFDJKqkX85dA+2L5cpR9SdIVGQOtBcOdv0HOkWyb+o6vgpQYweQgsmwwpSV5HKSKSLwrgmrkiBcSWPwCrpFokpBjE3gQtBrhEeuW3sOIr+P0DMMFQu4MrG6l1of6qIyIFlpJqEX85Mkiximb+EAEgOBTO7uu29DRIWOhWp/z9Q3ivF8Sc6+a7rttVybWIFDgq/xDxl02L3ADFyLJeRyISeIJD3GqVnZ9wAxt7jIB9W115yFvtYMM8ryMUETktSqpF/OXIIEURObXQcDj3X672uvcbcDDJTck340lIO+R1dCIiOaKkWsQf9m1zMx4oqRbJueBQaHEtDJ4DLa6DX0bC2xfBtmVeRyYiki0l1SL+oEVfRHKvWEno9Sr0nwj7t8HoDjD7RUg77HVkIiInpaRaxB82LXKzGlQ62+tIRAqu+t1h8Dz3+MPT8Ob5bhEZEZEApKRaxB82LYKKjdwiGCKSe5Hl4Kr34NrP3IqMH1wBE6+F3X97HZmIyHGUVIvkt4wM2KxBiiL5qm4XGDwXOj0Ba36A11vDzy9BeqrXkYmIAEqqRfJf4lq3SpySapH8FVIM2g2DoQuhbmeY+R94qz1sXOh1ZCIiuU+qjTHVjDE/GmOWG2OWGWPu8rU/aYzZZIz53bddkumch40x8caYlcaYizO1d/O1xRtjHsrbRyoi0g7Dwndg1xqvI5F/0iBFEf+KioGrP4B+H8HB3fBOF/jmPkhO9DoyESnC8rKiYhpwr7V2sTGmJLDIGDPdt2+ktfbFzAcbYxoB/YDGQBVghjGmnm/360AXIAFYaIyZYq39Kw+xFX6/joIfnwETBI0vhwuGQaUmXkcl4JLq0Ego38DrSEQKtwY9oGY7+OEZWDAa4t6Bys2hdnuo1R6qnwehEV5HKSJFRK6TamvtFmCL7/k+Y8xyoOopTukNTLTWHgLWGWPigVa+ffHW2rUAxpiJvmOVVJ/MzniY/QLU7wHlznI91n9+DvW6Qbt7oVqr7K8h/rNpEVRpDkHBXkciUviFl4JLhkPLAbD8K1g3G+a86ua4Di4GtdpB3YuhXle3wqmIiJ/kpaf6KGNMTaAFMB9oCww1xlwPxOF6s3fjEu7M684mcCwJ3/iP9tb5EVehZC18fTeEhEPPkVCyIlxwDyx4G+a94f4MWrOdS65rdwBjTn29vZvh7zlQt6v75SR5s305bPkDzrvd60hEipZKTd3W8RE4tB82zHUDGldNhe/ud1u5elDrQog5F6rGQtk62f+MFBHJoTwn1caYEsDnwN3W2r3GmDeBpwHrexwB3ARk9ZPLknVdtz3Jew0CBgFUr149r6F753AyBIdBcC5u/x8TYP3PxxJqgIgy0P4BOG8wLBrnemnevwyqtHTJdf1LICiL27xqGky6FQ4mQrEoOPdmlwyWqJCnj1dkpSS5qb4iyrj/FiLijWIl3GwhdbtAt/+6sSerp7ntj49h4Rh3XEQZVybS5SkoU8PbmEWkwDPWZpm/5uxkY0KBr4Gp1tqXsthfE/jaWtvEGPMwgLX2v759U4EnfYc+aa292Nd+3HEnExsba+Pi4nIdu98kJ7qej4gyJ+7bt9X9STJurKvzO6uT+7PkWZ0hsiykHXJzryaugaQE16NSvv6x8w/sgtdioVxdGPh91okyuOv8/hH8+jLsXg/lG7oR842vcIl8eqpbSOHXUVDR17OzZCL8NcUl+y2ug4aXQoVGLsE+kz0563+BUlUguvaZe8/8kJEBH18Hq76HG76Cmm29jkhEspKRDjtWQsJCSFgAyya79s5PQuzNJ/+5KiJFljFmkbU2NtvjcptUG2MMMB5ItNbenam9sq/eGmPMPUBra20/Y0xj4CNcHXUVYCZQF9eDvQroBGwCFgLXWGuXner9PUmq1/8K4VFZDwhMSXIJ89w3ICPNDZCp29XVOUeUcQlu3LsuoW3Wz52zehoc2AEYl0ju2wI24/jr1usObe9y15s8GJZ+Arf+7BYWyU56GiybBD+PgB3LoXQN14O67AvYOB/OGeh6cY4M5NkZD3NGwe8TIMM392tEGZdcl6sLpWJcnFFVjz0/3cVNrD15kh4/Az7sC6Wrw+1zICzy9K7tpdkvuMFS3Z5T6YdIQbJnA3x1lysVqdHWLY9eto7XUYlIADkTSfUFwM/AUuBIJvgI0B9ojivhWA/cminJfhRXCpKGKxf5ztd+CfAyEAy8a619Nrv39ySpHt0BNv/mksymV0LTvlCysut5nvUcJO+Cs/tB6Wqux3LrUneeCQKMS6YvvO9YL2xGBmz5DVZPd3Mbl6kJ0XXc/shysORjmP+WK8+o1NRdr9290Onx04s7IwNWfQezX3SLkoSVgEtHuc+QleRE917bl7tkfPty2BXvPt8/RZSBUlXdVqU5tBrkYv+n1TNg+mMQXhr6fQjFo4/fv2sNvN3RfWnZswHOHwoXZ/vPIDDEz4APrnT384q3VaMpUtBYC79/CN8/AumHoEFPNyaldnv3JV9EijS/J9Ve8ySpPrDT9fwu/Qw2+sZcFi8HyTtdqUaXp11ieUTSJtcbvXs9tLw+d70fh5PdD/u5r7mR7LfOyv0UUdbChnlQqnLuRsGnHnQDG/duhr2b3Ja0yb1OSoBtf7oBlOfcCG3ucD3a21fAtEdd4lmmJuzd4n5JDfji2C+rlCQY09nd30E/wq+vwKKxcPN0iMn237B/paa4L0XBoceS5dSDsGcjJG1w5Tozn3JfKv41vWD1rovI8fZucX9xWj0NDmx3bdG1Xd117Q7u5/w/OwREpNBTUu1vu9e7aew2LYaWN7gBMf7sobTW1QLmZnDjmbJjlSuBWfKxS0RrtYO1s1zPeIcH4dxbXB3jhP6ubOS6z91czhP6w5qZMGCyOydlL7xxvhtsdOtst4ramZJ6EP7+Fdb86P4cvD3TzI5BoS65Tk0+/pwSlWDgt/qTsUhhYa37C926We5n2Ppf4PA+wEDls12SXaMNVGutJFukCFBSLd7Z/bcbBLl8CjS6DDo87AZiHrFtGXzQx/XC1+3svpxc8iK0uuXYMaumwUd9of1D0PHh46+flADFSuXfFIAZGS6BjnvX9ainH3IDNquf735xBvkGd6YfdltEaYiq7nraS1dzJUCak1qk8EpPdaV/a2fB2p/cAMf0w25fuXouua7T0Y2h0V+rRAodJdUS2PZscIn1zlWuXKTnyyf29H8xyCXct86G4mXd86Wful9uJggqNnaJb/Xz3MDJnStd79L25W4GlTK1ju2POdf1fGeWnAi/feCS6d3rILI8NLnSzcZSo83pD8IUkaIh9aD7ObRhLmyY7wZ+p+yB0OIusW7Sx83udGCH+wvejhWwa7X7OdXMN+5GRAoMJdUS+JITYcU3cPbVEBJ24v4Du+D1VmDTXd21zYDKzdzUgKkH3S+0hIXHl2OERLhpCKNru19iW/8ELJhgKFnJ9S6lHXJb+iF3TvU2bo7uhr2yjkNE5FQyMtzPoz8/h78mZz2oO7y0S7wxbgBk8+ugYU8toy5SACiplsJh5Xfw47NuasGmfaF8veP3p6e6mUr2b3f7Stc8fp7ZlCSXeG+Y5wYhhYS5AZ8hYa7Wu0EP1+MtIpIf0tNcLfbfc9xg7XL13Rf9yHJuLM7vH7lpS5M2uNmOzhnoZk2KqprtpUXEG0qqRUREAlFGhlsZN+4dWP6VK2dr0gfOH+L+GiciASWnSXUATyUhIiJSCAUFuRKQ2u1d7/W8/8Fv77uZkyo2PbbEekyrwJ7xSUSOo55qERERrx3c40pDVn7r6rMz0lx5SJ2L3Oq8Z3WGEhW8jlKkSFL5h4iISEGUkuSm7ls9za24u3+ba6/c3CXXJSu5qT6PbJHlodq5LgkXkXyn8g8REZGCKDwKGvV2m7WwdYlLrldPh19ecjMhncBAxSZQ43w3b3ZkOTfFX2hxN8NIeGmIKHP8QG4RyVfqqRYRESkoDh9wC2dlpB3b9mxwJSN/zzlxmtHMTJCb8794Od9KkAawLnHHuqS7dPVjW6kqUCzKzfFfrKRL0P25crBIgFJPtYiISGETFnniqo3RtdygR3DTjO5Y6UpIUpPddjjZvT6wA5J3woGdroYbC5hjvdd7NsC6n31LsmfBBEFUjFtFslw9KFfXJd8hERASDqHh7jGshFvxNiT8WBKesheSNrr3OLADyjd0M53kdm0AayEtRfN8S0BRUi0iIlJYBIdCpSa5P99aOLjbJb97N8Ph/XBon2/b69p3rnK94ifrET8iKNQl1xlpLqn/p5AIiIl1q96WqOgWBDuY6B4PH4DiZVy9eGQFV86SnAjb//JtK1xsVVu6wZx1OrlrBYee/HNlpEFGukv+RfxA5R8iIiJyejIyYN9mSEpwPcZph9xj6sFjCXjKXvdogiCq2rGykuLRbtGuDfNccr51ybE68WKlXBlKWAmX3B/YARmpx943ogxUaAwVG7mSlHU/w6Y4d35YSbc/I9Wtnpue5nueevw1Iiu4849cp3QN1+Md6utxD4t0ZTInS9ClyFH5h4iIiPhHkK8UJComd+dH13YDMQEO7Xc90xFlTiwHOdJzfmCHG8BZouKJdd0H98C62W4ly8MH3IwowaEQHHbseVCoL0k2sHsdbFsGce9C2sGTBGhcYl2ykpvKMLK8b7Bn6RMfI8q45xmpLpaUPS7m1IMuUQ+LdF8SjpTuHHl+pEY9I90dm5bituAwX4IfceI85RkZvh731GM97+mpx9fYZ6S5+Ev4Ys6qDt5ad67mQc9XupsiIiLinWIl3JYVY1zPdvHok58fURoa9XLb6chId4vv7N0EqSkuwU496BLzAztg31Y3neG+rbArHg4mwaEsylhyzbikP3Mv+j8d+WJwJHHmNKsLQsJ9XwwquXMP7j622Qz3pajsWRBdx33RCSvu2q11jyYo05cB3xeC4mXdl4xiJf0zcPVwsisx2r3e/dXj8H73xevQXqjT0ZX7BCgl1SIiIlL0BAVD2Tpuy6mMdFcfnrInU6+07zEo5NjUhRGlXU/0kST9sK83/sjz1GT3PP2w65EODXfHB4e5JDvVl+CnJruE+khve1CIizs4NNNc5cGZ9oW43ueMDDiwHfZtcV8K9m11CXCFhr74yoAJdonrrnhIiHNJ6+kIiXC94WElfYl4Btj0Y88zMrJutxmAcTEUL+vq5SOi3SDa7ctd3X5WXx5Cwt05SqqzZ4zpBowCgoEx1trnPA5JRERE5Jig4Ox7zgsia91A0PRDrnca4x5teqYvA74vBAd2uoR9v29LTXbHHtmCgjO9DnbJ/D/bbYZ7v+RdsGcjbP7NJcxVz4EW10H5+q73PLyU6x0vVrJA1LgHRFJtjAkGXge6AAnAQmPMFGvtX95GJiIiIlLIGQORZb2OosALlKWVWgHx1tq11trDwESgt8cxiYiIiIjkSKAk1VWBjZleJ/jajmOMGWSMiTPGxO3YseOMBSciIiIiciqBklRnNXz0hCp1a+1oa22stTa2fPnyZyAsEREREZHsBURNNa5nulqm1zHA5lOdsGjRop3GmL/9GlXWqgMbPHjfokT32P90j/1P99i/dH/9T/fY/3SP/S8/7nGNnBwUECsqGmNCgFVAJ2ATsBC4xlq7zNPAsmCM2WGtVTe5H+ke+5/usf/pHvuX7q//6R77n+6x/53JexwQPdXW2jRjzFBgKm5KvXcDMaH22eN1AEWA7rH/6R77n+6xf+n++p/usf/pHvvfGbvHAZFUA1hrvwW+9TqOHMjP5ZQka7rH/qd77H+6x/6l++t/usf+p3vsf2fsHgfKQMWCZLTXARQBusf+p3vsf7rH/qX763+6x/6ne+x/Z+weB0RNtYiIiIhIQaaeahERERGRPFJSLSIiIiKSR0qqRURERETySEm1iIiIiEgeKakWEREREckjJdUiIiIiInmkpFpEREREJI+UVIuIiIiI5JGSahERERGRPFJSLSIiIiKSR0qqRURERETySEm1iIiIiEgeKakWEREREckjJdUiIiIiInmkpFpEREREJI9CvA4gt8qVK2dr1qzpdRgiIiIiUogtWrRop7W2fHbHFdikumbNmsTFxXkdhoiIiIgUYsaYv3NynMo/RERERETySEm1iIiIiEgeKakWKeLmb5nPm7+/SdKhpNM6Lz0j3U8RiYiIFDwFtqZaRPLuqzVf8fivj5Nm03h/+fsMajqI/g37Uyy42EnP2Z2ym9d+e43J8ZO5q+VdXN/4+jMYsYiInCmpqakkJCSQkpLidShnRHh4ODExMYSGhubqfCXVIkXU+GXjeTHuRVpVasUdLe5g9JLRjFg0ggkrJjC0xVA6VutIibASR49PzUjlk5Wf8Prvr5Ocmky9MvV4Ie4FthzYwn2x9xEcFOzhpxERkfyWkJBAyZIlqVmzJsYYr8PxK2stu3btIiEhgVq1auXqGkqqRYoYay0jF41k7LKxdKnRhefaPUdYcBhvdH6D+VvmMyJuBI/88ggAFSIqUKt0LWqWqknc1jjWJK3hvMrn8eC5D1K7dG1ejHuR9/96n60HtvLfdv8lPCTc409XtFlrmRw/mdiKsVQrVc3rcESkgEtJSSkSCTWAMYayZcuyY8eOXF9DSbVIEWKt5Yk5TzApfhJX17+ah1s9fFwPc+vKrZnYcyJzN89leeJy1iWtY13SOr5e+zXlIsoxquMoOlbrePQH7APnPkDlyMq8sPAFbpl2C69c9Aplwsuc9P23HdhGVLEoJd9+Mn7ZeEYsGkF0eDSj0xQVfAAAIABJREFUu4ymfnR9r0MSkQKuKCTUR+T1syqpFilCvl77NZPiJ3FL01u4o8UdWf4ACTJBtK3alrZV2x5ts9YCWf/AGdBoAJUiK/HQ7Ie49ttrea3Ta9SOqn3Ccd+v/55Hf36UumXq8u7F71I8tHg+fjJZsGUBIxePpG3VtsTvjmfg1IG82flNmpVv5nVoIiJFgmb/ECkiElMSGb5wOM3KN2Noi6Gn9Y3cGHPK47vU6MI7F7/DgdQDXPfNdczZPOfoPmstby95m/tn3U+NqBosT1zOA7MfIC0jLU+fR47ZemAr98++n5qlajKi/Qje6/4epYuV5pZptzB/y3yvwxMRyTVjDAMGDDj6Oi0tjfLly9OzZ08Po8qakmqRIuKFhS+wP3U/T5z/BEEm///Xb16hORN6TKBSiUoMnjGYCSsmkJqeyuNzHueV317hklqXMKHHBB5u9TCzEmbx3ILnjvaAS+4dTj/MvbPuJSUthZEdRxIZGkmVElUY3208VUtUZfCMwcz8e6bXYYqI5EpkZCR//vknBw8eBGD69OlUrVr1tK6RlnZmOnGUVIsUAXM2zeHrtV9zU5ObqFumrt/ep0qJKrzf/X3axbTj/+b/H5dOvpTJ8ZO5rdltPNfuOYoFF6Nfg34MbDKQj1d+zNhlY/0WS1ExfOFwluxYwjMXPHNc2U354uUZe/FY6pWpx90/3c1t029j2a5lfovDWstXa75i7Z61fnsPESmaunfvzjfffAPAhAkT6N+//9F9CxYsoE2bNrRo0YI2bdqwcuVKAMaNG0ffvn259NJL6dq1KwMGDODLL788et61117LlClT8jVO1VSLFBJpGWm8teQtSoSWoE/dPkenw0tOTeapeU9Rs1RNBp09yO9xRIZG8nKHlxn12ygmrpjIsxc8S686vY475u6Wd7N1/1ZGLhpJpeKVuKT2JX6PqzCa/vd0Pl75MQMbD6RLjS4n7C8dXpqx3cYyccVExvw5hn5f96NLjS4MbT6U2qVPrHvPi3HLxvHSopeoWqIqX/T6QjXzIoXM8wueZ0Xiiny9ZoPoBjzY6sFsj+vXrx9PPfUUPXv2ZMmSJdx00038/PPP7hoNGjB79mxCQkKYMWMGjzzyCJ9//jkAc+fOZcmSJURHRzNr1ixGjhxJ7969SUpKYs6cOYwfPz5fP4+SapFC4HD6YR76+SGm/z0dgLf+eIt+DfpxTcNrGL9sPJv2b2LsxWNPuahLfgoOCmbYOcO4q8VdWc5fHWSCeOaCZ9h+cDuP/vooJcJKcGHMhWcktkB1IPUAIUEhp/XfaNyf46hZqiZ3trzzpMeEh4RzY5Mb6VOvD+//9T7jl41n5oaZXFTtIq5teC3nVDwnzyPev1rzFS8teonYirEs2raIEXEjeOz8x/J0TRGRI84++2zWr1/PhAkTuOSS4zthkpKSuOGGG1i9ejXGGFJTU4/u69KlC9HR0QC0b9+eIUOGsH37dr744gv69OlDSEj+psFKqkUKuOTUZO756R7mbJ7DA+c+QIsKLXj3z3cZs3QM45eNJ82m0aduH2IrxZ7x2E61IExYcBivXPQKg6YN4u4f72ZUx1G0i2l3BqMLHH/u/JMhM4dQLqIc47qNo2RYyWzPWZG4giU7l/DguQ8SEpT9j/KSYSUZ3Hww/Rr0471l7/HZ6s+YsWEGDaMbck3Da2hVqRW7D+0m8WAiiSmJJB1KIjgomNCgUMKCwwgLCqN+dH3qlK5z3HV/2fQLj//6OK0rt+aNTm/w6m+vMm7ZODpV70Sbqm1yfU9EJLDkpEfZn3r16sV9993HTz/9xK5du462P/bYY3Ts2JFJkyaxfv16OnTocHRfZGTkcdcYMGAAH374IRMnTuTdd9/N9xiVVIsUYHsP72XIjCEs2bmEp9o8xeV1LwfgpQ4vsS5pHeOWjWPtnrUMix3mcaRZKxVWire6vMUt025xifVFo7ig6gVeh3VGzdo4i/tn30+psFKs3bOWu3+8mzc7v0lYcNgpz/t05acUCy7GpXUuPa33iw6P5u5z7ubWZrfy9dqv+fCvD3ns15z3Kp9d7myuqHsF3Wp1c/+2fhpG3TJ1ebnDy4QFhzG0xVBmJ8zm8TmP80XvLygVVuq04hMRycpNN91EVFQUTZs25aeffjranpSUdHTg4rhx4055jRtvvJFWrVpRqVIlGjdunO8xKqkWKaB2HtzJ7TNuJ35PPC9c+AJda3Y9bn+tqFr8p81/PIou56KKRfF217e5Zdot3PXDXbx60au0qdqGvYf3smLXCpYnLic6PPq0k8eC4JOVn/Ds/GdpGN2Q1zq9xtzNc3nkl0f496//5rl2z510lpbk1GS+WfcNF9e8mKhiUbl674iQCPrW68uVda9kwdYFbNy3kejwaMpGlCU6PJqoYlFkZGRwOOMwh9MPk5KWwq+bf2XS6kk8OfdJnl/4PCEmhOjwaN7o/MbRGv5iwcV49oJnue7b6xi+YDjPXPBMru+PiMgRMTEx3HXXXSe0P/DAA9xwww289NJLXHTRRae8RsWKFWnYsCGXXXaZX2I0BXVKq9jYWBsXF+d1GCKeWLNnDYNnDCYxJZGRHUcWit7dpENJ/Gvav1i7Zy0VIyuycd/G4/Y/1Oohrm14rUfR5S9rLa/89gpjlo7hwpgLeeHCF44O7BuzdAyjFo/ixsY3cm/svVme/9mqz/jP3P/wfvf3aV6h+ZkMHWstS3Yu4YvVX7AicQXDLxxOjVI1Tjju1d9eZfSS0bzS8RU6Vu94RmMUkfyxfPlyGjZs6HUY+SY5OZmmTZuyePFioqKy7pDI6jMbYxZZa7OtoVRPtUgBM2/LPIb9OIxiIcUY120cjcvl/5+wvBBVLIq3u7zN0/OexmK5/KzLaVS2EfWj6/PMvGcYvnA41UpWKxQDGj9d9Sljlo6hb72+PNL6keNqom9ucjPbDmxj3LJxVCxekesaXXfC+Z+s/IS6Zep6slqiMYZm5Ztl+963nX0bszbO4tFfHqV/w/5cVe8qKkZWPENRiogcb8aMGdx0000MGzbspAl1XqmnWqQAmbR6Ek/NfYqaUTV5vdPrVClRxeuQzojk1GQGTh3IuqR1vN/9fepH1/c6pFzbdmAbvb/sTdNyTRndZXSWM2+kZ6Rz76x7+WHDDzxx/hP0qdfn6L5lO5fR75t+PNL6Efo36H/CuYFkw94NvBD3ArM2ziLYBNOlRheuaXgNzco3y/OMIyLif4Wtpzon8tJTnaPFX4wxpY0xnxljVhhjlhtjzjfGRBtjphtjVvsey/iONcaYV4wx8caYJcaYlpmuc4Pv+NXGmBsytZ9jjFnqO+cVo5+2Isex1vL676/z+JzHObfSubzX/b0ik1ADFA8tzqsXvUqpsFIMmTmE7cnbvQ4pV6y1PDv/WdIz0nn8vMdPmlgGBwXz/IXP06ZqG56c+ySfrPzk6L5PV31KREgEPWsH3hK9/1S9VHVevehVvrniG65peA2/bPqFAd8NoO3Ettw09SaeX/A8X8Z/ydYDW70OVUROoqB2vuZGXj9rTldUHAV8b61tADQDlgMPATOttXWBmb7XAN2Bur5tEPAmgDEmGngCaA20Ap44koj7jhmU6bxuefpUIoWItZaRi0fyvz/+x2VnXcbrnV/P0ZRrhU2F4hV4vdPr7Du8j6Ezh7Jw60I27d9EWsaZWX42P8zYMIMfN/7I4OaDqVaq2imPLRZcjFc6vkL7mPY8Pe9pPlr+EfsO7+Pbdd/SrWa3AvVvoFrJatx/7v3M6DuDp9s+Tfea3TmUfojPV3/Ov3/9N1d+dSU7D+70OkwR+Yfw8HB27dpVJBJray27du0iPDw819fItvzDGFMK+AOobTMdbIxZCXSw1m4xxlQGfrLW1jfGvOV7PiHzcUc2a+2tvva3gJ9824++hB1jTP/Mx52Myj+kKLDWMiJuBOP/Gs/V9a/mkdaPnHRGiKJidsJs7vrhLtKsS6aDTTCVIivRtUZX7jnnnoAtK9h7eC+9J/emfER5PurxUY7mlgZITU/l3ln38uPGH2lVqRULti5gQo8JNCnXxM8R+196RjpLdy5l4NSBXFzzYp5r95zXIYlIJqmpqSQkJJCSkuJ1KGdEeHg4MTExhIaGHteenwMVawM7gLHGmGbAIuAuoKK1dguAL7Gu4Du+KpB52H6Cr+1U7QlZtJ/AGDMI16NN9erVcxC6SMFlrWX4wuF8sPwD+jfoz8OtHg7YhPFMujDmQqZdOY01SWvYvH8zCfsSWJ64nLHLxhJTMoar6l/ldYhZGrloJIkpibzW6bUcJ9QAocGhjOgwggdnP8j0v6fTMLohjcsWjsGpwUHBNK/QnH81/Rf/++N/9KrTizZVtGCMSKAIDQ2lVq1aXodRYOTkJ3sI0BK4w1o73xgzimOlHlnJ6re+zUX7iY3WjgZGg+upPlXQIgWZtZbnFjzHRys+4rqG1/HAuQ8ooc6kfPHylC9e/ujrDJvB4JmDeX7B8zQt15SGZQNrYM3CrQv5bNVn3NDohlwlxKFBoQy/cDhvL3mb86qcV+j+Lfyr6b/4bt13PDvvWT7v9TnhIbn/86uIiFdy8nfkBCDBWjvf9/ozXJK9zVf2ge9xe6bjMxcLxgCbs2mPyaJdpMiK2xanhPo0BJkg/nvBfykdXpp7Z93LvsP7vA4JcCUfI+JGcOv0W4kpEcPg5oNzfa2QoBBub347LSq0yMcIA0Ox4GI8dt5jbNi3gbeXvu11OCIiuZJtUm2t3QpsNMYcmcOqE/AXMAU4MoPHDcCXvudTgOt9s4CcByT5ykSmAl2NMWV8AxS7AlN9+/YZY87zzfpxfaZriRRJczfPJdgEM7TFUCXUOVQmvAwvtn+Rzfs388ScJ44OrLHW8tv233j0l0cZvnA4u1N2+z2W1IxUPlr+ET2+6MH4ZePpUbsH73V/7+gCL3Ki1pVb06tOL979813W7FnjdTgiIqctp4V9dwAfGmPCgLXAQFxC/okx5mZgA9DXd+y3wCVAPJDsOxZrbaIx5mlgoe+4p6y1ib7ntwPjgAjgO98mUmQt2LqAJuWaEBka6XUoBUqLCi24s+WdjFw0kvf+eo9SYaX4aMVHrEhcQYnQEhxMO8jk+MkMbjaYqxtcTWhQaLbX3HVwF/F74onfE8/q3atZv3c9Tco2oU+9PtSKOr7WcO/hvXy95msmrJjA+r3raV2pNfedex8Nohv46yMXKvfG3sushFk8NfcpxnYbW+QH5YpIwaLFX0QCzIHUA1ww4QIGNhnInS3v9DqcAifDZnDHD3cwO2E2AGeVPotrGl5Dj1o92Lx/My/EvcCczXOoWaomd7e8m/OrnH9CD/Lew3v5bu13TIqfxLJdy462RxWLolqJaqxIXEGaTePcSufSt15fKkdW5vPVn/P9uu9JSU+hUdlG3N7sdtrHtNdfGk7TpNWTeHzO4zSIbsBV9a+iR60e6uEXEU/ldPYPJdUiAebnhJ8ZPHMwb3d9m/Mqn+d1OAVS0qEkxv45lrZV2xJbMfa4xNZay8+bfuaFhS+wfu96gkwQtaNq07hsYxpEN+DPXX8y4+8ZHEo/RN0ydelRqweNyjaibpm6lA0vizGGnQd3Mjl+Mp+t+oxN+zcBEBESwSW1LqFv/b6FZnYOL1hrmRQ/iQ+Xf8iq3auIDI2kZ+2eDGg0gBqlangdnogUQUqqRQqol+Je4oPlHzCn/xzNguBHqRmpzN08l6U7l7Js5zKW7VpGYkoiJUNLckntS7i87uU0im50yp7mDJvBvM3z2Jmyk4uqXUSJsBJn8BMUbtZa/tjxB5+s/ISp66cSGhzKqI6jaF25tdehiUgRo6RapIC6+uurKR5SnLHdxnodSpFirWV78naiikXpy0yA2XpgK7fPuJ2/9/7Nc+2eo2vNrl6HJCJFSE6Tao0CEQkgSYeSWJG4glaVWnkdSpFjjKFiZEUl1AGoUmQlxnUbR+Oyjblv1n18vOJjr0MSETmBkmqRALJo2yIybAatKiupFsksqlgUo7uO5sKYC3lm/jO8+fubFNS/tIpI4aSkWiSALNi6gPDgcJqWa+p1KCIBJyIkgpEdR9KrTi/e+OMN3vnzHa9DEhE5KqfzVIvIGbBg6wJaVGhBWHCY16GIBKTQoFCebvs0qRmpjFo8iqolqtK9VnevwxIRUU+1SKBITElk9e7VKv0QyUaQCeKZts/QskJLHv3lURZvW+x1SCIiSqpFAsXCrW6xUQ1SFMleWHAYozq6nuo7f7yT9UnrvQ5JRIo4JdUiAWLBlgVEhkbSqGwjr0MRKRBKh5fmjU5vEGyCGTxzMIkpiV6HJCJFmJJqkQCxYOsCzql4DiFBGuogklPVSlXjlYteYXvydu758R5SM1K9DklEiigl1SIBYNuBbazfu16lHyK50Kx8M55q8xSLty/mpbiXvA5HRIoodYmJBIAFWxcAqqcWya1Lal/C0p1L+WD5BzQp14QetXt4HZKIFDHqqRYJAPO2zKNUWCnqR9f3OhSRAmtY7DBaVmjJk3OeZGXiSq/DEZEiRkm1iMeSU5OZuWEmHap1IMjof0mR3AoNCmVEhxGUDCvJPT/dw97De70OSUSKEP0GF/HYzA0zOZB6gMvOuszrUEQKvHIR5Xipw0ts2b+Fh2Y/RHJqstchiUgRoaRaxGOT4ycTUyKG2IqxXociUig0r9Cch1o9xM+bfqb3l72Ztn4a1lqvwxKRQk5JtYiHEvYlsGDrAnqf1RtjjNfhiBQaVze4mvHdxlMqrBT3zrqXW6bfwto9a70OS0QKsRwn1caYYGPMb8aYr32vaxlj5htjVhtjPjbGhPnai/lex/v218x0jYd97SuNMRdnau/ma4s3xjyUfx9PJLBNWTMFg6F3nd5ehyJS6LSs2JKPe37MI60f4a9df9FnSh9umXYLzy94ni9Wf8EfO/5QeYiI5JvTmVLvLmA5UMr3+nlgpLV2ojHmf8DNwJu+x93W2rOMMf18x11tjGkE9AMaA1WAGcaYer5rvQ50ARKAhcaYKdbav/L42UQCWobN4Mv4L2lduTWVS1T2OhyRQikkKIT+Dfpzcc2LeXvJ2/y2/Tc+W/UZKekpAFQsXpFJvSdRMqykx5GKSEGXo55qY0wM0AMY43ttgIuAz3yHjAeOjLLq7XuNb38n3/G9gYnW2kPW2nVAPNDKt8Vba9daaw8DE33HihRqC7cuZPOBzRqgKHIGRIdH82CrB5nYcyLzr53Pt1d8yzNtn2Fb8jY+Wv6R1+GJSCGQ0/KPl4EHgAzf67LAHmttmu91AlDV97wqsBHAtz/Jd/zR9n+cc7L2ExhjBhlj4owxcTt27Mhh6CKBaXL8ZEqGlqRT9U5ehyJSpASZIKqVrEbvs3rTIaYD7y9/nwOpB7wOS0QKuGyTamNMT2C7tXZR5uYsDrXZ7Dvd9hMbrR1trY211saWL1/+FFGLBLb9h/cz4+8ZdKvVjfCQcK/DESmybm12K0mHkpi4YqLXoYhIAZeTnuq2QC9jzHpcacZFuJ7r0saYIzXZMcBm3/MEoBqAb38UkJi5/R/nnKxdpNCaun4qKekpKv0Q8ViTck1oW7Ut45eN16BFEcmTbJNqa+3D1toYa21N3EDDH6y11wI/Alf6DrsB+NL3fIrvNb79P1g3QegUoJ9vdpBaQF1gAbAQqOubTSTM9x5T8uXTiQSoyfGTqR1Vm6blmnodikiRd9vZt7H70G4+XfWp16GISAGWl3mqHwSGGWPicTXT7/ja3wHK+tqHAQ8BWGuXAZ8AfwHfA0Ostem+uuuhwFTc7CKf+I4VKZR+2fQLv+/4ncvPulxzU4sEgOYVmnNe5fMY++dYUtJSvA5HRAooU1BXmYqNjbVxcXFehyFyWrYe2Erfr/pSvnh5PrzkQyJCIrwOSUSAuK1xDJw6kIdaPcS1Da/1OhwRCSDGmEXW2myXPdaKiiJnSGpGKvfNuo/D6Yd5qf1LSqhFAkhspVhiK8by7tJ3OZR+yOtwRKQAUlItcoaMWjSKP3b8wX/a/IeaUTW9DkdE/uG2Zrex/eB2Pl7xsdehiEgBpKRa5Az4YcMPjP9rPP3q96NbrW5ehyMiWWhVqRVtqrThjT/eYEey1kIQkdOjpFrEzxL2JfDvX/5N47KNuf/c+70OR0ROwhjDI60f4XD6YV6Ie8HrcESkgFFSLeJnLy9+mQwyeLH9i4QFh3kdjoicQo1SNbi56c18t+475m+Z73U4IlKAKKkW8aMdyTuY+fdM+tTtQ0zJGK/DEZEcuLnJzcSUiOGZec+Qmp7qdTgiUkAoqRbxo89Wf0aaTePq+ld7HYqI5FB4SDiPtH6E9XvXM/6v8V6HIyIFhJJqET9JzUjls5Wf0bZKW6qXqu51OCJyGtrFtKNz9c689cdbbNq/yetwRKQAUFIt4iezNs5i+8Ht6qUWKaAebPUgxhienvc06RnpXocjIgFOSbWIn0xcMZHKkZW5MOZCr0MRkVyoFFmJYecM49dNv/J/8/+PgroCsYicGSFeByBSGK3ds5b5W+dzV8u7CA4K9jocEcmlfg36sfnAZsb+OZYy4WUY2mKo1yGJSIBSUi3iB5+s+oTQoFAuP+tyr0MRkTy6p+U9JB1K4q0lb1EmvAzXNrzW65BEJAApqRbJZ8mpyXwZ/yVda3albERZr8MRkTwyxvDYeY+xJ2UPzy14jqhiUfSs3dPrsEQkwKimWiSffbPuG/an7qdf/X5ehyIi+SQkKITh7YdzbqVzeeyXx5idMNvrkEQkwCipFslHa/es5b1l71G/TH2alW/mdTgiko+KBRfjlY6vULdMXe796V5+2/6b1yGJSABRUi2SD/7Y8Qd3/XAXvb/szdYDWxnSfAjGGK/DEpF8ViKsBG92fpOKkRUZMnMIq3av8jokEQkQSqpF8mBt0lpumnoT1317HXHb4rj17FuZeuVUOlbv6HVoIuInZSPK8laXt4gIjuC26beRsC/B65BEJABkm1QbY6oZY340xiw3xiwzxtzla482xkw3xqz2PZbxtRtjzCvGmHhjzBJjTMtM17rBd/xqY8wNmdrPMcYs9Z3zilEXnxQAm/dv5pZptxC/O577Y+9n+pXTGdpiKNHh0V6HJiJ+VrVEVd7q8haH0g8xaPogdh7c6XVIIuKxnPRUpwH3WmsbAucBQ4wxjYCHgJnW2rrATN9rgO5AXd82CHgTXBIOPAG0BloBTxxJxH3HDMp0Xre8fzQR/0lMSeTW6bdyMO0gYy4ew/WNr6d4aHGvwxKRM+isMmfxeqfX2XlwJ9d8cw3fr/teC8SIFGHZJtXW2i3W2sW+5/uA5UBVoDcw3nfYeOAy3/PewHvWmQeUNsZUBi4GpltrE621u4HpQDffvlLW2rnW/TR6L9O1RAJOcmoyQ2YMYcuBLbx20WvUK1PP65BExCPNKzRndJfRlAorxf2z7+f6765n6Y6lXoclIh44rZpqY0xNoAUwH6hord0CLvEGKvgOqwpszHRagq/tVO0JWbSLBJzU9FTu/vFulicu58X2L9KyYsvsTxKRQq15heZ83PNj/tPmP2zct5Frvr2G+2fdz1drvmLNnjWkZ6R7HaKInAE5XvzFGFMC+By421q79xRlz1ntsLlozyqGQbgyEapXr55dyCJ5Zq1ly4EtrNq9ilW7V/Hrpl9ZvH0xT7d9mg7VOngdnogEiOCgYK6oewUX17yYd5a+wwfLP+D79d8DEBESQYPoBvSu05sr6l6hmYFECqkcJdXGmFBcQv2htfYLX/M2Y0xla+0WXwnHdl97AlAt0+kxwGZfe4d/tP/ka4/J4vgTWGtHA6MBYmNjVbgmfpGSlsKvm35l6t9T+WXTL+w7vO/ovpgSMfy79b+57CxVKInIiSJDI7mz5Z0Mbj6YdUnr+GvXX/y16y8WbVvEk3OfZMHWBTxx/hMagyFSCGWbVPtm4ngHWG6tfSnTrinADcBzvscvM7UPNcZMxA1KTPIl3lOB/8s0OLEr/8/efcdJUZ8PHP882273eqfcURUQVBAlNuwFRaPYE42KPYlRg1EjmvizxWgSNWossSF2IIkGC4o1IhYUFAFR6eXg4HrfvW3f3x+znAcccG1v9o7n/WJeuzs7M/vsc8Pus9/5znfgJmNMhYjUisjBWN1KLgT+0QnvTalWC0VCzNkwh9mrZ/NR0Uc0hBvITMrkuP7HsU/uPgzNGsqQrCGkuFPsDlUp1Q24HC6GZA1hSNYQJuw5gaiJ8tTip3hk4SN8X/E9fz/q7wzOHGx3mEqpTiS7OlNZRA4DPgYWA9HY7JuxCuAZQH9gHXB2rEAW4GGsETwagIuNMfNj27okti7AXcaYZ2LzxwBTAR/wFnC12UVgY8aMMfPnz2/Tm1WJb1P9JlLdqaR6UuP+WsYYFpUt4vWVr/P2mrepbqwmKymLYwccy7gB4/hJ75/gcrS6h5RSSu3S58Wfc+OcG/GH/dx6yK2cPPhku0NSSu2CiCwwxozZ5XLddfgfLap7nkWli7jsncvok9KHZ098lkxvZqdsN2qifF3yNUvLl1LmL2ua1tasZUPdBpKcSRzT/xhOGXwKh/Q9RAtppVRclTSUcMNHN/BVyVdcOepKfjXqV9rPWqkEpkW16lZWVK7gotkXkexKptxfzrDsYTw17ql29zuMmiiLShcxe81s3lnzDiV+q8u/2+Em15dLri+X/OR8jiw8kuMHHN8lLeNKKbVFKBri9k9vZ+bKmZw55Ez+ePAf9Qe9UgmqtUW1/g9WXaYh1IBDHHhd3q3mb6jbwC/f/SUeh4enT3iaFZUruPZ/1zLpw0k8fOzDeJyerZZfWbUSf9hPmifNmtxpBCIBvi3/liVlS1havpRvSr6hxF+C2+HmsILDuG7gdRzS9xAykzK1RUgpZTu3w82dY+8kPzmfJxc/Sbm/nL8e+Vd8Lp/doSml2klbqlW7GWOoaqyipKGEkoYSIiZCn5Sv1H+NAAAgAElEQVQ+9EntQ5o7DRFhfe165hTNYU7RHL7c9CUuh4txA8YxYc8JHNDrACoCFUx8ayKVjZVMPXFq04VU/rviv9zyyS2cMPAE/nL4X2iMNPL2mreZ/sN0lpYv3WlchamF7JO7D0cUHsFR/Y4izZPWFelQSql2mfb9NP4878/sm7cvDx79ILm+XLtDUko1o90/VKssq1zGF8VfkOvLpXdKb3qn9CbPl0coGmoqlpsm/9aPSxtKCUaDLW43xZ1Cuied4vpiAAZlDOKIgiOoC9Xx9pq3qQ/VU5BagNvhZnPDZp44/gn2y99vq21MXTKV+xbcx/75+7O8cjm1oVr2zNyTs4eeTUFqATXBGmqDtdSF6nCIg+HZw9k7Z+9O64utlFJd5b2173HjnBsREU7b8zQuHHEh/dP1egxKJQItqtUOGWP4bONnPLv0WT7d+Ol2zzvEQdREt5vvc/nIT87/cfLlb/XYIQ6K64vZVL+JjXUbqQxUMjJvJEcUHrHVl4M/7Of9de8zc8VMFpUu4v6j7mdswdgWY33oq4eY+u1Ujh9wPD8b9jNG54/W7htKqR5pTfUapn47lddWvkbERDi2/7Gcu9e57J2zt45rrZSNtKju4TbVb+Jfy/5FOBrG6/SS5EoiyZlEfaiejXUbKaorYkPtBsoD5eR4c7ZqhZ67cS7LK5eT68vlvL3O45Q9TqE2WNtUEG+q37R9AZ2cT6o7tdMLWmPMLrcZioZwO9yd+rpKKZWoShtKefG7F5nxwwxqQ9bFpwpSCxiSNYShWUMZmTuS/fL3IyMpI24xRE2UqsYqMpMycYgjbq+jVHegRXUP1RBqYMqSKUz9diqhaAiHOAhHw1stk+3NpjC1kILUAnJ8OZQHytlcv5nNDdY0KGMQF464kJMGnbTdSYBKKaUSQ32onnnF81heuZzlVctZXrmctTVriZgIAHtk7MHoXqMZlTeKETkjGJwxuMURRLZ8z++qAaPcX86nGz9tmioCFXgcHgrSCihItaYcbw4p7hTSPGmkelLJT85n75y9deQS1aNpUd3NRaIRIiaCwWCMIWqivLP2HR766iFK/aWMHzieSQdMom9qXyLRCI2RRgKRAF6nd6eHCVvTMqyUUioxBcIBlpQtYWHpQr7a/BULSxY2tWYnOZMYljWMPbP2pD5UT2lDKZsbNlPaUGo9lz3MmrKG0S+tH8X1xayuXs2amjWsqlrFyuqVAGQlZXFI30MYkTOCMn8ZRbVFbKjbQFFtUdNrNZfuSWds37EcXng4YwvGku3N7tKcKBVvWlQnsNpgLQ2hBjxOjzU5PFQHq/mm5BsWli5sulBJKBrabt2RuSO54Sc3bHdSn1JKqd1P1ERZU7OG78q/Y2n5Ur6r+I6VVStJ86SRn5xPni+P/OR8GkIN/FD5A8sql+EP+5vWd4qTfmn9GJQxiH1y92Fs37EMzxm+wy4f4WiY+lA9daE66oJ1rK5ZzdyiuXy84WMqAhUIQv/0/gzNGspe2XsxLGsYfVP74nK4cIoTp8OJx+Eh15erDTyq29CiOsE0Rhr5cP2HvLHyDeZumNt0+G5bboebvXP2ZlTeKDKSMpo+dARhQPoAjul/jPZvU0op1S6RaIT1tespqiuib2pf+qX2w+3s+DkrURPlu/LvmLthLt9VfMcPFT9QVFe0w+WzvdmMzB3JqPxRjMobxYD0ASQ5k/C6vHgcHkQEYwzhaJhgNEgwEpuiQUKR0K7nxeY7xEGON4dcXy55yXnk+fL0pE/VZlpUdzFjDEvLl/LGqjco95fjc/vwOr34XD4qAhW8t/Y9akO15Cfnc/Lgk+mX1o9gJGh9YESCeJyepn5x2s9ZKaVUd1cXrGNZ5bKm6xhETZSIidAQarAu0lX6DWtq1rS4rtvhbvFobWdIdiWTl5xnFdq+PPKS8+id3LvphP5eyb3I9eXidDjj8vqq+9ErKsbByqqVBCNBkt3J+Fw+fC4fDaEGZq2excwVM1lZvRKPw0PvlN4EwgH8ET+BcACXw8Vx/Y/jlD1O4cDeB+p/VKWUUj1eqieV/Xvtv9NlqgJVLCpbxKb6TTRGGpumUCSEy+Fq6iLpdrqb7u9ontthPU5yJjU9jpgIZf4yyhrKKAuUUdpQSpm/jFJ/KaUNpSwtX0rJ+hICkcBWcbnEZRXbKb3pndybXim9mu73TrEeZ3uz9cix2oq2VLfBpbMv5YtNX7T43Ki8UZy6x6mcOOhE0j3pWz2nJwcqpZRSickYQ02wpmlI2U31m9jUsInN9ZvZ1GA93ly/ebuLnbkdbvKT85tauLOSskj1pJLqTiXNk0ayOxmPw4PL4cLtcON2uPG5faS6U0lxp5DqTiXJmaT1QTegLdVxMGn/SZT4S/CH/fjDfhpCDQAcUXgEgzIG7XA9/Q+jlFJKJSYRISMpg4ykDIZlD2txGWMMFYEKNjds3r7wrt/EwpKFVDVWUR+qb9Nre51eCtMK6Z/Wn/7p/emX1o9cXy7Z3myyvFlkebNIc6dpHdFNaFHdBvvm7Wt3CEoppZTqYiJCji+HHF8OI3JG7HC5SDRCfbieumAd9aF6wtEwoWio6dYf9jeNnFIXqqPcX05RbRFratYwd8Pc7VrD4cfCuzCtkMJU6zYzKZM0TxrpnnTSPGlke7PJTMrU4ttmWlQrpZRSSnUCp8NJuid9u26grRE1UUoaSij3l1PZWElloLKpdbyotoiiuiLmFc/bakjE5pKcSVv1+24+9UnpQ++U3qS4Uzr6FtVOaFGtlFJKKWUzhziaiuAdMcZQ2VhJTWMNNcEaaoO11ARrKPeXs6l+E8X1xWxq2MRnxZ9R5i8jaqJbrZ/mTqNXSi/6pPQhPzmfVHdqUz/wFHcK2d5scpNzyfXmkuPL0StltpFmSymllFKqGxARsr3ZrbpqZSgaorShdKs+4MV1xU19wb+r+I76UP0OW74FIcub1TT0YI7PGu871Z261Shoya4f7/tcPrwuL1ET3arby5biXhBEBEGw/knTPLfDjdflJcmZhM/lI8mZ1O2K+oSJVkROBB4EnMBTxph7bA5JKaWUUqpbcjvc9E3tS9/UvjtdLhQN0RBqoDZYS0WgwhqCMDaV+kubhiRcWb2SMn8Z4Wi4i96BNbThlkLb6/Jy4YgLOW/4eV32+m2VEEW1iDiBR4DjgSLgSxF5zRiz1N7IlFJKKaV6LrfD3TT6SWFa4U6XNcY0FeFbRkLzh/00hBuabgPhAE5xNg0j6HK4cIgDg2nahsH8eIsBYxX3gUiAQDhAY6SRQDiw3eNeKb26IiXtlhBFNXAgsMIYswpARKYBEwAtqpVSSimlEoCIWBfccXrIJNPucBJOolwKqABY3+xxUWyeUkoppZRSCS9RiuqWBlbc7lKPInKFiMwXkfmlpaVdEJZSSimllFK7lihFdRHQr9njQmDjtgsZY54wxowxxozJy8vrsuCUUkoppZTaGTFmuwbhrg9CxAUsA44FNgBfAucZY77dyTqlwNquiXAr/YF1Nrzu7kRzHH+a4/jTHMeX5jf+NMfxpzmOv87I8QBjzC5bcxOiqAYQkZOAB7CG1JtijLnL5pBaJCKlrUmsaj/NcfxpjuNPcxxfmt/40xzHn+Y4/royx4ky+gfGmFnALLvjaIUquwPYDWiO409zHH+a4/jS/Maf5jj+NMfx12U5TpQ+1d1Jtd0B7AY0x/GnOY4/zXF8aX7jT3Mcf5rj+OuyHGtR3XZP2B3AbkBzHH+a4/jTHMeX5jf+NMfxpzmOvy7LccL0qVZKKaWUUqq70pZqpZRSSimlOkiLaqWUUkoppTpIi2qllFJKKaU6SItqpZRSSimlOkiLaqWUUkoppTpIi2qllFJKKaU6SItqpZRSSimlOkiLaqWUUkoppTpIi2qllFJKKaU6SItqpZRSSimlOkiLaqWUUkoppTpIi2qllFJKKaU6SItqpZRSSimlOkiLaqWUUkoppTpIi2qllFJKKaU6yGV3AO2Vm5trBg4caHcYSimllFKqB1uwYEGZMSZvV8t126J64MCBzJ8/3+4wlFJKKaVUDyYia1uznHb/UEoppZRSqoO0qFZKKaWUUqqDtKhWSqkeIur3s/bCidTNmWN3KEoptdvptn2qlVJKba3qlVdo+OILnFlZpB5xhN3hKKUSTCgUoqioiEAgYHcoCcnr9VJYWIjb7W7X+lpUK6VUD2DCYSqmPANA/aefYsJhxKUf8UqpHxUVFZGWlsbAgQMREbvDSSjGGMrLyykqKmLQoEHt2oZ2/1BKqR6g5u3ZhDZsIP2UU4jW1uJftMjukJRSCSYQCJCTk6MFdQtEhJycnA614mtRrZRS3ZwxhvKnnsKzxx70uvkmcDio+/hju8NSSiUgLah3rKO50aJaKaW6ufq5n9D4/ffkXHIJrqwsfKNGUT/3E7vDUkqp7YgIF1xwQdPjcDhMXl4eP/3pT9u0nY0bN3LWWWcB8L///a/N68eDFtVKKRUTLisjGgzaHUablT/1FK5evcg4xfpSSTlsLIElSwhXVNgcmVJKbS0lJYUlS5bg9/sBePfddykoKGjTNsLhMH379uXf//53PEJsNy2qlVIKCCxdyorjx7Hplv+zO5Q28S9eTMO8eWRPnIh4PACkHn44GEP9J5/aHJ1SSm1v/PjxvPnmmwC8/PLLnHvuuU3PffHFFxx66KGMHj2aQw89lB9++AGAqVOncvbZZ3PKKacwbtw41qxZwz777LPVdqPRKEOGDKG0tLTp8Z577klZWRkXXXQR11xzDYceeiiDBw+OS0GuRbVSarcXKilh/a+vxPj9VM+aRTj2gdwdlD/5FI60NDLPOadpnnfvvXFmZlI/d26XxWGCQYLr12O6YUu/Uqpr/fznP2fatGkEAgEWLVrEQQcd1PTcXnvtxZw5c/j666+54447uPnmm5ue++yzz3j22Wf54IMPWtyuw+Hg/PPP58UXXwTgvffeY9SoUeTm5gJQXFzM3LlzeeONN5g8eXKnvy8db0kptVuLBgIU/eYqIrW1FDzwABsmTaJy+gzyrvqN3aHtUuPq1dS++y45l1+OMzWlab44naQceih1n3yCiUYRR+e3n4RLSyl7/AmCq1YSXLuOUHExRKPkXH4Z+ddd1+mvp5TqXJv+/Gcav/u+U7eZNHwvejcrgndk5MiRrFmzhpdffpmTTjppq+eqq6uZOHEiy5cvR0QIhUJNzx1//PFkZ2fvdNuXXHIJEyZMYNKkSUyZMoWLL7646bnTTjsNh8PBiBEj2Lx5cxvf3a5pS7VSqtuL+v1EamvbvJ4xhuKbbyawZAkF9/6N9BNPIOWIw6mcPq1btLhWTHkGcbvJvvCC7Z5LOfxwImVlNH7fuV+aWxTffjuV06cTqavHN3o0ub/6JUlD9qT+83lxeT2lVM9y6qmncv3112/V9QPglltu4eijj2bJkiW8/vrrWw1xl5KSsu1mttOvXz969erFBx98wLx58xg/fnzTc0lJSU33jTGd8C62pi3VSqluzUSjrLv0MqINDQx69ZU2DYlU9sij1Mx6i/zrryPtmGMAyD7/fNZf8Utq3nmXjJ+eHK+wOyxcWkr1f/9Lxhln4Iod2mwuZeyhANTN/QTviBGd+tp1cz+h7r33yfvd78i94vKm+SYSpfzpp4n6/Th8vk59TaVU52pNi3I8XXLJJWRkZLDvvvvyv//9r2l+dXV104mLU6dObde2L7vsMs4//3wuuOACnE5nJ0TbOtpSrZTq1qpfeQX/V1/R+P33BNpwwZO6jz+m7OGHyTj9dLIvvbRpfsphh+Ee0J/KF16IR7idpuK55zGRCDmXXNzi8+78fJL22ov6Th6v2oRCbL77btz9+5N90cStnvON3g/CYQJLlnTqayqlep7CwkJ++9vfbjf/97//PTfddBNjx44lEom0a9unnnoqdXV1W3X96Aq7LKpFZIqIlIjIkmbzskXkXRFZHrvNis0XEXlIRFaIyCIR2b/ZOhNjyy8XkYnN5h8gIotj6zwkOiq5UqqVIlVVlNx7H96RIxGfj6pXXm3VeiYapeRv9+Ie0J/et9+2Veu2OBxkn3ce/oUL8S/5Nl6hd0ikro7KadNIGzcOz4ABO1wu9fDDaPj6ayJ1dZ322pUvvURw5Up6TZ6MIzbayBa+UaMAaPh6Yae9nlKqZ6lr4fPoqKOO4o033gDgkEMOYdmyZXzyySfceeedrFmzBoCLLrqIhx9+uGmdgQMHsiT2A775+gDffPMNo0aNYq+99mqaN3Xq1KZxrXcUR0e1pqV6KnDiNvMmA+8bY4YA78ceA4wHhsSmK4DHwCrCgVuBg4ADgVu3FOKxZa5ott62r6WUUi0q+fsDRGpr6XPnHaSPO56aWbOItuISszVvvknjsmXkXXPNdoUhQMYZZyDJyVTGziCPt8B331F8++2UPfkkphUtM1XTpxOtrSXnsst2ulzKYYdDOEzD5593Spzh8nJK//EwKYcfTurRR233vCsrC8/gwfi//rpTXk8ppdrqnnvu4cwzz+Tuu+/u8tfeZVFtjJkDbHsFgQnAs7H7zwKnNZv/nLF8DmSKSB/gBOBdY0yFMaYSeBc4MfZcujHmM2P1GH+u2baUUmqH/IsXUzVjBtnn/wLvsGFknH4G0dpaat97f6frmWCQ0of+QdLw4aQ3O4GlOWdaGhkTTqXmzTfjdgEVE4lQ+/77rL1wIqtPP4Pqf/+H0vvuZ91FFxPaXLLD9aLBIBVTnyX5kIPx7bP3Tl8jefR+OJKTqeukofVK/v53ooEAvW6avMO+677R++H/+uu4nASklFK7MnnyZNauXcthhx3W5a/d3j7VvYwxxQCx2/zY/AJgfbPlimLzdja/qIX5LRKRK0RkvojML+1G48gqpTqXiUTYdPsduHJzyb36agCSD/wJ7oICql95ZafrVv3nP4TWryd/0m93OtRc9nnnYYJBqv7V+RcICG3axKqTf0rRb64iWLSe/BtuYMjcj+lz9934lyxh9emnU7eDy4zXvPYa4dLSXbZSA4jHQ/LBB1M141+sOPY41l44kY033UzZPx8nUl3dppj9i5dQ/Z9XyL7gApIGD97hcsmjRxOpqiK4ek2btq+UUt1dZ4/+0VLThWnH/BYZY54AngAYM2aMNoMotZuq+te/CCxZQt9778WZmgpYfaEzTjuNskcfJVRcjLtPn+3Wi/r9lD36GL4DDiDliCN2+hpJQ4aQfPDBlD/1FA0L5uPMyMSZkYEzM5OUgw7EN2ZMm0Yaaa7i+ecJrl9Pwf33kTZuHOKyPoozTz8N3777sOHaa1l/+eXkXHYZ2ZdcjCvL6i1nolHKn55C0ojhpBx6aKteK//660kaMoTQxo2ENmyg/tNPqX71VWrff5/+zzyz1fjWOxINBNh02204s7PJvfLXO13WN3o0AP6vvyZp8KBWxaiU6jrGmHZ/dvV0HT3C1t6W6s2xrhvEbrccqywC+jVbrhDYuIv5hS3MV0qpFjUsWEDJ/X8n+aCDSD9564sGZJx+GhhD9cyZLa5b8cILhEtLyf/dta36Usm/dhK+UaOIVFTiX7iQ6tdeo+zhh1l7wYWsPOFEyh57jNDGtn1kRYNBql95lbRjjib9pJOaCuotkvbck4EzZpBxxumUP/kky484kqKrr6H2gw+onT2b4OrV5Fx6aau/FJMGDyL/2kkU/O2vDHzpRYZ89D8KH32EwNKlFF155S77oBtjKP7DHwl8+y19br8NZ1raTpf3DBqEIyMD/0LtV61UovF6vZSXl2v3rBYYYygvL8fr9bZ7G9KaxIrIQOANY8w+scd/A8qNMfeIyGQg2xjzexE5GbgKOAnrpMSHjDEHxk5UXABsGQ3kK+AAY0yFiHwJXA3MA2YB/zDGzNpVTGPGjDHz589v27tVSnVbJhik9B8PU/7UU7gLC+n/1JMtjnyx9sKJhDZvYo+3396q8IzU1LDi+HH49htF/8cfb3cc0YYGat99l6pXXqVh3jwQIf3kk+n717+06sqF1W++ycbrrqffk0+SevjO+/wFvv+e6lf/S/UbbxApLwfAXVjIHm+/tV0x3lbVr7/Oxt/fSOpRR1H40IOI293icmX//CelDzxI3qRJ5P7ql63a9rpf/pLQhg3s0exsfKWU/UKhEEVFRVtdUEX9yOv1UlhYiHubz0MRWWCMGbOr9Xf5qSwiLwNHAbkiUoQ1isc9wAwRuRRYB5wdW3wWVkG9AmgALgaIFc93Al/GlrvDGLPl7J9fY40w4gPeik1KKdUksGwZG39/I43ff0/m2WfTa/KNOHZwZa2MM06nePJN+BcsIHmM9RkYDQYpfeABotXV5E+a1KFYHMnJZEyYQMaECQSLiqh47jkqn3se3/6jyT7vvF2uXzV9Bu7CwqaLs+yMd6+98N40mfzrr6Nu7lxq336btPHjO1xQA2SccgqR2lo233EnG2+6ucUfBTXvvEPpAw+Sfsop5PzyilZvO3n0aEo/mkOkuhpnRkaHY1VKdQ63282gQdotK152+clsjDl3B08d28KyBvjNDrYzBZjSwvz5wD67ikMptXuq/fBDNvx2Eo60NAoffZS0Y47e6fLp48ax+c4/UfXqqyTtNZyqGTOomDqVcEkJmWefhXf48E6LzVNYSK+bbiK4YiWl995H2lFH4e7bd4fLN65aTcMXX5B37bWtatXeQtxu0o4+mrSjd/7e2yr7vPOI1tZR+ve/E6mqIm3c8aQceCDuAQNo/O47Nt44Ge+okfT5051t6oPp2y/Wr/qbb0jdRd91pZTqKfQy5UqphFY1fQau3FwG/msGrpycXS7vSE4mbfyJ1LzxJrXvvU+0uprkgw6iz91/bvXJfW0hIvS+4w5WnXoqxbfdRr/HH99hAVo1Ywa4XGSecXqnx9FeObHLjFe88Dz1saH3XHl5mHAYZ2Ym/R5+GEdSUpu26dt3H3A6afj6ay2qlVK7Db1MuVIqYRlj8C9aRPJBB7WqoN4i62c/h0iE5J+MYeD0aQx4diqpY8fG7Yx3T2EB+ddeS/2cj6l5/fUWl4k2NlL96qukHXssrry8uMTRHiJC7i+vYMicOQx+axa9b7+d5AMPxNWnN/0efaRdsTpSUvAOG4Zfr6yolNqNaEu1UiphhTZsJFJRgW/kvm1az7fvPgz7ZmGbulh0VNZ551Izaxab7/ozKWPHbvcjoHb2bCLV1WT97Jwui6ktRISkQYNIGjSoU2L0jR5N1auvYsLhTukDrpRSiU5bqpVSCSuw6BsAvPuObPO6XVlQA4jTSZ8/3Um0oYHNd9213fOV02fg7t+f5IMP7tK47OIbPRrT0EDjsmV2h6KUUl1Cmw+UUgnLv2gx4vHgHTbU7lBaJWmPPcj9zZWUPvAg4dIyUsaOJWXsoYgnCf+CBeRff12XF/t2SR69HwANX3+Nd8QIm6NRSqn406JaKZWw/IsW4R0xYodjKCeinEsvxQRD1H74IaUPPEDpAw+A0wluNxmnJ84JivHm6tsXV36+1a/6F7+wOxyllIo7LaqVUgnJhEIEli5N2D7IOyJuN3nXXE3eNVcTLi+n/rPPqf/0U5L23LNNJ1t2dyKCb/Ro/F99ZXcoSinVJbSoVkolpMblyzGBQLv6UycKV04OGT89mYyfnmx3KLZIPuhAamfPJvDDD3iHDbM7HKWUiqvdo3OfUqrb8S9aDIBvVPctqnd36ePHg8tF9czX7A5FKaXiTotqpVRC8i9ahDMrC3dhod2hqHZyZWWReuSRVL/+GiYctjscpZSKKy2qlVIJKbB4Ed6R+8btgi2qa2RMOJVIaRn1n31udyhKKRVXWlQrpRJOpK6OxhUr8XXj/tTKknrUUTgyMqieOdPuUJRSKq60qFZKJZzAkm/BGO1P3QM4PB7STxpP7XvvEamrszscpZSKGy2qlVIJx79oEQDeffaxORLVGTInTMAEAtTOfsfuUJRSKm60qFZKJZzA4kW4B/THlZVldyiqE3hHjcIzYIB2AVFK9WhaVCulEo7/m0Xan7oHEREyTptAwxdfECzaYHc4SikVF1pUK6USSmjzZsIlJfhGalHdk6SfcioANa/rmNVKqZ5Ji2qlVELxf/MNAL6R+9ociepMnsICkn/yE6r/OxNjjN3hKKVUp9OiWimVUAKLF4PbTdLw4XaHojpZxmkTCK5di3/hQrtDUUqpTqdFtVIqofgXLcY7bBiOpCS7Q1GdLO2EE5DkZKqmTbM7FKWU6nRaVCulEkakqorA4sXan7qHcqamknnWmVS/OYtQcbHd4SilVKfSoloplRBMKETRtddiQiEyTj/d7nBUnORMnAjGUPHsc3aHopRSnUqLaqVUQth8z19o+Oxzet9xB7599aIvPZW7oID08eOpmjGDSE2N3eEopVSn0aJaKWW7ymnTqXzxRbIvvpjM00+zOxwVZzmXXkK0oYHK6dPtDkUppTqNFtVKKVvVz/uCTX/6EylHHE7+9dfZHY7qAt7hw0k59BAqn3ueaDBodzhKKdUptKhWStkmuG4dG377Wzz9+lFw332I02l3SKqLZF96KeHSUmpef93uUJRSqlNoUa2UskXjypWsveBCjDH0e+xRnGlpdoekulDKoYeSNHw45VOewUSjdoejlFIdpkW1UqrL+Zd8y9pfnI+JRBjw3LN4Bg60OyTVxUSEnEsuJrhyJXUffWR3OEop1WFaVCululTDl1+ybuJEHMnJDHzxBbzDhtkdkrJJ+okn4urTh/LHn9DWaqVUt6dFtVKqy9TNmcO6yy7H1asXA156Ec+AAXaHpGwkbjd5v7kS/8KFVL70st3hKKVUh2hRrZTqEpGaGjZc+zs8ewxmwAvP4+7d2+6QVALIOPNMUo44nJJ776Vx1Wq7w1FKqXbrUFEtImtEZLGILBSR+bF52SLyrogsj91mxeaLiDwkIitEZJGI7N9sOxNjyy8XkYkde0tKqURUOW060fp6+t51F7XjqbIAAB+VSURBVK7sbLvDUQlCROjzpz/hSEpi4+TJmHDY7pCUUqpdOqOl+mhjzH7GmDGxx5OB940xQ4D3Y48BxgNDYtMVwGNgFeHArcBBwIHArVsKcaVUzxANBql4/jlSxo7FO3y43eGoBOPOz6f3bbcSWLSI8ieftDscpZRql3h0/5gAPBu7/yxwWrP5zxnL50CmiPQBTgDeNcZUGGMqgXeBE+MQl1LKJjWvvUaktIycyy61OxSVoNLHjyf95JMpfeRR/Eu+tTscpZRqs44W1QZ4R0QWiMgVsXm9jDHFALHb/Nj8AmB9s3WLYvN2NF8p1QOYaJTyp6eQNGI4yQcfbHc4KoH1/r9bcGVns/HGG4kGAnaHo5RSbdLRonqsMWZ/rK4dvxGRI3ayrLQwz+xk/vYbELlCROaLyPzS0tK2R6uU6nJ1//sfwdWrybn0UkRa+u+ulMWZkUGfP/+Z4MqVbLxxMiYSsTskpZRqtQ4V1caYjbHbEuBVrD7Rm2PdOojdlsQWLwL6NVu9ENi4k/ktvd4TxpgxxpgxeXl5HQldKdVFyp96GndBAeknnGB3KKobSD1sLPk33kjt7NlsuvNOjGmxjUUppRJOu4tqEUkRkbQt94FxwBLgNWDLCB4TgZmx+68BF8ZGATkYqI51D5kNjBORrNgJiuNi85RS3VzDV1/h/+orsi+6CHG57A5HdRM5F19EzmWXUjVtOmUPP2J3OEop1Sod+ZbrBbwaO5zrAl4yxrwtIl8CM0TkUmAdcHZs+VnAScAKoAG4GMAYUyEidwJfxpa7wxhT0YG4lFIJovzpKTgzMsg88wy7Q1HdTN511xGuqKTskUdw5mSTfd55doeklFI71e6i2hizChjVwvxy4NgW5hvgNzvY1hRgSntjUUolnsZVq6j74ANyf/1rHMnJdoejuhkRoc8dtxOpqmLznX/C4fGQceaZ2i9fKZWw9IqKSqm4KLn3Phw+H1nn/8LuUFQ3JS4XBfffR/KYMRT/8RZWn3kmtR9+qP2slVIJSYtqpVSnq//0U+o++ICcX/1Kr56oOsTh9dL/mSn0uftuorV1FP36Stb87OfUfTxXi2ulVEKR7vqhNGbMGDN//ny7w1BKbcOEw6w+/Qyifj+D33wDR1KS3SGpHsKEQlTPnEnZo48R2rgR3+jR5F1zNckHH6zdQpRScSMiC5pdOXyHtKVaKdWpqv79bxqXLyf/hhu0oFadStxuMs86iz3efovet91GqLiYdRdfwroLLqT+iy/sDk8ptZvTlmqlVKeJ1NSw8oQTSdpzT/o/96y2Hqq4ijY2UvWvf1P++OOES0vx7rsv6SefRPr48bh79bI7PKVUD6Et1UqpLlf2yKNEqqrodfNNWlCruHMkJZF9/i/Y49136PWHP0AkQsk9f2HFUUez9vwLqJw2nWhjo91hKqV2E1pUK6U6RePq1VS8+CKZZ52Jd/hwu8NRuxGH10v2Becz6JX/MPitWeRefRXhyko23XYbK8ePp+qVV/WS50qpuNOiWinVYdFgkE233oYjKYm83/7W7nDUbixp0CDyrrySwW+8Tv9npuDKzqH45ptZfdpp1H7wgY4YopSKGy2qlVIdYoJBNky6loYvvqDXH/6AKzfX7pCUQkRIOeQQBv5rBgUPPIAJhSm68jdsvP4GTChkd3hKqR5Ii2qlVLuZcJgN199A3Qcf0OuWP5J5xul2h6TUVkSE9BNPYPDrr5F7zdXUvPkmRddeSzQYtDs0pVQPo0W1UqpdTCTCxt/fSO0775A/+Uayf6FXTlSJS9xu8q68kl5/+AN1771P0VVXEQ0E7A5LKdWDaFGtlGozE41SfPMfqJk1i7zrfkfORRfZHZJSrZJ9wfn0vuN26j+ey/pf/opofb3dISmleggtqpVSbWKMYdOdd1I9cya5V19F7uWX2x2SUm2Sdc459P3LPTR8+SVrL76EwHff2R2SUqoH0KJaKdUmpX9/gKqXp5Fz2aXkXnml3eEo1S4Zp55KwYMPEFq7ltVnnMnGm24mtHmz3WEppboxLaqVUq1W9uSTlD/xBJnnnEPeddfpBV5Ut5Z+/PHs8e47ZF9yMTVvvMHKE06k5MEHifr9doemlOqGtKhWSrVK5bTplN53P+knnUTvW/9PC2rVIzjT0+l1ww0MfmsWacceS/lj/2TtxIsIV1baHZpSqpvRoloptUvVb7zJpttvJ/XII+n7l3sQp9PukJTqVJ7CQgruu5fCh/9B4w8/sPa8XxDasMHusJRS3YgW1Uqpnar98EM2Tp5M8gEHUPDgA4jbbXdISsVN2nHH0f/ppwiXl7Pm3PMILFtmd0hKqW5Ci2ql1A7Vz/uCDZOuxTtsGIX/fAyH12t3SErFXfKYMQx4/nkA1p5/ATVvv03g++8JbdhApK5OL3WulGqRdNcPhzFjxpj58+fbHYZSPZZ/8WLWTbwIV58+DHjheVxZWXaHpFSXCm3YwLrLLie4evVW88XjIeeyy8i98teIy2VTdEqpriIiC4wxY3a1nH4aKKW207h8OesvuxxnVhb9pzytBbXaLbkLChj0n3/jX7SYSE010dpaIjW1+L/5hrJHH6Xuk7kU/PWveAYMsDtUpVQC0KJaKbWVcGUl6y69DDxu+j8zBXevXnaHpJRtHMnJpBx80Hbza94aR/Gtt7Hq9DPoffNNZJx5po6Io9RuTvtUK6W2Uv7PxwmXldHvn//E07+/3eEolZDSx49n8Gsz8Y0cSfEfb2HtBRdQOW0a4bIyu0NTStlE+1QrpZoEizawavx40k89hb533WV3OEolPBONUvnCi1S+9BLBNWvA4SD5gANIGzeOlLFj8QwaqC3YSnVzre1TrUW1UqrJhht+T+0777DH7Ldx9+5tdzhKdRvGGBqXL6d29jvUzH6b4IqVALjy80k++CBSDjqYpKFDcGZm4szKwpGSosW2Ut2EnqiolGqTwNKl1Lz+OjmXX64FtVJtJCJ4hw7FO3QoeVdfRXDtWuo/n0fDvM+p/+RTal57fesV3G48hYXk33A9acccY0/QSqlOpS3VSikA1l1yKYFvv2WPd9/BmZ5udzhK9RjGGIIrVhAsKiJSWUWkspJIVRV1H31E47JlpJ98Mr3+cDOu7Gy7Q1VKtUBbqpVSrVb3ySfUf/opvW6arAW1Up1MREgaMoSkIUO2mp939VWUPfkkZf98nPpPP6X3LX8kbfx47RaiVDelLdVK7eZMNMrqs84iWl3D4Ldm4fB47A5Jqd1KYNkyiv/wRwKLF+PMyyVp8B54Bg+ybgcOwJWbizM7B1d2FuJ22x2uUjsVqa2lcdkya1q+nMZly4kGg7iysnBmZ+PMzsKVnY0zK3Y/Nt/du3fC7t/aUq2U2iljDI3LllP1r3/RuPQ7+v7tb1pQK2UD79ChDHz5JapefRX/V18TXLWKmjfeJFpbu92yzsxMXH374Cnsh7tfIZ5+/XBmZyMOBzgcIII4nThS03BmZlgnRqang9OJ8fuJxiYTCuHu3RuHz2fDO+44EwwSrqrCmZm5y88tEwzSuHo1jcuWEVy9GmdmFp5BA/EMHIi7b1/E6eyaoHswEwxS+8GHVL3yH+rnfgLRKACO1FSShg7FmZ5OuLSUwLJlRCoqMI2N221DvF68e++Nb+RIfKNG4d17BK68PBxeb1e/nXbTlmqldiPRxkYCS5ZQ+8EH1L73HqG160CEtBNOoOD++6wvZqWU7YwxhEtLCRUVES4rI1JRQbisnHBZKaGNGwmtLyJUVIQJhVq3QRFo4fve1asXnn79cA/oj8PrswrugJ+oPxArfAxNdcKW1Zttx4TDROvqiNbVEamrwzQ04C4oIGn4XniHj8A7fDjuPr0x4TAmFLKmcBhHcgrO9DQcaWk409Iw0ajV17yiwrqtriba2IgJBq11giHCmzYRXLOGxjWrCRVtgEgEsAo3Z3Y2riyrJd/wY7zRmmoaV6+BcLjltLjduPr2wZWdgzMnG1dWNs7MDKKNjUTr64nW1Vvvr76eaH0dkbp6ovX1mMZGnOnpOLe0vmZlWi2uWdnWvKzMWGtsVtPk8HgwxmACASI1tURrqonU1BCpqSFaU0OkuoZofR2OlJSmIxPOnBwcycmYcBgiEUwkAtEo4nYjSUmIJwmHN8l6u8FgU74idfUE16whuGoVjatXEVy1mmhd3dZ/P7cLV24errwfJ2d6Oo5kH47kZBzJyYjbTXTLdoPW3w9jrP1JrK5N/m8WUf3aa0QqK3H17k3GhAkk7z+apKFDcfXuvV13JmMMpqGBcOzvHa6oIFJeQeOyH/B/s4jAt99utV87UlOtozW5OWSdcw4Zp57aun2+E+mQekopQiUlNMybh3/hN/gXLSLw/fcQCoHLRcrBB5N2/PGkHXsMrtxcu0NVSrWRiUYJl5QQqawEEyt+owYiYSK1dUSqqohUVxOpqoJoBPHFiiVfMuJ0ENq4keDadQTXrSO4fh0EQ9YyPh/i8+Jwe6zWb7CKqOa3YBVVTpdV1Kam4EhNQ7xJBNeupfG77wlt2NCp71e8XjwDBuAZNMjqFpOfT7S6mnBFJZHycsKVFRAKxwo+a3KkpJC0554kDR2Kd9hQPAMHEqmpIbh6tVV0rllDaONGaxtbCrzqahxJSThSU3GkpOBITcGZsuV+Ko7UVMTjJlpTEysMK60fArEfAy39eAHr6pxbflh0GYcDd79CkgYOwpmZ+eN8Eau1v6yMcGkp4ZISovX17XsNt5u0Y44h86wzSTn00A63/JtgkMAPP9C4bBnh0jLC5daPyUhZORmnn07mGad3aPvt0e2KahE5EXgQcAJPGWPu2dnyWlQrtT0TiRBYvJjajz6i/qM5BJYuBUCSk/Htsw++UaPwjRpJ8oEH6gmJSqm4ilRXE/j+B8JlpYjHY7Wuut2I00m0oSHWQltLpLYGcTiaWnld2Vk4MjJweL3W8rF1HampCX80zYTDVutzrMW9qeiusopu8XhwpKVbrdwZ6db9DOuxIz0dZ0oKkfp6q8AvLydSUUnU70dcTqtYdbrAIRAKEW0MYhobMY0Bq9tPs1yJz2f9ABk4sNXd+qINDU1HG6INDdZRi2DQ2uaWye0GBDBNPx5cOTlbF+w9ULcqqkXECSwDjgeKgC+Bc40xS3e0jhbVandiQiGi9fWxQ491Px5uraoiuHZt7JDoGoJr1mIaGsDhwDd6NKlHHEHq4YeRNGyY9htUSiml2qG7nah4ILDCGLMKQESmAROAHRbVSnVEtKGBxlWrEZcTHA7EGWsFiPU7NMbE+g/Gfo3HJmMMzowM3L16tbhdE43iX7Cg6Vd+tN76xW+CjVtv15hYH7VGq99gY5Cov+HH/nt1dUTq66z16+owgcCO34zDgbuwEM/AASQfMAbfqFGkHja2x7ccKKWUUokkUYrqAmB9s8dFwEE2xaJ2A43Ll7PmZz9v17oZZ55B37vu2uHzay+4sNXbajrZxOu1+vClpeFITcGVl4dn0KBY/70U68SV1FQcKalN85ypqTjS03EXFOioHUoppZTNEqWobmmk++36pYjIFcAVAP379493TKoH8wwcSOGjj1hnUm85ozp2JjkQOxlHtjrDecuJL+6Cwh1uVxwO+k+disPnjZ0UlIIjJdkqeptto6n/W4L3D1RKKaVU6yRKUV0E9Gv2uBDYuO1CxpgngCfA6lPdNaGpnsiZkUHaMcfEZdspB+tBFqWUUmp3kyjNZF8CQ0RkkIh4gJ8Dr9kck1JKKaWUUq2SEC3VxpiwiFwFzMYaUm+KMeZbm8NSSimllFKqVRJiSL32EJFSYK0NL90fWGfD6+5ONMfxpzmOP81xfGl+409zHH+a4/jrjBwPMMbk7WqhbltU20VESluTWNV+muP40xzHn+Y4vjS/8ac5jj/Ncfx1ZY4TpU91d1JldwC7Ac1x/GmO409zHF+a3/jTHMef5jj+uizHWlS3XbXdAewGNMfxpzmOP81xfGl+409zHH+a4/jrshxrUd12T9gdwG5Acxx/muP40xzHl+Y3/jTH8ac5jr8uy7H2qVZKKaWUUqqDtKVaKaWUUkqpDtKiWimllFJKqQ7SolrZRkTE7hh6Os1x/GmO409zHH+a4/jTHMePiDhjt7bmWIvqZkRksIgU2h1HTyYiw0RkXwCjHfrjQkT2FpGjQHMcL7ofx5/ux/Gn+3F8ichhIvKYiFwJmuN4EJGxIvIs8EcRybY7x3qiIiAiHqyzQw8FNgDPAy8bY/wiInb/kXoCEXEBjwOHAcXA68AMY8x6zXHnEBEH8DBwDNbVo+YBM40x80XEYYyJ2hpgD6D7cfzpfhx/uh/Hn4jsDzwLPAicBiwHnjXGLLQ1sB5ERAYDrwJ/B44A/MAsY8ybdsWkLdWW/YBUY8xQ4I9Yf5wLRMStHy6dZgBWjocBvwbygCtFxKc57jSZQBowHPgFUA5cJyKpWoh0Gt2P4y8L3Y/jbQCQpvtxXB0IfGmMeQq4DGgAThKRXHvD6lF+AnxnjJkKXAcsBH4qIv3sCmi3LapFZLiI9Ik9dAB7xn6hfwK8DewFHG5bgD1ArDtNcuyhF/hJ7IfKd8BrQApwpm0B9gAiMkBEvLGH2cAhQLIxphT4D1AB/Ca2rPbna4fYIdw9Yw+T0P2404nIWVsOkQPp6H7c6URkfxEZGnvoBsboftx5ROQcEfmdiBwam/UVkCoivY0xm4APgFxgrG1BdnMicoqIXCUiB8dmfQn0E5F+xphK4BOsqyeebleMu11RLSJ7isjrwJPA6yKyN/ADMBc4IbbYO0ANsI+IJNkTafclIn1EZA7wAjAz1mfvB+At4MLYYt8AXwOjRCTTnki7LxEZISL/BaZi7cdDjTErgM+BSbHFioFXgNEi0ldboNpORPYD5gDnikiaMWYp8D5wfmwR3Y87QERSReQ/wPVApYi4jDGrsb4cdT/uBCIySETeBB4BnheR440x36P7cacQEaeI/B9wY2zW4yJyClAPrAGOjM3/COvKfv1i6+mPw1aK1RSvAzdgHcl6RkROMMasAj4Dzokt+gOwFMhp1tjUpXa7ohq4E1hgjDkM+Bi4GuuwTDFWC1SOMaYCWAkcZoxp1J1/17bJ0c+wDnsdivXr/AbgIKwvygNFpMAYUw8UAYVY/aDULmzJsYjsBTwGfGiMORrrC/Hh2GJTgLEiMsgYEwY2AwHAZ0PI3U4L/9cLgHcBJz8eufoYOCRW4Ol+3Ebb5LgfsNkYc7Ax5mUgEps/FWs/Hqz7cdttk+PrgYXGmEOAmfzYsKH7cScwxkSAYcB1xpj7gdux6goXVl2xn4iMiO3HPxBrRdUfh20yBphrjDnCGHMnVj/1y2PPzQX2FZEDY3+LDcBYY0zAjkB3i6JaRHqLiCvW6lwJfBd7ygALsD6o3wIygAtiz83E+rWTrjt/q3i3ue8GMMbcjdUn8ifARqwvx+tiy72PVbSkd12Y3dqWHFcDk40xD8Ye3wkki0ge8AXWYce/AhhjlmD1n2zs4li7q21bNyqxTjCKYBUgbqz9thTrxyLoftxWzXM8EquQI9b941YROQyrtelT4F7Q/bgdvNBUXNcDodj8dGC5iAzAOgJTgu7HbSYiF4rIkc1a9TcDWbEjLf/GapQ7DiunAeBPseUKgC9jJ4qqnYjl+KhY3fY+8Fyzp8uxPpfBOjr7NfCAiKQCewPrmnU97VI9uqgWkWNF5GPgUeAhY0wj1s5+kogsxjq7fBhWa2oj8F/gUhG5G+uQwjysDyS1AyJyvIi8C/xNRH4em70aKBeR/rHH04FRWB8uTwMniMj9wGKsVtbaLg67W9kmx+cYY4qNMZ81a43aFwgaY0qNMXXAHUChiPxDRJYAa4FqPeKyY81y/Ndm+zFYuf0Ka3SgJKwTmQ8HngKO1/249bbJ8bmx2V8BxSIyBasfdRXwB2ACcD+QLyIP637cOi18VhislrwhIvI1cCJWC+p0rPOGngaOE5G/o/vxTomlj4h8CEzEOon2kVghV4b1WZEaW/xBrAa6EmPM7UBVrAvOz4GnYq3Wahst5Pg8rKOvycaY4lijBkAfrG4gGGM2xRqYPo8tez7wF2NMQ9e/AyugHjkBQ7GK4rOAfGA2cEjsudHA9GbLTgHuit3fG7gEOMvu95DoE7BnLMcTYjl9CbgKa4d/BvgpPw7b+CxwS+z+IOAU4Ay730OiTy3k+AXg5thz7tjtOODhbdbLxxoi8lS730OiTzvI8R9jz52C9SE9mB8Ljgtiz+l+3P4cv4h1xMoF3Id1xHDL/nwB8ETsvu7H7c/xS8D1seeGAa80W/b/sBqaAAbqfrzL3Dpjt0OBF2L3XVgNdk9jjbw0G2vksOTY8zOAa2P33UCe3e8jkaed5PgfW/bdZsu8DhwXu5/fbNk0u99HjzoEIdb4phhr2KX9gC+MMf8WkXSsL8P1sV86Blgl1kDhFVhnl58tImKM+Rb41qa3kPC2yfFBWP3TZ8aeex/rC/JZrG4IhwN1wP+w/hOMja27Gqs1W7VgFzn+ALhfRJ4yxpTEVjkW68sUEbkFeMYYU4R1aFe1oJU5fgzrB+LlwK3Am1gnMaeIiFP3451rRY7vwypIZmIVgWdjFYLfAGeKNSZ1Cbof79Aucvwe1n78PNboKetFZLixRvv4AJgUy/EarBPq1DZi3TTuAJwiMgura0wEwBgTFpGrgE1YR1VewmqJ7oN1JCCE1YUJY0wIq8uY2kYrcnwNsFFEjjTGfCTWdUVKgWUichfWEHpHGWv0D9uPsvSY7h8icjHWiRZ3xmYtAg4QkSexDmvlY/Uz/QewCutQ46Wxfnx/A2ab2M8d1bIWcrwYa1SEgbHHLqwP579gXVigCLhPRCYDD2AV12onWpFjN1YXpntjywtwANZJXR9hHdKt7MKQu51W5ng1ViH9b+A9rKNck4AlWB/c2gVhJ1r5WbEa+KsxZg7W58N1InIjMA2ry4KOkLATrdyPV8Wer8UacvMaEfkt1ufze1gNTKoFInIk1hGULGAFVh5DwNEiciA0/Zi5HfibMeZZrB/dF8a62riw/iZqB1qZY4NVdN8eW80LXITVzzoNq8U6Yb7zesQVFWN9ml4AtvTDOdcY84NYJ25dBNQZYx4Ta4iVjVgFdTpwErAPcK8xZp4twXcTLeT4PGPM9yLyANAL6I/1JfmX2DTRGFMqIuOxTlL8wBgz157ou4c25vge4Aqs/XkBVl/U64wxX9sRe3fRxhz/FaurR1mz9d2xVie1A+34rLjEGLNJRH4C7A8sMsZ8Zk/03UM79uOzYvOOwxpJ4TFjzOd2xN5diMjhwEBjzPOxx49iFcl+4GpjzAGxIwX5WKMvXWusK1L2xuoCssqu2LuLNub4IeB3QF+sbqb3m0S8OqXd/U86awL6x27vwbrEOFgt8U8Dhzdb7lFgvN3xdsdpmxxPj913YrWAHBZ73A9rOCyv3fF2x6mNOXZi/Wrf3+64u9PUhhw/AyTFHjvsjrs7TfpZkVA5fhbw2B1vd5uAZKyTk7f04/0FcHfs/kKsog+sHykv2x1vd5zamONpdsfbmqnHdP8wxqyL3X0A2ENExhvr0MwK4AkRGSYiN2Od9PK9XXF2Z9vkeJBYg69HgGrzYyv0r7DG/dbWvHZoY47FGBMwxnxlR6zdVRty7AfCsXX08thtoJ8V8deGHNfz4/jfqpWMMQ3GmMZYTgGO58d+0RcDw0XkDeBlrFFsVBu1MccL/r+9+3m5dIzjOP7+EIkpC4WSHYUsTMMwCz/Kj2wtCE2NZoEsRin2SDayV/gL6LFRQ0RSfoUaLDQj8iNZPJGmSWp8La5bnsZG3c917udc837VU899n7P4ns/i9O063/u6YOePhA0x/nGqJA8B+6vqxun6OdrDA2cAT1TVD0vWN4Ip4/ur6ubpei9tK6yzmH7OXbK+EZhxf2bcnxn3Z8b9JDmTNnv+Om3l9FiSy2jb6F0NfFtVPy1Z47obKePhmurpaea/krxCe2r8BG1rmy+qypOitsEpGf9M2+P7LeBoVX2zbHVjMOP+zLg/M+7PjPuaVkbPpu1Nv0HbcneT1vz9vmRtoxgp42HGP/4xfbmcSxtsvwf4vqo+tqHePqdkfB8t48N+gW8fM+7PjPsz4/7MuK9qK4+7afO+jwEbVXVg3Zq9nWykjIfap3qLR2gzTrdXO0VR28+M+zPj/sy4PzPuz4z7+pE2TvO8+XYzRMbDjX/Avz+HLV3HyMy4PzPuz4z7M+P+zFjaGYZsqiVJkqRVGm6mWpIkSVo1m2pJkiRpJptqSZIkaSabakmSJGkmm2pJkiRpJptqSdJ/TEcHS5L+J5tqSVpzSZ5O8uiW62eSHEryeJJPkhxJ8uSW119L8mmSr5I8uOX+8SRPJfkI2LfijyFJa82mWpLW30vAAWgHgQD3Ar8AlwN7gWuAPUlumt5/sKr2ANcCh5JcMN0/D/iyqq6vqvdX+QEkad2Neky5JJ02quq7JJtJdgMXAZ8D1wF3TP8D7KI12e/RGum7pvuXTvc3gZPAq6usXZJGYVMtSWN4EXgAuBh4GbgVeLaqXtj6piS3ALcB+6rqRJJ3gXOml/+oqpOrKliSRuL4hySNYQO4k7ZC/cb0dzDJLoAklyS5EDgf+HVqqK8AbliqYEkaiSvVkjSAqvozyTvAb9Nq85tJrgQ+SAJwHNgPHAYeTnIE+Br4cKmaJWkkqaqla5AkzTQ9oPgZcHdVHV26Hkk63Tj+IUlrLslVwDHgbRtqSVqGK9WSJEnSTK5US5IkSTPZVEuSJEkz2VRLkiRJM9lUS5IkSTPZVEuSJEkz2VRLkiRJM/0NxMSxJcwe0QkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset.plot(subplots=True, figsize=(12, 10), grid=False, title=\"Number of births per year\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = top1000.pivot_table('prop', 'year', 'sex', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f24641322d0>"
      ]
     },
     "execution_count": 53,
     "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": [
    "table.plot(title='Sum of table1000.prop by year and sex', yticks=np.linspace(0, 1.2, 13), xticks=range(1880, 2030, 20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "df = boys[boys.year == 2010]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "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>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>John</td>\n",
       "      <td>M</td>\n",
       "      <td>9655</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.087383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>William</td>\n",
       "      <td>M</td>\n",
       "      <td>9532</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.086269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>James</td>\n",
       "      <td>M</td>\n",
       "      <td>5927</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.053642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>945</th>\n",
       "      <td>Charles</td>\n",
       "      <td>M</td>\n",
       "      <td>5348</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.048402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>George</td>\n",
       "      <td>M</td>\n",
       "      <td>5126</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.046393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>Frank</td>\n",
       "      <td>M</td>\n",
       "      <td>3242</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.029342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>Joseph</td>\n",
       "      <td>M</td>\n",
       "      <td>2632</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.023821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>M</td>\n",
       "      <td>2534</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>Henry</td>\n",
       "      <td>M</td>\n",
       "      <td>2444</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>Robert</td>\n",
       "      <td>M</td>\n",
       "      <td>2415</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>952</th>\n",
       "      <td>Edward</td>\n",
       "      <td>M</td>\n",
       "      <td>2364</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>Harry</td>\n",
       "      <td>M</td>\n",
       "      <td>2152</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>954</th>\n",
       "      <td>Walter</td>\n",
       "      <td>M</td>\n",
       "      <td>1755</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.015884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>Arthur</td>\n",
       "      <td>M</td>\n",
       "      <td>1599</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>Fred</td>\n",
       "      <td>M</td>\n",
       "      <td>1569</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>Albert</td>\n",
       "      <td>M</td>\n",
       "      <td>1493</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.013512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>Samuel</td>\n",
       "      <td>M</td>\n",
       "      <td>1024</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.009268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>959</th>\n",
       "      <td>David</td>\n",
       "      <td>M</td>\n",
       "      <td>869</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.007865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>Louis</td>\n",
       "      <td>M</td>\n",
       "      <td>828</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.007494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>961</th>\n",
       "      <td>Joe</td>\n",
       "      <td>M</td>\n",
       "      <td>731</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.006616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>962</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>M</td>\n",
       "      <td>730</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.006607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>Clarence</td>\n",
       "      <td>M</td>\n",
       "      <td>730</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.006607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>Richard</td>\n",
       "      <td>M</td>\n",
       "      <td>728</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.006589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965</th>\n",
       "      <td>Andrew</td>\n",
       "      <td>M</td>\n",
       "      <td>644</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.005829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>M</td>\n",
       "      <td>643</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.005819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>967</th>\n",
       "      <td>Ernest</td>\n",
       "      <td>M</td>\n",
       "      <td>615</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.005566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>968</th>\n",
       "      <td>Will</td>\n",
       "      <td>M</td>\n",
       "      <td>588</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.005322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>Jesse</td>\n",
       "      <td>M</td>\n",
       "      <td>569</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.005150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>Oscar</td>\n",
       "      <td>M</td>\n",
       "      <td>544</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.004923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>Lewis</td>\n",
       "      <td>M</td>\n",
       "      <td>517</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.004679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275847</th>\n",
       "      <td>Simeon</td>\n",
       "      <td>M</td>\n",
       "      <td>211</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275848</th>\n",
       "      <td>Yadiel</td>\n",
       "      <td>M</td>\n",
       "      <td>211</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275849</th>\n",
       "      <td>Ira</td>\n",
       "      <td>M</td>\n",
       "      <td>211</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275850</th>\n",
       "      <td>Ledger</td>\n",
       "      <td>M</td>\n",
       "      <td>211</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275851</th>\n",
       "      <td>Avi</td>\n",
       "      <td>M</td>\n",
       "      <td>211</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275852</th>\n",
       "      <td>Maddux</td>\n",
       "      <td>M</td>\n",
       "      <td>210</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275853</th>\n",
       "      <td>Seamus</td>\n",
       "      <td>M</td>\n",
       "      <td>210</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275854</th>\n",
       "      <td>Jad</td>\n",
       "      <td>M</td>\n",
       "      <td>209</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275855</th>\n",
       "      <td>Jeremias</td>\n",
       "      <td>M</td>\n",
       "      <td>209</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275856</th>\n",
       "      <td>Kylen</td>\n",
       "      <td>M</td>\n",
       "      <td>209</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275857</th>\n",
       "      <td>Rashad</td>\n",
       "      <td>M</td>\n",
       "      <td>208</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275858</th>\n",
       "      <td>Santos</td>\n",
       "      <td>M</td>\n",
       "      <td>208</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275859</th>\n",
       "      <td>Dominique</td>\n",
       "      <td>M</td>\n",
       "      <td>207</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275860</th>\n",
       "      <td>Gianluca</td>\n",
       "      <td>M</td>\n",
       "      <td>207</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275861</th>\n",
       "      <td>Jovanni</td>\n",
       "      <td>M</td>\n",
       "      <td>207</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275862</th>\n",
       "      <td>Craig</td>\n",
       "      <td>M</td>\n",
       "      <td>207</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275863</th>\n",
       "      <td>Cedric</td>\n",
       "      <td>M</td>\n",
       "      <td>207</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275864</th>\n",
       "      <td>Bishop</td>\n",
       "      <td>M</td>\n",
       "      <td>205</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275865</th>\n",
       "      <td>Brenden</td>\n",
       "      <td>M</td>\n",
       "      <td>204</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275866</th>\n",
       "      <td>Anton</td>\n",
       "      <td>M</td>\n",
       "      <td>203</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275867</th>\n",
       "      <td>Camron</td>\n",
       "      <td>M</td>\n",
       "      <td>203</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275868</th>\n",
       "      <td>Giancarlo</td>\n",
       "      <td>M</td>\n",
       "      <td>203</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275869</th>\n",
       "      <td>Lyle</td>\n",
       "      <td>M</td>\n",
       "      <td>203</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275870</th>\n",
       "      <td>Alaric</td>\n",
       "      <td>M</td>\n",
       "      <td>202</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275871</th>\n",
       "      <td>Decker</td>\n",
       "      <td>M</td>\n",
       "      <td>202</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275872</th>\n",
       "      <td>Eliezer</td>\n",
       "      <td>M</td>\n",
       "      <td>202</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275873</th>\n",
       "      <td>Ramiro</td>\n",
       "      <td>M</td>\n",
       "      <td>202</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275874</th>\n",
       "      <td>Yisroel</td>\n",
       "      <td>M</td>\n",
       "      <td>202</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275875</th>\n",
       "      <td>Marquis</td>\n",
       "      <td>M</td>\n",
       "      <td>201</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275876</th>\n",
       "      <td>Turner</td>\n",
       "      <td>M</td>\n",
       "      <td>201</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>137997 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             name sex  births  year      prop\n",
       "942          John   M    9655  1880  0.087383\n",
       "943       William   M    9532  1880  0.086269\n",
       "944         James   M    5927  1880  0.053642\n",
       "945       Charles   M    5348  1880  0.048402\n",
       "946        George   M    5126  1880  0.046393\n",
       "947         Frank   M    3242  1880  0.029342\n",
       "948        Joseph   M    2632  1880  0.023821\n",
       "949        Thomas   M    2534  1880  0.022934\n",
       "950         Henry   M    2444  1880  0.022119\n",
       "951        Robert   M    2415  1880  0.021857\n",
       "952        Edward   M    2364  1880  0.021395\n",
       "953         Harry   M    2152  1880  0.019477\n",
       "954        Walter   M    1755  1880  0.015884\n",
       "955        Arthur   M    1599  1880  0.014472\n",
       "956          Fred   M    1569  1880  0.014200\n",
       "957        Albert   M    1493  1880  0.013512\n",
       "958        Samuel   M    1024  1880  0.009268\n",
       "959         David   M     869  1880  0.007865\n",
       "960         Louis   M     828  1880  0.007494\n",
       "961           Joe   M     731  1880  0.006616\n",
       "962       Charlie   M     730  1880  0.006607\n",
       "963      Clarence   M     730  1880  0.006607\n",
       "964       Richard   M     728  1880  0.006589\n",
       "965        Andrew   M     644  1880  0.005829\n",
       "966        Daniel   M     643  1880  0.005819\n",
       "967        Ernest   M     615  1880  0.005566\n",
       "968          Will   M     588  1880  0.005322\n",
       "969         Jesse   M     569  1880  0.005150\n",
       "970         Oscar   M     544  1880  0.004923\n",
       "971         Lewis   M     517  1880  0.004679\n",
       "...           ...  ..     ...   ...       ...\n",
       "275847     Simeon   M     211  2017  0.000115\n",
       "275848     Yadiel   M     211  2017  0.000115\n",
       "275849        Ira   M     211  2017  0.000115\n",
       "275850     Ledger   M     211  2017  0.000115\n",
       "275851        Avi   M     211  2017  0.000115\n",
       "275852     Maddux   M     210  2017  0.000114\n",
       "275853     Seamus   M     210  2017  0.000114\n",
       "275854        Jad   M     209  2017  0.000114\n",
       "275855   Jeremias   M     209  2017  0.000114\n",
       "275856      Kylen   M     209  2017  0.000114\n",
       "275857     Rashad   M     208  2017  0.000113\n",
       "275858     Santos   M     208  2017  0.000113\n",
       "275859  Dominique   M     207  2017  0.000113\n",
       "275860   Gianluca   M     207  2017  0.000113\n",
       "275861    Jovanni   M     207  2017  0.000113\n",
       "275862      Craig   M     207  2017  0.000113\n",
       "275863     Cedric   M     207  2017  0.000113\n",
       "275864     Bishop   M     205  2017  0.000112\n",
       "275865    Brenden   M     204  2017  0.000111\n",
       "275866      Anton   M     203  2017  0.000111\n",
       "275867     Camron   M     203  2017  0.000111\n",
       "275868  Giancarlo   M     203  2017  0.000111\n",
       "275869       Lyle   M     203  2017  0.000111\n",
       "275870     Alaric   M     202  2017  0.000110\n",
       "275871     Decker   M     202  2017  0.000110\n",
       "275872    Eliezer   M     202  2017  0.000110\n",
       "275873     Ramiro   M     202  2017  0.000110\n",
       "275874    Yisroel   M     202  2017  0.000110\n",
       "275875    Marquis   M     201  2017  0.000110\n",
       "275876     Turner   M     201  2017  0.000110\n",
       "\n",
       "[137997 rows x 5 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "prop_cumsum = df.sort_values(by='prop', ascending=False).prop.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "260877    0.011544\n",
       "260878    0.020939\n",
       "260879    0.029990\n",
       "260880    0.038952\n",
       "260881    0.047850\n",
       "260882    0.056594\n",
       "260883    0.065178\n",
       "260884    0.073443\n",
       "260885    0.081552\n",
       "260886    0.089635\n",
       "Name: prop, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum.values.searchsorted(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = boys[boys.year == 1900]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "in1900 = df.sort_values(by='prop', ascending=False).prop.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "in1900.values.searchsorted(0.5) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_quantile_count(group, q=0.5):\n",
    "    group = group.sort_values(by='prop', ascending=False)\n",
    "    return group.prop.cumsum().values.searchsorted(q) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "diversity = top1000.groupby(['year', 'sex']).apply(get_quantile_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "diversity = diversity.unstack('sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>39</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex    F   M\n",
       "year        \n",
       "1880  38  14\n",
       "1881  38  14\n",
       "1882  38  15\n",
       "1883  39  15\n",
       "1884  39  16"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diversity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f245e5f5a90>"
      ]
     },
     "execution_count": 69,
     "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": [
    "diversity.plot(title=\"Number of popular names in top 50%\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}