babynames.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# US Baby Names 1880-2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "names1880 = pd.read_csv('datasets/babynames/yob1880.txt', names=['name', 'sex', 'births'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "    </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>794</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     794\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "F     90993\n",
       "M    110493\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880.groupby('sex').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = range(1880, 2011)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pieces = []\n",
    "columns = ['names', 'sex', 'births']\n",
    "\n",
    "for year in years:\n",
    "    path = 'datasets/babynames/yob%d.txt' % year\n",
    "    frame = pd.read_csv(path,names=columns)\n",
    "    \n",
    "    frame['year'] = year\n",
    "    pieces.append(frame)\n",
    "\n",
    "names = pd.concat(pieces,ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>names</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>794</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>1690754</th>\n",
       "      <td>Zaviyon</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690755</th>\n",
       "      <td>Zaybrien</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690756</th>\n",
       "      <td>Zayshawn</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690757</th>\n",
       "      <td>Zayyan</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690758</th>\n",
       "      <td>Zeal</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690759</th>\n",
       "      <td>Zealan</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690760</th>\n",
       "      <td>Zecharia</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690761</th>\n",
       "      <td>Zeferino</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690762</th>\n",
       "      <td>Zekariah</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690763</th>\n",
       "      <td>Zeki</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690764</th>\n",
       "      <td>Zeriah</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690765</th>\n",
       "      <td>Zeshan</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690766</th>\n",
       "      <td>Zhyier</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690767</th>\n",
       "      <td>Zildjian</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690768</th>\n",
       "      <td>Zinn</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690769</th>\n",
       "      <td>Zishan</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690770</th>\n",
       "      <td>Ziven</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690771</th>\n",
       "      <td>Zmari</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690772</th>\n",
       "      <td>Zoren</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690773</th>\n",
       "      <td>Zuhaib</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690774</th>\n",
       "      <td>Zyeire</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690775</th>\n",
       "      <td>Zygmunt</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690776</th>\n",
       "      <td>Zykerion</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690777</th>\n",
       "      <td>Zylar</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690778</th>\n",
       "      <td>Zylin</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690779</th>\n",
       "      <td>Zymaire</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690780</th>\n",
       "      <td>Zyonne</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690781</th>\n",
       "      <td>Zyquarius</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690782</th>\n",
       "      <td>Zyran</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690783</th>\n",
       "      <td>Zzyzx</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1690784 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             names 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     794  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",
       "1690754    Zaviyon   M       5  2010\n",
       "1690755   Zaybrien   M       5  2010\n",
       "1690756   Zayshawn   M       5  2010\n",
       "1690757     Zayyan   M       5  2010\n",
       "1690758       Zeal   M       5  2010\n",
       "1690759     Zealan   M       5  2010\n",
       "1690760   Zecharia   M       5  2010\n",
       "1690761   Zeferino   M       5  2010\n",
       "1690762   Zekariah   M       5  2010\n",
       "1690763       Zeki   M       5  2010\n",
       "1690764     Zeriah   M       5  2010\n",
       "1690765     Zeshan   M       5  2010\n",
       "1690766     Zhyier   M       5  2010\n",
       "1690767   Zildjian   M       5  2010\n",
       "1690768       Zinn   M       5  2010\n",
       "1690769     Zishan   M       5  2010\n",
       "1690770      Ziven   M       5  2010\n",
       "1690771      Zmari   M       5  2010\n",
       "1690772      Zoren   M       5  2010\n",
       "1690773     Zuhaib   M       5  2010\n",
       "1690774     Zyeire   M       5  2010\n",
       "1690775    Zygmunt   M       5  2010\n",
       "1690776   Zykerion   M       5  2010\n",
       "1690777      Zylar   M       5  2010\n",
       "1690778      Zylin   M       5  2010\n",
       "1690779    Zymaire   M       5  2010\n",
       "1690780     Zyonne   M       5  2010\n",
       "1690781  Zyquarius   M       5  2010\n",
       "1690782      Zyran   M       5  2010\n",
       "1690783      Zzyzx   M       5  2010\n",
       "\n",
       "[1690784 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_births = names.pivot_table('births', index='year', columns='sex', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>2006</th>\n",
       "      <td>1896468</td>\n",
       "      <td>2050234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1916888</td>\n",
       "      <td>2069242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1883645</td>\n",
       "      <td>2032310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1827643</td>\n",
       "      <td>1973359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1759010</td>\n",
       "      <td>1898382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex         F        M\n",
       "year                  \n",
       "2006  1896468  2050234\n",
       "2007  1916888  2069242\n",
       "2008  1883645  2032310\n",
       "2009  1827643  1973359\n",
       "2010  1759010  1898382"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1801b893da0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ld5CdZ2fR9mhu/HAjU99Zzw0f/M6/d2Tz+prDrNkfT25+wYUDjJwDnS4zV84nHavnijVQ614Hey7c/A2M+BNs/gC2N7DJNvH74fCPcMl9xVczGPUwtB9qpn5Xs0VVmZZJPvCI1roXMAJ4QCnVG3gC+Flr3R342XoNMBHobj3uAd4HExiAZ4BLgOHAM4XBwcpzT5H9JljpVTqHEKKIHfPBv9dFA6sr9pzmq82R3HtFFx4Y040Xp/dlw9EkBr+whke+201yRi5+nq64ONmIzyjgnV+OcPeCbTy+aM+Fg9hsMP0DcHKBRXeBPa+eK9fApEbB9k9h0C3QuovpUux6pek6+v3thtMduOFNcPGE4fcUT7fZ4LK/QmqkaaFUQ4XBRGt9Wmu9w3p+DjgAtAemAYVhdz5wrfV8GrBAG5sAX6VUIDAeWKO1TtZapwBrgAnWNm+t9UattQYWlDhWVc4hhAAzVhKzHQbfVuzixIycfP6xNIIBIb48enUoADcODWHOld0Y1qk1n84expq/XM6ns4fz1d0j+N/LPIh4bjyzR3Viyc4Y9sWmXTiHTzBc8ybE7jDThZuzda+av5c/Zv46OcOMr6D3NDM+seIRx4+hpJyEvd+bddY8Wl+8vcdEM7ni97eqFfyqNDVYKdUJs/bbZiBAa30aTMBRSrW1srUHoorsFm2llZceXUo61TjH6RLlvQfTcsHf35/w8PBi9fHx8eHcuXMVV7wO+fr60qdPn/Ovv/rqKzp27Fhm/uzsbMLDw0lPT7+oPo1VU6oLNIz6dDsylyDlzO/pweQXKcuK47kkZeRxf18bG9ZdmMo+2BUGdwFO7+e3Iv+L0tPT2fL7eoa6a751gSe/2shfh7oXOVMrQtuNpd2619iWFUJGy051XbUaqYvPxmbPZdTOr0loezmHdh0DinT7+c+iS4iNDts+ISougWPd7qq181a2LqogH//E3+kQuRgPFJsYTG4Z+wX6XU3o4XfZ9Z+3SW3Vv0rlqXQwUUq1BBYBD2utz5Zz17/SNuhqpJdbnMrso7WeC8wFCA0N1WFhYcW2HzhwAC8vrwpOVbdatGjBnj17Ks5ocXd3Z9CgQYSHh1OyPo1VU6oLNID65GXBptugz7WMvmrq+eRz2Xk8vPZXwkL9uWt65a4pKFqXaNdj/N+PB3Hv0I8RXfwuZBocCq/3ZFibLBgRVosVqX118tkcWQPrsgkcex+B3Us59pgrYWUbQrbMJWT0zdBzUq2ctty6aA0xO2DfYjN2du606X6bOJeRfa8r+6B5I+DNbxmYsRamV+26uErN5lJKuWACyZda68VWcnxh15L1N8FKjwZCiuweDMRWkB5cSnp1ziGE2P21ud5hyKxiyfN/P0lqZh5/GdejWoedNbIT7bzd+eeqg+ii3SDegeAVaL68mqNDK8G1JXS+vOw8V78IgQPM9TmpUWXnqw35ufD5tfDxlWbFgnb94eZv4cHtUF4gAXBxh+H3mnvaJB4uP28JFbZMrJlVnwAHtNavF9m0DJgFvGz9XVok/UGl1NeYwfY0q4tqNfC/RQbdrwae1FonK6XOKaVGYLrPbgPers45qlTzEp5bvo/9sVWYb18JvYO8eeaaPuXmycrKYuDAgQB07tyZJUuW1GoZRDOzbwmsfMzMzOl04ZqStMw85q49zrheAQwI8a3Wod1dnPjLVd15fNFevtkaxYzhHS5sDBpsrqhubgoK4NCPZrDd2a3sfM5u5nqOD6+A72fDrOXg0qL65902j6Fb34Tun0L7IcW3rX4SjofDuOfMD4oWrUo9RJkG32ZuR7DnGxj7dKV3q0zLZBRwK3ClUmqX9ZiE+YK/Sil1BLjKeg2wEjgOHAU+Av4EoLVOBl4AtlqP5600gPuBj619jgE/WulVOkdj1KJFC3bt2sWuXbskkIia2bEAvr/DfLncsgiUIj0nnw9+O8bY138jPSefh8d1r9EpbhgSwqVd/Hjhh/1EJWde2BA0CJKOmKvtm5PTu0wXUmgluq78usL09yF6G3w7q/oz4FIjYfXf8cyIhHkTzOdeaMcC2Pqxmbo9+uGqBxIArwDoMsbcgbOgoOL8lgpbJlrr9ZQ+RgEwtpT8GnigjGPNAy66u47WehvQt5T0pKqeo7oqakEI0aDF74dlf4auY+GmL8DVg+w8OxP/vZao5Cwu696Gh8YOpm97nxqdxmZTvHrjACa8sZZHvt3NwntG4GRTJpiA+XItr7unqTn0IygbdL+6cvl7XWNmwC1/CJbcZy4ctDlV/nxam5YnsH3I6wxNWWo+91//F9x9zDU/Xa+Ecc9WuSrF9L8RltwLUZW/mZ8s9ChEU7BtHji5mbsmunoAsGxXLFHJWbz/x8FM7Fd7M+fb+7bgH9f05rHv9/DxuuPce0XXC8EkdmfzCyYdLjU3GKusIbebdbD++wy4e8Pk14vfW6Y8B3+Aw6vgqhdIz+sCkxfDlrkQH2FahUGDYfxLVQtQpek5BVw8TOukkiSYCNHY5aSbQfc+157/UtNaM2/DCXq282JC33a1fsrrhwTz84EEXll9iD5BPozu3gZ8OzSvcZOUUxC/1wyuV9Xoh83qBOvfMC2Kcc+Wn//MUTi4HDa+BwF9YcT9sG6DCRoj7q9O6QFYdySRjceS+MtVPXBxKjLq4dYSek6GiMVl71yCrM3lYOnp6Y4ugmjsIr6H3HMw9M7zSRuPJXEw7hx3jOpMOdP4q00p093V1d+TB77awckzGaZ10pxmdBWuY1WZ8ZLSjH0Ght5hAsraV6HAfnGe/Fz47nZ4Z4i5dbJP+wsrD9RAvr2AV1cf4rZ5W3gv/Bgv/rD/4kz9bzIBr5IkmAjRmGkNWz+Btn2KLZsyb8MJ/DxdmTowqM5O3dLNmY9vG4ZScNeCbeS2HQCppxr+8uu1IS3GXCnea6oZWLfEpGZx/xfb2RNdiS9hpWDSq2a67i8vwOu94ccnIGqr+VzzsuGbW8wMvcsfg4cj4J7wqt8ls4R8ewG3f7qVd349yg1Dgrl9ZCfmbzzFt1tLTFnuMgY82lT6uNLNJURjFrsD4vbA5NfO97ufPJPBzwcT+POV3XF3qWHfeQU6+Hnwxo0Dmf3ZVrbmdWIUmK6ubhfNm2la/vusaUlc/UKx5DfXHObHiDh+PZTAWzMGcXWfCroYbU4wfe6FLqVtn8Dm902XYYvWZkLDlDfNEii1ZPGOGNYfPcNzU/swa2Qn8u0FHEtM56n/RNAtoCWDO1gzwJycYdRDwEOVOq60TIRozLZ9ahbu63cjAJm5+fxtyV6cbYpbRnSoYOfacVn3Nri72Fifbl17HNvEu7qitpqB6ZEPFrvdbWRSJot3xjB9UHtC23lz7xfbWbDxZMXHc3I2rZMZX8JjR+Ha980y9mcOw7T3ajWQ5OYX8O+fj9A/2IfbLjXLNjk72Xh75iD8vdx4dtm+4hekjqr8VfDSMhGisbLnm5te9ZoC7t6czc7jjk+3siMyhVdvGEBbL/eKj1ELnJ1s9AnyYWucHVp3hdhdVTuAPd+0qmo6A6k+5GbCykehZTsY/ddim9799ShONsUTE3vi7e7CnK938o+l+2jl4co1AyrZ3ejuAwNvNg+tKz/Lq5K+3RZFTGoWL03vW2wszdfDlQev7MaTi/ey7sgZLu/hX+VjS8tEiMYqajNkJUPoJLLz7Nzy8WZ2RaXyzs2D+cPg4Ir3r0X9g32IiE2joP0QiNxYtRVyv7rBDDI3dDnn4Mvr4fRumPRKsfuBRCVnsmhHNDOHhRDg7U4LVyfenjmIYZ1a8ch3u9l+qhrjSLUcSLLz7Lzzy1GGdGzFFaUEiz8Mbk87b3fe+fVotY4vwUSIxurQSnByhW5j+XZbFHui03hr5iAm1eI1JZU1MMSX7LwCYgPHQWYSnPitcjsmHoZjv5hrJ3Icu4L3Rc7GwgeXwVc3mXuSLJgGkZvMbYt7TyuW9b3wY9iU4r6wC4Px7i5OfHjrUIJ83Ll7wXYikzJLnqHexJ/N5vkf9hN3NptHrupR6gw/N2cn7rm8C1tOJLP1ZNWDnwQTB1NKceutt55/nZ+fj7+/P1OmTHFgqYTD2fPKv6eE1iaYdL6cPGdPPvztOEM6tmJiHVxTUhn9g816X7+rweDmbVaqrYydn5u/9lyznlRDsuoJM26RdNTckyRuL9z0OfS7vli25IxcFu2I5vqhwQT6FF9vq7WnK5/OHo69QHPfF9vJzitl+m8dSc/JZ/nuWO79fBsjX/6FrzZHcsOQYEZ2K3uG1szhHfDzdOWdX6reOpFg4mCenp5ERESQlZUFwJo1a2jfvn0Fe4kmrcAOb/SFje+WnSfxECQfh9CJLNkZQ0xqFg+O6VYn15RURic/D7zdndl5OttcPX1guZnaWh57HuxeCD0mgJuPaZ00FId/gv1LzZTcP2+Hvx6Eh/eaWVclfLM1itz8AmaP7FTqoTq38eSNmwaw//RZnl22r44LDgUFmmeX7WPwC2v488KdbD+Vyl2jOxP+aBj/umFAufu2cHXijtGd+e1wIgdOV22dNQkmDcDEiRNZsWIFAAsXLmTmzJkOLpFwqLOxkB5nFuwrq3VyaCUA9u4TeD/8GH2CvAkLrfqgaW1RStE/2NdcX9HvOsg5a5YxL8/hVZCRCENmm6nEh3+q0sKCNWbPh1//D3Z+WTw9NxNWPgJtQs2CiWAts39xq89eoPli0yku7eJH94Cy7410Zc8AHhjTla+3RvHdtpovQb9mfzxvbs9md9TF17P8c9VBPvv9JFMHBPHdfZey+W9jeXJSLzq18azUsf94SQdcnW18U/K6kwrIbK5CPz5hmrG1qV0/mFjx7UxnzJjB888/z5QpU9izZw933HEH69atq92yiMYj5aT19wREb73oHu6ACSaBA1lxysaJMxm8/8fBDmuVFOof7MPctcfJDhmLu0cbc4vYXteUvcOOz82sqG7jzP1X9i2G0zsvXlK9LmSfNYP+x342r2O2w8R/wtkYcw1JaiTcvhKcXcs9zM8H4olJzeLpKb0qPOVfxvVgx6lU/rZkLy5ONq4dVL0eiDPpOTz2/W5SM+1Me3cDNwwJZsbwDnRp48ninTF8uPY4t47oyPPT+lTr34SvhysT+rRj8Y5onpjYs9L7STBpAPr378/JkydZuHAhkybVzl3YRCNWGExQZs2tksEkNcosYz7mb3y+8SRd/D0ZX9HFcfVgQIgv+QWa/fGZDO5zrfnFn3MO3Er5xX72tGm5jHrYXGfRbZxZfffw6joNJjZ7tmkB/fdZOHMIrvk3JJ+ADW+awJIaCcoJrngcOo2q8HifbzpFoI8743oFVJjX2cnGB7cM4Z7Pt/HwN7uITsnkgWp0TT63fD+ZOXaeHuFOgmsQ8zac4LvtF+58PqFPO56dWr1AUmjGsBCW7Y5lVURcpfeRYFKoEi2IujR16lQeffRRwsPDSUpKcmhZhIOlnjJfaL2mmF/rE16+8Av5xDpYfDc4uZLcaTLbVp3kobHdsdkc2yoBGGANwu+OSmVw3+tMN93e70u/6G7jO+bvYGvyiacfBA83XV9j/lb7hSuww6I7Gb1/Oeh8cPeFP34PXceY7QF94Ld/miu+h98D3hVfF3IsMZ11R87w2PhQnJ0qN2Lg4+HCgjuH88Sivbz602ESzuXwXBW++H8+EM/y3bH8ZVwPujrHcGdYL2aP6sy+2DROnMkgv0Bz+8hO5rYANTCiix8dWnvw9dbISu8jwaSBuOOOO/Dx8aFfv36Eh4c7ujjCgXTKSZKd23K67TX03b8Ujvxkfrmv/Rese82sBXXzt/wU5YXWNIhWCUA7H3faermxJzoNLh1hgsN/n4Ee44t/OadFw5aPYMDN5r7khUInmBbD2dhKfZlXyaGVsG8JcYFXEzTuT9BhRPE7Hfa/0TwqSWvN/644gLuLjZuGhVS8QxFuzk68fuMA/L3cmLv2ODaleOaa3mUGlOw8OxuPJ7HpWBLfb48mNMCL+8O68vv6GMC87+18avcCVZtNcdOwEP61+lDl96nVEohqCw4O5qGHKrcGjmja8s8c52B2a146GAie/rD+dXh/JKx7FQbMhHt+g8D+rN4XR0jrFvRsV/bAb30bEOLLtlPJaKXM6rb2PHPzpqITCcJfBjSEPVGh6YzeAAAgAElEQVR8527jzN8TdTBeuMmsd3W4x32mNVKTW+YCy/ec5ueDCTx6dShtWpZzu94yKKV4cmJP7hrdmc9+P8nTSyM4En8Oe0HxCRdJ6TlMf+93Zn+6lXkbTtC1bUvenDEQV+e6/+q+fkhwlVo40jJxsNKWoA8LCyMsLKz+CyMahtRTROp+bDyZxrkR1+K16yPzC/7WJeYuephrCDYcTeK2Szs6fOC9qKt6B7Bmfzw7IlMZ0rErXPW8WX5k2ycw7C5zkeKuL+GS+8DX/KLfGZmCs81Gv8Ce5iLM+L3ATbVXqNhdcGqDue9Ibs2XbEnOyOXZZfsYEOLL7FGdq30cpRR/n9wLDXyy/gRfbIrE09WJsNC23H15F4J83fnjR5uJTM7krZmDuKpXAC1c62/JmQBvd8aEtuV4JfNLMBGiIcnNwCXrDFHaTPP9xuNm7po2yCwE6HKhKyP8UAK59oKKV6WtZxP7tuMfSyNYvCOaIR1bmXusHFwBKx6Bta+ZsR8XD7jsEcB04dy9YBveLVz45ZEw8O8JcRG1W6jNH5jFMAfdCpuruG5YCVqbazjOZefxynX9azw2oZTi6Sm9mTm8A7uiUtkZmcKy3bGs2Hualm7OFGjNZ7OHc2nXKtzJsRb9e8ZAPrm9cnmlm0uIhiTVDHhG6bb0a+/D1xHn0ANvLhZIAH7aF4+fp6v5wm5AvNxdGN+nHct3x5KTbwebDW6cbyYRdBptbi185dPgaa7CXrY7ljPpuRxPzOBowjkznT6+FoPJuXgzCWDQH6GFb40OlZtfwCPf7WbZ7lj+fGV3Qmuxe7Fb25ZcPySYl6b3Y+OTY3lqci96B3qz4A7HBRIAT7fKtzeafctEa92gugnKo8tbXkM0Dda04PQW7Zk5vAN/W7KXfbFn6dve53yWjJx8fj2YwKR+gTX+ZVwX/jA4mKW7YvnlQIK597y7T6m3ltVaM2/9CUJatyAqOYvV++LpFtDXdIOdiweviqfbVmj7p1CQZ7rVqiglI5cnF++llacL/dr78sOeWH4/lsQjV/XgwSu71bxsZWjp5sxdl3Xhrsu6VJy5AWnWwcTd3Z2kpCT8/PwafEDRWpOUlIS7e/0sKy4cxAomqnUnJvcL5Nll+1i8I4aUzFw+23CSPTFpJJ7LAWB831r4sq0Do7u1oa2XG4t2xJhgUobCWwu/cl1/vtoSyep9cTww2bqLYPzemgcTrWHvd9D58mJ3Q6zcrpr/WbSHXw8m4OHqxMItUbg4Kd64aQDTB9XvisyNRbMOJsHBwURHR5OYmOjoolSKu7s7wcHyD7lJSzlFBu74+gXi4+HCmJ7+zNtwgnkbTuDv5caYUH9CWnnQxb8lYT3aOrq0pXKyKaYPas8n60+QlJ6DXxmznT5Zf4I2Lc2thZMycvnnqoPEthhMEJhxk8LZXdUVt9cs0njpg2Vmycq14+KkLrpO5ItNp1izP56nJvfiztGdiUrOwtlJEeRbs1lgTVmzDiYuLi507lz92RhC1DZ78gmiCvwJ8TPrKN1zeVfSc/KZPiiYawYE4ubcCG4ghenq+nDtcT7fdIqHx/W4aPvxxHR+PpjAQ2PNrYXH9wngn6sOsvpYNrO9g2tn3GTfEuviz6mlbt4fe5bb5m2hrZcbH88aej5QHDh9lhdWHOCKHv7cMaozSik6+HnUvDxNXLMOJkI0NPakE0TqtnS0vryGdGzFl3eNcHCpqi60nReT+wfy3q/HmNwv8KJFEF9ZdYgWLk7cMsLcOraLf0t6BLRk9b44ZrfrW/N18rQ2qwd0ucJcXV/C9lPJzP50Ky1cnYhKzmTqOxv453X9WH/0DAu3ROLt7sKrNwxoECsLNBYym0uIhkJrnNIiidJt6dC68f8Sfm5qHzzdnHjs+z3FLsb7/dgZVu2L44ExXfH3utAFNr5PO7acSCardS84c6TiJezLc3qXGX/qM/18Up69gEPJdv656iC3fLyF1p6uLLp/JIv/NJIWrjbunL+NBRtPMalfIIvuv7RY2UTFpGUiREORkYiTPYtI3ZZrmkAwadPSjWen9uGhr3fx6YYT3HVZF+wFmueX76e9b4uLZiuN6xXA278cJaKgA8O0HRIPQNCg6p08YjHYnM29VTAXeU769zoik7Nxth1nZLc2vHbDgPMBY+kDo1m0PZoJfdsR0gTee0eQYCJEQ5FyCoA4W9sm86t46oAglu+O5cUVB/j1UAKd/Dw5GHeOd28ejLtL8fGfXoHeuDgpduQEMwzMIHxVgkn8PrOasru3GS/pMgY8WgPw+cZTRCZncmdfVx66Pgxvd5diu7b2dOXuyxvXVNyGRoKJEA2FNS0437thLZFSE0op3rhpIJ9tOMnXW6PYcDSJ4Z1bM6nfxVfuuzrb6BHgxYYkZ+518azaILzW8O1tZvZWoSufBsx1OR+tO84VPfy5LDjzokAiaocEEyEaCiuYuPp1cmgxapuXuwt/HtudB8Z0Y9upFLr6e5YZLHsHevPLwQR0YG9UVZZVidluAsnVL5n1y+w50M7covbLzadIzshlztjunDuxuzaqJEohA/BCNBA6NZIz2oeANq0dXZQ6YbMphnduXeZ1JwB9grxJysgly38AxGyDzOTKHXz31+DsDoNvg4DepnvMZiMr187ctccZ3a1Ng1t6pqmRYCJEA5GXdpp47Xt+WnBz1DvILBsTEXAt5GfDzs8r3ik/FyK+h56TzXhJEQu3RHIm3bRKRN2SYCJEA5GfdppE7dskpgVXV69Acz3Klsx20HG0uVtjgb38nY6ugawU6D+jWLLWmoVbIhkY4svwzk2ztdeQSDARooGwZcST0MyDiZe7Cx39PNh/+iwMv9usonzkp/J32r3Q3ETMutdLoX2xZzmSkM71Q2QJovogwUSIhqDAjmt2Mgn4Etyq+QYTMOMm+2LPmm4rryDYMhfs+bDzS3NflJPrL9y58cwROLQK+t0ATsXnEy3eEYOrk40p/ctebFLUHpnNJURDkJmEDTuZrm3q9W56DVHvQG9W7o3jXB54Db0Dfn0R3h0GycfB5mK6vtqEAhrOHDZ3Zxx0S7Fj5NkLWLY7hrG92uLr4eqYijQz0jIRoiE4FwdAvkfDXAm4PvWxBuEPxp2DIbPA1cvcVOumL+CJUzDtXfDwA58Qc9OtBzZDQJ9ix1h3JJEz6blMH9TeEVVolqRlIkRDkB5v/rZsWLfhdYTeQWZG1r6YNIZ16gyPHDC33bVZv30H3XJRS6SkRTtiaOXhQlioBOf6IsFEiIbAapk4+0r/flsvN/w8Xc0gPIBb5W6P++rqQ6zcexpPN2cOxp3l5uEdcHWWzpf6UuE7rZSap5RKUEpFFEl7VikVo5TaZT0mFdn2pFLqqFLqkFJqfJH0CVbaUaXUE0XSOyulNiuljiilvlFKuVrpbtbro9b2ThWdQ4jGKv/saQBatA5ycEkcTylF7yBv9sacrfQ+KRm5zF17HFdnG21aujKiix+zRnaqu0KKi1QmbH8GTCgl/Q2t9UDrsRJAKdUbmAH0sfZ5TynlpJRyAt4FJgK9gZlWXoB/WsfqDqQAd1rpdwIpWutuwBtWvjLPUbVqC9GwZKfEkqY98Pf1qThzMzC6WxsOnD7Lobhzlcq/eGcMufYC3pwxkE9nD+fzOy+hi3/LOi6lKKrCYKK1XgtUck0DpgFfa61ztNYngKPAcOtxVGt9XGudC3wNTFNmgZ4rge+t/ecD1xY51nzr+ffAWCt/WecQotHKSz1Ngm5FgLe7o4vSINw4NAQ3ZxvzN56sMG/hxYmDOvjSs513hflF3ajJmMmDSqnbgG3AI1rrFKA9sKlInmgrDSCqRPolgB+QqrXOLyV/+8J9tNb5Sqk0K3955yhGKXUPcA+Av78/4eHhVa9lA5Went5k6tOU6gLVq0/3hBMkaF8iD+8lPK7hNLQd+dkMD7Dx/bZIRrU8g6dL2asoH0mxczQhmzv6ulZY1qb0b62h1aW6weR94AVAW39fA+4ASvvENaW3gHQ5+SlnW3n7FE/Uei4wFyA0NFSHhYWVlq1RCg8Pp6nUpynVBapXn3ObzpFAZyZfOZo25SyEWN8c+dn490hj8lvrOe3e8aIbaRW17NtdtHSL59Ebx+DhWv5XWlP6t9bQ6lKtqQ5a63ittV1rXQB8xIVupmggpEjWYCC2nPQzgK9SyrlEerFjWdt9MN1tZR1LiMZJa1rknOEMrWgtF9id1yfIh2GdWrFg4ylSMnL5Zmsk/1x1kJ2RKWit0VpzOP4cK/ac5tpBQRUGElG3qvXuK6UCtdanrZfTgcKZXsuAr5RSrwNBQHdgC6Y10V0p1RmIwQyg36y11kqpX4HrMeMos4ClRY41C9hobf/Fyl/WOYRonLJTcda5ZLv6YbM1jZti1ZZZIzvx4Fc7GfLiGgo0KAXvhx+jcxtPcvMLiEnNwtXZxi0jOjq6qM1ehcFEKbUQCAPaKKWigWeAMKXUQEz30kngXgCt9T6l1LfAfiAfeEBrbbeO8yCwGnAC5mmt91mneBz4Win1IrAT+MRK/wT4XCl1FNMimVHROYRolNITAMiTq98vMr5PO6YNDKJNSzemDgiis78nq/bGsXxPLO4uTtwf1pWwUP9mv55ZQ1BhMNFazywl+ZNS0grzvwS8VEr6SmBlKenHKWU2ltY6G7ihKucQolGyLljES65+L8nFyca/ZxS/D/yNw0K4cVhIGXsIR5HLQ4WoL6lRsGwOZKcVT7eWUnHxkavfReMlwUSI+vLj47BjPmyfXyw5L9XMH3GXq99FIybBRIj6cPS/cGiFuU/5tk+K3T0wMzmWLO1Kq1ZtHFhAIWpGgokQdS0/F358gnyfTnwf9CiknDTBxZKXGkuC9iXAR65+F42XBBMh6tqWuZB0hPfc7+KJwz3IdPWHLR9d2J4eTwK+spSKaNQkmAhR1za9R1LASF4/1RmbsyuLbFfB0TWQdAwA58wE0zKRYCIaMQkmQtSlzGQ4G8PCpB50b+vF05N78VbqKLRyhvWvQ4Ed95xEklVrvN3lCm7ReEkwEaIuxZtrczdntuOFa/syfXAwGa5t2Og7BXZ+AXPDcLdnkOXWBrMothCNkwQTIeqQtoJJYPchjOjiR0s3Z6YNDOLOMzPInPYRZCQCkOMhFyyKxk2CiRB1KDN6L8m6Jf1Cu59Pmzm8A1l5BSzKuQQe3MpLrg9xzP8qB5ZSiJqTYCJEHco7HcGhgg70bu97Pq1fex/6tvfmtZ8O8cuJTL7IGomfr9zUSTRuEkyEqCsFBXikHOKQDqFnO6/zyUop3p45mECfFtzx2Tay8uwEeDece5gIUR0STISoK6mncC3IIsmzG55uxWdqdW7jyZI/jeS2S83S6T0CvEo7ghCNhsxFFKKuJOwHoKBt71I3u7s48fy0vswZ2x0/T7kplmjcpGUiRB3JjtkLgG/HfuXma9PSTaYFi0ZPWiZC1JGMyD0kFPjTPUSWlhdNn7RMhKgjtjMHOKQ70DtIZmqJpk+CiWgUzqTn8OyyfUSnZDq6KJWTn4N3xikinTvR1kvW3BJNnwQT0WCcy86joECXuu3ZZfv47PeT3PnZNs5l59Vzyaoh8RBO2Mlu3dPRJRGiXkgwEQ3CscR0Rr78C/d+sR17iYDy68EEfthzmvF9AjiamM6chTvJtxc4qKSVk386AgC39n0dXBIh6ocEE+Fw2fma+z7fTp69gDX743ll1cHz2zJy8nnqPxF0b9uSt2cO5rmpffj1UCIvrTzgwBJXLGPvMpK0F+06SzARzYPM5hIOpbXm04gcjiXa+fzOS1gVEceHa4/j7+WGv5cby3fHEpOaxXf3XYqrs41bRnTkeGIG8zacoIt/S24d0dHRVbiIS24qXifX8LF9PGODWzu6OELUCwkmwqG+2xbN5jg7j40PZVS3NlzSuTUnkzJ4cYVpebg525hzZTeGdbrwpfz3yb04mZTBs8v20cnPg8u6+3M6LYuMHDvd2rZ0VFXOC4gPx6bz+bXFeO7y83R0cYSoFxJMhMNorflo3XE6edu4/4quADg72fjw1iGsPXyGDq096B7QEhen4r2xTjbFWzMHcf37v/OnL3bg7+3G8cQMnG2K/zwwir7tfRxRHUNr/GN+YltBD0aPHIXNJhcjiuZBxkyEw+yITOFIQjpjQpyLfel6uDozoW87egd5XxRICrV0c+bjWUMJae1Bh9Ye/G1ST1p7uvLIt7vJybfXVxUuFrUZn+wYvi8Yw41DQxxXDiHqmbRMhMMs3BKFp6sTlwRW759hcCsPVj502fnX3dt6Mfuzrby+5jBPTuxVW8Wskrytn5GjW2DvPQ1/L1kJWDQf0jIR9ebV1YeYNW8L57LzOJudxw97Ypk6sD3uzrXTFTSmZ1tmDg9h7trjbD2ZXCvHrJKMM6j9S1hmH8GMUY4JZkI4igQTUS+ycu18uuEEvx1OZPanW/lqcyTZeQXMHF67XUF/n9ybkFYePPz1LtIy6/fiRr3+TZQ9lx/dpzC4Q6t6PbcQjibBRNSLnw/Gk5FrZ/aoTuyMSuXlHw/SO9CbfpUdLM8+C2v/BcnHy83W0s2Zt2YOIv5sNk8s3oPWpV9RX+vOxVOw5SP+Yx9Jj04dZRVg0exIMBH1YumuWAK83Xhqcm/euGkgTjbF7FGdKvelqzUsfwh+eRHeuxTWvQ72Iq2Os7Gw6C54ewicWMfAEF/+Z0IoP0bE8f5vx/h0wwlu+OB3Xl19qM7ql7/2VbQ9lxWtbmNUexmKFM2P/KsXdS41M5fwQwnMurQTTjbF1AFBjAn1x8vdpXIH2DEf9i2GkXMg5QT8/Bxseh/aDwafYNi1EAryoWUAzL8GRj/MXVc8yYajSbyyygQQnxYu7IpK5baRHWtv4cXYXaAUaI3a9inf5l/BPdeOIztyb+0cX4hGRIKJqHM/RsSRZ9dMG9j+fNpFgURr2PqxCQg9p4DNajTH74MfH4cuY2Dccyb98GqIWAyxO+HIGgidCONfAk9/WPUErH8DW+Jh/n3TPP6z6zSju/vjhJ3xb4TzxcZT/PXq0BrX6eyO7/Feduf513btxL5udzOzix/hkTU+vBCNjgQTUeeW7oqhi78nfduXc1+PXV/CykfNc/+e0O96iN4Gx38DN2/4w9wLAabHePMAKLCDzenCcaa+Df69YPWT+G58mdvHPWtaEN/cwne+Qcze/Bh/GtMNdxcnqktnpVCw4n/YX9CRdwuuw1uncdoWwAtTx1T7mEI0dhJMRJ2KTslk84lkHh7bo8zxEY+MaNjwGHS6DIbcDuteM+Mjvh1h8K0w9E5o2bb0E9hKCQoj7oczh2D9G5CdZrrBtJ0B9ija5xxk2a6e3Dis+rPITnz9OB3zkzkw7N+8NmESO06l4ObiREhrj2ofU4jGToKJqDMFBZonF+/FzdnGHwa3Lz1TXja9978Kzu7wh4/AOxD6/AEyEkyXV3VmRSkFk16FpGOwbR50GAnT3kHPDeMxlx95af0ALu3qR8K5bPxbutPBr/JB4Mz+3+hy6huWe07n2slTcLIpRnZrU/UyCtHESDARdeajdcdZd+QM//eHfmX/at/4Ni0zTsDMb0wgAdOd5dWuZid3coGbvjDjK32mg7MratidXLb+32QnHOGyV84B0NrTlQ2PX0kL14q7vdLiI7F/fzexug39b3kFJ1l3S4jzZGqwqBO7o1L51+pDTOrXjhnldSntXUSqTx8InVD7hWjhCwNuAmdX8/qS+8HJmbndNvLKdf157preJGfk8P2O6GK7xaRm8euhBD5ed5yVe0+TnpNPdGwsSR9OoaU9lZNj36djUBndbkI0U9IyEbWuoEDz6He7CfB25/+m9y/7WpIzRyHxAInd7sK3PgrmFYAaMJPQ3V8TqmLQ8RGMbunP3Wtf4ebhHXCyKV776RALftnFONsOxjltRwFfEcglTgfoRQzHrprHyNFX10dphWhUJJiIWvfb4USOJKTz5k0D8fEo51qSg8sBONNmBN3rqWyMfhhOrAVtR3W+nK4Hf2BA5n/574G+uDnbiPvtY3a4f4ITduxeQeTaWuB6didaQ+LV79Br5LT6KqkQjUqFwUQpNQ+YAiRorftaaa2Bb4BOwEngRq11ijI/Qf8NTAIygdu11jusfWYBT1mHfVFrPd9KHwJ8BrQAVgIPaa11dc4hGoaP1x+nnbc7k/sHFt+QmwkFeeBuLaFyYDkEDSbH3b/+Cte6Czy0yzwvKEB/eDmPJCzhvjUT0Gdj+N5lASpkOEx4CaegwbRQCuz5kJ9NoJvjb7wlRENVmTGTz4CSHdpPAD9rrbsDP1uvASYC3a3HPcD7cD74PANcAgwHnlFKFa6E976Vt3C/CdU5h2gY9sWmseFoEreP6nTxvUiW3APvjYT0BEiLgZjt0GuKYwoKYLOhxvyNYB1Hr8QV/M3+AW4uTtiumwvth1yYSebkDBJIhChXhcFEa70WKLme9zRgvvV8PnBtkfQF2tgE+CqlAoHxwBqtdbLWOgVYA0ywtnlrrTdqsyLfghLHqso5RAPwyfoTeLg6MXNYh+IbcjPg8E9wNhq+nQX7lpj0XlPrv5BFhU7E3m4gL7jOZ5Tag+2q58C3Q8X7CSGKqe6YSYDW+jSA1vq0Uqpwakt7IKpIvmgrrbz06FLSq3OO0yULqZS6B9N6wd/fn/Dw8KrVsgFLT09vcPVJyS5g6c4sxoQ4s3PLhmLb2iRuoq89h9jA8QRFrqYgagtZHiFsjYhxeF1a+0+lf9wuUn16syujK9SwLI6uT21qSnWBplWfhlaX2h6AL23ajq5GenXOcXGi1nOBuQChoaE6LCysgkM3HuHh4TS0+izYeBK73seTN4yiq3+JbqH/fAfuPgTd9SX8/By239/Gc8hNhIWFOb4u+gro3gHfzpcRVtPrW2iYn011NaW6QNOqT0OrS3WDSbxSKtBqMQQCCVZ6NFD0ooJgINZKDyuRHm6lB5eSvzrnEA4WmZSJu4uNLm08i28osMPhVdD9anMx4dhnzfpZjhwvKUop6H+Do0shRKNW3YsWlwGzrOezgKVF0m9TxgggzeqqWg1crZRqZQ28Xw2stradU0qNsGZp3VbiWFU5h3CwmNQsgnxbXHxdSfQ2yDxjVvcFM6A96I8XZnUJIRq9ykwNXohpVbRRSkVjZmW9DHyrlLoTiAQKf9atxEzZPYqZtjsbQGudrJR6Adhq5Xtea104qH8/F6YG/2g9qOo5hOPFpGbR3rfFxRsOrQSbM3QbV/+FEkLUiwqDidZ6ZhmbxpaSVwMPlHGcecC8UtK3AX1LSU+q6jmEY8WkZNEnqJRl5g/9CJ1GS0tEiCZM1uYStSIr105SRu7FLZOEA2Y5+NBJjimYEKJeSDARtSImNQuA9q2KBJO8bFhyr2mR9JZlSIRoymRtLlErzgcT3yJLza9+Ek7vhhkLa76kvBCiQZOWiagVsSVbJnu+MzemGjkHekoXlxBNnQQTUStiUrJwsikCvNzMulsr/godLoWx/3B00YQQ9UCCiagVMalZtPN2x9mmTCCx58G175uLFIUQTZ4EE1ErYlKyTBdXxCJztfvYp6F1Z0cXSwhRTySYiFoRk5pFaMsc+PF/zPLtl9zn6CIJIeqRzOYSNZZvLyDubDZj8rZCZhLcthRsTo4ulhCiHknLRNRY3Nls7AWakIIYcPOBgIsWNBBCNHESTESNxaSYacF+ebHQutOFOxQKIZoNCSaixgovWPTKjDL3WBdCNDsSTESNxaRk4YQd53PR0EpmcAnRHEkwETUWk5pFX8+zqIJ8aZkI0UxJMBE1FpOaxUBP6/Y0cm2JEM2SBBNRYzEpWfR0O2NeSMtEiGZJgomokYICTUxqFp1tCeDsDi1ldWAhmiMJJqJGTiRlkJNfQAjxZvDdJv+khGiO5H++qJGImDQA2uRGy3iJEM2YBBNRIxExabg7g+s5ucZEiOZMgomokYiYs1zaNh+VnwWtOjm6OEIIB5FgIqpNa01EbBojW501CdIyEaLZkmAiqi0yOZNz2fn095BrTIRo7iSYiGqLiDEtks5OCWBzBp8ODi6REMJRJJiIaouITcPFSeGXGw0+IeAkt8cRormSYCKqLSImjR4BXjilnJTxEiGaOQkmolq01kTEpNEvyBuST8h4iRDNnAQTUS2xadmkZOYxrE025KRBm1BHF0kI4UASTES1FF75PtA5yiQE9ndgaYQQjibBRFTL7qhUnGyKDrlHAQUBfRxdJCGEA0kwEVVmL9As3RXLiC6tcUmMMIPvbl6OLpYQwoEkmIgq++1wAjGpWfzxko4Qtxfa9XN0kYQQDibBRFTZl5si8fdy46ou7pByUsZLhBASTETVxKRm8euhBG4aGoJL4n6T2E6CiRDNnQQTUSXfbIlEAzOGh0DcHpMo3VxCNHsSTESl5dsL+HprFGE9/Alu5WHGSzzbgpfcqleI5k6Ciai0iNizJJzL4Q+Dg01C3B5plQghAAkmogrOX6gY4gv5uZBwUAbfhRCABBNRBftiz+Lt7kxwqxaQeBAK8qRlIoQAahhMlFInlVJ7lVK7lFLbrLTWSqk1Sqkj1t9WVrpSSr2llDqqlNqjlBpc5DizrPxHlFKziqQPsY5/1NpXlXcOUbf2x6bRJ8gHpVSRwXdpmQghaqdlMkZrPVBrPdR6/QTws9a6O/Cz9RpgItDdetwDvA8mMADPAJcAw4FnigSH9628hftNqOAcoo7k2Qs4EHeOPkHeJiF6G7i2lKXnhRBA3XRzTQPmW8/nA9cWSV+gjU2Ar1IqEBgPrNFaJ2utU4A1wARrm7fWeqPWWgMLShyrtHOIOnIsMZ3c/AL6tveBggI4tBK6jQWbk6OLJoRoAGp6azwN/KSU0sCHWuu5QIDW+jSA1vq0Uqqtlbc9EFVk32grrbz06FLSKeccxSil7sG0bPD39yc8PLy69Wxw0tPT67U+G2LyAMiIOcTOU0sZlB7PflSvLcQAABMcSURBVN2NhFooQ33Xpa41pfo0pbpA06pPQ6tLTYPJKK11rPVlvkYpdbCcvKqUNF2N9EqzgttcgNDQUB0WFlaV3Ru08PBw6rM+a5fvx93lFDMmjcFp9U/g5Ebva/9C71pY4LG+61LXmlJ9mlJdoGnVp6HVpUbdXFrrWOtvArAEM+YRb3VRYf1NsLJHAyFFdg8GYitIDy4lnXLOIepIRGwaPdt544SGA8tMF5esFCyEsFQ7mCilPJVSXoXPgauBCGAZUDgjaxaw1Hq+DLjNmtU1AkizuqpWA1crpVpZA+9XA6utbeeUUiOsWVy3lThWaecQdaCgQHMg9ix923tD7A44GwO9pjq6WEKIBqQm3VwBwBJrtq4z8JXWepVSaivwrVLqTiASuMHKvxKYBP/f3n1HV1XlCxz//tJISCAFQu8oJTSFgILMKKCA6Iwi6iC6RLCiPnEW41PHN+tZRkcdn68sy+golqfgExnHBiIqiAUEEqSEGiCBUEJJIyQhubn7/bF34KIpQEhuye+z1l05d599TvYvO/f87mn7kAmUANMAjDF5IvIEsMrVe9wYk+emZwBvAjHAQvcCeLqG36EawO78Eo4c89CvQzxs/DuERULv8XUvqJRqMs44mRhjdgCDqik/DIypptwA99SwrtnA7GrKVwP9T/V3qIaRsbcIgH7tW8APH0GPiyFGb+1RSp2gd8CrOm3YU0hEmNA76iAUZEOfK/zdJKVUgNFkomq1cW8RH67ZQ+92LWhWlG0L26T4t1FKqYCjyUTV6LN1+5j08g8YA09fM9A+VREgsZs/m6WUCkD1vc9Ehaglmw9wz5x0hnRN5OWbBtOmRTRkZEFENMS19XfzlFIBRpOJ+gVPpZcnF2yiR3Isc26/gGYRbsiU/CxI6ApS3f2kSqmmTA9zqV+Yl5ZD5oFiHhzf50QiAcjP1kNcSqlqaTJRJykp9/D84q0M6ZrI2BSfw1nG2D0TTSZKqWpoMlHHGWN4acl2Dh45xh8n9EF8D2eV5kP5EUjs6r8GKqUClp4zUXi9hi835fLi0u2s3V3AFQPaM6Rr0smV8nfan7pnopSqhiaTJm5dTgGPfpxB+q4COifF8OTE/lw7pNMvK+plwUqpWmgyaaLKKip5/NONzF25i1axUTwzaQCTBnciIryGI5/57obFBD3MpZT6JU0mTVDe0XJuf3s1adn53DayO/ddei4toyNrXyg/C5q3hmZxjdJGpVRw0WQSZMo9Xu5+N53BcZVcchrLlZR7yD5cQtahozzz+Wb2FZbx0o2DmTCg/amtQK/kUkrVQpNJkFmUsZ8vN+WyPT6MGcacfMVVNUrLK3lhyTb+vmwn5ZVeAFrFRjHn9gsZ0vU0Rv4tyIaOqfVpulIqhGkyCTJzftyFCOws9JKWnU9qt6Rq63m9hoUb9vPUgk3sKShl4vkdGdO3DV2TYunZJpbmUafR9ZUeKNgN/a89S1EopUKNJpMgsuNgMct3HObeUefwxneZvPbtzuPJZFVWHrvzSkiMjaKotIKXlmxnS+4Rerdtwft3DmdY9+qTzikpygFTqfeYKKVqpMkkgK3cmcfjn2Ywc0wvLktpy9yVu4gIE24e0ZWs7Gw+27ifXYdL+GpzLo99svGkZXsmx/Lfk8/jyoEdCA+r51haelmwUqoOmkwC1OKNudw7J52KSi93vZPGs5MG8kFaDpeltKVNi2jGdIlgUbaHW95cyY6DRxnXry0PjOtDYWkFnkovqd2S6p9EqlRdFqzJRClVA00mflRYUsGn6/eybnchmQeL2V9YRoeEaNq0jGbh+n0M6BjPC1MGM+v9tcyatxaAKRd0ASAxOozfDOzAP9bsYfLQzjw5ccDZSx4/l58FYRHQsmPDrF8pFfQ0mTQyr9fww/bD/N/q3SzK2E+5x0ur2Ch6tokjtVsi+wrLSMvKZ2xKO/7j+kHENovgrenDuGdOOgePHOOinq2Pr+tPV6Ywtl9bxvVrV+dVXfVyYBPEd4aw8LrrKqWaJE0mDWxr7hG+3nyACo+XorIKFqzfz56CUuJjIrlhaGeuS+1M/47xta4jJiqc2bcMxes1hPnsfSTGRjG+/yneJ3KmVr8BWxfCiH9p2N+jlApqmkwaUFp2PlNnr6T4mAeAMIGLzmnNg5f3YWxKW6IjT++bflhDHcaqybYv4bNZcO5YGPNo4/5upVRQ0WRyFq3PKaSorII+7VqQdbiEqbNX0jouioUzf0X7+GjCw6RhD0edTfvWwbyp0LYfXPsGhOu/ilKqZrqFOEO5RWXk5JcSJpCTX8rs73eyZlfB8fki0DWpOe/dMZx28dF+bOkZKNwDc66H6HiY8r6Ox6WUqpMmkzOwYU8h1/7tB8oqvMfLurZqzmO/7UfP5Dg27y/iYPExpo3oHnyJpKzIJpJjxXDrImjZwOdklFIhQZPJaTpwpIzb315NUvMonri6P2EixESFM9Tnvo6R57auYy0BqKwINn0CK1+Bg5vhxnn2EJdSSp0CTSanyFPppajMw13/m0ZBSQUfzBhOvw61X4XVIIyBtXOJOtas/uvKz4KtiyDzK9j5DXjKILE7THoNeo6u//qVUk2GJpNa7M4r4Z0V2cxPz+FQcfnx8pdvHOyfRAKwYT78cwYp8Skw9hp7cuZ0HdoGS5+268JAUg8YPBUGXAedUs9snUqpJk2TiY/8o+XMXbWLbbnF7DhYzPo9hYgIl/VtS9/2LYmLjqBvuxaMOMdPh7HKimDRIxCdQELhRlg7F86bUn3dSg9kfwc5q23yyNsB5UfBU2r3SCKi4aKZMGSqTSZKKVUPmkwAYwzz0/fw1IJNHD1aTKv4lvRIjuOeUedww7AudEiIOXmBskL49nl7ldOgKRBfwzAjlR5Y9lfI3QApV0OfCXYjXrQHivZBaR6U5p94VZbbZ4Z0GwnFB2yyyPoWBk6GobfC0r9AcS7c9iWF799N/Bd/gl7joXA3fPmoXUdCF4iIgW1f2PWDHQYlqQfEtbG/v/8kGHYnxCU36N9VKdV0NJlkEl2Wa2/C63EJ7F9H6dfPItnfsTFmCJ9UDKPsyGHebb6cvtEboXl3SL4IIjvBsr1QfBA6DbHP8zh6EObfCoU5YLzw9ZPQ+QKIjAZvJbTqCQN/ZzfeH0yH7O/t4243f2o35F6Pff2C2PGvvBU+ReHQ6hxY+ACkv2WHNUmdBp1S2dprBkPTZsEbE+DQFohJgvYDITcDSgvsOY9+V0P3iyG6ZSP9lZVSTVWTSSYRnhJ4dxImOh4pK6TcxPKNdyAjPWv4d5ZCJJj43tD7fntYaNOndg8krg1EJ9ghRb7+MyD22//0LyC2Nfw0B3YshYpSkDBYNw/S3rSJIDwKJr4CA66HXctPJJTErtCyEzRPgphEiEmAZvE2yexNh6zvICrW7kHEJkPGh/D5w7b+6D8BcDSuOwy/G5a/CENvg1GP2PUopZQfNJlkstV04aGIaYwoXcFGTwf297qRWb9JJSmhGexaAVGxSPtBJ04+e732gVDhkfZ9wW57wrqsEEbeb2/oAxj9iH1VOVYMmz+D3SvsRr7q8tpuF9lXbcKioMuF9uWr/zX2cJanzCaUKpc+DiNm6uEqpZTfNZlkEhkehqf3layKvIpx/dqdfC9IdRv5sDAg7MT7hM42idSlWRwM+p19nU1Rze3LV1iYJhKlVEBoMskkubnw3HWD/N0MpZQKSWF1V1FKKaVqp8lEKaVUvWkyUUopVW9BnUxEZLyIbBGRTBF5yN/tUUqppipok4mIhAMvApcDKcANIpLi31YppVTTFLTJBBgGZBpjdhhjyoH3gKv83CallGqSgjmZdAR2+7zPcWVKKaUaWTDfZ1LdOOnmpAoidwB3ACQnJ7N06dJGaFbjKC4uDpl4QikWCK14QikWCK14Ai2WYE4mOUBnn/edgL2+FYwxrwKvAojIkVGjRm1pvOY1uNbAIX834iwJpVggtOIJpVggtOJprFi6nkqlYE4mq4BzRaQ7sAeYDNTwcA8AthhjUhulZY1ARFaHSjyhFAuEVjyhFAuEVjyBFkvQJhNjjEdE7gUWAeHAbGNMhp+bpZRSTVLQJhMAY8wCYIG/26GUUk1dMF/Ndbpe9XcDzrJQiieUYoHQiieUYoHQiiegYhFjTN21lFJKqVo0pT0TpZRSDUSTiVJKqXoL6mQiIrNF5ICIbPApO09EVojITyKyWkSGufJ4EflERNaKSIaITPNZZqqIbHOvqQEUyyARWS4i613bW/rMe9gNcLlFRMb5lAfE4JenE4+IXCYiaa48TURG+ywzxJVnisj/iEh1N6sGTCw+87uISLGI/MGnLOj6xs0b6OZluPnRrjyo+kZEIkXkLVe+SUQe9lnG730jIp1FZIlrW4aIzHTlSSKy2G2fFotIoisX93fPFJF1IjLYZ12Nv00zxgTtC/g1MBjY4FP2BXC5m54ALHXTfwSecdPJQB4QBSQBO9zPRDedGCCxrAIudtPTgSfcdAqwFmgGdAe2Yy+PDnfTPVxsa4GUAOqbmuI5H+jgpvsDe3yWWQkMx454sLCqbwM1Fp/584F5wB/c+2DtmwhgHTDIvW8FhAdj32DvQ3vPTTcHsoBugdI3QHtgsJtuAWx1n/VngYdc+UOc2I5NcH93AS4EfnTlftmmBfWeiTFmGTYpnFQMVH2riufEXfEGaOG+PcW55TzAOGCxMSbPGJMPLAbGN3Tbf66GWHoDy9z0YmCSm74K+6E4ZozZCWRiB74MmMEvTyceY8waY0xVP2UA0SLSTETaAy2NMcuN/ZS8DVzd8K0/2Wn2DSJyNfYD7HvfU1D2DTAWWGeMWeuWPWyMqQzSvjFArIhEADFAOVBEgPSNMWafMSbdTR8BNmHHG7wKeMtVe4sTf+ergLeNtQJIcP3il21aUCeTGtwP/FVEdgPPAVW7si8AfbHJZT0w0xjjJbAHjNwA/NZNX8eJ4WNqanMgxwI1x+NrErDGGHMM2/Ycn3mBFE+1sYhILPAg8NjP6gdr3/QCjIgsEpF0EflXVx50fQN8ABwF9gG7gOeMMXkEYN+ISDfsHvuPQFtjzD6wCQdo46oF1HYgFJPJDOD3xpjOwO+B1135OOAnoANwHvCCO5Za54CRfjQduEdE0rC7veWuvKY2B3IsUHM8AIhIP+AZ4M6qomrWESjx1BTLY8B/GmOKf1Y/kGOBmuOJAEYCN7qfE0VkDIEdT02xDAMqsduA7sAsEelBgMUiInHYw6T3G2OKaqtaTZnftgNBfQd8DaYCM930POA1Nz0NeNrtkmeKyE6gDzZrX+KzfCdgaaO0tA7GmM3YwwyISC/gCjertkEuax380p9qiQcR6QR8CNxsjNnuinOwMVQJmHhqieUC4FoReRZIALwiUgakEZx9kwN8Y4w55OYtwJ6jeIfg65spwOfGmArggIh8D6Riv8UHRN+ISCQ2kbxrjPmHK84VkfbGmH3uMNYBV17TdsAv27RQ3DPZC1zspkcD29z0LmAMgIi0xR5X3YEd22usiCS6qyTGujK/E5E27mcY8G/A39ysj4HJ7rxCd+Bc7MnQ44NfikgUdvDLjxu/5dWrKR4RSQA+Ax42xnxfVd/t0h8RkQvdua6bgY8aveHVqCkWY8yvjDHdjDHdgP8CnjLGvECQ9g32szBQRJq7cw0XAxuDsW+w24DR7iqoWOxJ680ESN+4v+PrwCZjzPM+sz7GfknG/fzIp/xmF8+FQKHrF/9s0xr7ioWz+QLmYo9/VmCz8a3YXfE07BUZPwJDXN0O2Cu91mOPqd7ks57p2JPYmcC0AIplJvaKjq3A07gRC1z9R7BXoGzB5yoa7BUeW928RwKsb6qNB/uBP4o9DFn1auPmpbr+2o497yWBHMvPlnsUdzVXsPaNq38T9mKCDcCzPuVB1TfYC2/muVg2Ag8EUt+4bZfBXj1X9TmYgL2C7ivsF+OvgCRXX7CPLt+O3a6l+qyr0bdpOpyKUkqpegvFw1xKKaUamSYTpZRS9abJRCmlVL1pMlFKKVVvmkyUUkrVmyYTpZRS9abJRKkgIiLh/m6DUtXRZKJUAxGRJ6qeSeHePyki94nIAyKyyj2D4jGf+f8U+zyXDBG5w6e8WEQeF5EfsUO+KxVwNJko1XBexw2D4Yb2mAzkYoe/GYYdcHSIiPza1Z9ujBmCvbP8PhFp5cpjsc/ruMAY811jBqDUqQrFgR6VCgjGmCwROSwi5wNtgTXAUOxYSWtctThsclmGTSATXXlnV34YO9Lt/MZsu1KnS5OJUg3rNeAWoB0wGzvY6F+MMa/4VhKRS4BLgeHGmBIRWQpEu9llxpjKxmqwUmdCD3Mp1bA+xD7lbih25NZFwHT3zApEpKMb5TYeyHeJpA92RFulgobumSjVgIwx5SKyBChwexdfiEhfYLkdcZxi7Ki8nwN3icg67EjQK/zVZqXOhI4arFQDcife04HrjDHb6qqvVLDSw1xKNRARScE+T+IrTSQq1OmeiVJKqXrTPROllFL1pslEKaVUvWkyUUopVW+aTJRSStWbJhOllFL19v/mIVeteLywNQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_births.plot(title='Total births / year grouped by sex', grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_prop(group):\n",
    "    group['prop'] = group.births / group.births.sum()\n",
    "    return group\n",
    "\n",
    "names = names.groupby(['year', 'sex']).apply(add_prop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>names</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>794</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008726</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>1690754</th>\n",
       "      <td>Zaviyon</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690755</th>\n",
       "      <td>Zaybrien</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690756</th>\n",
       "      <td>Zayshawn</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690757</th>\n",
       "      <td>Zayyan</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690758</th>\n",
       "      <td>Zeal</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690759</th>\n",
       "      <td>Zealan</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690760</th>\n",
       "      <td>Zecharia</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690761</th>\n",
       "      <td>Zeferino</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690762</th>\n",
       "      <td>Zekariah</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690763</th>\n",
       "      <td>Zeki</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690764</th>\n",
       "      <td>Zeriah</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690765</th>\n",
       "      <td>Zeshan</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690766</th>\n",
       "      <td>Zhyier</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690767</th>\n",
       "      <td>Zildjian</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690768</th>\n",
       "      <td>Zinn</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690769</th>\n",
       "      <td>Zishan</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690770</th>\n",
       "      <td>Ziven</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690771</th>\n",
       "      <td>Zmari</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690772</th>\n",
       "      <td>Zoren</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690773</th>\n",
       "      <td>Zuhaib</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690774</th>\n",
       "      <td>Zyeire</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690775</th>\n",
       "      <td>Zygmunt</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690776</th>\n",
       "      <td>Zykerion</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690777</th>\n",
       "      <td>Zylar</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690778</th>\n",
       "      <td>Zylin</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690779</th>\n",
       "      <td>Zymaire</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690780</th>\n",
       "      <td>Zyonne</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690781</th>\n",
       "      <td>Zyquarius</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690782</th>\n",
       "      <td>Zyran</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690783</th>\n",
       "      <td>Zzyzx</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1690784 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             names 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     794  1880  0.008726\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",
       "1690754    Zaviyon   M       5  2010  0.000003\n",
       "1690755   Zaybrien   M       5  2010  0.000003\n",
       "1690756   Zayshawn   M       5  2010  0.000003\n",
       "1690757     Zayyan   M       5  2010  0.000003\n",
       "1690758       Zeal   M       5  2010  0.000003\n",
       "1690759     Zealan   M       5  2010  0.000003\n",
       "1690760   Zecharia   M       5  2010  0.000003\n",
       "1690761   Zeferino   M       5  2010  0.000003\n",
       "1690762   Zekariah   M       5  2010  0.000003\n",
       "1690763       Zeki   M       5  2010  0.000003\n",
       "1690764     Zeriah   M       5  2010  0.000003\n",
       "1690765     Zeshan   M       5  2010  0.000003\n",
       "1690766     Zhyier   M       5  2010  0.000003\n",
       "1690767   Zildjian   M       5  2010  0.000003\n",
       "1690768       Zinn   M       5  2010  0.000003\n",
       "1690769     Zishan   M       5  2010  0.000003\n",
       "1690770      Ziven   M       5  2010  0.000003\n",
       "1690771      Zmari   M       5  2010  0.000003\n",
       "1690772      Zoren   M       5  2010  0.000003\n",
       "1690773     Zuhaib   M       5  2010  0.000003\n",
       "1690774     Zyeire   M       5  2010  0.000003\n",
       "1690775    Zygmunt   M       5  2010  0.000003\n",
       "1690776   Zykerion   M       5  2010  0.000003\n",
       "1690777      Zylar   M       5  2010  0.000003\n",
       "1690778      Zylin   M       5  2010  0.000003\n",
       "1690779    Zymaire   M       5  2010  0.000003\n",
       "1690780     Zyonne   M       5  2010  0.000003\n",
       "1690781  Zyquarius   M       5  2010  0.000003\n",
       "1690782      Zyran   M       5  2010  0.000003\n",
       "1690783      Zzyzx   M       5  2010  0.000003\n",
       "\n",
       "[1690784 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  sex\n",
       "1880  F      1.0\n",
       "      M      1.0\n",
       "1881  F      1.0\n",
       "      M      1.0\n",
       "1882  F      1.0\n",
       "      M      1.0\n",
       "1883  F      1.0\n",
       "      M      1.0\n",
       "1884  F      1.0\n",
       "      M      1.0\n",
       "1885  F      1.0\n",
       "      M      1.0\n",
       "1886  F      1.0\n",
       "      M      1.0\n",
       "1887  F      1.0\n",
       "      M      1.0\n",
       "1888  F      1.0\n",
       "      M      1.0\n",
       "1889  F      1.0\n",
       "      M      1.0\n",
       "1890  F      1.0\n",
       "      M      1.0\n",
       "1891  F      1.0\n",
       "      M      1.0\n",
       "1892  F      1.0\n",
       "      M      1.0\n",
       "1893  F      1.0\n",
       "      M      1.0\n",
       "1894  F      1.0\n",
       "      M      1.0\n",
       "            ... \n",
       "1996  F      1.0\n",
       "      M      1.0\n",
       "1997  F      1.0\n",
       "      M      1.0\n",
       "1998  F      1.0\n",
       "      M      1.0\n",
       "1999  F      1.0\n",
       "      M      1.0\n",
       "2000  F      1.0\n",
       "      M      1.0\n",
       "2001  F      1.0\n",
       "      M      1.0\n",
       "2002  F      1.0\n",
       "      M      1.0\n",
       "2003  F      1.0\n",
       "      M      1.0\n",
       "2004  F      1.0\n",
       "      M      1.0\n",
       "2005  F      1.0\n",
       "      M      1.0\n",
       "2006  F      1.0\n",
       "      M      1.0\n",
       "2007  F      1.0\n",
       "      M      1.0\n",
       "2008  F      1.0\n",
       "      M      1.0\n",
       "2009  F      1.0\n",
       "      M      1.0\n",
       "2010  F      1.0\n",
       "      M      1.0\n",
       "Name: prop, Length: 262, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.groupby(['year', 'sex']).prop.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pieces = []\n",
    "\n",
    "for year, group in names.groupby(['year', 'sex']):\n",
    "    pieces.append(group.sort_values(by='births', ascending=False)[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "top1000 = pd.concat(pieces, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>names</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>794</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008726</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>261847</th>\n",
       "      <td>Yair</td>\n",
       "      <td>M</td>\n",
       "      <td>201</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261848</th>\n",
       "      <td>Talan</td>\n",
       "      <td>M</td>\n",
       "      <td>201</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261849</th>\n",
       "      <td>Keyon</td>\n",
       "      <td>M</td>\n",
       "      <td>201</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261850</th>\n",
       "      <td>Kael</td>\n",
       "      <td>M</td>\n",
       "      <td>201</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261851</th>\n",
       "      <td>Demarion</td>\n",
       "      <td>M</td>\n",
       "      <td>200</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261852</th>\n",
       "      <td>Gibson</td>\n",
       "      <td>M</td>\n",
       "      <td>200</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261853</th>\n",
       "      <td>Reagan</td>\n",
       "      <td>M</td>\n",
       "      <td>200</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261854</th>\n",
       "      <td>Cristofer</td>\n",
       "      <td>M</td>\n",
       "      <td>199</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261855</th>\n",
       "      <td>Daylen</td>\n",
       "      <td>M</td>\n",
       "      <td>199</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261856</th>\n",
       "      <td>Jordon</td>\n",
       "      <td>M</td>\n",
       "      <td>199</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261857</th>\n",
       "      <td>Dashawn</td>\n",
       "      <td>M</td>\n",
       "      <td>198</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261858</th>\n",
       "      <td>Masen</td>\n",
       "      <td>M</td>\n",
       "      <td>198</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261859</th>\n",
       "      <td>Rowen</td>\n",
       "      <td>M</td>\n",
       "      <td>197</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261860</th>\n",
       "      <td>Yousef</td>\n",
       "      <td>M</td>\n",
       "      <td>197</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261861</th>\n",
       "      <td>Thaddeus</td>\n",
       "      <td>M</td>\n",
       "      <td>197</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261862</th>\n",
       "      <td>Kadin</td>\n",
       "      <td>M</td>\n",
       "      <td>197</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261863</th>\n",
       "      <td>Dillan</td>\n",
       "      <td>M</td>\n",
       "      <td>197</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261864</th>\n",
       "      <td>Clarence</td>\n",
       "      <td>M</td>\n",
       "      <td>197</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261865</th>\n",
       "      <td>Slade</td>\n",
       "      <td>M</td>\n",
       "      <td>196</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261866</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>M</td>\n",
       "      <td>196</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261867</th>\n",
       "      <td>Sheldon</td>\n",
       "      <td>M</td>\n",
       "      <td>196</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261868</th>\n",
       "      <td>Keshawn</td>\n",
       "      <td>M</td>\n",
       "      <td>195</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261869</th>\n",
       "      <td>Menachem</td>\n",
       "      <td>M</td>\n",
       "      <td>195</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261870</th>\n",
       "      <td>Joziah</td>\n",
       "      <td>M</td>\n",
       "      <td>195</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261871</th>\n",
       "      <td>Bailey</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261872</th>\n",
       "      <td>Camilo</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261873</th>\n",
       "      <td>Destin</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261874</th>\n",
       "      <td>Jaquan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261875</th>\n",
       "      <td>Jaydan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261876</th>\n",
       "      <td>Maxton</td>\n",
       "      <td>M</td>\n",
       "      <td>193</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>261877 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            names 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     794  1880  0.008726\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",
       "261847       Yair   M     201  2010  0.000106\n",
       "261848      Talan   M     201  2010  0.000106\n",
       "261849      Keyon   M     201  2010  0.000106\n",
       "261850       Kael   M     201  2010  0.000106\n",
       "261851   Demarion   M     200  2010  0.000105\n",
       "261852     Gibson   M     200  2010  0.000105\n",
       "261853     Reagan   M     200  2010  0.000105\n",
       "261854  Cristofer   M     199  2010  0.000105\n",
       "261855     Daylen   M     199  2010  0.000105\n",
       "261856     Jordon   M     199  2010  0.000105\n",
       "261857    Dashawn   M     198  2010  0.000104\n",
       "261858      Masen   M     198  2010  0.000104\n",
       "261859      Rowen   M     197  2010  0.000104\n",
       "261860     Yousef   M     197  2010  0.000104\n",
       "261861   Thaddeus   M     197  2010  0.000104\n",
       "261862      Kadin   M     197  2010  0.000104\n",
       "261863     Dillan   M     197  2010  0.000104\n",
       "261864   Clarence   M     197  2010  0.000104\n",
       "261865      Slade   M     196  2010  0.000103\n",
       "261866    Clinton   M     196  2010  0.000103\n",
       "261867    Sheldon   M     196  2010  0.000103\n",
       "261868    Keshawn   M     195  2010  0.000103\n",
       "261869   Menachem   M     195  2010  0.000103\n",
       "261870     Joziah   M     195  2010  0.000103\n",
       "261871     Bailey   M     194  2010  0.000102\n",
       "261872     Camilo   M     194  2010  0.000102\n",
       "261873     Destin   M     194  2010  0.000102\n",
       "261874     Jaquan   M     194  2010  0.000102\n",
       "261875     Jaydan   M     194  2010  0.000102\n",
       "261876     Maxton   M     193  2010  0.000102\n",
       "\n",
       "[261877 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "boys = top1000[top1000.sex == 'M']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "girls = top1000[top1000.sex == 'F']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_births = top1000.pivot_table('births', index='year', columns='names', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 131 entries, 1880 to 2010\n",
      "Columns: 6868 entries, Aaden to Zuri\n",
      "dtypes: float64(6868)\n",
      "memory usage: 6.9 MB\n"
     ]
    }
   ],
   "source": [
    "total_births.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset = total_births[['John', 'Harry', 'Mary', 'Marilyn']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000001801A717160>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000180211E96A0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001802135FA90>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x0000018021386EF0>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = top1000.pivot_table('prop', index='year', columns='sex', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18021497550>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.plot(title='Sum of table1000.prop by year and sex', grid=True, yticks=np.linspace(0, 1.2, 13), xticks=range(1880, 2020, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = boys[boys.year == 2010]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>names</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>260877</th>\n",
       "      <td>Jacob</td>\n",
       "      <td>M</td>\n",
       "      <td>21875</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.011523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260878</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>M</td>\n",
       "      <td>17866</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.009411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260879</th>\n",
       "      <td>Michael</td>\n",
       "      <td>M</td>\n",
       "      <td>17133</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.009025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260880</th>\n",
       "      <td>Jayden</td>\n",
       "      <td>M</td>\n",
       "      <td>17030</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260881</th>\n",
       "      <td>William</td>\n",
       "      <td>M</td>\n",
       "      <td>16870</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260882</th>\n",
       "      <td>Alexander</td>\n",
       "      <td>M</td>\n",
       "      <td>16634</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260883</th>\n",
       "      <td>Noah</td>\n",
       "      <td>M</td>\n",
       "      <td>16281</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260884</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>M</td>\n",
       "      <td>15679</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260885</th>\n",
       "      <td>Aiden</td>\n",
       "      <td>M</td>\n",
       "      <td>15403</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260886</th>\n",
       "      <td>Anthony</td>\n",
       "      <td>M</td>\n",
       "      <td>15364</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260887</th>\n",
       "      <td>Joshua</td>\n",
       "      <td>M</td>\n",
       "      <td>15238</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260888</th>\n",
       "      <td>Mason</td>\n",
       "      <td>M</td>\n",
       "      <td>14728</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.007758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260889</th>\n",
       "      <td>Christopher</td>\n",
       "      <td>M</td>\n",
       "      <td>14135</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.007446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260890</th>\n",
       "      <td>Andrew</td>\n",
       "      <td>M</td>\n",
       "      <td>14093</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.007424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260891</th>\n",
       "      <td>David</td>\n",
       "      <td>M</td>\n",
       "      <td>14042</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.007397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260892</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>M</td>\n",
       "      <td>13954</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.007350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260893</th>\n",
       "      <td>Logan</td>\n",
       "      <td>M</td>\n",
       "      <td>13943</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.007345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260894</th>\n",
       "      <td>Elijah</td>\n",
       "      <td>M</td>\n",
       "      <td>13735</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.007235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260895</th>\n",
       "      <td>James</td>\n",
       "      <td>M</td>\n",
       "      <td>13714</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.007224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260896</th>\n",
       "      <td>Joseph</td>\n",
       "      <td>M</td>\n",
       "      <td>13657</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.007194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260897</th>\n",
       "      <td>Gabriel</td>\n",
       "      <td>M</td>\n",
       "      <td>12722</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.006701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260898</th>\n",
       "      <td>Benjamin</td>\n",
       "      <td>M</td>\n",
       "      <td>12280</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.006469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260899</th>\n",
       "      <td>Ryan</td>\n",
       "      <td>M</td>\n",
       "      <td>11886</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.006261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260900</th>\n",
       "      <td>Samuel</td>\n",
       "      <td>M</td>\n",
       "      <td>11776</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.006203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260901</th>\n",
       "      <td>Jackson</td>\n",
       "      <td>M</td>\n",
       "      <td>11693</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.006159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260902</th>\n",
       "      <td>John</td>\n",
       "      <td>M</td>\n",
       "      <td>11424</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.006018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260903</th>\n",
       "      <td>Nathan</td>\n",
       "      <td>M</td>\n",
       "      <td>11269</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.005936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260904</th>\n",
       "      <td>Jonathan</td>\n",
       "      <td>M</td>\n",
       "      <td>11028</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.005809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260905</th>\n",
       "      <td>Christian</td>\n",
       "      <td>M</td>\n",
       "      <td>10965</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.005776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260906</th>\n",
       "      <td>Liam</td>\n",
       "      <td>M</td>\n",
       "      <td>10852</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.005716</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>261847</th>\n",
       "      <td>Yair</td>\n",
       "      <td>M</td>\n",
       "      <td>201</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261848</th>\n",
       "      <td>Talan</td>\n",
       "      <td>M</td>\n",
       "      <td>201</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261849</th>\n",
       "      <td>Keyon</td>\n",
       "      <td>M</td>\n",
       "      <td>201</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261850</th>\n",
       "      <td>Kael</td>\n",
       "      <td>M</td>\n",
       "      <td>201</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261851</th>\n",
       "      <td>Demarion</td>\n",
       "      <td>M</td>\n",
       "      <td>200</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261852</th>\n",
       "      <td>Gibson</td>\n",
       "      <td>M</td>\n",
       "      <td>200</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261853</th>\n",
       "      <td>Reagan</td>\n",
       "      <td>M</td>\n",
       "      <td>200</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261854</th>\n",
       "      <td>Cristofer</td>\n",
       "      <td>M</td>\n",
       "      <td>199</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261855</th>\n",
       "      <td>Daylen</td>\n",
       "      <td>M</td>\n",
       "      <td>199</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261856</th>\n",
       "      <td>Jordon</td>\n",
       "      <td>M</td>\n",
       "      <td>199</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261857</th>\n",
       "      <td>Dashawn</td>\n",
       "      <td>M</td>\n",
       "      <td>198</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261858</th>\n",
       "      <td>Masen</td>\n",
       "      <td>M</td>\n",
       "      <td>198</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261859</th>\n",
       "      <td>Rowen</td>\n",
       "      <td>M</td>\n",
       "      <td>197</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261860</th>\n",
       "      <td>Yousef</td>\n",
       "      <td>M</td>\n",
       "      <td>197</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261861</th>\n",
       "      <td>Thaddeus</td>\n",
       "      <td>M</td>\n",
       "      <td>197</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261862</th>\n",
       "      <td>Kadin</td>\n",
       "      <td>M</td>\n",
       "      <td>197</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261863</th>\n",
       "      <td>Dillan</td>\n",
       "      <td>M</td>\n",
       "      <td>197</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261864</th>\n",
       "      <td>Clarence</td>\n",
       "      <td>M</td>\n",
       "      <td>197</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261865</th>\n",
       "      <td>Slade</td>\n",
       "      <td>M</td>\n",
       "      <td>196</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261866</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>M</td>\n",
       "      <td>196</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261867</th>\n",
       "      <td>Sheldon</td>\n",
       "      <td>M</td>\n",
       "      <td>196</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261868</th>\n",
       "      <td>Keshawn</td>\n",
       "      <td>M</td>\n",
       "      <td>195</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261869</th>\n",
       "      <td>Menachem</td>\n",
       "      <td>M</td>\n",
       "      <td>195</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261870</th>\n",
       "      <td>Joziah</td>\n",
       "      <td>M</td>\n",
       "      <td>195</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261871</th>\n",
       "      <td>Bailey</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261872</th>\n",
       "      <td>Camilo</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261873</th>\n",
       "      <td>Destin</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261874</th>\n",
       "      <td>Jaquan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261875</th>\n",
       "      <td>Jaydan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261876</th>\n",
       "      <td>Maxton</td>\n",
       "      <td>M</td>\n",
       "      <td>193</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              names sex  births  year      prop\n",
       "260877        Jacob   M   21875  2010  0.011523\n",
       "260878        Ethan   M   17866  2010  0.009411\n",
       "260879      Michael   M   17133  2010  0.009025\n",
       "260880       Jayden   M   17030  2010  0.008971\n",
       "260881      William   M   16870  2010  0.008887\n",
       "260882    Alexander   M   16634  2010  0.008762\n",
       "260883         Noah   M   16281  2010  0.008576\n",
       "260884       Daniel   M   15679  2010  0.008259\n",
       "260885        Aiden   M   15403  2010  0.008114\n",
       "260886      Anthony   M   15364  2010  0.008093\n",
       "260887       Joshua   M   15238  2010  0.008027\n",
       "260888        Mason   M   14728  2010  0.007758\n",
       "260889  Christopher   M   14135  2010  0.007446\n",
       "260890       Andrew   M   14093  2010  0.007424\n",
       "260891        David   M   14042  2010  0.007397\n",
       "260892      Matthew   M   13954  2010  0.007350\n",
       "260893        Logan   M   13943  2010  0.007345\n",
       "260894       Elijah   M   13735  2010  0.007235\n",
       "260895        James   M   13714  2010  0.007224\n",
       "260896       Joseph   M   13657  2010  0.007194\n",
       "260897      Gabriel   M   12722  2010  0.006701\n",
       "260898     Benjamin   M   12280  2010  0.006469\n",
       "260899         Ryan   M   11886  2010  0.006261\n",
       "260900       Samuel   M   11776  2010  0.006203\n",
       "260901      Jackson   M   11693  2010  0.006159\n",
       "260902         John   M   11424  2010  0.006018\n",
       "260903       Nathan   M   11269  2010  0.005936\n",
       "260904     Jonathan   M   11028  2010  0.005809\n",
       "260905    Christian   M   10965  2010  0.005776\n",
       "260906         Liam   M   10852  2010  0.005716\n",
       "...             ...  ..     ...   ...       ...\n",
       "261847         Yair   M     201  2010  0.000106\n",
       "261848        Talan   M     201  2010  0.000106\n",
       "261849        Keyon   M     201  2010  0.000106\n",
       "261850         Kael   M     201  2010  0.000106\n",
       "261851     Demarion   M     200  2010  0.000105\n",
       "261852       Gibson   M     200  2010  0.000105\n",
       "261853       Reagan   M     200  2010  0.000105\n",
       "261854    Cristofer   M     199  2010  0.000105\n",
       "261855       Daylen   M     199  2010  0.000105\n",
       "261856       Jordon   M     199  2010  0.000105\n",
       "261857      Dashawn   M     198  2010  0.000104\n",
       "261858        Masen   M     198  2010  0.000104\n",
       "261859        Rowen   M     197  2010  0.000104\n",
       "261860       Yousef   M     197  2010  0.000104\n",
       "261861     Thaddeus   M     197  2010  0.000104\n",
       "261862        Kadin   M     197  2010  0.000104\n",
       "261863       Dillan   M     197  2010  0.000104\n",
       "261864     Clarence   M     197  2010  0.000104\n",
       "261865        Slade   M     196  2010  0.000103\n",
       "261866      Clinton   M     196  2010  0.000103\n",
       "261867      Sheldon   M     196  2010  0.000103\n",
       "261868      Keshawn   M     195  2010  0.000103\n",
       "261869     Menachem   M     195  2010  0.000103\n",
       "261870       Joziah   M     195  2010  0.000103\n",
       "261871       Bailey   M     194  2010  0.000102\n",
       "261872       Camilo   M     194  2010  0.000102\n",
       "261873       Destin   M     194  2010  0.000102\n",
       "261874       Jaquan   M     194  2010  0.000102\n",
       "261875       Jaydan   M     194  2010  0.000102\n",
       "261876       Maxton   M     193  2010  0.000102\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "prop_cumsum = df.sort_values(by='prop', ascending=False).prop.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "260877    0.011523\n",
       "260878    0.020934\n",
       "260879    0.029959\n",
       "260880    0.038930\n",
       "260881    0.047817\n",
       "260882    0.056579\n",
       "260883    0.065155\n",
       "260884    0.073414\n",
       "260885    0.081528\n",
       "260886    0.089621\n",
       "Name: prop, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum.values.searchsorted(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = boys[boys.year == 1900]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "in1900 = df.sort_values(by='prop', ascending=False).prop.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "in1900.values.searchsorted(0.5) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "diversity = top1000.groupby(['year', 'sex']).apply(get_quantile_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "diversity = diversity.unstack('sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diversity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18021758278>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diversity.plot(title='Number of popular names in top 50%', grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_last_letter = lambda x: x[-1]\n",
    "last_letters = names.names.map(get_last_letter)\n",
    "last_letters.name = 'last_letter'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = names.pivot_table('births', index=last_letters, columns=['sex', 'year'], aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "subtable = table.reindex(columns=[1910, 1960, 2010], level='year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "      <th colspan=\"3\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>108376.0</td>\n",
       "      <td>691247.0</td>\n",
       "      <td>670605.0</td>\n",
       "      <td>977.0</td>\n",
       "      <td>5204.0</td>\n",
       "      <td>28438.0</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>694.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>3912.0</td>\n",
       "      <td>38859.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>5.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>946.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>15476.0</td>\n",
       "      <td>23125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6750.0</td>\n",
       "      <td>3729.0</td>\n",
       "      <td>2607.0</td>\n",
       "      <td>22111.0</td>\n",
       "      <td>262112.0</td>\n",
       "      <td>44398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>133569.0</td>\n",
       "      <td>435013.0</td>\n",
       "      <td>313833.0</td>\n",
       "      <td>28655.0</td>\n",
       "      <td>178823.0</td>\n",
       "      <td>129012.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                 F                            M                    \n",
       "year             1910      1960      2010     1910      1960      2010\n",
       "last_letter                                                           \n",
       "a            108376.0  691247.0  670605.0    977.0    5204.0   28438.0\n",
       "b                 NaN     694.0     450.0    411.0    3912.0   38859.0\n",
       "c                 5.0      49.0     946.0    482.0   15476.0   23125.0\n",
       "d              6750.0    3729.0    2607.0  22111.0  262112.0   44398.0\n",
       "e            133569.0  435013.0  313833.0  28655.0  178823.0  129012.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex  year\n",
       "F    1910     396416.0\n",
       "     1960    2022062.0\n",
       "     2010    1759010.0\n",
       "M    1910     194198.0\n",
       "     1960    2132588.0\n",
       "     2010    1898382.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "letter_prop = subtable / subtable.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "      <th colspan=\"3\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
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       "      <th>last_letter</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.273390</td>\n",
       "      <td>0.341853</td>\n",
       "      <td>0.381240</td>\n",
       "      <td>0.005031</td>\n",
       "      <td>0.002440</td>\n",
       "      <td>0.014980</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>0.002116</td>\n",
       "      <td>0.001834</td>\n",
       "      <td>0.020470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000538</td>\n",
       "      <td>0.002482</td>\n",
       "      <td>0.007257</td>\n",
       "      <td>0.012181</td>\n",
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       "      <th>d</th>\n",
       "      <td>0.017028</td>\n",
       "      <td>0.001844</td>\n",
       "      <td>0.001482</td>\n",
       "      <td>0.113858</td>\n",
       "      <td>0.122908</td>\n",
       "      <td>0.023387</td>\n",
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       "    <tr>\n",
       "      <th>e</th>\n",
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       "      <td>0.215133</td>\n",
       "      <td>0.178415</td>\n",
       "      <td>0.147556</td>\n",
       "      <td>0.083853</td>\n",
       "      <td>0.067959</td>\n",
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       "    <tr>\n",
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       "      <td>0.000010</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.000783</td>\n",
       "      <td>0.004325</td>\n",
       "      <td>0.001188</td>\n",
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       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.002250</td>\n",
       "      <td>0.009488</td>\n",
       "      <td>0.001404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>0.051529</td>\n",
       "      <td>0.036224</td>\n",
       "      <td>0.075852</td>\n",
       "      <td>0.045562</td>\n",
       "      <td>0.037907</td>\n",
       "      <td>0.051670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>0.001526</td>\n",
       "      <td>0.039965</td>\n",
       "      <td>0.031734</td>\n",
       "      <td>0.000844</td>\n",
       "      <td>0.000603</td>\n",
       "      <td>0.022628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k</th>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000356</td>\n",
       "      <td>0.036581</td>\n",
       "      <td>0.049384</td>\n",
       "      <td>0.018541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l</th>\n",
       "      <td>0.043189</td>\n",
       "      <td>0.033867</td>\n",
       "      <td>0.026356</td>\n",
       "      <td>0.065016</td>\n",
       "      <td>0.104904</td>\n",
       "      <td>0.070367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m</th>\n",
       "      <td>0.001201</td>\n",
       "      <td>0.008613</td>\n",
       "      <td>0.002588</td>\n",
       "      <td>0.058044</td>\n",
       "      <td>0.033827</td>\n",
       "      <td>0.024657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>0.079240</td>\n",
       "      <td>0.130687</td>\n",
       "      <td>0.140210</td>\n",
       "      <td>0.143415</td>\n",
       "      <td>0.152522</td>\n",
       "      <td>0.362771</td>\n",
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       "    <tr>\n",
       "      <th>o</th>\n",
       "      <td>0.001660</td>\n",
       "      <td>0.002439</td>\n",
       "      <td>0.001243</td>\n",
       "      <td>0.017065</td>\n",
       "      <td>0.012829</td>\n",
       "      <td>0.042681</td>\n",
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       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.003172</td>\n",
       "      <td>0.005675</td>\n",
       "      <td>0.001269</td>\n",
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       "    <tr>\n",
       "      <th>q</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000180</td>\n",
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       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>0.013390</td>\n",
       "      <td>0.006764</td>\n",
       "      <td>0.018025</td>\n",
       "      <td>0.064481</td>\n",
       "      <td>0.031034</td>\n",
       "      <td>0.087477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>0.039042</td>\n",
       "      <td>0.012764</td>\n",
       "      <td>0.013332</td>\n",
       "      <td>0.130815</td>\n",
       "      <td>0.102730</td>\n",
       "      <td>0.065145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>0.027438</td>\n",
       "      <td>0.015201</td>\n",
       "      <td>0.007830</td>\n",
       "      <td>0.072879</td>\n",
       "      <td>0.065655</td>\n",
       "      <td>0.022861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>u</th>\n",
       "      <td>0.000684</td>\n",
       "      <td>0.000574</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.001221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.001434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.001182</td>\n",
       "      <td>0.006329</td>\n",
       "      <td>0.007711</td>\n",
       "      <td>0.016148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000727</td>\n",
       "      <td>0.003965</td>\n",
       "      <td>0.001851</td>\n",
       "      <td>0.008614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.110972</td>\n",
       "      <td>0.152569</td>\n",
       "      <td>0.116828</td>\n",
       "      <td>0.077349</td>\n",
       "      <td>0.160987</td>\n",
       "      <td>0.058168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.002439</td>\n",
       "      <td>0.000659</td>\n",
       "      <td>0.000704</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.001831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                 F                             M                    \n",
       "year             1910      1960      2010      1910      1960      2010\n",
       "last_letter                                                            \n",
       "a            0.273390  0.341853  0.381240  0.005031  0.002440  0.014980\n",
       "b                 NaN  0.000343  0.000256  0.002116  0.001834  0.020470\n",
       "c            0.000013  0.000024  0.000538  0.002482  0.007257  0.012181\n",
       "d            0.017028  0.001844  0.001482  0.113858  0.122908  0.023387\n",
       "e            0.336941  0.215133  0.178415  0.147556  0.083853  0.067959\n",
       "f                 NaN  0.000010  0.000055  0.000783  0.004325  0.001188\n",
       "g            0.000144  0.000157  0.000374  0.002250  0.009488  0.001404\n",
       "h            0.051529  0.036224  0.075852  0.045562  0.037907  0.051670\n",
       "i            0.001526  0.039965  0.031734  0.000844  0.000603  0.022628\n",
       "j                 NaN       NaN  0.000090       NaN       NaN  0.000769\n",
       "k            0.000121  0.000156  0.000356  0.036581  0.049384  0.018541\n",
       "l            0.043189  0.033867  0.026356  0.065016  0.104904  0.070367\n",
       "m            0.001201  0.008613  0.002588  0.058044  0.033827  0.024657\n",
       "n            0.079240  0.130687  0.140210  0.143415  0.152522  0.362771\n",
       "o            0.001660  0.002439  0.001243  0.017065  0.012829  0.042681\n",
       "p            0.000018  0.000023  0.000020  0.003172  0.005675  0.001269\n",
       "q                 NaN       NaN  0.000030       NaN       NaN  0.000180\n",
       "r            0.013390  0.006764  0.018025  0.064481  0.031034  0.087477\n",
       "s            0.039042  0.012764  0.013332  0.130815  0.102730  0.065145\n",
       "t            0.027438  0.015201  0.007830  0.072879  0.065655  0.022861\n",
       "u            0.000684  0.000574  0.000417  0.000124  0.000057  0.001221\n",
       "v                 NaN  0.000060  0.000117  0.000113  0.000037  0.001434\n",
       "w            0.000020  0.000031  0.001182  0.006329  0.007711  0.016148\n",
       "x            0.000015  0.000037  0.000727  0.003965  0.001851  0.008614\n",
       "y            0.110972  0.152569  0.116828  0.077349  0.160987  0.058168\n",
       "z            0.002439  0.000659  0.000704  0.000170  0.000184  0.001831"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x180215afc18>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes  =plt.subplots(2, 1, figsize=(10, 8))\n",
    "letter_prop['M'].plot(kind='bar', rot=0, ax=axes[0], title='Male')\n",
    "letter_prop['F'].plot(kind='bar', rot=0, ax=axes[1], title='Female')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "letter_prop = table / table.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "dny_ts = letter_prop.loc[['d', 'n', 'y'], 'M'].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>last_letter</th>\n",
       "      <th>d</th>\n",
       "      <th>n</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>0.083055</td>\n",
       "      <td>0.153213</td>\n",
       "      <td>0.075760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>0.083247</td>\n",
       "      <td>0.153214</td>\n",
       "      <td>0.077451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>0.085340</td>\n",
       "      <td>0.149560</td>\n",
       "      <td>0.077537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>0.084066</td>\n",
       "      <td>0.151646</td>\n",
       "      <td>0.079144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>0.086120</td>\n",
       "      <td>0.149915</td>\n",
       "      <td>0.080405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "last_letter         d         n         y\n",
       "year                                     \n",
       "1880         0.083055  0.153213  0.075760\n",
       "1881         0.083247  0.153214  0.077451\n",
       "1882         0.085340  0.149560  0.077537\n",
       "1883         0.084066  0.151646  0.079144\n",
       "1884         0.086120  0.149915  0.080405"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dny_ts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1802161fa58>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dny_ts.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_names = pd.Series(top1000.names.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "lesley_like = all_names[all_names.str.lower().str.contains('lesl')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "632     Leslie\n",
       "2294    Lesley\n",
       "4262    Leslee\n",
       "4728     Lesli\n",
       "6103     Lesly\n",
       "dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lesley_like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered = top1000[top1000.names.isin(lesley_like)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "names\n",
       "Leslee      1082\n",
       "Lesley     35022\n",
       "Lesli        929\n",
       "Leslie    370429\n",
       "Lesly      10067\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered.groupby('names').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = filtered.pivot_table('births', index='year', columns='sex', aggfunc='sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = table.div(table.sum(1), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>2006</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex     F   M\n",
       "year         \n",
       "2006  1.0 NaN\n",
       "2007  1.0 NaN\n",
       "2008  1.0 NaN\n",
       "2009  1.0 NaN\n",
       "2010  1.0 NaN"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18021a90a20>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.plot(style={'M': 'k-', 'F': 'k--'}, grid=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}