{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis with Pandas \n",
    "[link](https://www.youtube.com/watch?v=w26x-z-BdWQ&list=PLyFizHxKlcupyh5YA-3ZFKGBwjoi8zqC4&index=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from pandas import Series, DataFrame\n",
    "pd.set_option('display.mpl_style', 'default') \n",
    "\n",
    "#pd.set_option('display.width', 5000) \n",
    "#pd.set_option('display.max_columns', 60)\n",
    "\n",
    "pd.options.display.max_rows = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>XOM</th>\n",
       "      <th>SPX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-01-02</th>\n",
       "      <td>7.40</td>\n",
       "      <td>21.11</td>\n",
       "      <td>29.22</td>\n",
       "      <td>909.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-03</th>\n",
       "      <td>7.45</td>\n",
       "      <td>21.14</td>\n",
       "      <td>29.24</td>\n",
       "      <td>908.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-06</th>\n",
       "      <td>7.45</td>\n",
       "      <td>21.52</td>\n",
       "      <td>29.96</td>\n",
       "      <td>929.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-07</th>\n",
       "      <td>7.43</td>\n",
       "      <td>21.93</td>\n",
       "      <td>28.95</td>\n",
       "      <td>922.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-08</th>\n",
       "      <td>7.28</td>\n",
       "      <td>21.31</td>\n",
       "      <td>28.83</td>\n",
       "      <td>909.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AAPL   MSFT    XOM     SPX\n",
       "2003-01-02  7.40  21.11  29.22  909.03\n",
       "2003-01-03  7.45  21.14  29.24  908.59\n",
       "2003-01-06  7.45  21.52  29.96  929.01\n",
       "2003-01-07  7.43  21.93  28.95  922.93\n",
       "2003-01-08  7.28  21.31  28.83  909.93"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_px = pd.read_csv('data/stock_px.csv', index_col=0, parse_dates= True)\n",
    "close_px.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14568ce10>"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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st6IoL13/12dWoTnVc6PU/hq3sBsfr57ATzv/Z2A/ff1f7Kwd86QX1ud5MtXZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1455ec490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "close_px.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x130b7ef50>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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i3gCcjogfB84AfapfAOeBSxHR263ect1aQM6Zy7GXaioxxrkGPA8cAx7PzGcz8xngSkQs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\nCDSfUx1xZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113c4b610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "close_px.ix[-1].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-09-12   NaN\n",
       "2011-09-13   NaN\n",
       "2011-09-14   NaN\n",
       "2011-09-15   NaN\n",
       "2011-09-16   NaN\n",
       "Name: AAPL, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = close_px['AAPL'][-20:]\n",
    "s2 = close_px['AAPL'][-25:-10]\n",
    "(s1+s2).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-09-12    379.94\n",
       "2011-09-13    384.62\n",
       "2011-09-14    389.30\n",
       "2011-09-15    392.96\n",
       "2011-09-16    400.50\n",
       "Name: AAPL, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.add(s2, fill_value=0).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>SPX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-10-03</th>\n",
       "      <td>374.60</td>\n",
       "      <td>24.53</td>\n",
       "      <td>1099.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-04</th>\n",
       "      <td>372.50</td>\n",
       "      <td>25.34</td>\n",
       "      <td>1123.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-05</th>\n",
       "      <td>378.25</td>\n",
       "      <td>25.89</td>\n",
       "      <td>1144.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-06</th>\n",
       "      <td>377.37</td>\n",
       "      <td>26.34</td>\n",
       "      <td>1164.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-07</th>\n",
       "      <td>369.80</td>\n",
       "      <td>26.25</td>\n",
       "      <td>1155.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-10</th>\n",
       "      <td>388.81</td>\n",
       "      <td>26.94</td>\n",
       "      <td>1194.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-11</th>\n",
       "      <td>400.29</td>\n",
       "      <td>27.00</td>\n",
       "      <td>1195.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-12</th>\n",
       "      <td>402.19</td>\n",
       "      <td>26.96</td>\n",
       "      <td>1207.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-13</th>\n",
       "      <td>408.43</td>\n",
       "      <td>27.18</td>\n",
       "      <td>1203.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-14</th>\n",
       "      <td>422.00</td>\n",
       "      <td>27.27</td>\n",
       "      <td>1224.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              AAPL   MSFT      SPX\n",
       "2011-10-03  374.60  24.53  1099.23\n",
       "2011-10-04  372.50  25.34  1123.95\n",
       "2011-10-05  378.25  25.89  1144.03\n",
       "2011-10-06  377.37  26.34  1164.97\n",
       "2011-10-07  369.80  26.25  1155.46\n",
       "2011-10-10  388.81  26.94  1194.89\n",
       "2011-10-11  400.29  27.00  1195.54\n",
       "2011-10-12  402.19  26.96  1207.25\n",
       "2011-10-13  408.43  27.18  1203.66\n",
       "2011-10-14  422.00  27.27  1224.58"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = close_px.ix[-10:, ['AAPL', 'MSFT', 'SPX']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL     389.424\n",
       "MSFT      26.370\n",
       "SPX     1171.356\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-10-03    499.453333\n",
       "2011-10-04    507.263333\n",
       "2011-10-05    516.056667\n",
       "2011-10-06    522.893333\n",
       "2011-10-07    517.170000\n",
       "2011-10-10    536.880000\n",
       "2011-10-11    540.943333\n",
       "2011-10-12    545.466667\n",
       "2011-10-13    546.423333\n",
       "2011-10-14    557.950000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(np.mean, axis=1)\n",
    "#df.mean(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2007-10-09 00:00:00')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_px.SPX.idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>a</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>foo</td>\n",
       "      <td>-0.025291</td>\n",
       "      <td>-0.148188</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>bar</td>\n",
       "      <td>-0.906232</td>\n",
       "      <td>0.513582</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>foo</td>\n",
       "      <td>0.367118</td>\n",
       "      <td>-0.523249</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>bar</td>\n",
       "      <td>-0.660170</td>\n",
       "      <td>-2.313452</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>foo</td>\n",
       "      <td>1.204531</td>\n",
       "      <td>0.129450</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>bar</td>\n",
       "      <td>0.284837</td>\n",
       "      <td>-1.768297</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              b         a         c    d\n",
       "2000-01-01  foo -0.025291 -0.148188  NaN\n",
       "2000-01-02  bar -0.906232  0.513582  NaN\n",
       "2000-01-03  foo  0.367118 -0.523249  NaN\n",
       "2000-01-04  bar -0.660170 -2.313452  NaN\n",
       "2000-01-05  foo  1.204531  0.129450  NaN\n",
       "2000-01-06  bar  0.284837 -1.768297  NaN"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'a': np.random.randn(6),\n",
    "                   'b': ['foo', 'bar']*3, \n",
    "                   'c': np.random.randn(6)},\n",
    "                     index=pd.date_range('1/1/2000', periods=6),\n",
    "                     columns = ['b', 'a', 'c', 'd'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Baby names "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "names = pd.read_csv('data/baby-names2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>257995</th>\n",
       "      <td>2008</td>\n",
       "      <td>Carleigh</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>girl</td>\n",
       "      <td>C642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257996</th>\n",
       "      <td>2008</td>\n",
       "      <td>Iyana</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>girl</td>\n",
       "      <td>I500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257997</th>\n",
       "      <td>2008</td>\n",
       "      <td>Kenley</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>girl</td>\n",
       "      <td>K540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257998</th>\n",
       "      <td>2008</td>\n",
       "      <td>Sloane</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>girl</td>\n",
       "      <td>S450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257999</th>\n",
       "      <td>2008</td>\n",
       "      <td>Elianna</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>girl</td>\n",
       "      <td>E450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        year      name      prop   sex soundex\n",
       "257995  2008  Carleigh  0.000128  girl    C642\n",
       "257996  2008     Iyana  0.000128  girl    I500\n",
       "257997  2008    Kenley  0.000127  girl    K540\n",
       "257998  2008    Sloane  0.000127  girl    S450\n",
       "257999  2008   Elianna  0.000127  girl    E450"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1880</td>\n",
       "      <td>John</td>\n",
       "      <td>0.081541</td>\n",
       "      <td>boy</td>\n",
       "      <td>J500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1880</td>\n",
       "      <td>William</td>\n",
       "      <td>0.080511</td>\n",
       "      <td>boy</td>\n",
       "      <td>W450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1880</td>\n",
       "      <td>James</td>\n",
       "      <td>0.050057</td>\n",
       "      <td>boy</td>\n",
       "      <td>J520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1880</td>\n",
       "      <td>Charles</td>\n",
       "      <td>0.045167</td>\n",
       "      <td>boy</td>\n",
       "      <td>C642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1880</td>\n",
       "      <td>George</td>\n",
       "      <td>0.043292</td>\n",
       "      <td>boy</td>\n",
       "      <td>G620</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>129995</th>\n",
       "      <td>1880</td>\n",
       "      <td>Emaline</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>girl</td>\n",
       "      <td>E545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129996</th>\n",
       "      <td>1880</td>\n",
       "      <td>Ester</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>girl</td>\n",
       "      <td>E236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129997</th>\n",
       "      <td>1880</td>\n",
       "      <td>Eulah</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>girl</td>\n",
       "      <td>E400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129998</th>\n",
       "      <td>1880</td>\n",
       "      <td>Eulalie</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>girl</td>\n",
       "      <td>E440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129999</th>\n",
       "      <td>1880</td>\n",
       "      <td>Euna</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>girl</td>\n",
       "      <td>E500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        year     name      prop   sex soundex\n",
       "0       1880     John  0.081541   boy    J500\n",
       "1       1880  William  0.080511   boy    W450\n",
       "2       1880    James  0.050057   boy    J520\n",
       "3       1880  Charles  0.045167   boy    C642\n",
       "4       1880   George  0.043292   boy    G620\n",
       "...      ...      ...       ...   ...     ...\n",
       "129995  1880  Emaline  0.000041  girl    E545\n",
       "129996  1880    Ester  0.000041  girl    E236\n",
       "129997  1880    Eulah  0.000041  girl    E400\n",
       "129998  1880  Eulalie  0.000041  girl    E440\n",
       "129999  1880     Euna  0.000041  girl    E500\n",
       "\n",
       "[2000 rows x 5 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[names.year == 1880]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  sex \n",
       "2000  boy     1000\n",
       "      girl    1000\n",
       "2001  boy     1000\n",
       "      girl    1000\n",
       "2002  boy     1000\n",
       "              ... \n",
       "2003  girl    1000\n",
       "2004  boy     1000\n",
       "      girl    1000\n",
       "2005  boy     1000\n",
       "      girl    1000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.groupby(['year', 'sex']).size().ix[2000:2005]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "boys = names[names.sex == 'boy']\n",
    "girls = names[names.sex == 'girl']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_max_record(group):\n",
    "    return group.ix[group.prop.idxmax()]\n",
    "\n",
    "#boys[boys.year == 2000].prop.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year           1880\n",
       "name           John\n",
       "prop       0.081541\n",
       "sex             boy\n",
       "soundex        J500\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_max_record(boys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</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>1880</th>\n",
       "      <td>1880</td>\n",
       "      <td>John</td>\n",
       "      <td>0.081541</td>\n",
       "      <td>boy</td>\n",
       "      <td>J500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>1881</td>\n",
       "      <td>John</td>\n",
       "      <td>0.080975</td>\n",
       "      <td>boy</td>\n",
       "      <td>J500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>1882</td>\n",
       "      <td>John</td>\n",
       "      <td>0.078314</td>\n",
       "      <td>boy</td>\n",
       "      <td>J500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>1883</td>\n",
       "      <td>John</td>\n",
       "      <td>0.079066</td>\n",
       "      <td>boy</td>\n",
       "      <td>J500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>1884</td>\n",
       "      <td>John</td>\n",
       "      <td>0.076476</td>\n",
       "      <td>boy</td>\n",
       "      <td>J500</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>2004</th>\n",
       "      <td>2004</td>\n",
       "      <td>Jacob</td>\n",
       "      <td>0.013196</td>\n",
       "      <td>boy</td>\n",
       "      <td>J210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>2005</td>\n",
       "      <td>Jacob</td>\n",
       "      <td>0.012148</td>\n",
       "      <td>boy</td>\n",
       "      <td>J210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>2006</td>\n",
       "      <td>Jacob</td>\n",
       "      <td>0.011331</td>\n",
       "      <td>boy</td>\n",
       "      <td>J210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>2007</td>\n",
       "      <td>Jacob</td>\n",
       "      <td>0.010948</td>\n",
       "      <td>boy</td>\n",
       "      <td>J210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>2008</td>\n",
       "      <td>Jacob</td>\n",
       "      <td>0.010355</td>\n",
       "      <td>boy</td>\n",
       "      <td>J210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>129 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year   name      prop  sex soundex\n",
       "year                                    \n",
       "1880  1880   John  0.081541  boy    J500\n",
       "1881  1881   John  0.080975  boy    J500\n",
       "1882  1882   John  0.078314  boy    J500\n",
       "1883  1883   John  0.079066  boy    J500\n",
       "1884  1884   John  0.076476  boy    J500\n",
       "...    ...    ...       ...  ...     ...\n",
       "2004  2004  Jacob  0.013196  boy    J210\n",
       "2005  2005  Jacob  0.012148  boy    J210\n",
       "2006  2006  Jacob  0.011331  boy    J210\n",
       "2007  2007  Jacob  0.010948  boy    J210\n",
       "2008  2008  Jacob  0.010355  boy    J210\n",
       "\n",
       "[129 rows x 5 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result =boys.groupby('year').apply(get_max_record)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1149b6790>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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07I47oLAw7mhqp76jZSYDHcysnZk1BfoAEyqUmQCcE10sHyh196Vmtp2ZtYz2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\nkruISA5SchcRyUFK7iIiOUjJXUQkB/1/zTUQ1jEO3EQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1149b6510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#proportion of names per year -> more diversity\n",
    "\n",
    "result.prop.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>1880</td>\n",
       "      <td>Travis</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>boy</td>\n",
       "      <td>T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2648</th>\n",
       "      <td>1882</td>\n",
       "      <td>Travis</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>boy</td>\n",
       "      <td>T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3672</th>\n",
       "      <td>1883</td>\n",
       "      <td>Travis</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>boy</td>\n",
       "      <td>T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4656</th>\n",
       "      <td>1884</td>\n",
       "      <td>Travis</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>boy</td>\n",
       "      <td>T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5602</th>\n",
       "      <td>1885</td>\n",
       "      <td>Travis</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>boy</td>\n",
       "      <td>T612</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>124165</th>\n",
       "      <td>2004</td>\n",
       "      <td>Travis</td>\n",
       "      <td>0.001164</td>\n",
       "      <td>boy</td>\n",
       "      <td>T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125173</th>\n",
       "      <td>2005</td>\n",
       "      <td>Travis</td>\n",
       "      <td>0.001114</td>\n",
       "      <td>boy</td>\n",
       "      <td>T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126162</th>\n",
       "      <td>2006</td>\n",
       "      <td>Travis</td>\n",
       "      <td>0.001186</td>\n",
       "      <td>boy</td>\n",
       "      <td>T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127169</th>\n",
       "      <td>2007</td>\n",
       "      <td>Travis</td>\n",
       "      <td>0.001087</td>\n",
       "      <td>boy</td>\n",
       "      <td>T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128176</th>\n",
       "      <td>2008</td>\n",
       "      <td>Travis</td>\n",
       "      <td>0.001025</td>\n",
       "      <td>boy</td>\n",
       "      <td>T612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        year    name      prop  sex soundex\n",
       "563     1880  Travis  0.000101  boy    T612\n",
       "2648    1882  Travis  0.000082  boy    T612\n",
       "3672    1883  Travis  0.000080  boy    T612\n",
       "4656    1884  Travis  0.000081  boy    T612\n",
       "5602    1885  Travis  0.000095  boy    T612\n",
       "...      ...     ...       ...  ...     ...\n",
       "124165  2004  Travis  0.001164  boy    T612\n",
       "125173  2005  Travis  0.001114  boy    T612\n",
       "126162  2006  Travis  0.001186  boy    T612\n",
       "127169  2007  Travis  0.001087  boy    T612\n",
       "128176  2008  Travis  0.001025  boy    T612\n",
       "\n",
       "[125 rows x 5 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# it goes element=wise \n",
    "\n",
    "boys[boys.name == 'Travis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>John</th>\n",
       "      <th>1880</th>\n",
       "      <td>0.081541</td>\n",
       "      <td>boy</td>\n",
       "      <td>J500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>William</th>\n",
       "      <th>1880</th>\n",
       "      <td>0.080511</td>\n",
       "      <td>boy</td>\n",
       "      <td>W450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James</th>\n",
       "      <th>1880</th>\n",
       "      <td>0.050057</td>\n",
       "      <td>boy</td>\n",
       "      <td>J520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charles</th>\n",
       "      <th>1880</th>\n",
       "      <td>0.045167</td>\n",
       "      <td>boy</td>\n",
       "      <td>C642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>George</th>\n",
       "      <th>1880</th>\n",
       "      <td>0.043292</td>\n",
       "      <td>boy</td>\n",
       "      <td>G620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kolten</th>\n",
       "      <th>2008</th>\n",
       "      <td>0.000090</td>\n",
       "      <td>boy</td>\n",
       "      <td>K435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Damari</th>\n",
       "      <th>2008</th>\n",
       "      <td>0.000089</td>\n",
       "      <td>boy</td>\n",
       "      <td>D560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugh</th>\n",
       "      <th>2008</th>\n",
       "      <td>0.000089</td>\n",
       "      <td>boy</td>\n",
       "      <td>H200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jensen</th>\n",
       "      <th>2008</th>\n",
       "      <td>0.000089</td>\n",
       "      <td>boy</td>\n",
       "      <td>J525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yurem</th>\n",
       "      <th>2008</th>\n",
       "      <td>0.000089</td>\n",
       "      <td>boy</td>\n",
       "      <td>Y650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>129000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  prop  sex soundex\n",
       "name    year                       \n",
       "John    1880  0.081541  boy    J500\n",
       "William 1880  0.080511  boy    W450\n",
       "James   1880  0.050057  boy    J520\n",
       "Charles 1880  0.045167  boy    C642\n",
       "George  1880  0.043292  boy    G620\n",
       "...                ...  ...     ...\n",
       "Kolten  2008  0.000090  boy    K435\n",
       "Damari  2008  0.000089  boy    D560\n",
       "Hugh    2008  0.000089  boy    H200\n",
       "Jensen  2008  0.000089  boy    J525\n",
       "Yurem   2008  0.000089  boy    Y650\n",
       "\n",
       "[129000 rows x 3 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# so it's better to do this\n",
    "\n",
    "idf = boys.set_index(['name', 'year'])\n",
    "idf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</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>0.000101</td>\n",
       "      <td>boy</td>\n",
       "      <td>T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>0.000082</td>\n",
       "      <td>boy</td>\n",
       "      <td>T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>0.000080</td>\n",
       "      <td>boy</td>\n",
       "      <td>T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>0.000081</td>\n",
       "      <td>boy</td>\n",
       "      <td>T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1885</th>\n",
       "      <td>0.000095</td>\n",
       "      <td>boy</td>\n",
       "      <td>T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>0.001164</td>\n",
       "      <td>boy</td>\n",
       "      <td>T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>0.001114</td>\n",
       "      <td>boy</td>\n",
       "      <td>T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>0.001186</td>\n",
       "      <td>boy</td>\n",
       "      <td>T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>0.001087</td>\n",
       "      <td>boy</td>\n",
       "      <td>T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>0.001025</td>\n",
       "      <td>boy</td>\n",
       "      <td>T612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          prop  sex soundex\n",
       "year                       \n",
       "1880  0.000101  boy    T612\n",
       "1882  0.000082  boy    T612\n",
       "1883  0.000080  boy    T612\n",
       "1884  0.000081  boy    T612\n",
       "1885  0.000095  boy    T612\n",
       "...        ...  ...     ...\n",
       "2004  0.001164  boy    T612\n",
       "2005  0.001114  boy    T612\n",
       "2006  0.001186  boy    T612\n",
       "2007  0.001087  boy    T612\n",
       "2008  0.001025  boy    T612\n",
       "\n",
       "[125 rows x 3 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idf.ix['Travis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a0ec390>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RLiecEPq1dCm88kr4ZtC/f/jG8Oyz4RvGMcdAZSW0b5+f9rTGsmXwl7+Eqa5/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\nUqLtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118ce6050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idf.ix['Travis'].prop.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Kennard     0.000027\n",
       "Danniel     0.000027\n",
       "Deryl       0.000028\n",
       "Grayling    0.000028\n",
       "Michial     0.000028\n",
       "              ...   \n",
       "Charles     0.019521\n",
       "Robert      0.029625\n",
       "William     0.034182\n",
       "James       0.035465\n",
       "John        0.041082\n",
       "Name: prop, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boys.groupby('name')['prop'].mean().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result = boys.groupby('name')['prop'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name         \n",
       "Aaden   count    1.000000\n",
       "        mean     0.000442\n",
       "        std           NaN\n",
       "        min      0.000442\n",
       "        25%      0.000442\n",
       "                   ...   \n",
       "Abbie   50%      0.000046\n",
       "        75%      0.000046\n",
       "        max      0.000046\n",
       "Abbott  count    2.000000\n",
       "        mean     0.000044\n",
       "dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = boys[boys.year == 2008].sort_values(by='prop', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128000    0.010355\n",
       "128001    0.019792\n",
       "128002    0.029093\n",
       "128003    0.037892\n",
       "128004    0.046594\n",
       "            ...   \n",
       "128995    0.795058\n",
       "128996    0.795147\n",
       "128997    0.795236\n",
       "128998    0.795325\n",
       "128999    0.795414\n",
       "Name: prop, dtype: float64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.prop.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "127"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to get the 50%\n",
    "int(df.prop.cumsum().searchsorted(0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# writting the function to use in each year\n",
    "\n",
    "def get_quantile_count(group, quantile=0.5):\n",
    "    df = group.sort_values(by='prop', ascending=False)\n",
    "    return int(df.prop.cumsum().searchsorted(quantile))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "q = 0.5\n",
    "boys_ct = boys.groupby('year').apply(get_quantile_count, quantile = q)\n",
    "girls_ct = girls.groupby('year').apply(get_quantile_count, quantile = q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x114220c50>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3ltCNMcaL5uyYwx0d7/DahVB3ltCNMcaLvt/xPXd2vrNczmUJ3RhjvCTxRCIJJxIY1GZQ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\nJCyhG2NMJWEJ3RhjKon/D0vuu68O+3KpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114220210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# people got creative choosing names in 1980's\n",
    "\n",
    "boys_ct.plot(label= 'boys')\n",
    "girls_ct.plot(label= 'girls')\n",
    "plt.legend(loc= 'best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "      <th>year_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1880.0</td>\n",
       "      <td>John</td>\n",
       "      <td>0.081541</td>\n",
       "      <td>boy</td>\n",
       "      <td>J500</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1880.0</td>\n",
       "      <td>William</td>\n",
       "      <td>0.080511</td>\n",
       "      <td>boy</td>\n",
       "      <td>W450</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1880.0</td>\n",
       "      <td>James</td>\n",
       "      <td>0.050057</td>\n",
       "      <td>boy</td>\n",
       "      <td>J520</td>\n",
       "      <td>998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1880.0</td>\n",
       "      <td>Charles</td>\n",
       "      <td>0.045167</td>\n",
       "      <td>boy</td>\n",
       "      <td>C642</td>\n",
       "      <td>997.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1880.0</td>\n",
       "      <td>George</td>\n",
       "      <td>0.043292</td>\n",
       "      <td>boy</td>\n",
       "      <td>G620</td>\n",
       "      <td>996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128996</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>Damari</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>boy</td>\n",
       "      <td>D560</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128997</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>Hugh</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>boy</td>\n",
       "      <td>H200</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128998</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>Jensen</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>boy</td>\n",
       "      <td>J525</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128999</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>Yurem</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>boy</td>\n",
       "      <td>Y650</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_rank</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>129001 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             year     name      prop  sex soundex  year_rank\n",
       "0          1880.0     John  0.081541  boy    J500     1000.0\n",
       "1          1880.0  William  0.080511  boy    W450      999.0\n",
       "2          1880.0    James  0.050057  boy    J520      998.0\n",
       "3          1880.0  Charles  0.045167  boy    C642      997.0\n",
       "4          1880.0   George  0.043292  boy    G620      996.0\n",
       "...           ...      ...       ...  ...     ...        ...\n",
       "128996     2008.0   Damari  0.000089  boy    D560        2.5\n",
       "128997     2008.0     Hugh  0.000089  boy    H200        2.5\n",
       "128998     2008.0   Jensen  0.000089  boy    J525        2.5\n",
       "128999     2008.0    Yurem  0.000089  boy    Y650        2.5\n",
       "year_rank     NaN      NaN       NaN  NaN     NaN        NaN\n",
       "\n",
       "[129001 rows x 6 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = boys.groupby('year')['prop']\n",
    "boys['year_rank'] = grouped.transform(Series.rank)\n",
    "\n",
    "boys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d93d310>"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nTpxoo3+mTvULoi4zZXSgR18QhfiBPnBg5izhtmKF1X6LiioOJ1y/3kKoXz94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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d9d11d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rank of the names per year\n",
    "\n",
    "idf = boys.set_index(['name', 'year'])\n",
    "idf.ix['Jose'].year_rank.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Births"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1880</td>\n",
       "      <td>boy</td>\n",
       "      <td>118405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1881</td>\n",
       "      <td>boy</td>\n",
       "      <td>108290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1882</td>\n",
       "      <td>boy</td>\n",
       "      <td>122034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1883</td>\n",
       "      <td>boy</td>\n",
       "      <td>112487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1884</td>\n",
       "      <td>boy</td>\n",
       "      <td>122745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>2005</td>\n",
       "      <td>girl</td>\n",
       "      <td>2024636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>2006</td>\n",
       "      <td>girl</td>\n",
       "      <td>2084511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>2007</td>\n",
       "      <td>girl</td>\n",
       "      <td>2109099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>2008</td>\n",
       "      <td>girl</td>\n",
       "      <td>2072756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>2009</td>\n",
       "      <td>girl</td>\n",
       "      <td>2001968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>260 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year   sex   births\n",
       "0    1880   boy   118405\n",
       "1    1881   boy   108290\n",
       "2    1882   boy   122034\n",
       "3    1883   boy   112487\n",
       "4    1884   boy   122745\n",
       "..    ...   ...      ...\n",
       "255  2005  girl  2024636\n",
       "256  2006  girl  2084511\n",
       "257  2007  girl  2109099\n",
       "258  2008  girl  2072756\n",
       "259  2009  girl  2001968\n",
       "\n",
       "[260 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "births = pd.read_csv('data/births.csv')\n",
    "births"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1880</td>\n",
       "      <td>John</td>\n",
       "      <td>0.081541</td>\n",
       "      <td>boy</td>\n",
       "      <td>J500</td>\n",
       "      <td>118405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1880</td>\n",
       "      <td>William</td>\n",
       "      <td>0.080511</td>\n",
       "      <td>boy</td>\n",
       "      <td>W450</td>\n",
       "      <td>118405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1880</td>\n",
       "      <td>James</td>\n",
       "      <td>0.050057</td>\n",
       "      <td>boy</td>\n",
       "      <td>J520</td>\n",
       "      <td>118405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1880</td>\n",
       "      <td>Charles</td>\n",
       "      <td>0.045167</td>\n",
       "      <td>boy</td>\n",
       "      <td>C642</td>\n",
       "      <td>118405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1880</td>\n",
       "      <td>George</td>\n",
       "      <td>0.043292</td>\n",
       "      <td>boy</td>\n",
       "      <td>G620</td>\n",
       "      <td>118405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257995</th>\n",
       "      <td>2008</td>\n",
       "      <td>Carleigh</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>girl</td>\n",
       "      <td>C642</td>\n",
       "      <td>2072756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257996</th>\n",
       "      <td>2008</td>\n",
       "      <td>Iyana</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>girl</td>\n",
       "      <td>I500</td>\n",
       "      <td>2072756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257997</th>\n",
       "      <td>2008</td>\n",
       "      <td>Kenley</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>girl</td>\n",
       "      <td>K540</td>\n",
       "      <td>2072756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257998</th>\n",
       "      <td>2008</td>\n",
       "      <td>Sloane</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>girl</td>\n",
       "      <td>S450</td>\n",
       "      <td>2072756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257999</th>\n",
       "      <td>2008</td>\n",
       "      <td>Elianna</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>girl</td>\n",
       "      <td>E450</td>\n",
       "      <td>2072756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>258000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        year      name      prop   sex soundex   births\n",
       "0       1880      John  0.081541   boy    J500   118405\n",
       "1       1880   William  0.080511   boy    W450   118405\n",
       "2       1880     James  0.050057   boy    J520   118405\n",
       "3       1880   Charles  0.045167   boy    C642   118405\n",
       "4       1880    George  0.043292   boy    G620   118405\n",
       "...      ...       ...       ...   ...     ...      ...\n",
       "257995  2008  Carleigh  0.000128  girl    C642  2072756\n",
       "257996  2008     Iyana  0.000128  girl    I500  2072756\n",
       "257997  2008    Kenley  0.000127  girl    K540  2072756\n",
       "257998  2008    Sloane  0.000127  girl    S450  2072756\n",
       "257999  2008   Elianna  0.000127  girl    E450  2072756\n",
       "\n",
       "[258000 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = pd.merge(names, births, on=['year', 'sex'])\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "      <th>births</th>\n",
       "      <th>persons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1880</td>\n",
       "      <td>John</td>\n",
       "      <td>0.081541</td>\n",
       "      <td>boy</td>\n",
       "      <td>J500</td>\n",
       "      <td>118405</td>\n",
       "      <td>9654.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1880</td>\n",
       "      <td>William</td>\n",
       "      <td>0.080511</td>\n",
       "      <td>boy</td>\n",
       "      <td>W450</td>\n",
       "      <td>118405</td>\n",
       "      <td>9532.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1880</td>\n",
       "      <td>James</td>\n",
       "      <td>0.050057</td>\n",
       "      <td>boy</td>\n",
       "      <td>J520</td>\n",
       "      <td>118405</td>\n",
       "      <td>5926.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1880</td>\n",
       "      <td>Charles</td>\n",
       "      <td>0.045167</td>\n",
       "      <td>boy</td>\n",
       "      <td>C642</td>\n",
       "      <td>118405</td>\n",
       "      <td>5347.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1880</td>\n",
       "      <td>George</td>\n",
       "      <td>0.043292</td>\n",
       "      <td>boy</td>\n",
       "      <td>G620</td>\n",
       "      <td>118405</td>\n",
       "      <td>5125.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     name      prop  sex soundex  births  persons\n",
       "0  1880     John  0.081541  boy    J500  118405   9654.0\n",
       "1  1880  William  0.080511  boy    W450  118405   9532.0\n",
       "2  1880    James  0.050057  boy    J520  118405   5926.0\n",
       "3  1880  Charles  0.045167  boy    C642  118405   5347.0\n",
       "4  1880   George  0.043292  boy    G620  118405   5125.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of persons\n",
    "\n",
    "merged['persons'] = np.floor(merged.prop * merged.births)\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name     sex \n",
       "Aaden    boy        959.0\n",
       "Aaliyah  girl     39660.0\n",
       "Aarav    boy        219.0\n",
       "Aaron    boy     508034.0\n",
       "         girl      1365.0\n",
       "                   ...   \n",
       "Zola     girl      4847.0\n",
       "Zollie   boy         60.0\n",
       "Zona     girl      3218.0\n",
       "Zora     girl      4551.0\n",
       "Zula     girl      3578.0\n",
       "Name: persons, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_name_sex = merged.groupby(['name', 'sex']).persons.sum()\n",
    "merged_name_sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name     sex \n",
       "Alabama  girl          3.0\n",
       "Daisye   girl          3.0\n",
       "Crissie  girl          3.0\n",
       "Dollye   girl          3.0\n",
       "Dema     girl          3.0\n",
       "                   ...    \n",
       "Mary     girl    4097626.0\n",
       "Michael  boy     4207352.0\n",
       "Robert   boy     4752198.0\n",
       "John     boy     5016124.0\n",
       "James    boy     5021269.0\n",
       "Name: persons, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_name_sex.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tdzGa1ar7sCQdAnwzIgZJOgK4HDg4IhYWjtkDuIk0SGJ74D6gb0SEpOnAOcBM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\nCZ9CSAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a1b4b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_10 = merged_name_sex.sort_values()[-5:].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### only boys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mboys = pd.merge(boys, births, on=['year', 'sex'])\n",
    "mboys['persons'] = np.floor(mboys.prop * mboys.births)\n",
    "persons = mboys.set_index(['year', 'name']).persons\n",
    "\n",
    "mgirls = pd.merge(girls, births, on=['year', 'sex'])\n",
    "mgirls['persons'] = np.floor(mgirls.prop * mgirls.births)\n",
    "gpersons = mgirls.set_index(['year', 'name']).persons\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  name   \n",
       "1880  John       9654.0\n",
       "      William    9532.0\n",
       "      James      5926.0\n",
       "      Charles    5347.0\n",
       "      George     5125.0\n",
       "                  ...  \n",
       "2008  Kolten      195.0\n",
       "      Damari      193.0\n",
       "      Hugh        193.0\n",
       "      Jensen      193.0\n",
       "      Yurem       193.0\n",
       "Name: persons, dtype: float64"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "persons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14536c810>"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WCx5PvxP3BiVRsxcEIagYWqzEJcQglwd+2OUBnYmpvUycFqWIIFatoL3VGuBe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\nTiqE2zr1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14523fa50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#persons.ix[:, 'Albert'].plot(label ='Albert')\n",
    "persons.ix[:, 'Alberto'].plot(label ='Alberto')\n",
    "gpersons.ix[:, 'Leticia'].plot(label ='Leticia')\n",
    "plt.legend(loc= 'best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>name</th>\n",
       "      <th>Aaden</th>\n",
       "      <th>Aarav</th>\n",
       "      <th>Aaron</th>\n",
       "      <th>Ab</th>\n",
       "      <th>Abb</th>\n",
       "      <th>Abbie</th>\n",
       "      <th>Abbott</th>\n",
       "      <th>Abdiel</th>\n",
       "      <th>Abdul</th>\n",
       "      <th>Abdullah</th>\n",
       "      <th>Abe</th>\n",
       "      <th>Abel</th>\n",
       "      <th>Abelardo</th>\n",
       "      <th>Abie</th>\n",
       "      <th>Abner</th>\n",
       "      <th>Abraham</th>\n",
       "      <th>Abram</th>\n",
       "      <th>Ace</th>\n",
       "      <th>Acey</th>\n",
       "      <th>Acie</th>\n",
       "      <th>Acy</th>\n",
       "      <th>Ada</th>\n",
       "      <th>Adalberto</th>\n",
       "      <th>Adam</th>\n",
       "      <th>Adams</th>\n",
       "      <th>Adan</th>\n",
       "      <th>Add</th>\n",
       "      <th>Addie</th>\n",
       "      <th>Addison</th>\n",
       "      <th>Adelard</th>\n",
       "      <th>...</th>\n",
       "      <th>Yoshio</th>\n",
       "      <th>Young</th>\n",
       "      <th>Yurem</th>\n",
       "      <th>Yusuf</th>\n",
       "      <th>Zachariah</th>\n",
       "      <th>Zachary</th>\n",
       "      <th>Zachery</th>\n",
       "      <th>Zack</th>\n",
       "      <th>Zackary</th>\n",
       "      <th>Zackery</th>\n",
       "      <th>Zaid</th>\n",
       "      <th>Zaiden</th>\n",
       "      <th>Zain</th>\n",
       "      <th>Zaire</th>\n",
       "      <th>Zakary</th>\n",
       "      <th>Zander</th>\n",
       "      <th>Zane</th>\n",
       "      <th>Zavier</th>\n",
       "      <th>Zayden</th>\n",
       "      <th>Zayne</th>\n",
       "      <th>Zeb</th>\n",
       "      <th>Zebulon</th>\n",
       "      <th>Zechariah</th>\n",
       "      <th>Zed</th>\n",
       "      <th>Zeke</th>\n",
       "      <th>Zenas</th>\n",
       "      <th>Zeno</th>\n",
       "      <th>Zigmund</th>\n",
       "      <th>Zion</th>\n",
       "      <th>Zollie</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>103.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>115.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>106.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8378.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>191.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>791.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7465.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1113.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>430.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>172.0</td>\n",
       "      <td>615.0</td>\n",
       "      <td>13703.0</td>\n",
       "      <td>615.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>1105.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>215.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>1141.0</td>\n",
       "      <td>1455.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>307.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7785.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>205.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>853.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2035.0</td>\n",
       "      <td>430.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6821.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>392.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>182.0</td>\n",
       "      <td>664.0</td>\n",
       "      <td>12265.0</td>\n",
       "      <td>624.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>214.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>910.0</td>\n",
       "      <td>1430.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>337.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8272.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>218.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>922.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2201.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6765.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1097.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>443.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>212.0</td>\n",
       "      <td>686.0</td>\n",
       "      <td>10978.0</td>\n",
       "      <td>524.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>996.0</td>\n",
       "      <td>419.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>227.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>1077.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>334.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1294.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8893.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>225.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>938.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2136.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6760.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1079.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>339.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>205.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>633.0</td>\n",
       "      <td>10179.0</td>\n",
       "      <td>461.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>946.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>238.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>1593.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>357.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1545.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>959.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>8496.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>199.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>863.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2132.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6075.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>264.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>193.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>9160.0</td>\n",
       "      <td>392.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>861.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1119.0</td>\n",
       "      <td>1557.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>364.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1586.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>129 rows × 3437 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "name  Aaden  Aarav   Aaron   Ab  Abb  Abbie  Abbott  Abdiel  Abdul  Abdullah   Abe   Abel  Abelardo  Abie  Abner  Abraham  Abram    Ace  Acey  Acie  Acy  Ada  Adalberto    Adam  Adams    Adan  Add  Addie  Addison  Adelard   ...    Yoshio  Young  Yurem  Yusuf  Zachariah  Zachary  Zachery   Zack  Zackary  Zackery   Zaid  Zaiden   Zain  Zaire  Zakary  Zander    Zane  Zavier  Zayden  Zayne   Zeb  Zebulon  Zechariah  Zed  Zeke  Zenas  Zeno  Zigmund    Zion  Zollie\n",
       "year                                                                                                                                                                                                                            ...                                                                                                                                                                                                                                            \n",
       "1880    NaN    NaN   101.0  4.0  NaN    NaN     4.0     NaN    NaN       NaN  49.0    8.0       NaN   NaN   26.0     80.0   20.0    NaN   NaN   NaN  NaN  NaN        NaN   103.0    NaN     NaN  NaN    8.0     18.0      NaN   ...       NaN   13.0    NaN    NaN        NaN      NaN      NaN   18.0      NaN      NaN    NaN     NaN    NaN    NaN     NaN     NaN     NaN     NaN     NaN    NaN   9.0      NaN        NaN  NaN   6.0    NaN   NaN      NaN     NaN     NaN\n",
       "1881    NaN    NaN    93.0  4.0  NaN    NaN     NaN     NaN    NaN       NaN  35.0   12.0       NaN   NaN   29.0     85.0   29.0    NaN   NaN   5.0  NaN  NaN        NaN   115.0    NaN     NaN  4.0    7.0     17.0      4.0   ...       NaN    8.0    NaN    NaN        NaN      NaN      NaN   25.0      NaN      NaN    NaN     NaN    NaN    NaN     NaN     NaN     NaN     NaN     NaN    NaN   9.0      NaN        NaN  NaN   NaN    NaN   NaN      NaN     NaN     NaN\n",
       "1882    NaN    NaN    85.0  5.0  NaN    NaN     NaN     NaN    NaN       NaN  50.0   10.0       NaN   NaN   31.0     91.0   25.0    8.0   NaN   NaN  NaN  NaN        NaN   113.0    NaN     NaN  NaN    5.0     20.0      NaN   ...       NaN    9.0    NaN    NaN        NaN      NaN      NaN   29.0      NaN      NaN    NaN     NaN    NaN    NaN     NaN     NaN     NaN     NaN     NaN    NaN  10.0      NaN        NaN  NaN   NaN    NaN   NaN      NaN     NaN     NaN\n",
       "1883    NaN    NaN   104.0  NaN  NaN    NaN     NaN     NaN    NaN       NaN  42.0   12.0       NaN   NaN   26.0     51.0   20.0    5.0   NaN   NaN  NaN  NaN        NaN   106.0    NaN     NaN  NaN    6.0     20.0      NaN   ...       NaN   12.0    NaN    NaN        NaN      NaN      NaN   28.0      NaN      NaN    NaN     NaN    NaN    NaN     NaN     NaN     NaN     NaN     NaN    NaN  13.0      NaN        NaN  NaN   5.0    NaN   5.0      NaN     NaN     NaN\n",
       "1884    NaN    NaN    96.0  NaN  5.0    NaN     NaN     NaN    NaN       NaN  45.0   13.0       NaN   NaN   33.0     67.0   28.0    5.0   NaN   NaN  NaN  NaN        NaN    82.0    NaN     NaN  NaN    7.0     16.0      6.0   ...       NaN   11.0    NaN    NaN        NaN      NaN      NaN   28.0      NaN      NaN    NaN     NaN    NaN    NaN     NaN     NaN     NaN     NaN     NaN    NaN  11.0      NaN        NaN  NaN   NaN    NaN   NaN      NaN     NaN     6.0\n",
       "...     ...    ...     ...  ...  ...    ...     ...     ...    ...       ...   ...    ...       ...   ...    ...      ...    ...    ...   ...   ...  ...  ...        ...     ...    ...     ...  ...    ...      ...      ...   ...       ...    ...    ...    ...        ...      ...      ...    ...      ...      ...    ...     ...    ...    ...     ...     ...     ...     ...     ...    ...   ...      ...        ...  ...   ...    ...   ...      ...     ...     ...\n",
       "2004    NaN    NaN  8378.0  NaN  NaN    NaN     NaN     NaN    NaN     191.0   NaN  791.0       NaN   NaN    NaN   1982.0  375.0    NaN   NaN   NaN  NaN  NaN        NaN  7465.0    NaN  1113.0  NaN    NaN    430.0      NaN   ...       NaN    NaN    NaN  172.0      615.0  13703.0    615.0  183.0   1105.0    485.0  172.0     NaN  215.0  172.0   210.0  1141.0  1455.0   179.0     NaN    NaN   NaN      NaN      307.0  NaN   NaN    NaN   NaN      NaN  1006.0     NaN\n",
       "2005    NaN    NaN  7785.0  NaN  NaN    NaN     NaN     NaN    NaN     205.0   NaN  853.0       NaN   NaN    NaN   2035.0  430.0    NaN   NaN   NaN  NaN  NaN        NaN  6821.0    NaN  1154.0  NaN    NaN    392.0      NaN   ...       NaN    NaN    NaN  182.0      664.0  12265.0    624.0  184.0   1065.0    492.0    NaN     NaN  214.0  210.0   201.0   910.0  1430.0   197.0     NaN    NaN   NaN      NaN      337.0  NaN   NaN    NaN   NaN      NaN  1114.0     NaN\n",
       "2006    NaN    NaN  8272.0  NaN  NaN    NaN     NaN     NaN    NaN     218.0   NaN  922.0       NaN   NaN    NaN   2201.0  415.0  240.0   NaN   NaN  NaN  NaN        NaN  6765.0    NaN  1097.0  NaN    NaN    443.0      NaN   ...       NaN    NaN    NaN  212.0      686.0  10978.0    524.0  198.0    996.0    419.0    NaN     NaN  227.0  247.0   220.0  1077.0  1405.0   249.0   225.0  194.0   NaN      NaN      334.0  NaN   NaN    NaN   NaN      NaN  1294.0     NaN\n",
       "2007    NaN    NaN  8893.0  NaN  NaN    NaN     NaN     NaN    NaN     225.0   NaN  938.0       NaN   NaN    NaN   2136.0  459.0  280.0   NaN   NaN  NaN  NaN        NaN  6760.0    NaN  1079.0  NaN    NaN    339.0      NaN   ...       NaN    NaN  205.0  275.0      633.0  10179.0    461.0  220.0    946.0    414.0    NaN     NaN  238.0  267.0     NaN  1050.0  1593.0   256.0   428.0  200.0   NaN      NaN      357.0  NaN   NaN    NaN   NaN      NaN  1545.0     NaN\n",
       "2008  959.0  219.0  8496.0  NaN  NaN    NaN     NaN   199.0    NaN     210.0   NaN  863.0       NaN   NaN    NaN   2132.0  475.0  323.0   NaN   NaN  NaN  NaN        NaN  6075.0    NaN  1110.0  NaN    NaN    264.0      NaN   ...       NaN    NaN  193.0  273.0      642.0   9160.0    392.0  236.0    861.0    379.0  219.0   229.0  273.0  253.0     NaN  1119.0  1557.0   303.0   561.0  264.0   NaN      NaN      364.0  NaN   NaN    NaN   NaN      NaN  1586.0     NaN\n",
       "\n",
       "[129 rows x 3437 columns]"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# unstack the column names\n",
    "persons.unstack('name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result = _"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x143fc53d0>"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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04guzo7OfkIieWA8Oi9GTMQGN6bGe2eYOkp8xuguq6DGeSEg80VHkiF0SkGMU\nGUkC4hTHy0FCREux00yzS641rEeSBDSmx3pmbzlodGcCeownEhJPdBTFcNWwHKPISBIQp+gtB8mZ\ngIiebGs6/pCCJyBrBfRGkoDG9FjPHO0aAdBnPJGQeKLDYDBQ5IzNDCE5RpGRJCBO0eYZ/cCwEIMp\ndppp7JFxAb2RJKAxvdUzvcEwvpBCpmV08+v1Fk+kJJ7oOaPAzrYmd9QfV45RZCQJiBMc9fSuEZAL\nrIhom1fq5OOGHq27IU4iSUBjeqtntrqD5NtHXwrSWzyRknii54zCDBq6/XT5QlF9XDlGkRn2iuFQ\nKMR3v/tdrr/+eq688kq2bNnC008/jcFgYPHixVRWVgJQW1vbd8nISNqFNmSNgIiVdKOBmcUONjf0\ncOGEHK27I44ZdhJ47bXXGD9+PNC7Pe+aNWtYtmwZACtWrKCyshJVVVm9enXE7alEb/XMtgi3jNBb\nPJGSeKJrbpmTmignAa1jijZd7h0UCATYsmULCxYswOfz0djYSElJCWZzb9mgqKiIpqYmFEWJSntx\nsVztSSut7iAV2cPfalmIkZhb6uT57a1ad0P0M6wxgZdeeomrrrqq7/qtLpcLu93OqlWr+MMf/oDd\nbqenpydq7alEb/XMSMtBeosnUhJPdI3LseINKlGdKqp1TNGmu3UCHo+HnTt3MmfOHKC3FORwOPB4\nPCxZsoSlS5fidrtxOp1RaxfaaYtgywghhmIwGJhT6uST+tT6sqdnQ5aDdu7cSTAY5PHHH6elpQVF\nUZg2bRqNjY1Ab1I4XsJRFCUq7adTXV3dVzM7njET/Xb/2LTuT0Onve9MIBniicZtiSe6t+eVTWPj\nkW6cbTt18e+RKrcHY1CP13iG4a233sLn83HllVeyefPmvtlBixYtYtasWQBs2bKlb7ZPJO0DWbt2\nLfPmzRtud8UIBUIKN/zfFl64dfaILzAvxHC1uAJ869mdrPrijIS8el0iqqmp4dJLLx3wdyNKAlpL\nxiTQ/8xGa43dfn740l6eunnGqB9DT/FEg8QTG49XHyIYVvn+Zyoifiy9xBQtsYjndElAFouJPq2y\ne6iIkzvml7G12cW7Bzq17krKkySgMT19g2mNwkIxPcUTDRJPbNhMafzgwgp++e5hOjzBiB5LLzFF\ni+wdJDTT4gpQKDODRJzMKHZw2aRcfrexQeuupDRJAhrT0xznZleAImdkSUBP8USDxBNbi2cVUn2g\ni+4I9hMpOltNAAAgAElEQVTSW0yR0t06AZE6WlwBihxyJiDiJ9tmYv7YTF7f0651V1KWJAGN6ame\n2dwToDDCJKCneKJB4om966bn88KONpRRTlTUY0yRkDEBoQlVVeVMQGhiemEGlnQDH8sqYk1IEtCY\nXuqZnb4Q5nQj9ggX7+glnmiReGLPYDBw7bQCXtjRNqz7N3T7+dvmZt4/2EVTj5933tFfTJHQ7fUE\nRHKTswChpUsn5fD7jQ0c6PAyLsc26P28wTDLX6tjYp6NzY091B31YgjbOOho4sLx2YzNsshV8UZI\nVgwLAN7e38G6vR0sv3yC1l0RKeqNPe387qMGHvnsJMqyBt7O/NG3DxJW4YfHVhqrqsqOFg/r9rXz\n3sEuPEGFibk2Fs8qZH55Vjy7r2unWzEsZwICODYoHOH0UCEicdnkXAJhhR+9vJeHrpyIyx9mT5sH\nU5qRiXk2DrR72dbs5jc3nNH3dwwGA9OLMphelMG/nTeWTm+Q2iY3P3v7EL+54YyIJzqkAhkT0Jhe\narTRKgfpJZ5okXji65qp+SyeWcR3n9/N/3xYz+FOP7ta3fzq3cOsqmnkvkvGYzOdOG7VP6Zsm4kL\nxmfz+ZkFPPLWwVHPONKSjAkITTS7AswulWs5CO19bkYB10/Pj6i2v3hmERsOdfPM1lYWzSyMYu+S\nj5wJaEwvc5xbXAGKo3AmoJd4okXi0cZIEsBAMaUZDfzgogr+trmZP33chDcYjmb3YkrWCQhNNEVh\noZgQelLitPD49VM42OHl1jXbeUNWJQ9IkoDG9FCjdflDqIDTEvkFPvQQTzRJPPp3uphKMy3cc8l4\nqq6YyKpNjby8c3hrEbQkeweJuGs+Nigs86tFspqcb+c/r57I/9U0sW6fnBH0N+Q6gb/+9a/s2rUL\no9HIN7/5TQoLC6mtre27JOTixYuprKwEiFr7YGSdQGy8f7CLl3a2UXXlRK27IkRM7W/38uOX93L3\nheWcMzZ11hFEtE7g5ptvBnovOP/cc8/xjW98g9WrV7Ns2TIAVqxYQWVlJaqqRqVdxF9Tj1/GA0RK\nGJ9rY/llE1j+eh0/v3YyY7MHXpSWSoZdDtq7dy9lZWU0NjZSUlKC2WzGbDZTVFREU1NT1NpTjR5q\ntC1RuI7AcXqIJ5okHv0baUzTizK47exSlr9eh8s/+usYxIou1wksX76czs5O/v3f/53m5mbsdjur\nVq1CVVXsdjs9PT19P0faXlxcHOuYxUmaXUGmFmZo3Q0h4ubqM/KoO+phxZsHuOeScTgtqbtkaliR\nP/jgg+zdu5df//rX3HbbbXg8Hm6//XYAVq5cidPpRFGUqLQPpbq6um8e7fGMmei3+8emxfO3uAoo\ndJiTJp5kOz4ST2xuV4YPUu82c/vTXr5+dim25h0YDL2/D4QUnnxlA20BAz+49iwyzGma9zfS24MZ\n9gZybW1tPPnkk/z4xz9m+fLlLFu2DFVVeeihh6iqqkJRlKi0n44MDMfG4j/W8j83TSXXHtlF5oVI\nRL3bUhzhqCdIebaFvAwzGw93MynfRo7NxNYmFz++eBzTEvhsOaKB4ccee4yenh7MZjO33XYbRqOR\nxYsXU1VV1TerB4hae6rpf2ajhQ5PkLCikmOLzumw1vFEm8Sjf5HGdEZBBr/83BSaewLUd/tp6gmw\nZHZR36Bx9f5Olr9Wxx3zy7hscm60uj2oeB+jId/5d9111ylts2bNYtasWTFrF/GzvcXN1EK7rBEQ\nKc1oMFCSaaEk03LK7xaOz6Y8x8rdL+yhxGlmRrFDgx7GjlxPIMX974Z6rOlGvjyvROuuCKFrGw53\n8fN3DvHL6xNvi+rTlYNkxXCK297iTuhapxDxcs7YLG6qLOSB1+sIhBStuxM1kgQ0puW87ZCisrfN\nG9Xpock2D13i0b94xrR4ZiFFDjNP1TTG7Dlk7yARN3VHvRQ7zWREeHF5IVKFwWDgzoVjeW1POzta\n3Fp3JypkTCCFPbu1hQMdPu66oFzrrgiRUN7e38EfNjby3zdOxZqu/+/SMiYgBrSjxc30IhkPEGKk\nLhyfw6Q8Gys/rNe6KxGTJKAxLWu0O1o8TI/yoHCy1ZwlHv3TKqbvnD+Wjxt6eGFHdK9RIGMCIi6O\neoJ4gmHGZJ06L1oIMTSnJZ2qKybyVE0jNfXdWndn1GRMIEVV7+/kld1HeUiuISBERLY09lC19gBV\nV0zQ7UaMMiYgTrFD1gcIERWzSpz8vwvKWfZaHc9tayWBvlcDkgQ0p1U9s8kViEkpKNlqzhKP/ukh\npgUVWTx+/RRe232Un647ENFiMhkTEHHR4QmSY5NdQ4WIltJMC7+4bgoA97yyD3cgrHGPhkeSgMa0\n2tGx3Rsi1x79C2kk2w6VEo/+6Skmc7qRn1w8jvG5vRvOtXuCI36MeMcjSSBFtcuZgBAxYTQY+NaC\nMSwcl8X3X9zD0VEkgniSJKAxLeqZ3mAYFbCbon/49VCfjSaJR//0GJPBYODL80q4bFIuP3xxZGcE\nMiYgYq7dEyLXli7XEBAixpbOLebiiTn88KW9NHb7te7OgCQJaEyLemaHN3alID3VZ6NB4tE/vcf0\n5XklfHZqHnc+v5v1+zqGvH+84xlyZPDJJ5+ksbERVVX51re+RWFhIbW1taxZs6bvspCVlZUAUWsX\nsdXuCcZkUFgIMbAbKwuZUezgp28e4OOGHv51wRjdbDw3ZC/uuOMOli9fzqJFi3j++edRVZXVq1dz\n3333ce+997JmzRqAqLWnGi3qmb0zg2JzJqDH+mwkJB79S5SYpuTb+c0NZ+ALKXznuV3sb/cOeL94\nxzPsr4M2m4309HQaGxspKSnBbO69vFpRURFNTU0oihKV9uLi4mjHKE4iawSE0EaGOY0fX1TB63va\n+eFLezm3PJPZJU5mlzooyNDmkpXD3jto5cqVXHPNNbjdbt577z0MBkPf8ujzzz8fVVWj0j558uRB\n+yB7B0XHo28fZHphBldPzde6K0KkrBZXgA8OdfFJg4stjT04LGnMKnZy9dS8qG/pEvHeQZs2baK0\ntJSysjIcDgcej4clS5awdOlS3G43Tqczau1D6X+qVF1dLbdHcbvdEyLHbtJNf+S23E7F27s/2UBu\n+y7uv2w8q788k+tyOwl3NnD/a3XsO+qJ+vMNZsgzgbq6Oqqrq7nlllsAUBSF5cuXs2zZMlRV5aGH\nHqKqqipq7aeTjGcC1dXVcZ8N8K1nd/K9C8qZkm+P+mNrEU8sSTz6l2wxPfnS+6zvdPLYdVMockan\nRHS6M4EhxwR+/vOfk5eXx4MPPkh5eTm33norixYtoqqqqm9WD4DRaGTx4sURt4vYa/cGybXJ7CAh\n9Gh6Zpj88kLufXUfv7huMg5LbN+rcj2BFBNWVK79/Sf889Y5pBtlsZgQevXLdw/T7Qtx7yXjIl7Y\nKdcTEH26/SEclnRJAELo3L/ML+NIl4+Xdx2N6fNIEtDYcAZuoqndE9tSULzjiTWJR/+SLabj8ZjT\njdxz8Xh+v7GRgx0DrymIBikMp5gOb+/MICGE/pXnWLn9nFK+/+JeZpc4mFqYwUUTssmP4poCGRNI\nMa/tPsonDT388KJxWndFCDFMDd1+tje7qW1y8f7BLr53wVjOq8ge9t+PaHaQSC4d3pCsFhYiwZRm\nWijNtHDZ5FyunOLmP9YdoKa+h9vPKYt4DyIZE9CYJmMCMSwHJWt9NlkkWzyQfDENFc/0ogyeuPEM\nevxhvvn3HXzc0BPR80kSSDHtXtlBVIhE57Ck85OLx/GtBWP42VsHefTtg3R4R3cFMxkTSDHff2EP\nX55XzJzSobfoEELonzsQ5k8fN/H6nnZunl3E52YUnDIFXNYJiD69q4VlTECIZJFhTuOO+WU8eu1k\nPjrSzV3/3M2RLt+w/74kAY3Fu57ZO0VU1gkMl8Sjf8kW02jjKc+28h9XTeTyybnc9c89vLyzbVh/\nT4rDKcQfUgiEFRzmNK27IoSIAYPBwPXTC5hT4uT+1/cRUlSum15w+r8jYwKpo7HHzw9f3MtTN8/Q\nuitCiBhr7PZz9wt7+Oa5ZTg798uYgIA9bR6Ko7Q1rRBC30oyLTx05UR+896R095PkoDG4lXPDCsq\nT9U08fmZhTF9HqnP6luyxQPJF1M045mQZ2P5ZeNPex9JAili3b4OMkxpzB+bqXVXhBBxNKPYcdrf\ny5hACgiGFb7+9A6+f2EFs0pO/4IQQiQfWSeQ4l7edZSxWVZJAEKIUwyZBHbu3MlPfvIT/vjHP/a1\n1dbWcv/997N8+XK2bt0a9fZUEut6pjsQ5s8fN3HrWSUxfZ7jpD6rb8kWDyRfTPGOZ8h1AsFgkBtv\nvJHdu3cDoKoqq1evZtmyZQCsWLGCysrKqLWL6Prr5mbOGpPJpBhcVF4IkfiGTAIzZ85k+/btfbcb\nGxspKSnBbO6dalhUVERTUxOKokSlvbi4OOpB6tnChQtj9tiNPX5e2tnGkzdNi9lznCyW8WhB4tG/\nZIsp3vGMeMWwy+XCbrezatUqVFXFbrfT09PT93Ok7amUBMKKSloMr/X72w0N3FhZSF6G7BUkhBjY\niAeGHQ4HHo+HJUuWsHTpUtxuN06nM2rtQ3n7nU/rZevfruYPr7xPWOmd4FRdXX1CPU3Ptzu9QRat\nquGxJ/43qo//zjvVNHb7eW5bK58cbqesZ29c43viiSfi+nwST2rH079NL/3RazyDGdYU0e3bt7Np\n0ya+8pWvoCgKy5cvZ9myZaiqykMPPURVVVXU2k9n7dq1PNeWxU8uHke7J8SKN/fT4w/jtKTxvYXl\nFDvNPL+9lVd2H+X8imxuObOEjGP75IQUlTRD794ax9U2udjZ4ubGysJTtl49WSCkYI7wCj79Pbu1\nhSc+qOeKQj/fv/7ciB5LUVW2Nbt5q66D9w50oaAys8jBDTMKhpwjHG3V1dVJdXou8ehfssUUi3hO\nN0V0yCTw3HPP8cknn9DV1cW0adO444472Lx5M08//TQGg4FFixYxa9YsALZs2cKaNWsibh/M2rVr\necudz9YmF93+MF+ZV8x10/JZu7eDlRvqCYZVzh+XxdVn5PPq7qN8eLiLSybmsqfNw/YWN3NLndx7\nyThspjR2t3q499V9VGRb8QTD3H1hORPzBh48rTvq5Xv/3M0t84pZNKvo9P/aw/StZ3cyt9TJ5kYX\nv77hjFE/zu5WD4+8fRADcNGEHC4Yn82YLMsJyU4IkdoiSgJ6snbtWubOncvre9oZn2tjcr8ZLy5/\niEBYPeHSiTta3HxwqIsZRRlMK8xg5YcN7D3q4Y75ZfznugPcuXAsC8qzeG1PO/+7oQGbyYgl3UiJ\n08y3zxtLocOMyx/i2//YxWen5vPyrqN8ZkIOt8wrxmAw0OUL0eYO0OUL4QspjMm0UpZlGbLOv7/d\ny72v7GPVF6fzlb9t4+FrJlOebR3Rv0W3L8Rz21r55442vrWgjIsm5MgHvxApKBxSUIH001QqkioJ\nRLJiWFVV/vJJM6s2NXL3heVcMSWv73feYJgObwhfUOHDw138Y1srP7yogn9sa6PAYeLb542lwxvk\nnlf2YTTAUXcQf1ilyGEi05qOOc3IkS4/be4A88qcfP3sUipybPT4QzxV08SuVjdVV0wk05rOkx/W\nk2Y08PWzS7n/7xuYUDGGr51VCsChDh9Wk5FCR++sqf3tXtbUtnC404fNZCTdaOBwp58ef4h5ZZn8\n24Ixuhr4lVNzfUu2eCA5YlIVFcOxL48nx6OqKl53EEVRUFVoberhwO42Du9vp6fLR8AfIi3dSGl5\nNuMm5zN9TikZTssJj3+6JJBS1xMwGAwsnVvM1WfkkXPSxdZtpjRspt7xgwl5NqYWZvAfbx6gJNPM\nfZeOAyDHZuJnn53MjhY35dlWCjJMp3z79gbDvLjzKN9/cS9zSh1sbnCxcFw2k/PtLH+9jhVXTuTN\nve08fM1kAGZmhXh+bwdfPbOEd/Z38qtjO/5lW9PJtZs40OHlhhkFXDctH1+w93oAY7IslGRaMMo3\nfyESlqqqHK5r5+MPDrFvRwvppjTsGWbUND+mcB0lY7NoPNTJ9k8acXX7SD/2+ZSTb2f85HyuuHEG\nWbl2bDYTgUCIQ3Xt1O1s5fe/qGb63FLOuXA8jsyhKwwpdSYwUp3eIOlGAw7LyHNlty/Ey7uOctYY\nJxPz7Ciqyn+tP0jdUS9Wk5Fffa53HEBVVb7x997xgXcOdLDiyomMy7Gx96iHhm4/51VkY4nigLQQ\nIjYC/hAH9rTR0thDfpGDkrFZOJxWwmGFYDCMxxXA3eOn46iHhkMd1B/oIN2UxtwFFUyfUwqouF0B\njra4OFzXTsOhTopKM5k+t5TS8uxhl3td3T4+qj7A1o1HGDshl8p5ZXT76qUcpAfBsMJDbx5g4bgs\nLp/8aSnqr5ub+Me2Nv7rmkkjHhsQQsReS0M3B/a24e7x4+r29/6/x08wEMZiSSfdnEbnUTclY7Mp\nGZNFW4uLxsNdeN0B0tKNpKcbsWWYcTgtZObYKC3PprQ8h7zCjJiN5fl9IXZvbWLrpnrOONskSUCv\nqqurWXDe+fhCSt901kSWDPXZ/iQe/Yt1TPt2tPDKM1uZPqcEZ5aVDKeFDKcFh9OCyZxOwB8iEAiT\nm5+BxRp5hT3eU0RTakxAr9KMhqRIAEIkm6019bz9yi5uumUeJWOzte5OTMiZgBAiYXlcAXq6vPh9\nIQwGA6UV2aSlRT6G5ur28c5ruzlc187nv3YWeYWJvQ27nAkIIRJGKKTgdQdwZg0+Pna0xcWGt/ez\nd3szWbl2zJY0goEw3R1epswsZtK0QkrLs7FYB58+7Xb56Wh1Y7GZcGRaCAUVWhq7aTjYyZaPDjPz\nrDF89c6FUSnx6FlyR5cAkq1GK/Hom17j8XmDHNnfzu5tzdTtbEVVVcoqcjj7gvFY7SYO17VTf7CD\n7k4v7h4/oaDC3AUV3P79C9lUs4GFC88DoLPdw64tjWx4az9N9V1k5dooK8+hrCIHR6aFpvpumo50\n0XSkE78vRG5BBn5fCI8rgMFooLDESWFJJl/61wVkD7KDQKzF+xhJEhBCxJ2qqjQ3dLN7axMHdrfR\ncdRDydhsJk0r4IIrpmC1m9heU8/rz20jHFYon5jHhKkFZOfayXBacGZaSRtg6nR2rp35F01k/kUT\nCYcVWhp7aDjYwd4dzbh7/BSVZjFpWiELL59ETl5G3wKtVCZjAkKIuOnp8lG78QjbauoxGA1MmVHE\nxGmFFJdlDfihLqJDxgSEEHGjKCpH9rfT3NBNe6ub7k4v4VDvgqmudi9nzCrm+qVzKCzNlP2udECS\ngMb0WqMdLYlH32IZj9cTYOumej758BBWq4myihyKyjKZUllEenoa6SYjeUUOzObofuzIMYqMJAEh\nxKioqorHHaDpSBfbaho4sKeNiVML+OwXZlMyNku+5ScIGRMQIsUpiorX3buvjc8XxGg0kpZmwJhm\nxHhs4LS91U1rUw9dHR687gAeV4DOdi9Go4H8IgdTZ5cwdVYJVpt+drQVn5IxASF0RlVV2ppdNB7u\npLm+G3ePn7xCBwXFTmwZJlS19z7H/0+/2xZrOrYMMza7ibR0I2lpRnzeIB1tHro6PIRCCqqi9t1f\nUVR6unx0tLnp7vBiTDOSbjKihNXeD35vEKvNhN1pxmo1oSgq4bCCoqgo4d7HycnPoKDIQcWkfOwZ\nZuwZZrJybdjsZq3/KUWEdJEEamtr+64wtnjxYiorK7XuUtxIPVPfohWPqqh0dXr7dojcs60ZgLJx\nORSVZlI+MY+jLS52bW3C7w1iMBowGACDAYOh9+fj5ZWAL4THHcDrCRAOKYTDChariZx8O9m5dtJN\naRgNBgxGjv1dA9m5diacUcC+/TuYM2c2oZDSu12J04LNbsIYhVW2WpHXXGQ0TwKqqrJ69WqWLVsG\nwIoVK1IqCYjkEQqGcXX76enyHfvjpb3NTVuzi/ZWN1abibzCDErGZvO5L8+loNgZ97p5Q+suCoqd\ncX1OoW+aJ4HGxkZKSkowm3tPK4uKimhqaqK4uFjjnsVHon+DUVWVgD+M3xfE7w1xxsTZtLe5MRoN\nBAPh3h0W/SH8vhDBQBi/r/d2KBTGaOytOZstaVhsJszmdLyewAlb9XqObdcbDivH/qgoYQWLzURu\nfga5+RlkZFqwZ5ixWNMxGnu/+WLglG/QQO+3awz9fj7+CzDwaUMgECLoD5NtH8/2jxswGHq3Mwj4\nQ/i8wb4Pe687gN8Xwu8LEvCHyMi0kpllxZFpxZltpawih9nnjCWv0HHaLQziJdFfbwNJtpjiHY/m\nScDlcmG321m1ahWqqmK32+np6UmZJBBtfXXkYzVhpV89+XiNNxgMEwz0+xP89MO674M7EAZV7f2k\nVNVjH3Qh/N4gfl8I37EPfb8/hMlkxGI19e6xYoBwUCGsqJhMaVis6ZgtaZgt6af8URWVcLi3Xt3a\n1EPAH8ZmN5HhtFBclokj04rdacFsTuurfRvTDKSlGfF6ArS3uuloc+Pu6f3Z7wueWEs/9m/w6b/N\n8f+A2vcfUFH7fj7OZO7tc1q6sffxFJV0UxomcxoWq4niskwmzyjqSz4Wqwmb3SQrUEXC0TwJOBwO\nPB4Pt99+OwArV67E6Rz8dPWpX78H9HvPqmq/n4//79M3df+5T6dMhOp//376vhGe9H4+9Rtl/8c+\nta99z6d+2if1pNs+nw+LxXLih9LxD6r+t08aKFQUte9DTen3oXe8bwaDoa+ubOhXVzYaDZjMaZjM\n6cf+3/vHfOxDz2RJx2xOw+G0YDAY+vqSX2Tq+7Cz2NKxHvu/xZJ+Qj25t5554an/GFFmtZnIycuI\n+fNIvVn/ki2mlBsTKC4uprGxEej9cBuqFDTjvGS78pZe6rMKEDj251RBIBgAVwDoHvxR7HY7NTU1\nMeifNiQe/Uu2mOIdjy7WCWzZsqVvdtCiRYuYNWuW1l0SQoiUoIskIIQQQhuJOzlYCCFExCQJCCFE\nCpMkIIQQKSztgQceeEDLDuzcuZNHH32UpqamvgHhdevWsXLlStavX09hYSGFhYWnba+treU3v/kN\n69evp6CgoK9dKwPF9MYbb/Db3/6W999/nylTpuBw9F64erC+6ymmkcTz5JNP8sILL7B+/XqmT59O\nRkZGQscDEAqF+M53vkN6ejqTJk0C9BUPjCym9vZ2Hn74YdatW8fhw4eZPXs2oK+YRhJPInwuDPS+\nGOl7P2bxqBrbsmWL+uGHH6pPPfVUX9vdd9+thsNh1e12q/fcc89p2xVFUe+77z7V7/erfr9fvf/+\n++Mew8lOjsnv9/f1t7u7W3300UdVVR2873qLabjx9FdbW6s++eSTqqomfjwvvvii+sgjj6ivvPKK\nqqr6i0dVRxbTY489pu7ateuEv6+3mEYST6J8Lqhq7/ti5cqVI37vxzIezctBM2fOPOFbF0B5eTm1\ntbVs3LiROXPmnLa9/7YTZrO5b9sJLZ0ck6qqhMNhQqEQdrudzs5OwuHwoH3XW0zDjac/m82GydS7\nTUIixxMIBNiyZQtnn3123/31Fg8MPyZFUWhubmbKlCkn/H29xTSSY5QonwvQ+75IT08f8Xs/lvFo\nvlhsINOmTeOtt95CUZQTVs4N1J4I205YLBZuvPFGVqxYgc1mw+1243a7B+378Z/1GtNg8WRmZvbd\n58033+Saa64B9H+MThfPSy+9xFVXXUVnZ2ff/fUeDwwek6IoBAIBHnnkEbxeL1dddRXnnHOO7mM6\n3TFKpM+F4++Lkb73Y/mZoLsk0NTUxNatW7nrrrsAePDBB5k5cyYdHR0Dto902wmtzJ8/n/nz5wPw\nox/9iMzMTFwu14B9VxRF9zENFM9xmzZtorS0lLKyMmDkW4NoYaB4PB4PO3fu5IYbbmD9+vV9902E\neGDgmMLhMBkZGdx9990oisKyZcuYM2dOQsQ0UDyDfV7oMZ7+74uGhoYRvfdj+ZmgmySg9u2Xo+Lx\neIDeATm32923h81A7SPddiKe1AHW4dXU1FBRUQEMvmWGoii6jGmoeADq6urYtm0bt9xyS1+bXo/R\nUPHs3LmTYDDI448/TktLC4qiMGPGDEpLS3UZDwwdU1paGnl5eXR2dpKbm9tXskvUY5Qonwsnvy9G\n+t6P5WeC5iuGn3vuOT755BO6urqYNm0ad9xxB8888wybNm0C4PLLL+eiiy4CGLRdb9tODBTTE088\nQUNDA1arle985zt935wH67ueYhpJPN/+9rfJy8vDaDRSXl7OrbfemtDxHPfWW2/h8/m48sorAX3F\nAyOLqa2tjZUrV+LxeFiwYEFf2U5PMY0knkT4XBjofbF582aefvrpYb/3YxWP5klACCGEdjSfHSSE\nEEI7kgSEECKFSRIQQogUJklACCFSmCQBIYRIYZIEhBAihUkSEEKIFCZJQAghUphuto0QQi98Ph93\n3XUXv/rVr0hPTyccDnPnnXfyyCOPYLVa+fOf/8yePXsIh8NcccUVXHjhhX1/d/Xq1ezZs4fu7m5y\ncnK4++67+7ZmWL9+Pdu3b8fn83H06FGmTp3KV77yFa3CFAKQJCDEKaxWK3PnzmXDhg2cd955bNq0\niRkzZmC323n99dcxGAw8+OCDhEIhli9fztSpU/su8HHVVVfxhS98AYCHH36YDRs2cP755/c9dm1t\nLffdd1/f5npCaE2SgBADuOyyy/jLX/7Ceeedx7p167jpppsA2Lx5M62trezduxfovd5AfX19XxJw\nOBxs376dhoYGAoEAHR0dJzzu2WefLQlA6IokASEGMGHCBNxuN3v37qWjo4PJkycDvbtwLl68mLPO\nOuuUv+P3+3nggQc488wzmTp1qi524RRiKDIwLMQgLrnkEh577DEuueSSvrazzz6bf/7zn/h8vlPu\n39DQQHp6OosWLWL8+PHs379/wK2QhdATORMQYhDnn38+f/rTn04Y+F24cCGdnZ088MADmM1mDAYD\nPx1fcxAAAAB9SURBVPnJT7BarVRUVJCXl8cPfvAD8vLymDFjxglXJBNCj2QraSEG8eGHH7Jr164T\nLpAjRLKRMwEhTnLkyBF++9vfkp6ezt133611d4SIKTkTEEKIFCYDw0IIkcIkCQghRAqTJCCEEClM\nkoAQQqQwSQJCCJHCJAkIIUQK+/9O+ygLICOMzgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x145389fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result['John'].plot()\n",
    "result['Juan'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ySabY2rVr8e5rx1C/LLkB3oJiOSxjrgvZTcE32v5uEz5/vw0sFguVtSrwYrzn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\nCIIgCAIgOgRBEAThItEhCIIgCAD8L+sSmQZZ6WVWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14523ff50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#persons.ix[:, 'Yurem'].plot(label ='Alberto')\n",
    "(gpersons.ix[:, 'Mary']/gpersons.ix[:, 'Mary'].max()).plot(label ='Mary')\n",
    "(gpersons.ix[:, 'Maria']/gpersons.ix[:, 'Maria'].max()).plot(label ='Maria')\n",
    "(gpersons.ix[:, 'Mia']/gpersons.ix[:, 'Mia'].max()).plot(label ='Mia')\n",
    "plt.legend(loc= 'best')\n",
    "plt.ylabel('Popul')\n",
    "plt.savefig('names.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Elections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "parties = {'Bachmann, Michelle': 'Republican',\n",
    "           'Cain, Herman': 'Republican',\n",
    "           'Gingrich, Newt': 'Republican',\n",
    "           'Huntsman, Jon': 'Republican',\n",
    "           'Johnson, Gary Earl': 'Republican',\n",
    "           'McCotter, Thaddeus G': 'Republican',\n",
    "           'Obama, Barack': 'Democrat',\n",
    "           'Paul, Ron': 'Republican',\n",
    "           'Pawlenty, Timothy': 'Republican',\n",
    "           'Perry, Rick': 'Republican',\n",
    "           \"Roemer, Charles E. 'Buddy' III\": 'Republican',\n",
    "           'Romney, Mitt': 'Republican',\n",
    "           'Santorum, Rick': 'Republican'}\n",
    "months = {'JAN': 1, 'FEB': 2, 'MAR':3, 'APR': 4, 'MAY':5, 'JUN':6, \n",
    "             'JUL':7, 'AUG':8, 'SEP':9, 'OCT':10, 'NOV':11, 'DEC':12}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fec = pd.read_csv('data/P00000001-ALL.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cmte_id</th>\n",
       "      <th>cand_id</th>\n",
       "      <th>cand_nm</th>\n",
       "      <th>contbr_nm</th>\n",
       "      <th>contbr_city</th>\n",
       "      <th>contbr_st</th>\n",
       "      <th>contbr_zip</th>\n",
       "      <th>contbr_employer</th>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th>contb_receipt_dt</th>\n",
       "      <th>receipt_desc</th>\n",
       "      <th>memo_cd</th>\n",
       "      <th>memo_text</th>\n",
       "      <th>form_tp</th>\n",
       "      <th>file_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>HARVEY, WILLIAM</td>\n",
       "      <td>MOBILE</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.6601e+08</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>20-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>HARVEY, WILLIAM</td>\n",
       "      <td>MOBILE</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.6601e+08</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>50.0</td>\n",
       "      <td>23-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>SMITH, LANIER</td>\n",
       "      <td>LANETT</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.68633e+08</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>05-JUL-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>749073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>BLEVINS, DARONDA</td>\n",
       "      <td>PIGGOTT</td>\n",
       "      <td>AR</td>\n",
       "      <td>7.24548e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>01-AUG-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>749073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>WARDENBURG, HAROLD</td>\n",
       "      <td>HOT SPRINGS NATION</td>\n",
       "      <td>AR</td>\n",
       "      <td>7.19016e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>300.0</td>\n",
       "      <td>20-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cmte_id    cand_id             cand_nm           contbr_nm  \\\n",
       "0  C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   \n",
       "1  C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   \n",
       "2  C00410118  P20002978  Bachmann, Michelle       SMITH, LANIER   \n",
       "3  C00410118  P20002978  Bachmann, Michelle    BLEVINS, DARONDA   \n",
       "4  C00410118  P20002978  Bachmann, Michelle  WARDENBURG, HAROLD   \n",
       "\n",
       "          contbr_city contbr_st   contbr_zip        contbr_employer  \\\n",
       "0              MOBILE        AL   3.6601e+08                RETIRED   \n",
       "1              MOBILE        AL   3.6601e+08                RETIRED   \n",
       "2              LANETT        AL  3.68633e+08  INFORMATION REQUESTED   \n",
       "3             PIGGOTT        AR  7.24548e+08                   NONE   \n",
       "4  HOT SPRINGS NATION        AR  7.19016e+08                   NONE   \n",
       "\n",
       "       contbr_occupation  contb_receipt_amt contb_receipt_dt receipt_desc  \\\n",
       "0                RETIRED              250.0        20-JUN-11          NaN   \n",
       "1                RETIRED               50.0        23-JUN-11          NaN   \n",
       "2  INFORMATION REQUESTED              250.0        05-JUL-11          NaN   \n",
       "3                RETIRED              250.0        01-AUG-11          NaN   \n",
       "4                RETIRED              300.0        20-JUN-11          NaN   \n",
       "\n",
       "  memo_cd memo_text form_tp  file_num  \n",
       "0     NaN       NaN   SA17A    736166  \n",
       "1     NaN       NaN   SA17A    736166  \n",
       "2     NaN       NaN   SA17A    749073  \n",
       "3     NaN       NaN   SA17A    749073  \n",
       "4     NaN       NaN   SA17A    736166  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fec['party'] = fec.cand_nm.map(parties)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cmte_id                 C00410118\n",
       "cand_id                 P20002978\n",
       "cand_nm        Bachmann, Michelle\n",
       "contbr_nm         HARVEY, WILLIAM\n",
       "contbr_city                MOBILE\n",
       "                      ...        \n",
       "memo_cd                       NaN\n",
       "memo_text                     NaN\n",
       "form_tp                     SA17A\n",
       "file_num                   736166\n",
       "party                  Republican\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.ix[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Democrat      593746\n",
       "Republican    407985\n",
       "Name: party, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cumulatives over the parties\n",
    "\n",
    "fec.party.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "party\n",
       "Democrat      1.335026e+08\n",
       "Republican    1.652488e+08\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.groupby('party')['contb_receipt_amt'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'20-JUN-11'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.contb_receipt_dt[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Convert dates to pythonic way\n",
    "import datetime as dt\n",
    "\n",
    "def convert_date(val):\n",
    "    x = val.split('-')\n",
    "    return dt.datetime(int('20'+ x[2]), months[x[1]], int(x[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fec.contb_receipt_dt= fec.contb_receipt_dt.map(convert_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "party       contb_receipt_dt\n",
       "Democrat    2011-04-04          642985.12\n",
       "            2011-04-05          308691.00\n",
       "            2011-04-06          247542.59\n",
       "            2011-04-07          252586.00\n",
       "            2011-04-08          295802.00\n",
       "                                  ...    \n",
       "Republican  2012-04-26          507141.33\n",
       "            2012-04-27          594893.56\n",
       "            2012-04-28           80869.16\n",
       "            2012-04-29           61421.02\n",
       "            2012-04-30          787737.81\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#group donations by date\n",
    "\n",
    "by_date = fec.groupby(['party','contb_receipt_dt'])['contb_receipt_amt'].sum()\n",
    "by_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1405877d0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JMYpL2eyYhSAIfdeprFLOnCpn8W9uROvsqHQcizOaJP66J58bIz1VUSi6I7qh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\nz3Lh1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114109690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Separate parties\n",
    "\n",
    "by_date.unstack('party').fillna(0).cumsum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th>file_num</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cand_nm</th>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Bachmann, Michelle</th>\n",
       "      <th>--</th>\n",
       "      <td>75.00</td>\n",
       "      <td>772097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100% DISABLED VETERAN</th>\n",
       "      <td>1869.50</td>\n",
       "      <td>6807894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100% DISABLED VIETNAM VETERAN</th>\n",
       "      <td>236.25</td>\n",
       "      <td>2260398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A/C AND HEAT</th>\n",
       "      <td>250.00</td>\n",
       "      <td>749016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACADEMIC EMPLOYMENT SPECIALIST</th>\n",
       "      <td>25.00</td>\n",
       "      <td>762366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Santorum, Rick</th>\n",
       "      <th>WRITER/PROFESSOR</th>\n",
       "      <td>910.00</td>\n",
       "      <td>12501040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X-RAY TECHNICIAN</th>\n",
       "      <td>2500.00</td>\n",
       "      <td>771852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XRAY TECH</th>\n",
       "      <td>380.00</td>\n",
       "      <td>3884634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTH MINISTER</th>\n",
       "      <td>475.00</td>\n",
       "      <td>2333109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTH MINISTRY</th>\n",
       "      <td>250.00</td>\n",
       "      <td>771852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>59313 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   contb_receipt_amt  file_num\n",
       "cand_nm            contbr_occupation                                          \n",
       "Bachmann, Michelle --                                          75.00    772097\n",
       "                   100% DISABLED VETERAN                     1869.50   6807894\n",
       "                   100% DISABLED VIETNAM VETERAN              236.25   2260398\n",
       "                   A/C AND HEAT                               250.00    749016\n",
       "                   ACADEMIC EMPLOYMENT SPECIALIST              25.00    762366\n",
       "...                                                              ...       ...\n",
       "Santorum, Rick     WRITER/PROFESSOR                           910.00  12501040\n",
       "                   X-RAY TECHNICIAN                          2500.00    771852\n",
       "                   XRAY TECH                                  380.00   3884634\n",
       "                   YOUTH MINISTER                             475.00   2333109\n",
       "                   YOUTH MINISTRY                             250.00    771852\n",
       "\n",
       "[59313 rows x 2 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = fec.groupby(['cand_nm', 'contbr_occupation']).sum()\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result =result.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "cand_nm                  \n",
       "Bachmann, Michelle  943                                   PRESIDENT\n",
       "                    915                                   PHYSICIAN\n",
       "                    569                                   HOMEMAKER\n",
       "                    610                       INFORMATION REQUESTED\n",
       "                    1084                                    RETIRED\n",
       "                                              ...                  \n",
       "Santorum, Rick      57975                                 PHYSICIAN\n",
       "                    57129      INFORMATION REQUESTED (BEST EFFORTS)\n",
       "                    57131    INFORMATION REQUESTED PER BEST EFFORTS\n",
       "                    57037                                 HOMEMAKER\n",
       "                    58452                                   RETIRED\n",
       "Name: contbr_occupation, dtype: object"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def top5_donors(group):\n",
    "    return group.sort_index(by='contb_receipt_amt')[-5:]\n",
    "result.groupby('cand_nm').apply(top5_donors)['contbr_occupation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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