notebooks/03-visualisation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%html\n",
    "<link rel=\"stylesheet\" href=\"static/hyrule.css\" type=\"text/css\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# pylab inline (either in the notebook or as an argument starting the notebook)\n",
    "# will by default load numpy as np and matplotlib.pylab as plt\n",
    "# Let's import everything else we used last time as well:\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from __future__ import division\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "pd.options.display.mpl_style = 'default'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration and Visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* learn how to group and aggregate data in pandas\n",
    "* learn how to visualise data with pandas\n",
    "* explain the difference between cast and melt in terms of a data frame\n",
    "* explore common visual relationships in data\n",
    "* learn two visualisation packages in python independent of pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code Dictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional Pandas Code\n",
    "\n",
    "Function         | Description\n",
    "-----------------|------------\n",
    "`.groupby()`     | Creates a groupby object in pandas. pass in either a string (one column) or a list (multiple columns)\n",
    "`.agg()`         | Aggregates data into a new DataFrame from a groupby object\n",
    "`.pivot_table()` | generates a pivot table, akin to those you'd expect in excel.\n",
    "`melt()`      | melts a DataFrame\n",
    "`.plot()`        | general plot using matplotlib of the columns provided, using the index as the x-axis\n",
    "`.hist()`        | generates a histogram using matplotlib of the columns provided, using the index as the x-axis\n",
    "`.boxplot()`     | generates a boxplot, which displays distribution via quartiles and outliers\n",
    "`pd.tools.plotting.scatter_matrix()` | a representation of histograms and scatterplots between columns <br /> Very common to import seperately using from pd.tools.plotting import scatter_matrix\n",
    "\n",
    "<br />\n",
    "### Matplotlib Basics\n",
    "\n",
    "Run this first to generate some data! They'll relate to the matplotlib basics below.\n",
    "\n",
    "```py\n",
    "X = np.linspace(-np.pi, np.pi, 256, endpoint=True)\n",
    "C, S = np.cos(X), np.sin(X)\n",
    "```\n",
    "\n",
    "Matplotlib functions will start with `plt`. In science we work with the concept of a `figure`, which is the space in which plots exist. While the below code is for `plot`, others are common as well (such as `hist`)\n",
    "\n",
    "Code | Description\n",
    "-----|------------\n",
    "`plt.figure(figsize=(8, 6), dpi=80)` | Sets a figure size of 8 by 6 inches, with a dpi of 80\n",
    "`plt.subplot(1, 1, 1)` | Create a new subplot from a grid of 1x1\n",
    "`plt.plot(X, C, color=\"blue\", linewidth=1.0, linestyle=\"-\")` | Plots cosine with a blue continuous line of width 1 (pixels)\n",
    "`plt.plot(X, S, color=\"green\", linewidth=1.0, linestyle=\"-\")` | Plots sine with a green continuous line of width 1 (pixels)\n",
    "`plt.xlim(-4.0, 4.0)` | Set x limits\n",
    "`plt.xticks(np.linspace(-4, 4, 9, endpoint=True))` | set x ticks\n",
    "`plt.ylim(-1.0, 1.0)` | Set y limits\n",
    "`plt.yticks(np.linspace(-1, 1, 5, endpoint=True))` | Set y ticks\n",
    "`plt.savefig(\"exercice_2.png\", dpi=72)` | Save figure using 72 dots per inch\n",
    "`plt.show()` | Show result on screen\n",
    "\n",
    "### Seaborn Basics\n",
    "\n",
    "We'll need to install seaborn first. You can do this with conda, a package installer for Anaconda. With this, conda might also update other packages. seaborn, once imported, takes over default matplotlib figures\n",
    "\n",
    "```sh\n",
    "cd ~/anaconda/bin\n",
    "conda install seaborn\n",
    "```\n",
    "\n",
    "Code | Description\n",
    "-----|------------\n",
    "`.set_style()`  | changes the current seaborn style. pass in parameters using a dictionary to update settings.\n",
    "`.axes_style()` | returns a dictionary of the current styles settings \n",
    "`.despine()`    | removes the top and right ticks on a chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lecture Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring the Wine Data Set\n",
    "\n",
    "wine data set: https://archive.ics.uci.edu/ml/machine-learning-databases/wine/\n",
    "\n",
    "### Aggregating Data\n",
    "\n",
    "Last class was heavily focused on manipulating and subsetting individual observations in a data set. Exploring the raw data can be benefited by exploring the data as a whole through grouping and aggregations. Data aggregations and segmentations allow us to better understand and evaluate the properties of our data, particularly when we have a target variable in mind."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity of ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315 of diluted wines</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 1</td>\n",
       "      <td> 14.23</td>\n",
       "      <td> 1.71</td>\n",
       "      <td> 2.43</td>\n",
       "      <td> 15.6</td>\n",
       "      <td> 127</td>\n",
       "      <td> 2.80</td>\n",
       "      <td> 3.06</td>\n",
       "      <td> 0.28</td>\n",
       "      <td> 2.29</td>\n",
       "      <td> 5.64</td>\n",
       "      <td> 1.04</td>\n",
       "      <td> 3.92</td>\n",
       "      <td> 1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 1</td>\n",
       "      <td> 13.20</td>\n",
       "      <td> 1.78</td>\n",
       "      <td> 2.14</td>\n",
       "      <td> 11.2</td>\n",
       "      <td> 100</td>\n",
       "      <td> 2.65</td>\n",
       "      <td> 2.76</td>\n",
       "      <td> 0.26</td>\n",
       "      <td> 1.28</td>\n",
       "      <td> 4.38</td>\n",
       "      <td> 1.05</td>\n",
       "      <td> 3.40</td>\n",
       "      <td> 1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 1</td>\n",
       "      <td> 13.16</td>\n",
       "      <td> 2.36</td>\n",
       "      <td> 2.67</td>\n",
       "      <td> 18.6</td>\n",
       "      <td> 101</td>\n",
       "      <td> 2.80</td>\n",
       "      <td> 3.24</td>\n",
       "      <td> 0.30</td>\n",
       "      <td> 2.81</td>\n",
       "      <td> 5.68</td>\n",
       "      <td> 1.03</td>\n",
       "      <td> 3.17</td>\n",
       "      <td> 1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 1</td>\n",
       "      <td> 14.37</td>\n",
       "      <td> 1.95</td>\n",
       "      <td> 2.50</td>\n",
       "      <td> 16.8</td>\n",
       "      <td> 113</td>\n",
       "      <td> 3.85</td>\n",
       "      <td> 3.49</td>\n",
       "      <td> 0.24</td>\n",
       "      <td> 2.18</td>\n",
       "      <td> 7.80</td>\n",
       "      <td> 0.86</td>\n",
       "      <td> 3.45</td>\n",
       "      <td> 1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 1</td>\n",
       "      <td> 13.24</td>\n",
       "      <td> 2.59</td>\n",
       "      <td> 2.87</td>\n",
       "      <td> 21.0</td>\n",
       "      <td> 118</td>\n",
       "      <td> 2.80</td>\n",
       "      <td> 2.69</td>\n",
       "      <td> 0.39</td>\n",
       "      <td> 1.82</td>\n",
       "      <td> 4.32</td>\n",
       "      <td> 1.04</td>\n",
       "      <td> 2.93</td>\n",
       "      <td>  735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   class  alcohol  malic acid   ash  alcalinity of ash  magnesium  total phenols  flavanoids  nonflavanoid phenols  proanthocyanins  color intensity   hue  od280/od315 of diluted wines  proline\n",
       "0      1    14.23        1.71  2.43               15.6        127           2.80        3.06                  0.28             2.29             5.64  1.04                          3.92     1065\n",
       "1      1    13.20        1.78  2.14               11.2        100           2.65        2.76                  0.26             1.28             4.38  1.05                          3.40     1050\n",
       "2      1    13.16        2.36  2.67               18.6        101           2.80        3.24                  0.30             2.81             5.68  1.03                          3.17     1185\n",
       "3      1    14.37        1.95  2.50               16.8        113           3.85        3.49                  0.24             2.18             7.80  0.86                          3.45     1480\n",
       "4      1    13.24        2.59  2.87               21.0        118           2.80        2.69                  0.39             1.82             4.32  1.04                          2.93      735"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = \"\"\"-- The attributes are (dontated by Riccardo Leardi, \n",
    "    riclea@anchem.unige.it )\n",
    "    1) Alcohol\n",
    "    2) Malic acid\n",
    "    3) Ash\n",
    "    4) Alcalinity of ash  \n",
    "    5) Magnesium\n",
    "    6) Total phenols\n",
    "    7) Flavanoids\n",
    "    8) Nonflavanoid phenols\n",
    "    9) Proanthocyanins\n",
    "    10)Color intensity\n",
    "    11)Hue\n",
    "    12)OD280/OD315 of diluted wines\n",
    "    13)Proline\"\"\"\n",
    "\n",
    "# we can generate the columns list using some string and list functions.\n",
    "columns = columns.split('\\n')\n",
    "columns = [i.strip() for i in columns][2:]\n",
    "columns = [i.split(')')[1].strip().lower() for i in columns]\n",
    "cols = ['class']\n",
    "cols.extend(columns)\n",
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data'\n",
    "wine = pd.read_csv(url, header=None)\n",
    "wine.columns = cols\n",
    "wine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.groupby.DataFrameGroupBy'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>alcalinity of ash</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>ash</th>\n",
       "      <th>color intensity</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>hue</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>malic acid</th>\n",
       "      <th>nonflavanoid phenols</th>\n",
       "      <th>od280/od315 of diluted wines</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>proline</th>\n",
       "      <th>total phenols</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</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 rowspan=\"8\" valign=\"top\">1</th>\n",
       "      <th>count</th>\n",
       "      <td> 59.000000</td>\n",
       "      <td> 59.000000</td>\n",
       "      <td> 59.000000</td>\n",
       "      <td> 59.000000</td>\n",
       "      <td> 59.000000</td>\n",
       "      <td> 59.000000</td>\n",
       "      <td>  59.000000</td>\n",
       "      <td> 59.000000</td>\n",
       "      <td> 59.000000</td>\n",
       "      <td> 59.000000</td>\n",
       "      <td> 59.000000</td>\n",
       "      <td>   59.000000</td>\n",
       "      <td> 59.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td> 17.037288</td>\n",
       "      <td> 13.744746</td>\n",
       "      <td>  2.455593</td>\n",
       "      <td>  5.528305</td>\n",
       "      <td>  2.982373</td>\n",
       "      <td>  1.062034</td>\n",
       "      <td> 106.338983</td>\n",
       "      <td>  2.010678</td>\n",
       "      <td>  0.290000</td>\n",
       "      <td>  3.157797</td>\n",
       "      <td>  1.899322</td>\n",
       "      <td> 1115.711864</td>\n",
       "      <td>  2.840169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>  2.546322</td>\n",
       "      <td>  0.462125</td>\n",
       "      <td>  0.227166</td>\n",
       "      <td>  1.238573</td>\n",
       "      <td>  0.397494</td>\n",
       "      <td>  0.116483</td>\n",
       "      <td>  10.498949</td>\n",
       "      <td>  0.688549</td>\n",
       "      <td>  0.070049</td>\n",
       "      <td>  0.357077</td>\n",
       "      <td>  0.412109</td>\n",
       "      <td>  221.520767</td>\n",
       "      <td>  0.338961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td> 11.200000</td>\n",
       "      <td> 12.850000</td>\n",
       "      <td>  2.040000</td>\n",
       "      <td>  3.520000</td>\n",
       "      <td>  2.190000</td>\n",
       "      <td>  0.820000</td>\n",
       "      <td>  89.000000</td>\n",
       "      <td>  1.350000</td>\n",
       "      <td>  0.170000</td>\n",
       "      <td>  2.510000</td>\n",
       "      <td>  1.250000</td>\n",
       "      <td>  680.000000</td>\n",
       "      <td>  2.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td> 16.000000</td>\n",
       "      <td> 13.400000</td>\n",
       "      <td>  2.295000</td>\n",
       "      <td>  4.550000</td>\n",
       "      <td>  2.680000</td>\n",
       "      <td>  0.995000</td>\n",
       "      <td>  98.000000</td>\n",
       "      <td>  1.665000</td>\n",
       "      <td>  0.255000</td>\n",
       "      <td>  2.870000</td>\n",
       "      <td>  1.640000</td>\n",
       "      <td>  987.500000</td>\n",
       "      <td>  2.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td> 16.800000</td>\n",
       "      <td> 13.750000</td>\n",
       "      <td>  2.440000</td>\n",
       "      <td>  5.400000</td>\n",
       "      <td>  2.980000</td>\n",
       "      <td>  1.070000</td>\n",
       "      <td> 104.000000</td>\n",
       "      <td>  1.770000</td>\n",
       "      <td>  0.290000</td>\n",
       "      <td>  3.170000</td>\n",
       "      <td>  1.870000</td>\n",
       "      <td> 1095.000000</td>\n",
       "      <td>  2.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td> 18.700000</td>\n",
       "      <td> 14.100000</td>\n",
       "      <td>  2.615000</td>\n",
       "      <td>  6.225000</td>\n",
       "      <td>  3.245000</td>\n",
       "      <td>  1.130000</td>\n",
       "      <td> 114.000000</td>\n",
       "      <td>  1.935000</td>\n",
       "      <td>  0.320000</td>\n",
       "      <td>  3.420000</td>\n",
       "      <td>  2.090000</td>\n",
       "      <td> 1280.000000</td>\n",
       "      <td>  3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td> 25.000000</td>\n",
       "      <td> 14.830000</td>\n",
       "      <td>  3.220000</td>\n",
       "      <td>  8.900000</td>\n",
       "      <td>  3.930000</td>\n",
       "      <td>  1.280000</td>\n",
       "      <td> 132.000000</td>\n",
       "      <td>  4.040000</td>\n",
       "      <td>  0.500000</td>\n",
       "      <td>  4.000000</td>\n",
       "      <td>  2.960000</td>\n",
       "      <td> 1680.000000</td>\n",
       "      <td>  3.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">2</th>\n",
       "      <th>count</th>\n",
       "      <td> 71.000000</td>\n",
       "      <td> 71.000000</td>\n",
       "      <td> 71.000000</td>\n",
       "      <td> 71.000000</td>\n",
       "      <td> 71.000000</td>\n",
       "      <td> 71.000000</td>\n",
       "      <td>  71.000000</td>\n",
       "      <td> 71.000000</td>\n",
       "      <td> 71.000000</td>\n",
       "      <td> 71.000000</td>\n",
       "      <td> 71.000000</td>\n",
       "      <td>   71.000000</td>\n",
       "      <td> 71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td> 20.238028</td>\n",
       "      <td> 12.278732</td>\n",
       "      <td>  2.244789</td>\n",
       "      <td>  3.086620</td>\n",
       "      <td>  2.080845</td>\n",
       "      <td>  1.056282</td>\n",
       "      <td>  94.549296</td>\n",
       "      <td>  1.932676</td>\n",
       "      <td>  0.363662</td>\n",
       "      <td>  2.785352</td>\n",
       "      <td>  1.630282</td>\n",
       "      <td>  519.507042</td>\n",
       "      <td>  2.258873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>  3.349770</td>\n",
       "      <td>  0.537964</td>\n",
       "      <td>  0.315467</td>\n",
       "      <td>  0.924929</td>\n",
       "      <td>  0.705701</td>\n",
       "      <td>  0.202937</td>\n",
       "      <td>  16.753497</td>\n",
       "      <td>  1.015569</td>\n",
       "      <td>  0.123961</td>\n",
       "      <td>  0.496573</td>\n",
       "      <td>  0.602068</td>\n",
       "      <td>  157.211220</td>\n",
       "      <td>  0.545361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td> 10.600000</td>\n",
       "      <td> 11.030000</td>\n",
       "      <td>  1.360000</td>\n",
       "      <td>  1.280000</td>\n",
       "      <td>  0.570000</td>\n",
       "      <td>  0.690000</td>\n",
       "      <td>  70.000000</td>\n",
       "      <td>  0.740000</td>\n",
       "      <td>  0.130000</td>\n",
       "      <td>  1.590000</td>\n",
       "      <td>  0.410000</td>\n",
       "      <td>  278.000000</td>\n",
       "      <td>  1.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td> 18.000000</td>\n",
       "      <td> 11.915000</td>\n",
       "      <td>  2.000000</td>\n",
       "      <td>  2.535000</td>\n",
       "      <td>  1.605000</td>\n",
       "      <td>  0.925000</td>\n",
       "      <td>  85.500000</td>\n",
       "      <td>  1.270000</td>\n",
       "      <td>  0.270000</td>\n",
       "      <td>  2.440000</td>\n",
       "      <td>  1.350000</td>\n",
       "      <td>  406.500000</td>\n",
       "      <td>  1.895000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td> 20.000000</td>\n",
       "      <td> 12.290000</td>\n",
       "      <td>  2.240000</td>\n",
       "      <td>  2.900000</td>\n",
       "      <td>  2.030000</td>\n",
       "      <td>  1.040000</td>\n",
       "      <td>  88.000000</td>\n",
       "      <td>  1.610000</td>\n",
       "      <td>  0.370000</td>\n",
       "      <td>  2.830000</td>\n",
       "      <td>  1.610000</td>\n",
       "      <td>  495.000000</td>\n",
       "      <td>  2.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td> 22.000000</td>\n",
       "      <td> 12.515000</td>\n",
       "      <td>  2.420000</td>\n",
       "      <td>  3.400000</td>\n",
       "      <td>  2.475000</td>\n",
       "      <td>  1.205000</td>\n",
       "      <td>  99.500000</td>\n",
       "      <td>  2.145000</td>\n",
       "      <td>  0.430000</td>\n",
       "      <td>  3.160000</td>\n",
       "      <td>  1.885000</td>\n",
       "      <td>  625.000000</td>\n",
       "      <td>  2.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td> 30.000000</td>\n",
       "      <td> 13.860000</td>\n",
       "      <td>  3.230000</td>\n",
       "      <td>  6.000000</td>\n",
       "      <td>  5.080000</td>\n",
       "      <td>  1.710000</td>\n",
       "      <td> 162.000000</td>\n",
       "      <td>  5.800000</td>\n",
       "      <td>  0.660000</td>\n",
       "      <td>  3.690000</td>\n",
       "      <td>  3.580000</td>\n",
       "      <td>  985.000000</td>\n",
       "      <td>  3.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">3</th>\n",
       "      <th>count</th>\n",
       "      <td> 48.000000</td>\n",
       "      <td> 48.000000</td>\n",
       "      <td> 48.000000</td>\n",
       "      <td> 48.000000</td>\n",
       "      <td> 48.000000</td>\n",
       "      <td> 48.000000</td>\n",
       "      <td>  48.000000</td>\n",
       "      <td> 48.000000</td>\n",
       "      <td> 48.000000</td>\n",
       "      <td> 48.000000</td>\n",
       "      <td> 48.000000</td>\n",
       "      <td>   48.000000</td>\n",
       "      <td> 48.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td> 21.416667</td>\n",
       "      <td> 13.153750</td>\n",
       "      <td>  2.437083</td>\n",
       "      <td>  7.396250</td>\n",
       "      <td>  0.781458</td>\n",
       "      <td>  0.682708</td>\n",
       "      <td>  99.312500</td>\n",
       "      <td>  3.333750</td>\n",
       "      <td>  0.447500</td>\n",
       "      <td>  1.683542</td>\n",
       "      <td>  1.153542</td>\n",
       "      <td>  629.895833</td>\n",
       "      <td>  1.678750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>  2.258161</td>\n",
       "      <td>  0.530241</td>\n",
       "      <td>  0.184690</td>\n",
       "      <td>  2.310942</td>\n",
       "      <td>  0.293504</td>\n",
       "      <td>  0.114441</td>\n",
       "      <td>  10.890473</td>\n",
       "      <td>  1.087906</td>\n",
       "      <td>  0.124140</td>\n",
       "      <td>  0.272111</td>\n",
       "      <td>  0.408836</td>\n",
       "      <td>  115.097043</td>\n",
       "      <td>  0.356971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td> 17.500000</td>\n",
       "      <td> 12.200000</td>\n",
       "      <td>  2.100000</td>\n",
       "      <td>  3.850000</td>\n",
       "      <td>  0.340000</td>\n",
       "      <td>  0.480000</td>\n",
       "      <td>  80.000000</td>\n",
       "      <td>  1.240000</td>\n",
       "      <td>  0.170000</td>\n",
       "      <td>  1.270000</td>\n",
       "      <td>  0.550000</td>\n",
       "      <td>  415.000000</td>\n",
       "      <td>  0.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td> 20.000000</td>\n",
       "      <td> 12.805000</td>\n",
       "      <td>  2.300000</td>\n",
       "      <td>  5.437500</td>\n",
       "      <td>  0.580000</td>\n",
       "      <td>  0.587500</td>\n",
       "      <td>  89.750000</td>\n",
       "      <td>  2.587500</td>\n",
       "      <td>  0.397500</td>\n",
       "      <td>  1.510000</td>\n",
       "      <td>  0.855000</td>\n",
       "      <td>  545.000000</td>\n",
       "      <td>  1.407500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td> 21.000000</td>\n",
       "      <td> 13.165000</td>\n",
       "      <td>  2.380000</td>\n",
       "      <td>  7.550000</td>\n",
       "      <td>  0.685000</td>\n",
       "      <td>  0.665000</td>\n",
       "      <td>  97.000000</td>\n",
       "      <td>  3.265000</td>\n",
       "      <td>  0.470000</td>\n",
       "      <td>  1.660000</td>\n",
       "      <td>  1.105000</td>\n",
       "      <td>  627.500000</td>\n",
       "      <td>  1.635000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td> 23.000000</td>\n",
       "      <td> 13.505000</td>\n",
       "      <td>  2.602500</td>\n",
       "      <td>  9.225000</td>\n",
       "      <td>  0.920000</td>\n",
       "      <td>  0.752500</td>\n",
       "      <td> 106.000000</td>\n",
       "      <td>  3.957500</td>\n",
       "      <td>  0.530000</td>\n",
       "      <td>  1.820000</td>\n",
       "      <td>  1.350000</td>\n",
       "      <td>  695.000000</td>\n",
       "      <td>  1.807500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td> 27.000000</td>\n",
       "      <td> 14.340000</td>\n",
       "      <td>  2.860000</td>\n",
       "      <td> 13.000000</td>\n",
       "      <td>  1.570000</td>\n",
       "      <td>  0.960000</td>\n",
       "      <td> 123.000000</td>\n",
       "      <td>  5.650000</td>\n",
       "      <td>  0.630000</td>\n",
       "      <td>  2.470000</td>\n",
       "      <td>  2.700000</td>\n",
       "      <td>  880.000000</td>\n",
       "      <td>  2.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             alcalinity of ash    alcohol        ash  color intensity  flavanoids        hue   magnesium  malic acid  nonflavanoid phenols  od280/od315 of diluted wines  proanthocyanins      proline  total phenols\n",
       "class                                                                                                                                                                                                                \n",
       "1     count          59.000000  59.000000  59.000000        59.000000   59.000000  59.000000   59.000000   59.000000             59.000000                     59.000000        59.000000    59.000000      59.000000\n",
       "      mean           17.037288  13.744746   2.455593         5.528305    2.982373   1.062034  106.338983    2.010678              0.290000                      3.157797         1.899322  1115.711864       2.840169\n",
       "      std             2.546322   0.462125   0.227166         1.238573    0.397494   0.116483   10.498949    0.688549              0.070049                      0.357077         0.412109   221.520767       0.338961\n",
       "      min            11.200000  12.850000   2.040000         3.520000    2.190000   0.820000   89.000000    1.350000              0.170000                      2.510000         1.250000   680.000000       2.200000\n",
       "      25%            16.000000  13.400000   2.295000         4.550000    2.680000   0.995000   98.000000    1.665000              0.255000                      2.870000         1.640000   987.500000       2.600000\n",
       "      50%            16.800000  13.750000   2.440000         5.400000    2.980000   1.070000  104.000000    1.770000              0.290000                      3.170000         1.870000  1095.000000       2.800000\n",
       "      75%            18.700000  14.100000   2.615000         6.225000    3.245000   1.130000  114.000000    1.935000              0.320000                      3.420000         2.090000  1280.000000       3.000000\n",
       "      max            25.000000  14.830000   3.220000         8.900000    3.930000   1.280000  132.000000    4.040000              0.500000                      4.000000         2.960000  1680.000000       3.880000\n",
       "2     count          71.000000  71.000000  71.000000        71.000000   71.000000  71.000000   71.000000   71.000000             71.000000                     71.000000        71.000000    71.000000      71.000000\n",
       "      mean           20.238028  12.278732   2.244789         3.086620    2.080845   1.056282   94.549296    1.932676              0.363662                      2.785352         1.630282   519.507042       2.258873\n",
       "      std             3.349770   0.537964   0.315467         0.924929    0.705701   0.202937   16.753497    1.015569              0.123961                      0.496573         0.602068   157.211220       0.545361\n",
       "      min            10.600000  11.030000   1.360000         1.280000    0.570000   0.690000   70.000000    0.740000              0.130000                      1.590000         0.410000   278.000000       1.100000\n",
       "      25%            18.000000  11.915000   2.000000         2.535000    1.605000   0.925000   85.500000    1.270000              0.270000                      2.440000         1.350000   406.500000       1.895000\n",
       "      50%            20.000000  12.290000   2.240000         2.900000    2.030000   1.040000   88.000000    1.610000              0.370000                      2.830000         1.610000   495.000000       2.200000\n",
       "      75%            22.000000  12.515000   2.420000         3.400000    2.475000   1.205000   99.500000    2.145000              0.430000                      3.160000         1.885000   625.000000       2.560000\n",
       "      max            30.000000  13.860000   3.230000         6.000000    5.080000   1.710000  162.000000    5.800000              0.660000                      3.690000         3.580000   985.000000       3.520000\n",
       "3     count          48.000000  48.000000  48.000000        48.000000   48.000000  48.000000   48.000000   48.000000             48.000000                     48.000000        48.000000    48.000000      48.000000\n",
       "      mean           21.416667  13.153750   2.437083         7.396250    0.781458   0.682708   99.312500    3.333750              0.447500                      1.683542         1.153542   629.895833       1.678750\n",
       "      std             2.258161   0.530241   0.184690         2.310942    0.293504   0.114441   10.890473    1.087906              0.124140                      0.272111         0.408836   115.097043       0.356971\n",
       "      min            17.500000  12.200000   2.100000         3.850000    0.340000   0.480000   80.000000    1.240000              0.170000                      1.270000         0.550000   415.000000       0.980000\n",
       "      25%            20.000000  12.805000   2.300000         5.437500    0.580000   0.587500   89.750000    2.587500              0.397500                      1.510000         0.855000   545.000000       1.407500\n",
       "      50%            21.000000  13.165000   2.380000         7.550000    0.685000   0.665000   97.000000    3.265000              0.470000                      1.660000         1.105000   627.500000       1.635000\n",
       "      75%            23.000000  13.505000   2.602500         9.225000    0.920000   0.752500  106.000000    3.957500              0.530000                      1.820000         1.350000   695.000000       1.807500\n",
       "      max            27.000000  14.340000   2.860000        13.000000    1.570000   0.960000  123.000000    5.650000              0.630000                      2.470000         2.700000   880.000000       2.800000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a groupby object:\n",
    "winegroups = wine.groupby('class')\n",
    "print type(winegroups)\n",
    "\n",
    "# groupby objects allow for some standard pandas calls:\n",
    "\n",
    "winegroups.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity of ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315 of diluted wines</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</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>1</th>\n",
       "      <td> 810.94</td>\n",
       "      <td> 118.63</td>\n",
       "      <td> 144.88</td>\n",
       "      <td> 1005.2</td>\n",
       "      <td> 6274</td>\n",
       "      <td> 167.57</td>\n",
       "      <td> 175.96</td>\n",
       "      <td> 17.11</td>\n",
       "      <td> 112.06</td>\n",
       "      <td> 326.170000</td>\n",
       "      <td> 62.660</td>\n",
       "      <td> 186.31</td>\n",
       "      <td> 65827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 871.79</td>\n",
       "      <td> 137.22</td>\n",
       "      <td> 159.38</td>\n",
       "      <td> 1436.9</td>\n",
       "      <td> 6713</td>\n",
       "      <td> 160.38</td>\n",
       "      <td> 147.74</td>\n",
       "      <td> 25.82</td>\n",
       "      <td> 115.75</td>\n",
       "      <td> 219.150000</td>\n",
       "      <td> 74.996</td>\n",
       "      <td> 197.76</td>\n",
       "      <td> 36885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 631.38</td>\n",
       "      <td> 160.02</td>\n",
       "      <td> 116.98</td>\n",
       "      <td> 1028.0</td>\n",
       "      <td> 4767</td>\n",
       "      <td>  80.58</td>\n",
       "      <td>  37.51</td>\n",
       "      <td> 21.48</td>\n",
       "      <td>  55.37</td>\n",
       "      <td> 355.019999</td>\n",
       "      <td> 32.770</td>\n",
       "      <td>  80.81</td>\n",
       "      <td> 30235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       alcohol  malic acid     ash  alcalinity of ash  magnesium  total phenols  flavanoids  nonflavanoid phenols  proanthocyanins  color intensity     hue  od280/od315 of diluted wines  proline\n",
       "class                                                                                                                                                                                             \n",
       "1       810.94      118.63  144.88             1005.2       6274         167.57      175.96                 17.11           112.06       326.170000  62.660                        186.31    65827\n",
       "2       871.79      137.22  159.38             1436.9       6713         160.38      147.74                 25.82           115.75       219.150000  74.996                        197.76    36885\n",
       "3       631.38      160.02  116.98             1028.0       4767          80.58       37.51                 21.48            55.37       355.019999  32.770                         80.81    30235"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winegroups.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        alcohol\n",
      "class          \n",
      "1      0.830508\n",
      "2      0.704225\n",
      "3      0.937500\n"
     ]
    }
   ],
   "source": [
    "# Groupby objects also:\n",
    "## can work through selected columns\n",
    "\n",
    "#print winegroups.alcohol.mean()\n",
    "#print winegroups[['alcohol', 'ash']].median()\n",
    "\n",
    "## can work through aggregation dictionaries, which can reference the origin\n",
    "## groupby functions, or construct new functions.\n",
    "\n",
    "#print winegroups.agg({'alcohol': 'median'})\n",
    "print winegroups.agg({'alcohol': lambda alcohol: alcohol.nunique() / alcohol.count()})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pivot Tables\n",
    "\n",
    "Pivot tables are another aggregational tool provided in pandas. Use pivot tables when:\n",
    "\n",
    "1. we want to easily compare results of variables across groups\n",
    "2. we want to easily plot results along the index.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pivoted_wine = wine.pivot_table(values='ash',\n",
    "                 columns='class',\n",
    "                 index='alcohol',\n",
    "                 aggfunc='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregation shapes\n",
    "\n",
    "Sometimes we'll need to \"melt\" or \"cast\" our aggregation into a different shape, mostly in the context of plotting or applying aggregations as new fields in the original data set. Consider the use case of `melt()` here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class  alcohol   1  2   3\n",
      "0        11.03 NaN  1 NaN\n",
      "1        11.41 NaN  1 NaN\n",
      "2        11.45 NaN  1 NaN\n",
      "3        11.46 NaN  1 NaN\n",
      "4        11.56 NaN  1 NaN\n",
      "    alcohol class  count\n",
      "51    12.85     1      1\n",
      "55    12.93     1      1\n",
      "59    13.05     1      4\n",
      "60    13.07     1      1\n",
      "63    13.16     1      1\n",
      "65    13.20     1      1\n",
      "67    13.24     1      2\n",
      "69    13.28     1      1\n",
      "70    13.29     1      1\n",
      "71    13.30     1      1\n",
      "75    13.39     1      1\n",
      "77    13.41     1      1\n",
      "79    13.48     1      1\n",
      "81    13.50     1      1\n",
      "82    13.51     1      1\n",
      "84    13.56     1      2\n",
      "85    13.58     1      1\n",
      "87    13.63     1      1\n",
      "88    13.64     1      1\n",
      "90    13.68     1      1\n"
     ]
    }
   ],
   "source": [
    "pivoted_wine_reset = pivoted_wine.reset_index()\n",
    "print pivoted_wine_reset.head()\n",
    "\n",
    "print pd.melt(pivoted_wine_reset,\n",
    "              id_vars=['alcohol'],\n",
    "              value_vars=[1, 2, 3],\n",
    "              value_name='count').dropna().head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregation Practice\n",
    "\n",
    "1. This time, create a group by object on a rounded 'alcohol' field. You might want to break this out into:\n",
    "    * create a new column with the rounded alcohol content\n",
    "    * groupby on the new column\n",
    "2. Explore the statistics from `.describe()` in this new groupby object. Across the new groups, what is interesting? What isn't?\n",
    "3. Use a pivot table to show the count of each target type per rounded alcohol level.\n",
    "4. Finally, melt the pivot table so that the columns represent each grouping and a count of that group.\n",
    "5. **take a step back** What would be the `groupby()` code to create the same data frame you created in question 4?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseball Stats\n",
    "\n",
    "We'll pull in the Lahman batting stats for data visualisation. There's only one table in the Lahman database for now; we'll add more as class moves forward. As mentioned before, pandas can read in from databases as well, using a db engine /connection.\n",
    "\n",
    "Below includes the columns and their meanings:\n",
    "```\n",
    "player         Player ID code\n",
    "year           Year\n",
    "stint          player's stint (order of appearances within a season)\n",
    "team           Team\n",
    "lgID           League\n",
    "G              Games\n",
    "AB             At Bats\n",
    "R              Runs\n",
    "H              Hits\n",
    "X2B            Doubles\n",
    "X3B            Triples\n",
    "HR             Homeruns\n",
    "RBI            Runs Batted In\n",
    "SB             Stolen Bases\n",
    "CS             Caught Stealing\n",
    "BB             Base on Balls\n",
    "SO             Strikeouts\n",
    "IBB            Intentional walks\n",
    "HBP            Hit by pitch\n",
    "SH             Sacrifice hits\n",
    "SF             Sacrifice flies\n",
    "GIDP           Grounded into double plays\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player</th>\n",
       "      <th>year</th>\n",
       "      <th>stint</th>\n",
       "      <th>team</th>\n",
       "      <th>lg</th>\n",
       "      <th>g</th>\n",
       "      <th>ab</th>\n",
       "      <th>r</th>\n",
       "      <th>h</th>\n",
       "      <th>X2b</th>\n",
       "      <th>X3b</th>\n",
       "      <th>hr</th>\n",
       "      <th>rbi</th>\n",
       "      <th>sb</th>\n",
       "      <th>cs</th>\n",
       "      <th>bb</th>\n",
       "      <th>so</th>\n",
       "      <th>ibb</th>\n",
       "      <th>hbp</th>\n",
       "      <th>sh</th>\n",
       "      <th>sf</th>\n",
       "      <th>gidp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0    </th>\n",
       "      <td> abercda01</td>\n",
       "      <td> 1871</td>\n",
       "      <td> 1</td>\n",
       "      <td> TRO</td>\n",
       "      <td> NA</td>\n",
       "      <td>   1</td>\n",
       "      <td>   4</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  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>115  </th>\n",
       "      <td> allenha01</td>\n",
       "      <td> 1872</td>\n",
       "      <td> 1</td>\n",
       "      <td> MID</td>\n",
       "      <td> NA</td>\n",
       "      <td>  16</td>\n",
       "      <td>  66</td>\n",
       "      <td>   8</td>\n",
       "      <td>  18</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   7</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</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>271  </th>\n",
       "      <td>  addybo01</td>\n",
       "      <td> 1873</td>\n",
       "      <td> 1</td>\n",
       "      <td> PH2</td>\n",
       "      <td> NA</td>\n",
       "      <td>  10</td>\n",
       "      <td>  51</td>\n",
       "      <td>  12</td>\n",
       "      <td>  16</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  10</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>  2</td>\n",
       "      <td>  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>396  </th>\n",
       "      <td>  addybo01</td>\n",
       "      <td> 1874</td>\n",
       "      <td> 1</td>\n",
       "      <td> HR1</td>\n",
       "      <td> NA</td>\n",
       "      <td>  50</td>\n",
       "      <td> 213</td>\n",
       "      <td>  25</td>\n",
       "      <td>  51</td>\n",
       "      <td>  9</td>\n",
       "      <td>  2</td>\n",
       "      <td>  0</td>\n",
       "      <td>  23</td>\n",
       "      <td>  4</td>\n",
       "      <td>  2</td>\n",
       "      <td>  1</td>\n",
       "      <td>  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>519  </th>\n",
       "      <td> abadijo01</td>\n",
       "      <td> 1875</td>\n",
       "      <td> 1</td>\n",
       "      <td> PH3</td>\n",
       "      <td> NA</td>\n",
       "      <td>  11</td>\n",
       "      <td>  45</td>\n",
       "      <td>   3</td>\n",
       "      <td>  10</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   4</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  3</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>737  </th>\n",
       "      <td>  addybo01</td>\n",
       "      <td> 1876</td>\n",
       "      <td> 1</td>\n",
       "      <td> CHN</td>\n",
       "      <td> NL</td>\n",
       "      <td>  32</td>\n",
       "      <td> 142</td>\n",
       "      <td>  36</td>\n",
       "      <td>  40</td>\n",
       "      <td>  4</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  5</td>\n",
       "      <td>  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>861  </th>\n",
       "      <td>  addybo01</td>\n",
       "      <td> 1877</td>\n",
       "      <td> 1</td>\n",
       "      <td> CN1</td>\n",
       "      <td> NL</td>\n",
       "      <td>  57</td>\n",
       "      <td> 245</td>\n",
       "      <td>  27</td>\n",
       "      <td>  68</td>\n",
       "      <td>  2</td>\n",
       "      <td>  3</td>\n",
       "      <td>  0</td>\n",
       "      <td>  31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  6</td>\n",
       "      <td>  5</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>958  </th>\n",
       "      <td> allisdo01</td>\n",
       "      <td> 1878</td>\n",
       "      <td> 1</td>\n",
       "      <td> PRO</td>\n",
       "      <td> NL</td>\n",
       "      <td>  19</td>\n",
       "      <td>  76</td>\n",
       "      <td>   9</td>\n",
       "      <td>  22</td>\n",
       "      <td>  2</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  1</td>\n",
       "      <td>  8</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>1038 </th>\n",
       "      <td> adamsge01</td>\n",
       "      <td> 1879</td>\n",
       "      <td> 1</td>\n",
       "      <td> SR1</td>\n",
       "      <td> NL</td>\n",
       "      <td>   4</td>\n",
       "      <td>  13</td>\n",
       "      <td>   0</td>\n",
       "      <td>   3</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  1</td>\n",
       "      <td>  1</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>1165 </th>\n",
       "      <td> ahearch01</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 1</td>\n",
       "      <td> TRN</td>\n",
       "      <td> NL</td>\n",
       "      <td>   1</td>\n",
       "      <td>   4</td>\n",
       "      <td>   1</td>\n",
       "      <td>   1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  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>1300 </th>\n",
       "      <td> ansonca01</td>\n",
       "      <td> 1881</td>\n",
       "      <td> 1</td>\n",
       "      <td> CHN</td>\n",
       "      <td> NL</td>\n",
       "      <td>  84</td>\n",
       "      <td> 343</td>\n",
       "      <td>  67</td>\n",
       "      <td> 137</td>\n",
       "      <td> 21</td>\n",
       "      <td>  7</td>\n",
       "      <td>  1</td>\n",
       "      <td>  82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 26</td>\n",
       "      <td>  4</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>1432 </th>\n",
       "      <td> ansonca01</td>\n",
       "      <td> 1882</td>\n",
       "      <td> 1</td>\n",
       "      <td> CHN</td>\n",
       "      <td> NL</td>\n",
       "      <td>  82</td>\n",
       "      <td> 348</td>\n",
       "      <td>  69</td>\n",
       "      <td> 126</td>\n",
       "      <td> 29</td>\n",
       "      <td>  8</td>\n",
       "      <td>  1</td>\n",
       "      <td>  83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 20</td>\n",
       "      <td>  7</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>1683 </th>\n",
       "      <td> allenmy01</td>\n",
       "      <td> 1883</td>\n",
       "      <td> 1</td>\n",
       "      <td> NY1</td>\n",
       "      <td> NL</td>\n",
       "      <td>   1</td>\n",
       "      <td>   4</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  2</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>1966 </th>\n",
       "      <td>   akejo01</td>\n",
       "      <td> 1884</td>\n",
       "      <td> 1</td>\n",
       "      <td> BL2</td>\n",
       "      <td> AA</td>\n",
       "      <td>  13</td>\n",
       "      <td>  52</td>\n",
       "      <td>   1</td>\n",
       "      <td>  10</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>   2</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2748 </th>\n",
       "      <td> alvorbi01</td>\n",
       "      <td> 1885</td>\n",
       "      <td> 1</td>\n",
       "      <td> SL5</td>\n",
       "      <td> NL</td>\n",
       "      <td>   2</td>\n",
       "      <td>   5</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  1</td>\n",
       "      <td>  2</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>3101 </th>\n",
       "      <td> allenmy01</td>\n",
       "      <td> 1886</td>\n",
       "      <td> 1</td>\n",
       "      <td> BSN</td>\n",
       "      <td> NL</td>\n",
       "      <td>   1</td>\n",
       "      <td>   3</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</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>3452 </th>\n",
       "      <td> allenmy01</td>\n",
       "      <td> 1887</td>\n",
       "      <td> 1</td>\n",
       "      <td> CL3</td>\n",
       "      <td> AA</td>\n",
       "      <td> 117</td>\n",
       "      <td> 463</td>\n",
       "      <td>  66</td>\n",
       "      <td> 128</td>\n",
       "      <td> 22</td>\n",
       "      <td> 10</td>\n",
       "      <td>  4</td>\n",
       "      <td>  77</td>\n",
       "      <td> 26</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3785 </th>\n",
       "      <td> albergu01</td>\n",
       "      <td> 1888</td>\n",
       "      <td> 1</td>\n",
       "      <td> CL3</td>\n",
       "      <td> AA</td>\n",
       "      <td> 102</td>\n",
       "      <td> 364</td>\n",
       "      <td>  51</td>\n",
       "      <td>  75</td>\n",
       "      <td> 10</td>\n",
       "      <td>  6</td>\n",
       "      <td>  1</td>\n",
       "      <td>  48</td>\n",
       "      <td> 26</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4136 </th>\n",
       "      <td> alvorbi01</td>\n",
       "      <td> 1889</td>\n",
       "      <td> 1</td>\n",
       "      <td> KC2</td>\n",
       "      <td> AA</td>\n",
       "      <td>  50</td>\n",
       "      <td> 186</td>\n",
       "      <td>  23</td>\n",
       "      <td>  43</td>\n",
       "      <td>  8</td>\n",
       "      <td>  9</td>\n",
       "      <td>  0</td>\n",
       "      <td>  18</td>\n",
       "      <td>  3</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 10</td>\n",
       "      <td> 35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4478 </th>\n",
       "      <td> abbotda01</td>\n",
       "      <td> 1890</td>\n",
       "      <td> 1</td>\n",
       "      <td> TL2</td>\n",
       "      <td> AA</td>\n",
       "      <td>   3</td>\n",
       "      <td>   7</td>\n",
       "      <td>   0</td>\n",
       "      <td>   1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>   1</td>\n",
       "      <td>  1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5053 </th>\n",
       "      <td> albergu01</td>\n",
       "      <td> 1891</td>\n",
       "      <td> 1</td>\n",
       "      <td> ML3</td>\n",
       "      <td> AA</td>\n",
       "      <td>  12</td>\n",
       "      <td>  41</td>\n",
       "      <td>   6</td>\n",
       "      <td>   4</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   2</td>\n",
       "      <td>  1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  7</td>\n",
       "      <td>  5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5461 </th>\n",
       "      <td> abbeybe01</td>\n",
       "      <td> 1892</td>\n",
       "      <td> 1</td>\n",
       "      <td> WAS</td>\n",
       "      <td> NL</td>\n",
       "      <td>  27</td>\n",
       "      <td>  75</td>\n",
       "      <td>   5</td>\n",
       "      <td>   9</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   3</td>\n",
       "      <td>  2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  9</td>\n",
       "      <td> 27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5771 </th>\n",
       "      <td> abbeybe01</td>\n",
       "      <td> 1893</td>\n",
       "      <td> 1</td>\n",
       "      <td> CHN</td>\n",
       "      <td> NL</td>\n",
       "      <td>   7</td>\n",
       "      <td>  26</td>\n",
       "      <td>   2</td>\n",
       "      <td>   6</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   2</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  2</td>\n",
       "      <td>  3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6057 </th>\n",
       "      <td> abbeybe01</td>\n",
       "      <td> 1894</td>\n",
       "      <td> 1</td>\n",
       "      <td> CHN</td>\n",
       "      <td> NL</td>\n",
       "      <td>  11</td>\n",
       "      <td>  39</td>\n",
       "      <td>   3</td>\n",
       "      <td>   5</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   4</td>\n",
       "      <td>  1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  2</td>\n",
       "      <td>  7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6351 </th>\n",
       "      <td> abbeybe01</td>\n",
       "      <td> 1895</td>\n",
       "      <td> 1</td>\n",
       "      <td> CHN</td>\n",
       "      <td> NL</td>\n",
       "      <td>   1</td>\n",
       "      <td>   3</td>\n",
       "      <td>   0</td>\n",
       "      <td>   1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6656 </th>\n",
       "      <td> abbeybe01</td>\n",
       "      <td> 1896</td>\n",
       "      <td> 1</td>\n",
       "      <td> BRO</td>\n",
       "      <td> NL</td>\n",
       "      <td>  25</td>\n",
       "      <td>  63</td>\n",
       "      <td>   7</td>\n",
       "      <td>  12</td>\n",
       "      <td>  1</td>\n",
       "      <td>  2</td>\n",
       "      <td>  0</td>\n",
       "      <td>   7</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  6</td>\n",
       "      <td> 13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6951 </th>\n",
       "      <td> abbated01</td>\n",
       "      <td> 1897</td>\n",
       "      <td> 1</td>\n",
       "      <td> PHI</td>\n",
       "      <td> NL</td>\n",
       "      <td>   3</td>\n",
       "      <td>  10</td>\n",
       "      <td>   0</td>\n",
       "      <td>   3</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7231 </th>\n",
       "      <td> abbated01</td>\n",
       "      <td> 1898</td>\n",
       "      <td> 1</td>\n",
       "      <td> PHI</td>\n",
       "      <td> NL</td>\n",
       "      <td>  25</td>\n",
       "      <td>  92</td>\n",
       "      <td>   9</td>\n",
       "      <td>  21</td>\n",
       "      <td>  4</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  14</td>\n",
       "      <td>  4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  1</td>\n",
       "      <td>  2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7554 </th>\n",
       "      <td> anderjo01</td>\n",
       "      <td> 1899</td>\n",
       "      <td> 1</td>\n",
       "      <td> BRO</td>\n",
       "      <td> NL</td>\n",
       "      <td> 117</td>\n",
       "      <td> 439</td>\n",
       "      <td>  65</td>\n",
       "      <td> 118</td>\n",
       "      <td> 18</td>\n",
       "      <td>  7</td>\n",
       "      <td>  4</td>\n",
       "      <td>  92</td>\n",
       "      <td> 25</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  4</td>\n",
       "      <td>  2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7902 </th>\n",
       "      <td> allenbo01</td>\n",
       "      <td> 1900</td>\n",
       "      <td> 1</td>\n",
       "      <td> CIN</td>\n",
       "      <td> NL</td>\n",
       "      <td>   5</td>\n",
       "      <td>  15</td>\n",
       "      <td>   0</td>\n",
       "      <td>   2</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   1</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8097 </th>\n",
       "      <td> anderjo01</td>\n",
       "      <td> 1901</td>\n",
       "      <td> 1</td>\n",
       "      <td> MLA</td>\n",
       "      <td> AL</td>\n",
       "      <td> 138</td>\n",
       "      <td> 576</td>\n",
       "      <td>  90</td>\n",
       "      <td> 190</td>\n",
       "      <td> 46</td>\n",
       "      <td>  7</td>\n",
       "      <td>  8</td>\n",
       "      <td>  99</td>\n",
       "      <td> 35</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  3</td>\n",
       "      <td>  4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8509 </th>\n",
       "      <td> adamsjo01</td>\n",
       "      <td> 1902</td>\n",
       "      <td> 1</td>\n",
       "      <td> SLN</td>\n",
       "      <td> NL</td>\n",
       "      <td>   1</td>\n",
       "      <td>   2</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8964 </th>\n",
       "      <td> abbated01</td>\n",
       "      <td> 1903</td>\n",
       "      <td> 1</td>\n",
       "      <td> BSN</td>\n",
       "      <td> NL</td>\n",
       "      <td> 136</td>\n",
       "      <td> 489</td>\n",
       "      <td>  61</td>\n",
       "      <td> 111</td>\n",
       "      <td> 18</td>\n",
       "      <td>  5</td>\n",
       "      <td>  1</td>\n",
       "      <td>  46</td>\n",
       "      <td> 23</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  4</td>\n",
       "      <td> 17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9364 </th>\n",
       "      <td> abbated01</td>\n",
       "      <td> 1904</td>\n",
       "      <td> 1</td>\n",
       "      <td> BSN</td>\n",
       "      <td> NL</td>\n",
       "      <td> 154</td>\n",
       "      <td> 579</td>\n",
       "      <td>  76</td>\n",
       "      <td> 148</td>\n",
       "      <td> 18</td>\n",
       "      <td> 10</td>\n",
       "      <td>  3</td>\n",
       "      <td>  54</td>\n",
       "      <td> 24</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  5</td>\n",
       "      <td> 13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9770 </th>\n",
       "      <td> abbated01</td>\n",
       "      <td> 1905</td>\n",
       "      <td> 1</td>\n",
       "      <td> BSN</td>\n",
       "      <td> NL</td>\n",
       "      <td> 153</td>\n",
       "      <td> 610</td>\n",
       "      <td>  70</td>\n",
       "      <td> 170</td>\n",
       "      <td> 25</td>\n",
       "      <td> 12</td>\n",
       "      <td>  3</td>\n",
       "      <td>  41</td>\n",
       "      <td> 30</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  8</td>\n",
       "      <td>  5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10177</th>\n",
       "      <td> abstebi01</td>\n",
       "      <td> 1906</td>\n",
       "      <td> 1</td>\n",
       "      <td> PIT</td>\n",
       "      <td> NL</td>\n",
       "      <td>   8</td>\n",
       "      <td>  20</td>\n",
       "      <td>   2</td>\n",
       "      <td>   4</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   3</td>\n",
       "      <td>  2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10614</th>\n",
       "      <td> abbated01</td>\n",
       "      <td> 1907</td>\n",
       "      <td> 1</td>\n",
       "      <td> PIT</td>\n",
       "      <td> NL</td>\n",
       "      <td> 147</td>\n",
       "      <td> 496</td>\n",
       "      <td>  63</td>\n",
       "      <td> 130</td>\n",
       "      <td> 14</td>\n",
       "      <td>  7</td>\n",
       "      <td>  2</td>\n",
       "      <td>  82</td>\n",
       "      <td> 35</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  8</td>\n",
       "      <td> 22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11060</th>\n",
       "      <td> abbated01</td>\n",
       "      <td> 1908</td>\n",
       "      <td> 1</td>\n",
       "      <td> PIT</td>\n",
       "      <td> NL</td>\n",
       "      <td> 146</td>\n",
       "      <td> 500</td>\n",
       "      <td>  43</td>\n",
       "      <td> 125</td>\n",
       "      <td> 16</td>\n",
       "      <td>  7</td>\n",
       "      <td>  1</td>\n",
       "      <td>  61</td>\n",
       "      <td> 22</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  7</td>\n",
       "      <td> 25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11530</th>\n",
       "      <td> abbated01</td>\n",
       "      <td> 1909</td>\n",
       "      <td> 1</td>\n",
       "      <td> PIT</td>\n",
       "      <td> NL</td>\n",
       "      <td>  36</td>\n",
       "      <td>  87</td>\n",
       "      <td>  13</td>\n",
       "      <td>  20</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>  16</td>\n",
       "      <td>  2</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12073</th>\n",
       "      <td> abbated01</td>\n",
       "      <td> 1910</td>\n",
       "      <td> 1</td>\n",
       "      <td> PIT</td>\n",
       "      <td> NL</td>\n",
       "      <td>   3</td>\n",
       "      <td>   3</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12611</th>\n",
       "      <td> ablesha01</td>\n",
       "      <td> 1911</td>\n",
       "      <td> 1</td>\n",
       "      <td> NYA</td>\n",
       "      <td> AL</td>\n",
       "      <td>   3</td>\n",
       "      <td>   4</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13178</th>\n",
       "      <td> adamsba01</td>\n",
       "      <td> 1912</td>\n",
       "      <td> 1</td>\n",
       "      <td> PIT</td>\n",
       "      <td> NL</td>\n",
       "      <td>  28</td>\n",
       "      <td>  53</td>\n",
       "      <td>   5</td>\n",
       "      <td>  12</td>\n",
       "      <td>  3</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>   3</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  8</td>\n",
       "      <td>  9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13808</th>\n",
       "      <td> acostme01</td>\n",
       "      <td> 1913</td>\n",
       "      <td> 1</td>\n",
       "      <td> WS1</td>\n",
       "      <td> AL</td>\n",
       "      <td>  12</td>\n",
       "      <td>  20</td>\n",
       "      <td>   3</td>\n",
       "      <td>   6</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>   1</td>\n",
       "      <td>  2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  4</td>\n",
       "      <td>  2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14424</th>\n",
       "      <td> acostme01</td>\n",
       "      <td> 1914</td>\n",
       "      <td> 1</td>\n",
       "      <td> WS1</td>\n",
       "      <td> AL</td>\n",
       "      <td>  39</td>\n",
       "      <td>  74</td>\n",
       "      <td>  10</td>\n",
       "      <td>  19</td>\n",
       "      <td>  2</td>\n",
       "      <td>  2</td>\n",
       "      <td>  0</td>\n",
       "      <td>   4</td>\n",
       "      <td>  3</td>\n",
       "      <td>  4</td>\n",
       "      <td> 11</td>\n",
       "      <td> 18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15219</th>\n",
       "      <td> acostme01</td>\n",
       "      <td> 1915</td>\n",
       "      <td> 1</td>\n",
       "      <td> WS1</td>\n",
       "      <td> AL</td>\n",
       "      <td>  72</td>\n",
       "      <td> 163</td>\n",
       "      <td>  20</td>\n",
       "      <td>  34</td>\n",
       "      <td>  4</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  18</td>\n",
       "      <td>  8</td>\n",
       "      <td>  4</td>\n",
       "      <td> 28</td>\n",
       "      <td> 15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  4</td>\n",
       "      <td>  9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16015</th>\n",
       "      <td> acostme01</td>\n",
       "      <td> 1916</td>\n",
       "      <td> 1</td>\n",
       "      <td> WS1</td>\n",
       "      <td> AL</td>\n",
       "      <td>   5</td>\n",
       "      <td>   8</td>\n",
       "      <td>   0</td>\n",
       "      <td>   1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  2</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16580</th>\n",
       "      <td> adamsbe01</td>\n",
       "      <td> 1917</td>\n",
       "      <td> 1</td>\n",
       "      <td> PHI</td>\n",
       "      <td> NL</td>\n",
       "      <td>  43</td>\n",
       "      <td> 107</td>\n",
       "      <td>   4</td>\n",
       "      <td>  22</td>\n",
       "      <td>  4</td>\n",
       "      <td>  1</td>\n",
       "      <td>  1</td>\n",
       "      <td>   7</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td> 20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17096</th>\n",
       "      <td> acostme01</td>\n",
       "      <td> 1918</td>\n",
       "      <td> 1</td>\n",
       "      <td> WS1</td>\n",
       "      <td> AL</td>\n",
       "      <td>   3</td>\n",
       "      <td>   2</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17602</th>\n",
       "      <td> adamsba01</td>\n",
       "      <td> 1919</td>\n",
       "      <td> 1</td>\n",
       "      <td> PIT</td>\n",
       "      <td> NL</td>\n",
       "      <td>  34</td>\n",
       "      <td>  92</td>\n",
       "      <td>   2</td>\n",
       "      <td>  17</td>\n",
       "      <td>  2</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>   4</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  6</td>\n",
       "      <td> 13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18136</th>\n",
       "      <td> acostjo01</td>\n",
       "      <td> 1920</td>\n",
       "      <td> 1</td>\n",
       "      <td> WS1</td>\n",
       "      <td> AL</td>\n",
       "      <td>  17</td>\n",
       "      <td>  25</td>\n",
       "      <td>   2</td>\n",
       "      <td>   6</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  4</td>\n",
       "      <td>  7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18651</th>\n",
       "      <td> acostjo01</td>\n",
       "      <td> 1921</td>\n",
       "      <td> 1</td>\n",
       "      <td> WS1</td>\n",
       "      <td> AL</td>\n",
       "      <td>  33</td>\n",
       "      <td>  30</td>\n",
       "      <td>   2</td>\n",
       "      <td>   2</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  6</td>\n",
       "      <td> 14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19171</th>\n",
       "      <td> acostjo01</td>\n",
       "      <td> 1922</td>\n",
       "      <td> 1</td>\n",
       "      <td> CHA</td>\n",
       "      <td> AL</td>\n",
       "      <td>   5</td>\n",
       "      <td>   5</td>\n",
       "      <td>   0</td>\n",
       "      <td>   1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>  1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19684</th>\n",
       "      <td> abramge01</td>\n",
       "      <td> 1923</td>\n",
       "      <td> 1</td>\n",
       "      <td> CIN</td>\n",
       "      <td> NL</td>\n",
       "      <td>   3</td>\n",
       "      <td>   1</td>\n",
       "      <td>   0</td>\n",
       "      <td>   1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20214</th>\n",
       "      <td> adamsba01</td>\n",
       "      <td> 1924</td>\n",
       "      <td> 1</td>\n",
       "      <td> PIT</td>\n",
       "      <td> NL</td>\n",
       "      <td>   9</td>\n",
       "      <td>  11</td>\n",
       "      <td>   1</td>\n",
       "      <td>   2</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20761</th>\n",
       "      <td> adamsba01</td>\n",
       "      <td> 1925</td>\n",
       "      <td> 1</td>\n",
       "      <td> PIT</td>\n",
       "      <td> NL</td>\n",
       "      <td>  33</td>\n",
       "      <td>  31</td>\n",
       "      <td>   3</td>\n",
       "      <td>   7</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  3</td>\n",
       "      <td>  6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21318</th>\n",
       "      <td> adamsba01</td>\n",
       "      <td> 1926</td>\n",
       "      <td> 1</td>\n",
       "      <td> PIT</td>\n",
       "      <td> NL</td>\n",
       "      <td>  19</td>\n",
       "      <td>   9</td>\n",
       "      <td>   1</td>\n",
       "      <td>   2</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21844</th>\n",
       "      <td> adamssp01</td>\n",
       "      <td> 1927</td>\n",
       "      <td> 1</td>\n",
       "      <td> CHN</td>\n",
       "      <td> NL</td>\n",
       "      <td> 146</td>\n",
       "      <td> 647</td>\n",
       "      <td> 100</td>\n",
       "      <td> 189</td>\n",
       "      <td> 17</td>\n",
       "      <td>  7</td>\n",
       "      <td>  0</td>\n",
       "      <td>  49</td>\n",
       "      <td> 26</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 42</td>\n",
       "      <td> 26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td> 16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22385</th>\n",
       "      <td> adamssp01</td>\n",
       "      <td> 1928</td>\n",
       "      <td> 1</td>\n",
       "      <td> PIT</td>\n",
       "      <td> NL</td>\n",
       "      <td> 135</td>\n",
       "      <td> 539</td>\n",
       "      <td>  91</td>\n",
       "      <td> 149</td>\n",
       "      <td> 14</td>\n",
       "      <td>  6</td>\n",
       "      <td>  0</td>\n",
       "      <td>  38</td>\n",
       "      <td>  8</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 64</td>\n",
       "      <td> 18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  4</td>\n",
       "      <td> 21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22915</th>\n",
       "      <td> adamssp01</td>\n",
       "      <td> 1929</td>\n",
       "      <td> 1</td>\n",
       "      <td> PIT</td>\n",
       "      <td> NL</td>\n",
       "      <td>  74</td>\n",
       "      <td> 196</td>\n",
       "      <td>  37</td>\n",
       "      <td>  51</td>\n",
       "      <td>  8</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  11</td>\n",
       "      <td>  3</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 15</td>\n",
       "      <td>  5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  1</td>\n",
       "      <td>  5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23445</th>\n",
       "      <td> adamssp01</td>\n",
       "      <td> 1930</td>\n",
       "      <td> 1</td>\n",
       "      <td> SLN</td>\n",
       "      <td> NL</td>\n",
       "      <td> 137</td>\n",
       "      <td> 570</td>\n",
       "      <td>  98</td>\n",
       "      <td> 179</td>\n",
       "      <td> 36</td>\n",
       "      <td>  9</td>\n",
       "      <td>  0</td>\n",
       "      <td>  55</td>\n",
       "      <td>  7</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 45</td>\n",
       "      <td> 27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  1</td>\n",
       "      <td> 15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23976</th>\n",
       "      <td> adairji01</td>\n",
       "      <td> 1931</td>\n",
       "      <td> 1</td>\n",
       "      <td> CHN</td>\n",
       "      <td> NL</td>\n",
       "      <td>  18</td>\n",
       "      <td>  76</td>\n",
       "      <td>   9</td>\n",
       "      <td>  21</td>\n",
       "      <td>  3</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>   3</td>\n",
       "      <td>  1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  1</td>\n",
       "      <td>  8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24485</th>\n",
       "      <td> adamsbo02</td>\n",
       "      <td> 1932</td>\n",
       "      <td> 1</td>\n",
       "      <td> PHI</td>\n",
       "      <td> NL</td>\n",
       "      <td>   4</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25012</th>\n",
       "      <td> adamssp01</td>\n",
       "      <td> 1933</td>\n",
       "      <td> 1</td>\n",
       "      <td> SLN</td>\n",
       "      <td> NL</td>\n",
       "      <td>   8</td>\n",
       "      <td>  30</td>\n",
       "      <td>   1</td>\n",
       "      <td>   5</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  1</td>\n",
       "      <td>  3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25503</th>\n",
       "      <td> adamssp01</td>\n",
       "      <td> 1934</td>\n",
       "      <td> 1</td>\n",
       "      <td> CIN</td>\n",
       "      <td> NL</td>\n",
       "      <td>  87</td>\n",
       "      <td> 278</td>\n",
       "      <td>  38</td>\n",
       "      <td>  70</td>\n",
       "      <td> 16</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  14</td>\n",
       "      <td>  2</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 20</td>\n",
       "      <td> 10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  2</td>\n",
       "      <td>  1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26024</th>\n",
       "      <td> allenet01</td>\n",
       "      <td> 1935</td>\n",
       "      <td> 1</td>\n",
       "      <td> PHI</td>\n",
       "      <td> NL</td>\n",
       "      <td> 156</td>\n",
       "      <td> 645</td>\n",
       "      <td>  90</td>\n",
       "      <td> 198</td>\n",
       "      <td> 46</td>\n",
       "      <td>  1</td>\n",
       "      <td>  8</td>\n",
       "      <td>  63</td>\n",
       "      <td>  5</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 43</td>\n",
       "      <td> 54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  1</td>\n",
       "      <td> 11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26537</th>\n",
       "      <td> allenet01</td>\n",
       "      <td> 1936</td>\n",
       "      <td> 1</td>\n",
       "      <td> PHI</td>\n",
       "      <td> NL</td>\n",
       "      <td>  30</td>\n",
       "      <td> 125</td>\n",
       "      <td>  21</td>\n",
       "      <td>  37</td>\n",
       "      <td>  3</td>\n",
       "      <td>  1</td>\n",
       "      <td>  1</td>\n",
       "      <td>   9</td>\n",
       "      <td>  4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  4</td>\n",
       "      <td>  8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27048</th>\n",
       "      <td> alexahu01</td>\n",
       "      <td> 1937</td>\n",
       "      <td> 1</td>\n",
       "      <td> CLE</td>\n",
       "      <td> AL</td>\n",
       "      <td>   7</td>\n",
       "      <td>  11</td>\n",
       "      <td>   0</td>\n",
       "      <td>   1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27574</th>\n",
       "      <td> allenet01</td>\n",
       "      <td> 1938</td>\n",
       "      <td> 1</td>\n",
       "      <td> SLA</td>\n",
       "      <td> AL</td>\n",
       "      <td>  19</td>\n",
       "      <td>  33</td>\n",
       "      <td>   4</td>\n",
       "      <td>  10</td>\n",
       "      <td>  3</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>   4</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  2</td>\n",
       "      <td>  4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28104</th>\n",
       "      <td> adamsbu01</td>\n",
       "      <td> 1939</td>\n",
       "      <td> 1</td>\n",
       "      <td> SLN</td>\n",
       "      <td> NL</td>\n",
       "      <td>   2</td>\n",
       "      <td>   1</td>\n",
       "      <td>   1</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28683</th>\n",
       "      <td> aderhmo01</td>\n",
       "      <td> 1940</td>\n",
       "      <td> 1</td>\n",
       "      <td> WS1</td>\n",
       "      <td> AL</td>\n",
       "      <td>   1</td>\n",
       "      <td>   2</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29224</th>\n",
       "      <td> adamsac01</td>\n",
       "      <td> 1941</td>\n",
       "      <td> 1</td>\n",
       "      <td> NY1</td>\n",
       "      <td> NL</td>\n",
       "      <td>  38</td>\n",
       "      <td>  12</td>\n",
       "      <td>   0</td>\n",
       "      <td>   1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29806</th>\n",
       "      <td> abernte01</td>\n",
       "      <td> 1942</td>\n",
       "      <td> 1</td>\n",
       "      <td> PHA</td>\n",
       "      <td> AL</td>\n",
       "      <td>   1</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30345</th>\n",
       "      <td> abernte01</td>\n",
       "      <td> 1943</td>\n",
       "      <td> 1</td>\n",
       "      <td> PHA</td>\n",
       "      <td> AL</td>\n",
       "      <td>   5</td>\n",
       "      <td>   4</td>\n",
       "      <td>   0</td>\n",
       "      <td>   1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30902</th>\n",
       "      <td> abernte01</td>\n",
       "      <td> 1944</td>\n",
       "      <td> 1</td>\n",
       "      <td> PHA</td>\n",
       "      <td> AL</td>\n",
       "      <td>   1</td>\n",
       "      <td>   1</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31471</th>\n",
       "      <td> adamsac01</td>\n",
       "      <td> 1945</td>\n",
       "      <td> 1</td>\n",
       "      <td> NY1</td>\n",
       "      <td> NL</td>\n",
       "      <td>  65</td>\n",
       "      <td>  16</td>\n",
       "      <td>   2</td>\n",
       "      <td>   3</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   1</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  2</td>\n",
       "      <td>  3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32050</th>\n",
       "      <td> abernwo01</td>\n",
       "      <td> 1946</td>\n",
       "      <td> 1</td>\n",
       "      <td> NY1</td>\n",
       "      <td> NL</td>\n",
       "      <td>  15</td>\n",
       "      <td>   8</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32732</th>\n",
       "      <td> abernwo01</td>\n",
       "      <td> 1947</td>\n",
       "      <td> 1</td>\n",
       "      <td> NY1</td>\n",
       "      <td> NL</td>\n",
       "      <td>   1</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33314</th>\n",
       "      <td> aberscl01</td>\n",
       "      <td> 1948</td>\n",
       "      <td> 1</td>\n",
       "      <td> CHN</td>\n",
       "      <td> NL</td>\n",
       "      <td>  12</td>\n",
       "      <td>  32</td>\n",
       "      <td>   1</td>\n",
       "      <td>   6</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>   6</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  5</td>\n",
       "      <td> 10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33887</th>\n",
       "      <td> aberscl01</td>\n",
       "      <td> 1949</td>\n",
       "      <td> 1</td>\n",
       "      <td> CHN</td>\n",
       "      <td> NL</td>\n",
       "      <td>   4</td>\n",
       "      <td>   7</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34460</th>\n",
       "      <td>  aberal01</td>\n",
       "      <td> 1950</td>\n",
       "      <td> 1</td>\n",
       "      <td> CLE</td>\n",
       "      <td> AL</td>\n",
       "      <td>   1</td>\n",
       "      <td>   2</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>  1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35037</th>\n",
       "      <td> abramca01</td>\n",
       "      <td> 1951</td>\n",
       "      <td> 1</td>\n",
       "      <td> BRO</td>\n",
       "      <td> NL</td>\n",
       "      <td>  67</td>\n",
       "      <td> 150</td>\n",
       "      <td>  27</td>\n",
       "      <td>  42</td>\n",
       "      <td>  8</td>\n",
       "      <td>  0</td>\n",
       "      <td>  3</td>\n",
       "      <td>  19</td>\n",
       "      <td>  3</td>\n",
       "      <td>  2</td>\n",
       "      <td> 36</td>\n",
       "      <td> 26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35653</th>\n",
       "      <td> abernbi01</td>\n",
       "      <td> 1952</td>\n",
       "      <td> 1</td>\n",
       "      <td> CLE</td>\n",
       "      <td> AL</td>\n",
       "      <td>   1</td>\n",
       "      <td>   1</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36285</th>\n",
       "      <td>  aberal01</td>\n",
       "      <td> 1953</td>\n",
       "      <td> 1</td>\n",
       "      <td> CLE</td>\n",
       "      <td> AL</td>\n",
       "      <td>   6</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  2</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36871</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1954</td>\n",
       "      <td> 1</td>\n",
       "      <td> ML1</td>\n",
       "      <td> NL</td>\n",
       "      <td> 122</td>\n",
       "      <td> 468</td>\n",
       "      <td>  58</td>\n",
       "      <td> 131</td>\n",
       "      <td> 27</td>\n",
       "      <td>  6</td>\n",
       "      <td> 13</td>\n",
       "      <td>  69</td>\n",
       "      <td>  2</td>\n",
       "      <td>  2</td>\n",
       "      <td> 28</td>\n",
       "      <td> 39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  3</td>\n",
       "      <td>  6</td>\n",
       "      <td>  4</td>\n",
       "      <td> 13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37447</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1955</td>\n",
       "      <td> 1</td>\n",
       "      <td> ML1</td>\n",
       "      <td> NL</td>\n",
       "      <td> 153</td>\n",
       "      <td> 602</td>\n",
       "      <td> 105</td>\n",
       "      <td> 189</td>\n",
       "      <td> 37</td>\n",
       "      <td>  9</td>\n",
       "      <td> 27</td>\n",
       "      <td> 106</td>\n",
       "      <td>  3</td>\n",
       "      <td>  1</td>\n",
       "      <td> 49</td>\n",
       "      <td> 61</td>\n",
       "      <td>  5</td>\n",
       "      <td>  3</td>\n",
       "      <td>  7</td>\n",
       "      <td>  4</td>\n",
       "      <td> 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38102</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1956</td>\n",
       "      <td> 1</td>\n",
       "      <td> ML1</td>\n",
       "      <td> NL</td>\n",
       "      <td> 153</td>\n",
       "      <td> 609</td>\n",
       "      <td> 106</td>\n",
       "      <td> 200</td>\n",
       "      <td> 34</td>\n",
       "      <td> 14</td>\n",
       "      <td> 26</td>\n",
       "      <td>  92</td>\n",
       "      <td>  2</td>\n",
       "      <td>  4</td>\n",
       "      <td> 37</td>\n",
       "      <td> 54</td>\n",
       "      <td>  6</td>\n",
       "      <td>  2</td>\n",
       "      <td>  5</td>\n",
       "      <td>  7</td>\n",
       "      <td> 21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38723</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1957</td>\n",
       "      <td> 1</td>\n",
       "      <td> ML1</td>\n",
       "      <td> NL</td>\n",
       "      <td> 151</td>\n",
       "      <td> 615</td>\n",
       "      <td> 118</td>\n",
       "      <td> 198</td>\n",
       "      <td> 27</td>\n",
       "      <td>  6</td>\n",
       "      <td> 44</td>\n",
       "      <td> 132</td>\n",
       "      <td>  1</td>\n",
       "      <td>  1</td>\n",
       "      <td> 57</td>\n",
       "      <td> 58</td>\n",
       "      <td> 15</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  3</td>\n",
       "      <td> 13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39338</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1958</td>\n",
       "      <td> 1</td>\n",
       "      <td> ML1</td>\n",
       "      <td> NL</td>\n",
       "      <td> 153</td>\n",
       "      <td> 601</td>\n",
       "      <td> 109</td>\n",
       "      <td> 196</td>\n",
       "      <td> 34</td>\n",
       "      <td>  4</td>\n",
       "      <td> 30</td>\n",
       "      <td>  95</td>\n",
       "      <td>  4</td>\n",
       "      <td>  1</td>\n",
       "      <td> 59</td>\n",
       "      <td> 49</td>\n",
       "      <td> 16</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  3</td>\n",
       "      <td> 21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39976</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1959</td>\n",
       "      <td> 1</td>\n",
       "      <td> ML1</td>\n",
       "      <td> NL</td>\n",
       "      <td> 154</td>\n",
       "      <td> 629</td>\n",
       "      <td> 116</td>\n",
       "      <td> 223</td>\n",
       "      <td> 46</td>\n",
       "      <td>  7</td>\n",
       "      <td> 39</td>\n",
       "      <td> 123</td>\n",
       "      <td>  8</td>\n",
       "      <td>  0</td>\n",
       "      <td> 51</td>\n",
       "      <td> 54</td>\n",
       "      <td> 17</td>\n",
       "      <td>  4</td>\n",
       "      <td>  0</td>\n",
       "      <td>  9</td>\n",
       "      <td> 19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40608</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1960</td>\n",
       "      <td> 1</td>\n",
       "      <td> ML1</td>\n",
       "      <td> NL</td>\n",
       "      <td> 153</td>\n",
       "      <td> 590</td>\n",
       "      <td> 102</td>\n",
       "      <td> 172</td>\n",
       "      <td> 20</td>\n",
       "      <td> 11</td>\n",
       "      <td> 40</td>\n",
       "      <td> 126</td>\n",
       "      <td> 16</td>\n",
       "      <td>  7</td>\n",
       "      <td> 60</td>\n",
       "      <td> 63</td>\n",
       "      <td> 13</td>\n",
       "      <td>  2</td>\n",
       "      <td>  0</td>\n",
       "      <td> 12</td>\n",
       "      <td>  8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41245</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1961</td>\n",
       "      <td> 1</td>\n",
       "      <td> ML1</td>\n",
       "      <td> NL</td>\n",
       "      <td> 155</td>\n",
       "      <td> 603</td>\n",
       "      <td> 115</td>\n",
       "      <td> 197</td>\n",
       "      <td> 39</td>\n",
       "      <td> 10</td>\n",
       "      <td> 34</td>\n",
       "      <td> 120</td>\n",
       "      <td> 21</td>\n",
       "      <td>  9</td>\n",
       "      <td> 56</td>\n",
       "      <td> 64</td>\n",
       "      <td> 20</td>\n",
       "      <td>  2</td>\n",
       "      <td>  1</td>\n",
       "      <td>  9</td>\n",
       "      <td> 16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41943</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1962</td>\n",
       "      <td> 1</td>\n",
       "      <td> ML1</td>\n",
       "      <td> NL</td>\n",
       "      <td> 156</td>\n",
       "      <td> 592</td>\n",
       "      <td> 127</td>\n",
       "      <td> 191</td>\n",
       "      <td> 28</td>\n",
       "      <td>  6</td>\n",
       "      <td> 45</td>\n",
       "      <td> 128</td>\n",
       "      <td> 15</td>\n",
       "      <td>  7</td>\n",
       "      <td> 66</td>\n",
       "      <td> 73</td>\n",
       "      <td> 14</td>\n",
       "      <td>  3</td>\n",
       "      <td>  0</td>\n",
       "      <td>  6</td>\n",
       "      <td> 14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42703</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1963</td>\n",
       "      <td> 1</td>\n",
       "      <td> ML1</td>\n",
       "      <td> NL</td>\n",
       "      <td> 161</td>\n",
       "      <td> 631</td>\n",
       "      <td> 121</td>\n",
       "      <td> 201</td>\n",
       "      <td> 29</td>\n",
       "      <td>  4</td>\n",
       "      <td> 44</td>\n",
       "      <td> 130</td>\n",
       "      <td> 31</td>\n",
       "      <td>  5</td>\n",
       "      <td> 78</td>\n",
       "      <td> 94</td>\n",
       "      <td> 18</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  5</td>\n",
       "      <td> 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43455</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1964</td>\n",
       "      <td> 1</td>\n",
       "      <td> ML1</td>\n",
       "      <td> NL</td>\n",
       "      <td> 145</td>\n",
       "      <td> 570</td>\n",
       "      <td> 103</td>\n",
       "      <td> 187</td>\n",
       "      <td> 30</td>\n",
       "      <td>  2</td>\n",
       "      <td> 24</td>\n",
       "      <td>  95</td>\n",
       "      <td> 22</td>\n",
       "      <td>  4</td>\n",
       "      <td> 62</td>\n",
       "      <td> 46</td>\n",
       "      <td>  9</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  2</td>\n",
       "      <td> 22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44209</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1965</td>\n",
       "      <td> 1</td>\n",
       "      <td> ML1</td>\n",
       "      <td> NL</td>\n",
       "      <td> 150</td>\n",
       "      <td> 570</td>\n",
       "      <td> 109</td>\n",
       "      <td> 181</td>\n",
       "      <td> 40</td>\n",
       "      <td>  1</td>\n",
       "      <td> 32</td>\n",
       "      <td>  89</td>\n",
       "      <td> 24</td>\n",
       "      <td>  4</td>\n",
       "      <td> 60</td>\n",
       "      <td> 81</td>\n",
       "      <td> 10</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  8</td>\n",
       "      <td> 15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44960</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1966</td>\n",
       "      <td> 1</td>\n",
       "      <td> ATL</td>\n",
       "      <td> NL</td>\n",
       "      <td> 158</td>\n",
       "      <td> 603</td>\n",
       "      <td> 117</td>\n",
       "      <td> 168</td>\n",
       "      <td> 23</td>\n",
       "      <td>  1</td>\n",
       "      <td> 44</td>\n",
       "      <td> 127</td>\n",
       "      <td> 21</td>\n",
       "      <td>  3</td>\n",
       "      <td> 76</td>\n",
       "      <td> 96</td>\n",
       "      <td> 15</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  8</td>\n",
       "      <td> 14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45734</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1967</td>\n",
       "      <td> 1</td>\n",
       "      <td> ATL</td>\n",
       "      <td> NL</td>\n",
       "      <td> 155</td>\n",
       "      <td> 600</td>\n",
       "      <td> 113</td>\n",
       "      <td> 184</td>\n",
       "      <td> 37</td>\n",
       "      <td>  3</td>\n",
       "      <td> 39</td>\n",
       "      <td> 109</td>\n",
       "      <td> 17</td>\n",
       "      <td>  6</td>\n",
       "      <td> 63</td>\n",
       "      <td> 97</td>\n",
       "      <td> 19</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  6</td>\n",
       "      <td> 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46520</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1968</td>\n",
       "      <td> 1</td>\n",
       "      <td> ATL</td>\n",
       "      <td> NL</td>\n",
       "      <td> 160</td>\n",
       "      <td> 606</td>\n",
       "      <td>  84</td>\n",
       "      <td> 174</td>\n",
       "      <td> 33</td>\n",
       "      <td>  4</td>\n",
       "      <td> 29</td>\n",
       "      <td>  86</td>\n",
       "      <td> 28</td>\n",
       "      <td>  5</td>\n",
       "      <td> 64</td>\n",
       "      <td> 62</td>\n",
       "      <td> 23</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  5</td>\n",
       "      <td> 21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47235</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1969</td>\n",
       "      <td> 1</td>\n",
       "      <td> ATL</td>\n",
       "      <td> NL</td>\n",
       "      <td> 147</td>\n",
       "      <td> 547</td>\n",
       "      <td> 100</td>\n",
       "      <td> 164</td>\n",
       "      <td> 30</td>\n",
       "      <td>  3</td>\n",
       "      <td> 44</td>\n",
       "      <td>  97</td>\n",
       "      <td>  9</td>\n",
       "      <td> 10</td>\n",
       "      <td> 87</td>\n",
       "      <td> 47</td>\n",
       "      <td> 19</td>\n",
       "      <td>  2</td>\n",
       "      <td>  0</td>\n",
       "      <td>  3</td>\n",
       "      <td> 14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48167</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1970</td>\n",
       "      <td> 1</td>\n",
       "      <td> ATL</td>\n",
       "      <td> NL</td>\n",
       "      <td> 150</td>\n",
       "      <td> 516</td>\n",
       "      <td> 103</td>\n",
       "      <td> 154</td>\n",
       "      <td> 26</td>\n",
       "      <td>  1</td>\n",
       "      <td> 38</td>\n",
       "      <td> 118</td>\n",
       "      <td>  9</td>\n",
       "      <td>  0</td>\n",
       "      <td> 74</td>\n",
       "      <td> 63</td>\n",
       "      <td> 15</td>\n",
       "      <td>  2</td>\n",
       "      <td>  0</td>\n",
       "      <td>  6</td>\n",
       "      <td> 13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49086</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1971</td>\n",
       "      <td> 1</td>\n",
       "      <td> ATL</td>\n",
       "      <td> NL</td>\n",
       "      <td> 139</td>\n",
       "      <td> 495</td>\n",
       "      <td>  95</td>\n",
       "      <td> 162</td>\n",
       "      <td> 22</td>\n",
       "      <td>  3</td>\n",
       "      <td> 47</td>\n",
       "      <td> 118</td>\n",
       "      <td>  1</td>\n",
       "      <td>  1</td>\n",
       "      <td> 71</td>\n",
       "      <td> 58</td>\n",
       "      <td> 21</td>\n",
       "      <td>  2</td>\n",
       "      <td>  0</td>\n",
       "      <td>  5</td>\n",
       "      <td>  9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49969</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1972</td>\n",
       "      <td> 1</td>\n",
       "      <td> ATL</td>\n",
       "      <td> NL</td>\n",
       "      <td> 129</td>\n",
       "      <td> 449</td>\n",
       "      <td>  75</td>\n",
       "      <td> 119</td>\n",
       "      <td> 10</td>\n",
       "      <td>  0</td>\n",
       "      <td> 34</td>\n",
       "      <td>  77</td>\n",
       "      <td>  4</td>\n",
       "      <td>  0</td>\n",
       "      <td> 92</td>\n",
       "      <td> 55</td>\n",
       "      <td> 15</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  2</td>\n",
       "      <td> 17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50857</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1973</td>\n",
       "      <td> 1</td>\n",
       "      <td> ATL</td>\n",
       "      <td> NL</td>\n",
       "      <td> 120</td>\n",
       "      <td> 392</td>\n",
       "      <td>  84</td>\n",
       "      <td> 118</td>\n",
       "      <td> 12</td>\n",
       "      <td>  1</td>\n",
       "      <td> 40</td>\n",
       "      <td>  96</td>\n",
       "      <td>  1</td>\n",
       "      <td>  1</td>\n",
       "      <td> 68</td>\n",
       "      <td> 51</td>\n",
       "      <td> 13</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  4</td>\n",
       "      <td>  7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51749</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1974</td>\n",
       "      <td> 1</td>\n",
       "      <td> ATL</td>\n",
       "      <td> NL</td>\n",
       "      <td> 112</td>\n",
       "      <td> 340</td>\n",
       "      <td>  47</td>\n",
       "      <td>  91</td>\n",
       "      <td> 16</td>\n",
       "      <td>  0</td>\n",
       "      <td> 20</td>\n",
       "      <td>  69</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td> 39</td>\n",
       "      <td> 29</td>\n",
       "      <td>  6</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>  2</td>\n",
       "      <td>  6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52663</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1975</td>\n",
       "      <td> 1</td>\n",
       "      <td> ML4</td>\n",
       "      <td> AL</td>\n",
       "      <td> 137</td>\n",
       "      <td> 465</td>\n",
       "      <td>  45</td>\n",
       "      <td> 109</td>\n",
       "      <td> 16</td>\n",
       "      <td>  2</td>\n",
       "      <td> 12</td>\n",
       "      <td>  60</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td> 70</td>\n",
       "      <td> 51</td>\n",
       "      <td>  3</td>\n",
       "      <td>  1</td>\n",
       "      <td>  1</td>\n",
       "      <td>  6</td>\n",
       "      <td> 15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53570</th>\n",
       "      <td> aaronha01</td>\n",
       "      <td> 1976</td>\n",
       "      <td> 1</td>\n",
       "      <td> ML4</td>\n",
       "      <td> AL</td>\n",
       "      <td>  85</td>\n",
       "      <td> 271</td>\n",
       "      <td>  22</td>\n",
       "      <td>  62</td>\n",
       "      <td>  8</td>\n",
       "      <td>  0</td>\n",
       "      <td> 10</td>\n",
       "      <td>  35</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td> 35</td>\n",
       "      <td> 38</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  2</td>\n",
       "      <td>  8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54456</th>\n",
       "      <td>  aasedo01</td>\n",
       "      <td> 1977</td>\n",
       "      <td> 1</td>\n",
       "      <td> BOS</td>\n",
       "      <td> AL</td>\n",
       "      <td>  13</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55440</th>\n",
       "      <td>  aasedo01</td>\n",
       "      <td> 1978</td>\n",
       "      <td> 1</td>\n",
       "      <td> CAL</td>\n",
       "      <td> AL</td>\n",
       "      <td>  29</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56400</th>\n",
       "      <td>  aasedo01</td>\n",
       "      <td> 1979</td>\n",
       "      <td> 1</td>\n",
       "      <td> CAL</td>\n",
       "      <td> AL</td>\n",
       "      <td>  37</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57361</th>\n",
       "      <td>  aasedo01</td>\n",
       "      <td> 1980</td>\n",
       "      <td> 1</td>\n",
       "      <td> CAL</td>\n",
       "      <td> AL</td>\n",
       "      <td>  40</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58311</th>\n",
       "      <td>  aasedo01</td>\n",
       "      <td> 1981</td>\n",
       "      <td> 1</td>\n",
       "      <td> CAL</td>\n",
       "      <td> AL</td>\n",
       "      <td>  39</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59255</th>\n",
       "      <td>  aasedo01</td>\n",
       "      <td> 1982</td>\n",
       "      <td> 1</td>\n",
       "      <td> CAL</td>\n",
       "      <td> AL</td>\n",
       "      <td>  24</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60247</th>\n",
       "      <td> abbotgl01</td>\n",
       "      <td> 1983</td>\n",
       "      <td> 1</td>\n",
       "      <td> SEA</td>\n",
       "      <td> AL</td>\n",
       "      <td>  14</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61253</th>\n",
       "      <td>  aasedo01</td>\n",
       "      <td> 1984</td>\n",
       "      <td> 1</td>\n",
       "      <td> CAL</td>\n",
       "      <td> AL</td>\n",
       "      <td>  23</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62237</th>\n",
       "      <td>  aasedo01</td>\n",
       "      <td> 1985</td>\n",
       "      <td> 1</td>\n",
       "      <td> BAL</td>\n",
       "      <td> AL</td>\n",
       "      <td>  54</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63235</th>\n",
       "      <td>  aasedo01</td>\n",
       "      <td> 1986</td>\n",
       "      <td> 1</td>\n",
       "      <td> BAL</td>\n",
       "      <td> AL</td>\n",
       "      <td>  66</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64252</th>\n",
       "      <td>  aasedo01</td>\n",
       "      <td> 1987</td>\n",
       "      <td> 1</td>\n",
       "      <td> BAL</td>\n",
       "      <td> AL</td>\n",
       "      <td>   7</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65300</th>\n",
       "      <td>  aasedo01</td>\n",
       "      <td> 1988</td>\n",
       "      <td> 1</td>\n",
       "      <td> BAL</td>\n",
       "      <td> AL</td>\n",
       "      <td>  35</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66335</th>\n",
       "      <td>  aasedo01</td>\n",
       "      <td> 1989</td>\n",
       "      <td> 1</td>\n",
       "      <td> NYN</td>\n",
       "      <td> NL</td>\n",
       "      <td>  49</td>\n",
       "      <td>   5</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  3</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67408</th>\n",
       "      <td>  aasedo01</td>\n",
       "      <td> 1990</td>\n",
       "      <td> 1</td>\n",
       "      <td> LAN</td>\n",
       "      <td> NL</td>\n",
       "      <td>  32</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68523</th>\n",
       "      <td> abbotji01</td>\n",
       "      <td> 1991</td>\n",
       "      <td> 1</td>\n",
       "      <td> CAL</td>\n",
       "      <td> AL</td>\n",
       "      <td>  34</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69609</th>\n",
       "      <td> abbotji01</td>\n",
       "      <td> 1992</td>\n",
       "      <td> 1</td>\n",
       "      <td> CAL</td>\n",
       "      <td> AL</td>\n",
       "      <td>  29</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70675</th>\n",
       "      <td> abbotji01</td>\n",
       "      <td> 1993</td>\n",
       "      <td> 1</td>\n",
       "      <td> NYA</td>\n",
       "      <td> AL</td>\n",
       "      <td>  32</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71855</th>\n",
       "      <td> abbotji01</td>\n",
       "      <td> 1994</td>\n",
       "      <td> 1</td>\n",
       "      <td> NYA</td>\n",
       "      <td> AL</td>\n",
       "      <td>  24</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72885</th>\n",
       "      <td> abbotji01</td>\n",
       "      <td> 1995</td>\n",
       "      <td> 1</td>\n",
       "      <td> CHA</td>\n",
       "      <td> AL</td>\n",
       "      <td>  17</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74138</th>\n",
       "      <td> abbotji01</td>\n",
       "      <td> 1996</td>\n",
       "      <td> 1</td>\n",
       "      <td> CAL</td>\n",
       "      <td> AL</td>\n",
       "      <td>  27</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75391</th>\n",
       "      <td> abbotje01</td>\n",
       "      <td> 1997</td>\n",
       "      <td> 1</td>\n",
       "      <td> CHA</td>\n",
       "      <td> AL</td>\n",
       "      <td>  19</td>\n",
       "      <td>  38</td>\n",
       "      <td>   8</td>\n",
       "      <td>  10</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>   2</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  6</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76627</th>\n",
       "      <td> abbotje01</td>\n",
       "      <td> 1998</td>\n",
       "      <td> 1</td>\n",
       "      <td> CHA</td>\n",
       "      <td> AL</td>\n",
       "      <td>  89</td>\n",
       "      <td> 244</td>\n",
       "      <td>  33</td>\n",
       "      <td>  68</td>\n",
       "      <td> 14</td>\n",
       "      <td>  1</td>\n",
       "      <td> 12</td>\n",
       "      <td>  41</td>\n",
       "      <td>  3</td>\n",
       "      <td>  3</td>\n",
       "      <td>  9</td>\n",
       "      <td> 28</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  2</td>\n",
       "      <td>  5</td>\n",
       "      <td>  2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77949</th>\n",
       "      <td> abbotje01</td>\n",
       "      <td> 1999</td>\n",
       "      <td> 1</td>\n",
       "      <td> CHA</td>\n",
       "      <td> AL</td>\n",
       "      <td>  17</td>\n",
       "      <td>  57</td>\n",
       "      <td>   5</td>\n",
       "      <td>   9</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  2</td>\n",
       "      <td>   6</td>\n",
       "      <td>  1</td>\n",
       "      <td>  1</td>\n",
       "      <td>  5</td>\n",
       "      <td> 12</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>  1</td>\n",
       "      <td>  4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79248</th>\n",
       "      <td> abbotje01</td>\n",
       "      <td> 2000</td>\n",
       "      <td> 1</td>\n",
       "      <td> CHA</td>\n",
       "      <td> AL</td>\n",
       "      <td>  80</td>\n",
       "      <td> 215</td>\n",
       "      <td>  31</td>\n",
       "      <td>  59</td>\n",
       "      <td> 15</td>\n",
       "      <td>  1</td>\n",
       "      <td>  3</td>\n",
       "      <td>  29</td>\n",
       "      <td>  2</td>\n",
       "      <td>  1</td>\n",
       "      <td> 21</td>\n",
       "      <td> 38</td>\n",
       "      <td>  1</td>\n",
       "      <td>  2</td>\n",
       "      <td>  2</td>\n",
       "      <td>  1</td>\n",
       "      <td>  2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80632</th>\n",
       "      <td>  abadan01</td>\n",
       "      <td> 2001</td>\n",
       "      <td> 1</td>\n",
       "      <td> OAK</td>\n",
       "      <td> AL</td>\n",
       "      <td>   1</td>\n",
       "      <td>   1</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81971</th>\n",
       "      <td> abbotpa01</td>\n",
       "      <td> 2002</td>\n",
       "      <td> 1</td>\n",
       "      <td> SEA</td>\n",
       "      <td> AL</td>\n",
       "      <td>   7</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83290</th>\n",
       "      <td>  abadan01</td>\n",
       "      <td> 2003</td>\n",
       "      <td> 1</td>\n",
       "      <td> BOS</td>\n",
       "      <td> AL</td>\n",
       "      <td>   9</td>\n",
       "      <td>  17</td>\n",
       "      <td>   1</td>\n",
       "      <td>   2</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>  2</td>\n",
       "      <td>  5</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84637</th>\n",
       "      <td> aardsda01</td>\n",
       "      <td> 2004</td>\n",
       "      <td> 1</td>\n",
       "      <td> SFN</td>\n",
       "      <td> NL</td>\n",
       "      <td>  11</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85983</th>\n",
       "      <td> abernbr01</td>\n",
       "      <td> 2005</td>\n",
       "      <td> 1</td>\n",
       "      <td> MIN</td>\n",
       "      <td> AL</td>\n",
       "      <td>  24</td>\n",
       "      <td>  67</td>\n",
       "      <td>   5</td>\n",
       "      <td>  16</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>   6</td>\n",
       "      <td>  2</td>\n",
       "      <td>  0</td>\n",
       "      <td>  7</td>\n",
       "      <td>  9</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>  3</td>\n",
       "      <td>  1</td>\n",
       "      <td>  2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87313</th>\n",
       "      <td> aardsda01</td>\n",
       "      <td> 2006</td>\n",
       "      <td> 1</td>\n",
       "      <td> CHN</td>\n",
       "      <td> NL</td>\n",
       "      <td>  45</td>\n",
       "      <td>   2</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88691</th>\n",
       "      <td> aardsda01</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 1</td>\n",
       "      <td> CHA</td>\n",
       "      <td> AL</td>\n",
       "      <td>  25</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90076</th>\n",
       "      <td> aardsda01</td>\n",
       "      <td> 2008</td>\n",
       "      <td> 1</td>\n",
       "      <td> BOS</td>\n",
       "      <td> AL</td>\n",
       "      <td>  47</td>\n",
       "      <td>   1</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  1</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91461</th>\n",
       "      <td> aardsda01</td>\n",
       "      <td> 2009</td>\n",
       "      <td> 1</td>\n",
       "      <td> SEA</td>\n",
       "      <td> AL</td>\n",
       "      <td>  73</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92849</th>\n",
       "      <td> aardsda01</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 1</td>\n",
       "      <td> SEA</td>\n",
       "      <td> AL</td>\n",
       "      <td>  53</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94205</th>\n",
       "      <td>  abadfe01</td>\n",
       "      <td> 2011</td>\n",
       "      <td> 1</td>\n",
       "      <td> HOU</td>\n",
       "      <td> NL</td>\n",
       "      <td>  29</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95594</th>\n",
       "      <td> aardsda01</td>\n",
       "      <td> 2012</td>\n",
       "      <td> 1</td>\n",
       "      <td> NYA</td>\n",
       "      <td> AL</td>\n",
       "      <td>   1</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97002</th>\n",
       "      <td> aardsda01</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 1</td>\n",
       "      <td> NYN</td>\n",
       "      <td> NL</td>\n",
       "      <td>  43</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98411</th>\n",
       "      <td>  abadfe01</td>\n",
       "      <td> 2014</td>\n",
       "      <td> 1</td>\n",
       "      <td> OAK</td>\n",
       "      <td> AL</td>\n",
       "      <td>  69</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>   0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          player  year  stint team  lg    g   ab    r    h  X2b  X3b  hr  rbi  sb  cs  bb  so  ibb  hbp  sh  sf  gidp\n",
       "0      abercda01  1871      1  TRO  NA    1    4    0    0    0    0   0    0   0   0   0   0  NaN  NaN NaN NaN   NaN\n",
       "115    allenha01  1872      1  MID  NA   16   66    8   18    1    0   0    7   0   0   0   1  NaN  NaN NaN NaN   NaN\n",
       "271     addybo01  1873      1  PH2  NA   10   51   12   16    1    0   0   10   0   1   2   0  NaN  NaN NaN NaN   NaN\n",
       "396     addybo01  1874      1  HR1  NA   50  213   25   51    9    2   0   23   4   2   1   0  NaN  NaN NaN NaN   NaN\n",
       "519    abadijo01  1875      1  PH3  NA   11   45    3   10    0    0   0    4   1   0   0   3  NaN  NaN NaN NaN   NaN\n",
       "737     addybo01  1876      1  CHN  NL   32  142   36   40    4    1   0   16 NaN NaN   5   0  NaN  NaN NaN NaN   NaN\n",
       "861     addybo01  1877      1  CN1  NL   57  245   27   68    2    3   0   31 NaN NaN   6   5  NaN  NaN NaN NaN   NaN\n",
       "958    allisdo01  1878      1  PRO  NL   19   76    9   22    2    0   0    7 NaN NaN   1   8  NaN  NaN NaN NaN   NaN\n",
       "1038   adamsge01  1879      1  SR1  NL    4   13    0    3    0    0   0    0 NaN NaN   1   1  NaN  NaN NaN NaN   NaN\n",
       "1165   ahearch01  1880      1  TRN  NL    1    4    1    1    0    0   0    0 NaN NaN   0   0  NaN  NaN NaN NaN   NaN\n",
       "1300   ansonca01  1881      1  CHN  NL   84  343   67  137   21    7   1   82 NaN NaN  26   4  NaN  NaN NaN NaN   NaN\n",
       "1432   ansonca01  1882      1  CHN  NL   82  348   69  126   29    8   1   83 NaN NaN  20   7  NaN  NaN NaN NaN   NaN\n",
       "1683   allenmy01  1883      1  NY1  NL    1    4    0    0    0    0   0    1 NaN NaN   0   2  NaN  NaN NaN NaN   NaN\n",
       "1966     akejo01  1884      1  BL2  AA   13   52    1   10    0    1   0    2   0 NaN   0 NaN  NaN    1 NaN NaN   NaN\n",
       "2748   alvorbi01  1885      1  SL5  NL    2    5    0    0    0    0   0    0 NaN NaN   1   2  NaN  NaN NaN NaN   NaN\n",
       "3101   allenmy01  1886      1  BSN  NL    1    3    0    0    0    0   0    0   0 NaN   0   1  NaN  NaN NaN NaN   NaN\n",
       "3452   allenmy01  1887      1  CL3  AA  117  463   66  128   22   10   4   77  26 NaN  36 NaN  NaN    5 NaN NaN   NaN\n",
       "3785   albergu01  1888      1  CL3  AA  102  364   51   75   10    6   1   48  26 NaN  41 NaN  NaN    7 NaN NaN   NaN\n",
       "4136   alvorbi01  1889      1  KC2  AA   50  186   23   43    8    9   0   18   3 NaN  10  35  NaN    0 NaN NaN   NaN\n",
       "4478   abbotda01  1890      1  TL2  AA    3    7    0    1    0    1   0    1   1 NaN   0 NaN  NaN    0 NaN NaN   NaN\n",
       "5053   albergu01  1891      1  ML3  AA   12   41    6    4    0    0   0    2   1 NaN   7   5  NaN    2 NaN NaN   NaN\n",
       "5461   abbeybe01  1892      1  WAS  NL   27   75    5    9    1    0   0    3   2 NaN   9  27  NaN    0 NaN NaN   NaN\n",
       "5771   abbeybe01  1893      1  CHN  NL    7   26    2    6    1    0   0    2   0 NaN   2   3  NaN    0 NaN NaN   NaN\n",
       "6057   abbeybe01  1894      1  CHN  NL   11   39    3    5    0    0   0    4   1 NaN   2   7  NaN    0 NaN NaN   NaN\n",
       "6351   abbeybe01  1895      1  CHN  NL    1    3    0    1    0    0   0    0   0 NaN   0   0  NaN    0   0 NaN   NaN\n",
       "6656   abbeybe01  1896      1  BRO  NL   25   63    7   12    1    2   0    7   0 NaN   6  13  NaN    0   4 NaN   NaN\n",
       "6951   abbated01  1897      1  PHI  NL    3   10    0    3    0    0   0    0   0 NaN   1 NaN  NaN    0   0 NaN   NaN\n",
       "7231   abbated01  1898      1  PHI  NL   25   92    9   21    4    0   0   14   4 NaN   7 NaN  NaN    1   2 NaN   NaN\n",
       "7554   anderjo01  1899      1  BRO  NL  117  439   65  118   18    7   4   92  25 NaN  27 NaN  NaN    4   2 NaN   NaN\n",
       "7902   allenbo01  1900      1  CIN  NL    5   15    0    2    1    0   0    1   0 NaN   0 NaN  NaN    1   0 NaN   NaN\n",
       "8097   anderjo01  1901      1  MLA  AL  138  576   90  190   46    7   8   99  35 NaN  24 NaN  NaN    3   4 NaN   NaN\n",
       "8509   adamsjo01  1902      1  SLN  NL    1    2    0    0    0    0   0    0   0 NaN   0 NaN  NaN    0   0 NaN   NaN\n",
       "8964   abbated01  1903      1  BSN  NL  136  489   61  111   18    5   1   46  23 NaN  52 NaN  NaN    4  17 NaN   NaN\n",
       "9364   abbated01  1904      1  BSN  NL  154  579   76  148   18   10   3   54  24 NaN  40 NaN  NaN    5  13 NaN   NaN\n",
       "9770   abbated01  1905      1  BSN  NL  153  610   70  170   25   12   3   41  30 NaN  35 NaN  NaN    8   5 NaN   NaN\n",
       "10177  abstebi01  1906      1  PIT  NL    8   20    2    4    0    0   0    3   2 NaN   0 NaN  NaN    0   1 NaN   NaN\n",
       "10614  abbated01  1907      1  PIT  NL  147  496   63  130   14    7   2   82  35 NaN  65 NaN  NaN    8  22 NaN   NaN\n",
       "11060  abbated01  1908      1  PIT  NL  146  500   43  125   16    7   1   61  22 NaN  58 NaN  NaN    7  25 NaN   NaN\n",
       "11530  abbated01  1909      1  PIT  NL   36   87   13   20    0    0   1   16   2 NaN  19 NaN  NaN    0   1 NaN   NaN\n",
       "12073  abbated01  1910      1  PIT  NL    3    3    0    0    0    0   0    0   0 NaN   0   0  NaN    0   0 NaN   NaN\n",
       "12611  ablesha01  1911      1  NYA  AL    3    4    0    0    0    0   0    0   0 NaN   0 NaN  NaN    0   0 NaN   NaN\n",
       "13178  adamsba01  1912      1  PIT  NL   28   53    5   12    3    1   0    3   0 NaN   8   9  NaN    0   1 NaN   NaN\n",
       "13808  acostme01  1913      1  WS1  AL   12   20    3    6    0    1   0    1   2 NaN   4   2  NaN    0   0 NaN   NaN\n",
       "14424  acostme01  1914      1  WS1  AL   39   74   10   19    2    2   0    4   3   4  11  18  NaN    0   0 NaN   NaN\n",
       "15219  acostme01  1915      1  WS1  AL   72  163   20   34    4    1   0   18   8   4  28  15  NaN    4   9 NaN   NaN\n",
       "16015  acostme01  1916      1  WS1  AL    5    8    0    1    0    0   0    0   0 NaN   2   0  NaN    0   0 NaN   NaN\n",
       "16580  adamsbe01  1917      1  PHI  NL   43  107    4   22    4    1   1    7   0 NaN   0  20  NaN    0   4 NaN   NaN\n",
       "17096  acostme01  1918      1  WS1  AL    3    2    0    0    0    0   0    0   0 NaN   0   1  NaN    0   0 NaN   NaN\n",
       "17602  adamsba01  1919      1  PIT  NL   34   92    2   17    2    1   0    4   0 NaN   6  13  NaN    0   3 NaN   NaN\n",
       "18136  acostjo01  1920      1  WS1  AL   17   25    2    6    1    0   0    1   0   0   4   7  NaN    0   2 NaN   NaN\n",
       "18651  acostjo01  1921      1  WS1  AL   33   30    2    2    0    0   0    0   1   0   6  14  NaN    0   1 NaN   NaN\n",
       "19171  acostjo01  1922      1  CHA  AL    5    5    0    1    0    0   0    0   0   0   1   1  NaN    0   0 NaN   NaN\n",
       "19684  abramge01  1923      1  CIN  NL    3    1    0    1    0    0   0    0   0   0   0   0  NaN    0   0 NaN   NaN\n",
       "20214  adamsba01  1924      1  PIT  NL    9   11    1    2    1    0   0    1   0   0   0   1  NaN    0   1 NaN   NaN\n",
       "20761  adamsba01  1925      1  PIT  NL   33   31    3    7    1    0   0    1   0   0   3   6  NaN    0   1 NaN   NaN\n",
       "21318  adamsba01  1926      1  PIT  NL   19    9    1    2    0    0   0    0   0 NaN   0   0  NaN    0   0 NaN   NaN\n",
       "21844  adamssp01  1927      1  CHN  NL  146  647  100  189   17    7   0   49  26 NaN  42  26  NaN    0  16 NaN   NaN\n",
       "22385  adamssp01  1928      1  PIT  NL  135  539   91  149   14    6   0   38   8 NaN  64  18  NaN    4  21 NaN   NaN\n",
       "22915  adamssp01  1929      1  PIT  NL   74  196   37   51    8    1   0   11   3 NaN  15   5  NaN    1   5 NaN   NaN\n",
       "23445  adamssp01  1930      1  SLN  NL  137  570   98  179   36    9   0   55   7 NaN  45  27  NaN    1  15 NaN   NaN\n",
       "23976  adairji01  1931      1  CHN  NL   18   76    9   21    3    1   0    3   1 NaN   1   8  NaN    0   2 NaN   NaN\n",
       "24485  adamsbo02  1932      1  PHI  NL    4    0    0    0    0    0   0    0   0   0   0   0    0    0   0   0     0\n",
       "25012  adamssp01  1933      1  SLN  NL    8   30    1    5    1    0   0    0   0 NaN   1   3  NaN    1   0 NaN     2\n",
       "25503  adamssp01  1934      1  CIN  NL   87  278   38   70   16    1   0   14   2 NaN  20  10  NaN    2   1 NaN     2\n",
       "26024  allenet01  1935      1  PHI  NL  156  645   90  198   46    1   8   63   5 NaN  43  54  NaN    1  11 NaN     5\n",
       "26537  allenet01  1936      1  PHI  NL   30  125   21   37    3    1   1    9   4 NaN   4   8  NaN    0   3 NaN     4\n",
       "27048  alexahu01  1937      1  CLE  AL    7   11    0    1    0    0   0    0   1   0   0   5  NaN    0   0 NaN   NaN\n",
       "27574  allenet01  1938      1  SLA  AL   19   33    4   10    3    1   0    4   0   0   2   4  NaN    0   1 NaN   NaN\n",
       "28104  adamsbu01  1939      1  SLN  NL    2    1    1    0    0    0   0    0   0 NaN   0   0  NaN    0   0 NaN     0\n",
       "28683  aderhmo01  1940      1  WS1  AL    1    2    0    0    0    0   0    0   0   0   0   0  NaN    0   0 NaN     0\n",
       "29224  adamsac01  1941      1  NY1  NL   38   12    0    1    0    0   0    0   0 NaN   0   3  NaN    0   0 NaN     0\n",
       "29806  abernte01  1942      1  PHA  AL    1    0    0    0    0    0   0    0   0   0   1   0  NaN    0   0 NaN     0\n",
       "30345  abernte01  1943      1  PHA  AL    5    4    0    1    0    0   0    0   0   0   0   2  NaN    0   0 NaN     0\n",
       "30902  abernte01  1944      1  PHA  AL    1    1    0    0    0    0   0    0   0   0   0   1  NaN    0   0 NaN     0\n",
       "31471  adamsac01  1945      1  NY1  NL   65   16    2    3    0    0   0    1   0 NaN   2   3  NaN    0   2 NaN     0\n",
       "32050  abernwo01  1946      1  NY1  NL   15    8    0    0    0    0   0    0   0 NaN   0   4  NaN    0   0 NaN     0\n",
       "32732  abernwo01  1947      1  NY1  NL    1    0    0    0    0    0   0    0   0   0   0   0    0    0   0   0     0\n",
       "33314  aberscl01  1948      1  CHN  NL   12   32    1    6    1    0   1    6   0 NaN   5  10  NaN    0   0 NaN     0\n",
       "33887  aberscl01  1949      1  CHN  NL    4    7    0    0    0    0   0    0   0 NaN   0   2  NaN    0   0 NaN     1\n",
       "34460   aberal01  1950      1  CLE  AL    1    2    0    0    0    0   0    0   0   0   1   1  NaN    0   0 NaN     0\n",
       "35037  abramca01  1951      1  BRO  NL   67  150   27   42    8    0   3   19   3   2  36  26  NaN    0   0 NaN     5\n",
       "35653  abernbi01  1952      1  CLE  AL    1    1    0    0    0    0   0    0   0   0   0   0  NaN    0   0 NaN     0\n",
       "36285   aberal01  1953      1  CLE  AL    6    0    0    0    0    0   0    0   0   0   2   0  NaN    0   0 NaN     0\n",
       "36871  aaronha01  1954      1  ML1  NL  122  468   58  131   27    6  13   69   2   2  28  39  NaN    3   6   4    13\n",
       "37447  aaronha01  1955      1  ML1  NL  153  602  105  189   37    9  27  106   3   1  49  61    5    3   7   4    20\n",
       "38102  aaronha01  1956      1  ML1  NL  153  609  106  200   34   14  26   92   2   4  37  54    6    2   5   7    21\n",
       "38723  aaronha01  1957      1  ML1  NL  151  615  118  198   27    6  44  132   1   1  57  58   15    0   0   3    13\n",
       "39338  aaronha01  1958      1  ML1  NL  153  601  109  196   34    4  30   95   4   1  59  49   16    1   0   3    21\n",
       "39976  aaronha01  1959      1  ML1  NL  154  629  116  223   46    7  39  123   8   0  51  54   17    4   0   9    19\n",
       "40608  aaronha01  1960      1  ML1  NL  153  590  102  172   20   11  40  126  16   7  60  63   13    2   0  12     8\n",
       "41245  aaronha01  1961      1  ML1  NL  155  603  115  197   39   10  34  120  21   9  56  64   20    2   1   9    16\n",
       "41943  aaronha01  1962      1  ML1  NL  156  592  127  191   28    6  45  128  15   7  66  73   14    3   0   6    14\n",
       "42703  aaronha01  1963      1  ML1  NL  161  631  121  201   29    4  44  130  31   5  78  94   18    0   0   5    11\n",
       "43455  aaronha01  1964      1  ML1  NL  145  570  103  187   30    2  24   95  22   4  62  46    9    0   0   2    22\n",
       "44209  aaronha01  1965      1  ML1  NL  150  570  109  181   40    1  32   89  24   4  60  81   10    1   0   8    15\n",
       "44960  aaronha01  1966      1  ATL  NL  158  603  117  168   23    1  44  127  21   3  76  96   15    1   0   8    14\n",
       "45734  aaronha01  1967      1  ATL  NL  155  600  113  184   37    3  39  109  17   6  63  97   19    0   0   6    11\n",
       "46520  aaronha01  1968      1  ATL  NL  160  606   84  174   33    4  29   86  28   5  64  62   23    1   0   5    21\n",
       "47235  aaronha01  1969      1  ATL  NL  147  547  100  164   30    3  44   97   9  10  87  47   19    2   0   3    14\n",
       "48167  aaronha01  1970      1  ATL  NL  150  516  103  154   26    1  38  118   9   0  74  63   15    2   0   6    13\n",
       "49086  aaronha01  1971      1  ATL  NL  139  495   95  162   22    3  47  118   1   1  71  58   21    2   0   5     9\n",
       "49969  aaronha01  1972      1  ATL  NL  129  449   75  119   10    0  34   77   4   0  92  55   15    1   0   2    17\n",
       "50857  aaronha01  1973      1  ATL  NL  120  392   84  118   12    1  40   96   1   1  68  51   13    1   0   4     7\n",
       "51749  aaronha01  1974      1  ATL  NL  112  340   47   91   16    0  20   69   1   0  39  29    6    0   1   2     6\n",
       "52663  aaronha01  1975      1  ML4  AL  137  465   45  109   16    2  12   60   0   1  70  51    3    1   1   6    15\n",
       "53570  aaronha01  1976      1  ML4  AL   85  271   22   62    8    0  10   35   0   1  35  38    1    0   0   2     8\n",
       "54456   aasedo01  1977      1  BOS  AL   13  NaN  NaN  NaN  NaN  NaN NaN  NaN NaN NaN NaN NaN  NaN  NaN NaN NaN   NaN\n",
       "55440   aasedo01  1978      1  CAL  AL   29  NaN  NaN  NaN  NaN  NaN NaN  NaN NaN NaN NaN NaN  NaN  NaN NaN NaN   NaN\n",
       "56400   aasedo01  1979      1  CAL  AL   37  NaN  NaN  NaN  NaN  NaN NaN  NaN NaN NaN NaN NaN  NaN  NaN NaN NaN   NaN\n",
       "57361   aasedo01  1980      1  CAL  AL   40  NaN  NaN  NaN  NaN  NaN NaN  NaN NaN NaN NaN NaN  NaN  NaN NaN NaN   NaN\n",
       "58311   aasedo01  1981      1  CAL  AL   39  NaN  NaN  NaN  NaN  NaN NaN  NaN NaN NaN NaN NaN  NaN  NaN NaN NaN   NaN\n",
       "59255   aasedo01  1982      1  CAL  AL   24  NaN  NaN  NaN  NaN  NaN NaN  NaN NaN NaN NaN NaN  NaN  NaN NaN NaN   NaN\n",
       "60247  abbotgl01  1983      1  SEA  AL   14  NaN  NaN  NaN  NaN  NaN NaN  NaN NaN NaN NaN NaN  NaN  NaN NaN NaN   NaN\n",
       "61253   aasedo01  1984      1  CAL  AL   23  NaN  NaN  NaN  NaN  NaN NaN  NaN NaN NaN NaN NaN  NaN  NaN NaN NaN   NaN\n",
       "62237   aasedo01  1985      1  BAL  AL   54  NaN  NaN  NaN  NaN  NaN NaN  NaN NaN NaN NaN NaN  NaN  NaN NaN NaN   NaN\n",
       "63235   aasedo01  1986      1  BAL  AL   66  NaN  NaN  NaN  NaN  NaN NaN  NaN NaN NaN NaN NaN  NaN  NaN NaN NaN   NaN\n",
       "64252   aasedo01  1987      1  BAL  AL    7  NaN  NaN  NaN  NaN  NaN NaN  NaN NaN NaN NaN NaN  NaN  NaN NaN NaN   NaN\n",
       "65300   aasedo01  1988      1  BAL  AL   35  NaN  NaN  NaN  NaN  NaN NaN  NaN NaN NaN NaN NaN  NaN  NaN NaN NaN   NaN\n",
       "66335   aasedo01  1989      1  NYN  NL   49    5    0    0    0    0   0    0   0   0   0   3    0    0   0   0     0\n",
       "67408   aasedo01  1990      1  LAN  NL   32    0    0    0    0    0   0    0   0   0   0   0    0    0   0   0     0\n",
       "68523  abbotji01  1991      1  CAL  AL   34  NaN  NaN  NaN  NaN  NaN NaN  NaN NaN NaN NaN NaN  NaN  NaN NaN NaN   NaN\n",
       "69609  abbotji01  1992      1  CAL  AL   29  NaN  NaN  NaN  NaN  NaN NaN  NaN NaN NaN NaN NaN  NaN  NaN NaN NaN   NaN\n",
       "70675  abbotji01  1993      1  NYA  AL   32  NaN  NaN  NaN  NaN  NaN NaN  NaN NaN NaN NaN NaN  NaN  NaN NaN NaN   NaN\n",
       "71855  abbotji01  1994      1  NYA  AL   24  NaN  NaN  NaN  NaN  NaN NaN  NaN NaN NaN NaN NaN  NaN  NaN NaN NaN   NaN\n",
       "72885  abbotji01  1995      1  CHA  AL   17  NaN  NaN  NaN  NaN  NaN NaN  NaN NaN NaN NaN NaN  NaN  NaN NaN NaN   NaN\n",
       "74138  abbotji01  1996      1  CAL  AL   27  NaN  NaN  NaN  NaN  NaN NaN  NaN NaN NaN NaN NaN  NaN  NaN NaN NaN   NaN\n",
       "75391  abbotje01  1997      1  CHA  AL   19   38    8   10    1    0   1    2   0   0   0   6    0    0   0   0     3\n",
       "76627  abbotje01  1998      1  CHA  AL   89  244   33   68   14    1  12   41   3   3   9  28    1    0   2   5     2\n",
       "77949  abbotje01  1999      1  CHA  AL   17   57    5    9    0    0   2    6   1   1   5  12    0    0   1   1     4\n",
       "79248  abbotje01  2000      1  CHA  AL   80  215   31   59   15    1   3   29   2   1  21  38    1    2   2   1     2\n",
       "80632   abadan01  2001      1  OAK  AL    1    1    0    0    0    0   0    0   0   0   0   0    0    0   0   0     0\n",
       "81971  abbotpa01  2002      1  SEA  AL    7    0    0    0    0    0   0    0   0   0   0   0    0    0   0   0     0\n",
       "83290   abadan01  2003      1  BOS  AL    9   17    1    2    0    0   0    0   0   1   2   5    0    0   0   0     1\n",
       "84637  aardsda01  2004      1  SFN  NL   11    0    0    0    0    0   0    0   0   0   0   0    0    0   0   0     0\n",
       "85983  abernbr01  2005      1  MIN  AL   24   67    5   16    1    0   1    6   2   0   7   9    0    1   3   1     2\n",
       "87313  aardsda01  2006      1  CHN  NL   45    2    0    0    0    0   0    0   0   0   0   0    0    0   1   0     0\n",
       "88691  aardsda01  2007      1  CHA  AL   25    0    0    0    0    0   0    0   0   0   0   0    0    0   0   0     0\n",
       "90076  aardsda01  2008      1  BOS  AL   47    1    0    0    0    0   0    0   0   0   0   1    0    0   0   0     0\n",
       "91461  aardsda01  2009      1  SEA  AL   73    0    0    0    0    0   0    0   0   0   0   0    0    0   0   0     0\n",
       "92849  aardsda01  2010      1  SEA  AL   53    0    0    0    0    0   0    0   0   0   0   0    0    0   0   0     0\n",
       "94205   abadfe01  2011      1  HOU  NL   29    0    0    0    0    0   0    0   0   0   0   0    0    0   0   0     0\n",
       "95594  aardsda01  2012      1  NYA  AL    1    0    0    0    0    0   0    0   0   0   0   0    0    0   0   0     0\n",
       "97002  aardsda01  2013      1  NYN  NL   43    0    0    0    0    0   0    0   0   0   0   0    0    0   0   0     0\n",
       "98411   abadfe01  2014      1  OAK  AL   69    0    0    0    0    0   0    0   0   0   0   0    0    0   0   0     0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sqlite3\n",
    "con = sqlite3.connect(\"../lahman.sqlite\")\n",
    "df = pd.read_sql(\"SELECT * from batting\", con)\n",
    "df = df.convert_objects(convert_numeric=True)\n",
    "\n",
    "# Reminder: Many functions from data frames will work on groupby objects!\n",
    "# What is below doing?\n",
    "df.groupby('year').head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Single Variable Plots: Histograms and Boxplots\n",
    "\n",
    "To best understand a feature on its own, we explore it using histograms. Histograms help us understand the distribution of data.\n",
    "\n",
    "#### What shouild we be looking for?\n",
    "\n",
    "1. Width of the data. Use this alongside standard deviation to understand scope.\n",
    "2. Distributions. NYU has a [nice guideline](http://pages.stern.nyu.edu/~adamodar/New_Home_Page/StatFile/statdistns.htm) to consider. We'll go over distributions as they arise. But in particular:\n",
    "3. Expected vs Actual: Is this the shape we expected? Does it make sense given the feature? \n",
    "4. Segments: What jumps out as interesting? For example, are we interested in observing an 80/20 problem, or interested in the majority?\n",
    "\n",
    "#### Ways we generate histograms:\n",
    "\n",
    "1. `df.col.hist()` : pandas integrates with matplotlib to build histograms on the fly. Pass in multiple columns to generate multiple histograms at the same time.\n",
    "2. `plt.hist()`: direct matplotlib code.\n",
    "\n",
    "#### Ways we generate boxplots:\n",
    "\n",
    "1. `df.col.boxplot()` : pandas integration for boxplots. Pass in multiple columns to generate multiple boxplots at the same time.\n",
    "2. `plt.boxplot()`: direct matplotlib code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x10de8cd50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10e8a1b90>]], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEKCAYAAAD5MJl4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nABgIBwCA4b8AXNPe95GSuTgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10de8c810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Histograms\n",
    "# pandas style\n",
    "df[['g', 'ab']].hist()\n",
    "\n",
    "# matplotlib style\n",
    "#sns.set_style('white') # sets seaborn \"white\" to the default matplotlib style\n",
    "#plt.figure(figsize=(8, 6), dpi=80)\n",
    "#plt.subplot(1, 1, 1)\n",
    "#plt.hist(df.g, bins=20)\n",
    "\n",
    "## Boxplots\n",
    "# pandas style\n",
    "#plt.figure(figsize=(8, 6), dpi=80)\n",
    "#df[['g', 'ab']].boxplot()\n",
    "#\n",
    "## matplotlib style\n",
    "#plt.figure(figsize=(8, 6), dpi=80)\n",
    "#plt.subplot(1, 1, 1)\n",
    "#plt.boxplot(df.g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Doing histograms faster\n",
    "\n",
    "1. How do we use a for loop to generate a plot of each numeric field? Consider checking the fields `dtype` before making the plots\n",
    "2. Wrap your histogram three-liner as a function. We should be in the habit of always generating figures and subplots, but it's less necessary to have to write all code each time you want to do so."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multivariable plots: Scatterplots\n",
    "\n",
    "While histograms allow us to explore fields individually, scatter plots allow us to compare and correlate data points. \n",
    "\n",
    "#### What to look for\n",
    "\n",
    "1. Degrees of correlation (+/-). Are the two features positively or negatively correlated? What's the impact of one on the other?\n",
    "2. Type of relationship. Is the relationship linear? Polynomial? Logarithmic? \n",
    "\n",
    "#### Ways we generate scatter plots:\n",
    "\n",
    "1. `df.plot(type='scatter')` : pandas integrates with matplotlib to build scatter plots on the fly.\n",
    "2. `plt.plot()`: direct matplotlib code. You'll have to specify the marker.\n",
    "2. `plt.scatter()`: simpler context for matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x110dd6b10>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAFwCAYAAAB3kDgfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fcd4650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/matplotlib/collections.py:571: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAFwCAYAAAB3kDgfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1102846d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "last_year = df[df.year == 2014]\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "# using plot and the '.' notation.\n",
    "plt.figure(figsize=(8, 6), dpi=80)\n",
    "plt.subplot(1, 1, 1)\n",
    "plt.plot(last_year.ab, last_year.h, '.')\n",
    "\n",
    "# using scatter, and setting additional parameters.\n",
    "plt.figure(figsize=(8, 6), dpi=80)\n",
    "plt.subplot(1, 1, 1)\n",
    "plt.scatter(last_year.ab, last_year.h, s=last_year.hr**2, alpha=0.3, c='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other useful pandas plots\n",
    "\n",
    "#### Trellis Plotting\n",
    "\n",
    "Trellis plotting is a technique to group histograms or scatterplots based on a segmentation. This will be familiar to R users (the `lattice` package adds this functionality, as well as ggplot's `facet_grid`). Trellis plotting helps us easily identify how a feature can change dependent on another. Trellis plotting is built into pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(480x320)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEZCAYAAACdNT5ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111086f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas.tools.rplot as rplot\n",
    "    \n",
    "plt.figure()\n",
    "plot = rplot.RPlot(last_year, x='hr')\n",
    "plot.add(rplot.TrellisGrid(['lg','.']))\n",
    "plot.add(rplot.GeomHistogram())\n",
    "print plot.render(plt.gcf())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A similar output can be done using the \"by\" argument in the plotting syntax (This would be more akin to `facet_wrap` in ggplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f40ebd0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f738a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEGCAYAAACZ0MnKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtcVPW6P/DPcJFkENEupolAVAq5oVA8mujJVMTUNrZl\ne0VT3HmJlMyUmwxQXIrS+jW6UTunXqJlWab5y71PRqaJN44GBmS2U9gkSBp4miFhBuZ7/vA4G+Q2\nA2sxFz7v16vXljXfedYzbL/zuNb6rmcphBACREREJnCwdAJERGQ7WDSIiMhkLBpERGQyFg0iIjIZ\niwYREZmMRYOIiEzGomFHhBCIiorCrl27LJ0KkUVcuXIFq1atwujRoxESEoL4+Hj89ttvlk7LrrBo\n2InGxkYkJycjLy/P0qkQWURjYyNWrlyJ33//HTt27MCWLVtw/vx5rFu3ztKp2RUnSydAXVdeXo51\n69ahqqoK7u7ulk6HyCJKSkpQUlKCvLw83HnnnQCAxMREzJs3D1qtFm5ubhbO0D7wSMMOFBQU4IEH\nHsCnn37KiUE9lqenJ9555x1jwWiKp6ikwyMNOzBjxgzMmDHD0mkQWZSHhwdCQkKabXvvvffg5eWF\nQYMGWSgr+8OiQUR2adu2bTh06BC2bdtm6VTsCosGEdmdzZs34+2330ZSUhLGjRtn6XTsCosGEdmV\ntLQ07Ny5EykpKZg9e7al07E7LBpEZDfeeust7Nq1CxkZGQgPD7d0OnaJRYOI7EJJSQmys7OxdOlS\nhISE4OrVq8bX+vfvD0dHRwtmZz9YNIjILnzxxRcQQmD79u3Yvn27cbtCocCBAwfwwAMPWDA7+6Hg\nk/uIiMhU7R5p6PV6xMbG4vLly3B0dMTLL78MR0dHxMbGwsHBAQ8++CBUKhUUCgXUajWOHDkCR0dH\nxMfHIyAgAGVlZSaPJbJVW7duxeHDh6HT6TBv3jwEBwd3eY4QWS3RjkOHDonVq1cLIYTIy8sT0dHR\nYvny5eL06dNCCCGSkpLEoUOHRFFRkVi4cKEQQoiKigrxpz/9SQghxLJly0weS2SLTp48KZYtWyaE\nEKK2tla8/fbbXZ4jRNas3TYiPj4+aGxshBACGo0Gzs7OKC4uRnBwMABg/PjxOH78OM6ePYuxY8cC\nAAYOHIjGxkZUV1ejpKTEpLE1NTVy1kUi2eTl5WHo0KFYuXIlli9fjscff7zLc4TImrV7esrV1RWX\nL19GWFgYrl+/juzsbOTn5xtfVyqV0Gg00Gq18PDwaLZdq9VCNLlc0tHYfv36tZrDmTNnOv3hiNoz\nYsSILseorq5GZWUltm7divLycixfvtysv/dNx7q6ukKj0bS7P84Hkoup86HdovHee+9h3LhxeOGF\nF3DlyhUsXLgQDQ0Nxte1Wi3c3d3h5uaG2tpa4/ba2lr06dMHDg4OJo+V4sN05MyZM3YdyxpzstZY\nUn359uvXD76+vnBycoKPjw9cXFzwyy+/GF83Z47U1taa1KVYqt/lLVL+/yN3XFuJKVdcOXM1Vbun\np/r27QulUgkAcHd3R0NDA/z9/XH69GkAwNGjRzFy5EgEBQXh2LFjEEKgoqICQgj069cPfn5+HY41\nGAzN/gVGZEtGjBiBb775BgBQVVWFuro6jB49uktzhMiatXuk8cwzzyA+Ph7z58+HXq/Hiy++iIcf\nfhgbNmyAXq+Hr68vwsLCoFAoMHLkSMyePRsGgwFJSUkAgNjY2A7HqlSqbvmgRHJ4/PHHkZ+fj1mz\nZhn/Pt93331dmiNE1qzDaxpvvvlmi+05OTkttkVHRyM6OrrZNm9vb5PHtufUT1dMHgsALo4O+IPn\nnbwDlLrFSy+91GJbV+cIkbWyiTvCo/eWmDV+gm8/pA/uL1M2RJb1y3WtWeOdHR3Qr4+rTNlQT2MT\nRYOI/mXp7gKzxi8b7Ylpj3jJlA31NB0WjU8//RR79+4FANTX1+P8+fPIycnBK6+8AicnJ4wdOxbR\n0dEwGAxITk7GhQsX4OzsjLS0NAwZMgQFBQVIT0+Ho6Njh2OJqGOVGp1Z4280GGTKhHqiDovGzJkz\nMXPmTABAamoqIiIioFKp8Pbbb8PT0xPPPvssSkpK8PPPP0Ov12P37t0oLCxEZmYmtmzZApVKBbVa\nbdJYIiKybu0uuW3qu+++w48//oipU6dCp9PB09MTABASEoITJ07g7NmzxidkBQYGoqioCFqtFnq9\n3qSxRERk/UwuGlu3bsXzzz8PrVYLNzc34/amd7w23e7o6Ija2lqTxxoMPIQmIrJ2Jl0I/+2333Dp\n0iWMGjUKWq22xZ2t7u7uqKura7bdYDBAqVSaPLbpnbFdZTAY8P3330Ona3nuV8o2DNYYyxpzsuZY\nUggPDzd2NRg8eDBmz56NtLS0Tl/zI7JmJhWN/Px8jBkzBgDg5uYGZ2dnlJeXY/DgwTh27Biio6Nx\n5coVHD58GFOnTkVBQQGGDh1q1lgpOTg4wM/PD05OzT+eNbazkDKWNeZkrbGkKjz19fVQKBTN7rUI\nDw/v0jU/f39/SXIjkoNJRaO0tLTZ6qaUlBSsXbsWjY2NCAkJQUBAAP7whz8gLy8Pc+bMAQBkZGSY\nPZbI1pw/fx43btxAVFQUGhoaEB0d3eo1v6tXr5p8zY9Fg6yZSUUjKiqq2c+BgYH48MMPm21TKBRI\nSUlp8V5zxhLZmt69eyMqKgoREREoLS3F0qVL0bdvX+PrSqUS5eXlJl/zKy8v79b8iczFm/uIusDb\n2xteXl7GP/fp0wfXr183vm7ONb9bHXGlVl9f3+HpOLmuE8kR11ZiyhXX0tf0WDSIuuCTTz7BhQsX\noFKpjF1uXV1dO3XNLy8vT5YL4S4uLhgxYlibr/f01uA9/fPfimsqFg2iLpg1axZiY2Mxb948KBQK\nZGRkQKFQdOmaH5E167BobN26FYcPH4ZOp8O8efMQHByM2NhYODg44MEHH4RKpYJCoYBarcaRI0fg\n6OiI+Ph4BAQEoKyszOSxRLbI2dkZb7zxRovtXbnmR2TN2r054tSpU/j222+xe/du7Ny5E1euXEFm\nZibWrFmDXbt2QQiB3NxcFBcXIz8/H3v27MGmTZuQmpoK4Oa/pkwdS0RE1q/dI428vDwMHToUK1eu\nhFarxbp16/DRRx8hODgYADB+/Hjk5eXBx8cHY8eOBQAMHDgQjY2NqK6uRklJiUlja2pq2nxGOBER\nWY92i0Z1dTUqKyuxdetWlJeXY/ny5RBCGF9v2hak6SNblUoltFqtWWNZNIiIrF+7RaNfv37w9fWF\nk5MTfHx84OLigl9++cX4+q0lgm5ubi3ahfTp06dZa5COxkqJbUSk0RNiEZF52i0aI0aMwI4dO7B4\n8WLjcsLRo0fj9OnTGDVqFI4ePYoxY8ZgyJAhyMrKQlRUFCorKyGEQL9+/eDn59fhWIPB0OzIQwps\nI9J19h6LhYeoc9otGo8//jjy8/Mxa9YsGAwGqFQq3HfffdiwYQP0ej18fX0RFhYGhUKBkSNHYvbs\n2TAYDEhKSgIAxMbGdjhWpVJ1ywclIqKu63DJ7UsvvdRiW9PmbLdER0e3uDHJ29vb5LFEtuzXX3/F\n008/jffeew8ODg5dXpZOZK2k60dO1EPp9XokJSWhd+/eEEKYtdS8tbFE1oxFg6iLXnvtNcydOxd3\n3303ALRYan78+HGcPXvWpGXpx48ft8yHIDIRiwZRF+zduxf9+/dHSEgIAEAI0eZS86arBFtblu7q\n6gqNRtN9yRN1gkm9p6R+MllbY4lszd69e6FQKHD8+HGcP38esbGxqKmpMb5uzrL0Wx1xpcYut5aJ\nKVdcS6/867BoyPFksrbGEtmanTt3Gv8cGRmJlJQUvPbaa11ali41drnt/phyxbWJLrdyPJmstbFE\n9kChUJi01Ly9ZelE1qzDoiHHk8laG2swGJodqhPZmqZH411dlk5krTosGlI+may9sVIWDLYRkUZP\niEVE5umwaEj5ZLL2xkqJbUS6zt5jsfAQdU6HRUOOJ5O1NZaIiKxbh0VDjieTtTWWiIisG688ExGR\nyVg0iIjIZCwaRERkMpPaiEjd9rm1sUS2qLGxEYmJiSgtLTVeq+vVqxdbo5Pd6vBIQ+q2z22NJbJF\nhw8fhoODAz744APExMRg48aNyMzMZGt0slsdFg2p2z63NrZpgzciWzJp0iRjAbh8+TL69u2L4uJi\ntkYnu9Vu0ZCy7XNHY4lslaOjI9avX4+0tDTMmDGDrdHJrrV7TUPKts8djZUS24hIoyfEksqrr76K\na9euISIiotnfO7ZGt43W4D3985uj3aIhR9vn28caDAZ4eHhI+qHYRqTr7D2WVBNv//79qKqqwrPP\nPos77rgDDg4OGD58OFujyxTXVmLKFdcmWqM3JUXb59vHqlQq8z4dkRUJDQ1FXFwcFixYgIaGBiQk\nJOD+++9na3SyWyYXDSnbPrc2lsgW9e7dG2+++WaL7WyNTvaKN/cREZHJWDSIiMhkLBpERGSyDq9p\nyNUmga1EiIhsT4dFo2mbhNOnT2Pjxo0AgDVr1iA4OBgqlQq5ubkYOHCgsU1CZWUlnn/+eXz88cfG\nNgmmjCUiIuvWYdGYNGkSJkyYAOBfbRKOHz/erPVBXl4efHx8TGqT0NbYmpoa9OvXT5YPSURE0jBp\nye2tNgm5ubl46623kJeXZ3ytaZuEpjfpddRKpLWxUhUN3hEujZ4Qq6v0ej3i4+NRUVEBnU6HFStW\nwNfXl11uyW6ZfJ+GFG0SuquVCO8I7zp7jyVV4Tlw4AD69++PrKwsXL9+HeHh4fDz8+vS6dtJkyZJ\nkhuRHDpcPbV//35s27YNAFq0SQCAo0ePYuTIkQgKCsKxY8cghEBFRUWLNgntjZWjlQhRdwgLC8Oq\nVasA3Gzo6eTk1OVO0ETWrMMjDTnaJLCVCNkLV1dXADePolevXo2YmBi8+uqrxtfNOX3LLrdkCzos\nGnK1SWArEbIXlZWViI6Oxvz58zF9+nRkZWUZX2OXW9vo8trTP785zGpYSETNXbt2DUuWLIFKpcLo\n0aMBwKTuzuxya98x5Yprc11uiai57OxsaDQabN68GZs3bwYAJCQkIC0tjV1uyS61WzTkWk7Iu8HJ\nXiQmJiIxMbHFdna5JXvVbtGQYzkh7wYnIrJd7RaNsLAwTJkyBUDbywl5NzgRUc/R7n0arq6uxqWB\nt5YTGgwG4+tNlxM2vTmvo7vBWxtLRETWr8ML4VItJ+yuu8EBthGRSk+IRUTmabdoyLGc8PaxctwN\nzjYiXWfvsVh4iDqn3aIhx3JC3g1ORGS72i0aci0n5N3gRES2iY97JZJAYWEhIiMjAQBlZWWYO3cu\n5s+fj+TkZOOCELVajYiICMyZMwfnzp1rdyyRtWLRIOqi7du3IzExEXq9HgCM9yft2rULQgjk5uai\nuLjYeH/Spk2bkJqa2uZYImvGokHURV5eXlCr1cajBLZGJ3tmUu+pwsJCvP7668jJyTGrNQjbiFBP\nEBoaip9//tn4szlPq+yO1ujscmuZmHLFtfTKvw6Lxvbt2/HZZ59BqVQCgFmtQdhGhHoic+5P6o7W\n6Oxy2/0x5YprDV1uOzw9JfWhd2tja2pqzPuERFbMlKdVtvdkSyJrZtKT+6Q49O5oLHtPka1TKBQA\nTLs/ia3RyVaZ/TyNzh56s41I98SyxpysOZZUBg8ejN27dwMw7/4ktkYnW2N20WAbEeuNZY05WWss\nayw8RLbA5KIh1aE324gQEdkuk4qG1IfebCNCRGSb7PYZ4aVX/wdXNPXNtmnQB8cuVLT7vkHuvXH/\nvbwoT/bl4pUaVPx2o9XX2psXnA90O7stGpW/1WHNZz+Y/b5NfxzGSUJ2p+K3G3hh/3mz38f5QLdj\nGxEiIjKZxY40DAYDkpOTceHCBTg7OyMtLQ1DhgyxVDpEFsO5QLbEYkXjyy+/hF6vx+7du1FYWIjM\nzExs2bLFUukYOUJ0eN0DaH4emOd9qSusdS4Aps+H2w1y7y1DNmQNLFY0zp49i3HjxgEAAgMDUVRU\nZKlUmqn+XY/kQxfNes//++PQNi8ytofFhgDrnQtA5+YDcHNOXDdh4cntOCesn8WKhlarhZubm/Fn\nR0dHGAyGZneR3xL3hI9Zse9x64XufJRNVyZWxW83TFrV1VQfZwdo9IYW27kKxjaZMxcA8+fDsLuV\nqK5r6FKO5qr+XY/kr37ueOBtOvoHWFt/x9uaE6ZwVt7V6aOpnjinLFY0bm8n0t4k8UK1ecG1N/8n\n+4m+5ifW+Kv57+vMewBAcwUA0BsAzGiJ3XDrPbdpL06NBjhz2bz0rLX1h73dzW3OXADMnw83rlaj\nN7pxPnTlff83J9rS1t/xtuaEqTrzRWjKnOqRrdHlEhQUhMOHD2Pq1KkoKCjA0KFDWx0nRxtgImti\n6lwAOB/I8hTCQg8lFkIgOTkZP/xw816KjIwM+PiYd9hNZA84F8iWWKxoEBGR7eHNfUREZDIWDSIi\nMpnVFg2DoXPL54jsjVxzwWAwoKqqinONzGJVDQv/+c9/IjMzE0VFRca16kOHDkVcXBwvDFKPItdc\niI+PR3p6OgoLC7F27Vp4eHhAq9UiIyMDjzzyiISfgOyVVRWNhIQErF27FoGBgcZtBQUFiIuLMz7P\nw1RCCOTm5uL48ePQaDRwd3fHyJEjjQ+C6u441hwLAEpKSnDixIlmsQICAmw+Jzny6g5SzoWmysvL\nAQAbN27E9u3b4e3tjaqqKqxZswa7du3qVEw5fr8HDx7Ek08+idraWqjVanz//fcYPnw4VqxYAaVS\n2amYzFWaXK2qaOj1+maTBECn//WTkpICIQTGjx8PV1dX1NbW4ujRozh27BjS0tK6PY41x1Kr1Th3\n7hxCQkLg6ekJrVYLtVoNf39/xMTE2HROUufVXaScC61xcnKCt7c3AGDAgAHoyiJKOX6/H3zwAZ58\n8kmkp6fD09MTiYmJOHHiBJKSkvDGG28wV0vmKqzIhg0bRGxsrPj888/FkSNHxMGDB0VsbKxISkoy\nO9a8efNa3T579myLxLHmWHPmzGmxzWAwiD/96U82n5PUeXUXKedCU3/84x9FeHi4mDJlivjoo49E\nXV2dSE5OFmvWrOl0TDl+vwsWLBBCCDF//vxWt3cWc+16rlZ1ITw5ORkTJkzAuXPn8MUXX6CwsBAT\nJkxAcnKy2bEMBgPy8/ObbTt9+jScnZ0tEseaYzU0NBhPW9zy888/w9HR0eZzkjqv7iLlXGhq3759\n2L17N1599VUEBgZCoVDgoYceQmZmZqdjyvH7LSsrw7vvvgtHR0eUlJQAAM6dOwe9Xt/pmHLlWlpa\n2qNytdub+8rKypCRkYGSkhIIIeDg4AA/Pz/ExsYaD8tNjZOZmYmSkhJjTyA/Pz/ExMRg2LBhZueU\nmZmJ4uJiCCGg0+kwfPhwbNiwwaycWvt8NTU1CAkJMfvzATfPlScnJ0On06F37964fv06XF1d8cor\nr7Q4RWLO59NqtRg9ejTWr19vdk6FhYVQqVTQ6XTG3kzOzs5ISUkxK6fW8mpoaIC/v3+nfu/UklRz\nrani4mKUlJSgqKgIgYGBmDx5MqKiopCcnAx/f/8u5SrVfL491+LiYgQEBBhzjYuLw6OPPtrlXKX4\nvmgr17Fjx+L555836/dqVdc0pOTl5YXs7Owux/npp5/w/fffw9nZGTExMZg+fToAIDIyEjk5OWbF\n0ul0AIDHHnsMM2bMQGJiIkpLS3Hx4kWz/xIYDAasW7cOwM0LZrGxsVi3bl2nzk1/+OGH2LdvH/Lz\n8xEXFwd3d3fU1dWZHSs/Px8PP/wwnnvuObz44ovo168ffvrpJ1y+fNnszxcZGYkNGzZg6tSpxi6w\nTTvBmqOxsRFOTk4YOXIkIiMjsX79ely6dAnFxcUsGhKQaq419fDDD+Phhx9GRESEcdtHH33U5bhS\nzudbqqqqsHnzZjg5OSE4OBh9+vTBRx991KWYgLTfF7f06tULhw8fhru7OwYMGIC5c+dCoVDgypUr\nLBqRkZHQ6/UtvvgUCoVZq0/++te/Yt++fRBCYPXq1dDpdHj66ac7lZNKpUJMTAwuX76M1atX4+9/\n/zvuuOMOLF26FE888YRZsZ555hn07t0bd999N4Cbh50qlQoAzP6L+vPPN1tYq9VqvPPOO51eUfP+\n++9j586dWL58ObKzs+Hj44OqqiqsWLECY8eONSunYcOG4fvvv8dnn32G559/HqNGjTLr/U1t2LAB\nzz33HDQaDZYvX479+/fD3d0dzzzzDKZNm9bpuHSTVHNN7piAtPO5rZj19fVdjglI+30hZUy7LRpr\n165FYmIi1Gp1p86D39KrVy94eHgAALZs2YJFixZh0KBBnYolhDB++Z08eRJ33XUXgJsrWcy1d+9e\nJCUlYe7cuQgJCenyv2pu5dGVFTW9evWCq6sr3Nzc4OnpaYzTXpvvtri4uCApKQnfffcdtm7dipSU\nFIwZMwZDhgzBwoULzYrV2NiIxx57DEIIbNy4Effeey8AWPU1DVsi1VyTOyYg7XyWMyYg7feFlDEd\nk7t6Zc1K3XvvvaitrUVDQwMeffRRuLu7G/8zx8mTJ3HmzBkEBQXBzc0N48ePx5o1a1BVVYUlS5aY\nFevMmTM4fPgwHn/8cYSGhgIAtm3bhtraWoSFhZkVy9XVFaGhoXj//fdRXFyM8vLyTv/rZseOHfjw\nww9RXV0NFxcXPPDAA0hLS0OvXr0wZcoUk+P8+uuveOedd/DQQw/hP//zP1FdXY23334bjzzyiNlH\nGp9++imefvppDBgwAE8++SRmzJgBBwcHVFdXIygoyKxYJ06cwN/+9jccPHgQdXV1qKiowHfffYff\nf//d7N87tSTVXJM7JiDtfJYzJiDt94WkMTu3cKvn0Ol04pNPPhG1tbXGbVevXhUvv/yy2bEaGhrE\noUOHmm3bt2+f+P3337uU4yeffNJiCZ256urqREFBgfjhhx9EfX29eP/994VOpzM7zsmTJ8Ubb7wh\nEhISxOuvvy4OHz7cqXz27t3bqfe1RqfTiUOHDol//OMfoqKiQqSnp4vNmzc3+/+UegYp57OcMYWQ\n5/tCiph2u3qKiIikZ1X3aRARkXVj0SAiIpOxaBARkclYNIiIyGQsGkREZDIWDSIiMhmLBhERmYxF\ng4iITMaiQUREJmPRsAOXLl1CVFQUgoKCEBISgjfeeAMNDQ2WTovIojZu3NjpbrDUNhYNG6fX67F0\n6VJ4eHjg008/xcaNG3HgwAFs3rzZ0qkRWUxRURH+4z/+AwqFwtKp2B27bY3eU1RVVSEwMBCpqalQ\nKpXw8vLClClTWjwmkqin0Ol0iIuLQ1BQEC5fvmzpdOwOjzRs3ODBg7Fx40YolUoAwPnz55Gbm4vH\nHnvMwpkRWcbmzZvh7e3NtvcyYdGwI9OmTUN4eDg8PDywaNEiS6dD1O2Ki4uxZ88eJCcnd+rRx9Qx\nFg078vrrr+Pdd99FXV0dnnvuOUunQ9Stbp2WWrduHe68805Lp2O3WDTsiJ+fH8aMGYP09HScPHkS\n//jHPyydElG32bJlCwYMGIDw8HBLp2LXeCHcxv3yyy8oLCzE5MmTjdsefPBBAEBNTY2l0iLqdgcO\nHMDVq1fx6KOPAgAaGhqMj4t95513MGLECAtnaB9YNGzcTz/9hFWrVuHrr7/GgAEDAADfffcdHBwc\n4Ovra+HsiLpPTk4OGhsbjT/v378fe/bswc6dO3HPPfdYMDP7wqJh44KDgzFs2DCsW7cOiYmJ+PXX\nX5GUlIS5c+eif//+lk6PqNsMGjSo2c8eHh5wdHSEp6enhTKyT7ymYeOcnJyQnZ2Nvn37Yv78+Xjh\nhRcwceJExMXFWTo1IotSKBS8uU8GCiHhurTGxkYkJiaitLQUCoUCKSkp0Ol0WL58Oby9vQEA8+bN\nw9SpU6FWq3HkyBE4OjoiPj4eAQEBUqVBZJX0ej3i4+NRUVEBnU6HFStWwNfXF7GxsXBwcMCDDz4I\nlUrFLzqyapKenjp8+DAcHBzwwQcf4PTp09i0aRMmTJiAJUuWYPHixcZxxcXFyM/Px549e1BZWYnn\nn38eH3/8sZSpEFmdAwcOoH///sjKysL169cRHh4OPz8/rFmzBsHBwVCpVMjNzcWkSZMsnSpRmyQt\nGpMmTcKECRMAAJcvX4a7uzuKi4tx6dIl5ObmwsvLC/Hx8Thz5gxCQkIAAAMHDkRjYyNqamrQr18/\nKdMhsiphYWGYMmUKAEAIAScnJ5SUlCA4OBgAMH78eOTl5bFokFWT/JqGo6Mj1q9fj7S0NMyYMQMB\nAQFYv349du7cCU9PT6jVatTW1sLNzc34HqVSCa1WK3UqRFbF1dXV+Hd99erViImJgcFgaPa6RqOx\nYIZEHZNl9dSrr76Ka9eu4c9//jM++OAD41LQyZMn4+WXX8bEiRNRW1trHF9bW4s+ffq0GuvMmTNy\npEhkkXX7lZWViI6Oxvz58zF9+nRkZWUZX6utrYW7u3u77+d8ILmYPB+EhPbt2ye2bt0qhBBCo9GI\nJ554QkRERIjCwkIhhBA7duwQWVlZoqioSCxatEgYDAZx+fJl8dRTT7UZ87//+7+lTNFk3C/3K7Wr\nV6+KsLAwceLECeO2ZcuWiVOnTgkhhNiwYYM4ePBguzGkylvKz2/vsawxJ0vGkvRIIzQ0FHFxcViw\nYAEaGhqQkJCAe++9F6mpqXB2dsY999xjbOE9cuRIzJ49GwaDASqVSso0iKxSdnY2NBoNNm/ebHze\nSUJCAtLS0qDX6+Hr68vOrGT1JC0avXv3xptvvtli++7du1tsi46ORnR0tJS7J7JqiYmJSExMbLE9\nJyfHrDjCzFXyXMJLUuId4UQ2ZvfpiyaPDRjohoeHDJAxG+ppWDSIbMzGo2Umj417wgcPD5ExGepx\n2EaEiIhMZhNHGuXX/sfs9wz0UMLJySY+HhGRzbCJb9WFuwrMGn//nb2xaWYA3Fk0iIgkJem3amsN\nC3v16tVqQzZzGhZqdY1tvtaa380cT0REppG1YeHGjRsBoEVDtoEDB7JhIRGRDZK1YWHfvn1x/Pjx\nFg3ZfHxWU+DPAAARMElEQVR8MHbsWABsWEhEZEskP+l/q2Fhbm4u3nrrLeTl5RlfUyqV0Gg00Gq1\n8PDwaLZdq9VKWjQuXbqIhvobXYphqT4/3C8RWStZGxZGRERAp9MZt2u1Wri7u8PNzc3khoWd5eNz\nP9yVd3T6/WfOnLFIQzvut/v2S0Tmk/Q+jf3792Pbtm0AgDvuuAMODg4YPnw4Tp8+DQA4evQoRo4c\niaCgIBw7dgxCCFRUVMBgMDQ78iAiIuske8PC+++/Hxs2bGjWkE2hULBhIRGRDeqWhoWtNWRjw0Ii\nItvDNiJERGQyFg2iblZYWIjIyEgAQHFxMcaPH4/IyEhERkbi4MGDFs6OqH3ss0HUjbZv347PPvsM\nSqUSAFBSUoLFixdj8eLFFs6MyDQ80iDqRl5eXlCr1cYHKRUVFeHrr7/GggULkJCQ0GwpOpE1kqxo\n6PV6vPTSS5g/fz4iIiLw1Vdfobi4GOPGjTMeev/tb38DAKjVakRERGDOnDk4d+6cVCkQWb3Q0FA4\nOjoafw4MDMT69euxc+dOeHp6Qq1WWzA7oo5JdnrqwIED6N+/P7KysnD9+nWEh4fjueeew5IlS5od\nehcXF7PvFNH/mTx5svHG1kmTJuGVV16RNH59fX2bNzJKeYOjvceyxpykjmUqyYpGWFgYpkyZAuDm\nM4ydnJxQXFyMS5cuITc3F15eXoiPj8eZM2cQEhICgH2niKKiopCYmIiAgACcOHECw4cPlzS+i4sL\nRowY1mK7lHfi23ssa8xJjlimkqxouLq6ArjZKmT16tV44YUXUF9fjz//+c/w9/dHdnY21Go13N3d\nZe87RWTtFAoFACA5ORmpqalwdnbGPffcg9TUVAtnRtQ+SVdPVVZWIjo6GvPnz8e0adOg0WiMh96T\nJ0/Gyy+/jIkTJ8redwpgw0Lu13oNHjwYu3fvBgD4+/sb/0xkCyQrGteuXcOSJUugUqkwevRoAM0P\nvY8fP47hw4cjKCgIWVlZiIqKQmVlpWx9p9iwkPvtaL9EZD7JikZ2djY0Gg02b96MzZs3AwDi4uKQ\nnp7e7NBbqVSy7xQRkY2SrGgkJiYiMTGxxfbWDr3Zd4qIyDbx5j4iIjIZiwYREZmMRYOIiEzGokFE\nRCaT7EK4Xq9HfHw8KioqoNPpsGLFCvj6+iI2NhYODg548MEHoVKpoFAooFarceTIETg6OiI+Ph4B\nAQFSpUFERDKStfeUn58f1qxZg+DgYKhUKuTm5mLgwIHsPUVEZKNk7T1VUlKC4OBgAMD48eORl5cH\nHx8fjB07FgB7TxER2RrJrmm4uroa+0itXr0aMTExMBgMxteVSiU0Gg20Wm2ztiG33kNERNZPtt5T\n06dPR1ZWlvE1rVYLd3d3uLm5sfcU92sV+yUi88nae8rPzw+nT5/GqFGjcPToUYwZMwZDhgxh7ynu\n1yr2S0Tmk7X3VEJCAtLS0qDX6+Hr64uwsDAoFAr2nqIerbCwEK+//jpycnJQVlbW6gpDImsle++p\nnJycFtvYe4p6qu3bt+Ozzz6DUqkEAGRkZLRYYThp0iQLZ0nUNt7cR9SNvLy8oFarIYQAgBYrDI8f\nP27J9Ig6xKJB1I1CQ0Ph6Oho/PlW8QBurkDUaDSWSIvIZJKuniIi8zg4/OvfbbW1tXB3d5c0fn19\nfZsX/aVcDGDvsawxJ6ljmYpFg8iCWlthKCUXFxeMGDGsxXYpV63ZeyxrzEmOWKZi0SCygFsrpGJj\nY7Fhw4ZmKwyJrJnkRaPpcsLi4mKsWLECXl5eAIB58+Zh6tSpbFhIPdrgwYONT7T09vZudYUhkbWS\ntGjcvpywpKQEixcvxuLFi41jiouL2bCQiMhGSbp66vblhEVFRfj666+xYMECJCQkoLa2FmfOnEFI\nSAiA5g0LiYjI+kl6pBEaGoqff/7Z+HNgYCBmz54Nf39/ZGdnQ61Ww93dvVnbkFsNC6XucsveU9wv\nEUlP1gvhkydPNjYjnDx5Ml5++WVMnDixWxoWsvcU99vRfonIfLLe3BcVFYVz584BAI4fP47hw4cj\nKCgIx44dgxACFRUVsjUsJCIi6clypHFrOWFycjJSU1Ph7OyMe+65B6mpqVAqlWxYSERkoyQvGk2X\nE/r7+xv/3BQbFhIR2Sb2niIiIpOxaBARkclYNIiIyGQsGkREZDLJi0ZhYSEiIyMBAGVlZZg7dy7m\nz5+P5ORk453iarUaERERmDNnjnFJLhERWT9Ji8b27duRmJgIvV4P4F+Psty1axeEEMjNzW3We2rT\npk1ITU2VMgUiIpKRrL2nWnuU5dmzZzF27FgA7D1FRGRrZO091fRRlkqlEhqNBlqttlt6TxHZkvDw\ncGM7HU9PT6Snp1s4I6LWydp7qumjLLVaLdzd3eHm5tYtvafYsJD7tRX19fVQKBR8rgbZBFmLRmuP\nshwyZAiysrIQFRWFyspK2XpPsWEh99vRfq3F+fPncePGDURFRaGhoQFr1qxBYGCgpdMiapWsvada\ne5SlQqFg7ymiJnr37o2oqChERESgtLQUf/nLX/Bf//VfzY7UiayFrL2n2nqUJXtPEf2Lt7e38ZHI\n3t7e8PDwwNWrVzFgwIAux66vr2/zqErKoy17j2WNOUkdy1Synp4ioo598sknuHDhAlQqFaqqqqDV\nanH33XdLEtvFxQUjRgxrsV3K04L2Hssac5IjlqlYNIgsbNasWYiNjcW8efOgUCiQkZHBU1NktVg0\niCzM2dkZb7zxhqXTIDIJ/zlDREQm65YjjaY3Lg0ePBizZ89GWloanJycMHbsWF4UJyKyEbIXjdZu\nXAoPD8fbb78NT09PPPvssygpKYG/v7/cqRARURfJfnqq6Y1LixYtQn5+PnQ6HTw9PQEAISEhOHHi\nhNxpEBGRBGQ/0rj9xqWlS5eib9++xteVSiXKy8vlToOIiCQge9G4/calPn364Pr168bXb/Wkkhp7\nT3G/RCQ92YvG7Tcu1dXVwdXVFeXl5Rg8eDDy8vJkuRDO3lPcb0f7JSLzyV40WrtxSaFQYO3atWhs\nbERISAgCAgLkToOIiCQge9Fo68alDz/8UO5dExGRxHhHOBHZlItXalDxW8fXKzXog2MXKgAAg9x7\n4/57+aA3KbBoEJFNqfjtBl7Yf96s92z64zAWDYmwjQgREZmMRYOIiExmt6enKmq0OHe52uz38dwn\nkenXDYB/XTvg3Ok8c37ft/S6o2/Hg2RgsaJhMBiQnJyMCxcuwNnZGWlpaRgyZIhk8as0dVj7/y+Y\n/T6e+6TuJvdc6AxeN+henfl9pz4xWKZs2mex01Nffvkl9Ho9du/ejbVr1yIzM9NSqRBZFOcC2RKL\nHWmcPXsW48aNAwAEBgaiqKjIUqk04wiBYxcqmi3XM0UfZwdo9Aaz98dDerLWuUDUGoUQQlhix4mJ\niQgNDcX48eMBABMmTEBubm6Lx1yy3QPJxRLtS1pj6lwAOB9IPqbOB4sdabi5uaG2ttb4s8FgaHWS\nWMvEJpKLqXMB4Hwgy7PYNY2goCAcPXoUAFBQUIChQ4daKhUii+JcIFtisdNTQggkJyfjhx9+AABk\nZGTAx8fHEqkQWRTnAtkSixUNIiKyPbwjnIiITMaiQUREJrPaomEwmH/Pg1T7raqqstj+iW5nC38X\n6+vru/T+uro66HQ6SXK5du2aJHGk/i6orq5GZ68GaLVaSXK4nU6nQ11dnVnvsaqi8c9//hMrV67E\n+PHjMXHiRPz7v/87nn32WVy6dEnW/cbHxwMACgsLMWXKFERHR2PatGkoKCiQdb9EbZFyLkRGRmLO\nnDmYPXt2s//mzJljdqyvvvoKEyZMwKRJk/D5558bty9dutSsOD/++CNWrlyJuLg45OXl4cknn8TU\nqVPx1VdfmZ3TpUuXjP9dvHgRK1euNP5sLim/Cz7++GOo1WoUFRUhLCwMixcvxpQpU5CXl2d2rMce\newx79uwx+323u3jxIlatWoUXX3wR3377LWbMmIEnn3yy2f+XHRJWZMGCBaKgoKDZtm+//VbMnj1b\n9v0KIcTChQvFpUuXhBBCXLlyRcybN0/W/RoMBnHo0CGRkpIi1q5dK1JTU8XBgweFwWCQdb+ff/65\nEEIIrVYrMjMzxaJFi0RWVpbQarWy7renfd6ukHIuFBQUiOnTp4vS0lJRXl7e7D9zzZo1S9TU1Ijq\n6moRGRkpPvnkE2O+5pg7d644deqU2Lt3rwgKChJXr14VGo2mU59v/PjxYsqUKWLBggViwYIFYuTI\nkcY/m0vK74KZM2eK2tpaERkZKS5evGiMNXPmTLNjRUREiJSUFLFgwQJx6tQps99/y7x580ReXp74\n+9//LkaNGiUqKytFbW2tiIiIMDmGVR1p6PV6BAYGNtv2yCOPdNv+nZyc4O3tDQAYMGBApw8lTZWS\nkoJvvvkGY8eOxdNPP40xY8bgxIkTSExMlHW/H3zwAQAgPT0dffv2RWJiIgYMGICkpCRZ99vTPm9X\nSDkXAgMD8dRTT+GHH37A4MGDm/1nrl69esHDwwP9+vXDli1bsGvXLpw8edLsOEIIjBo1CjNnzsSk\nSZNw1113wc3NDU5O5t9vvHfvXvj6+mLZsmXIycnBsGHDkJOTg5ycHLNj3SLFd0GvXr3g6uoKNzc3\neHp6GmO1deNme1xcXJCUlIR169Zhx44dmDZtGl555RXs2LHDrDiNjY147LHHEBoaCg8PD9x7771w\ndXWFs7OzyTGsqjX6Qw89hLi4OIwbN854l+zRo0dlv9lJo9Fg5syZuHHjBvbs2YOnnnoKmZmZGDhw\noKz7/fHHH7Fr165m2yZNmtSp0wadUVZWhrS0NADAAw88gC+++ELW/fW0z9sVUs+Fv/zlL5LkNWjQ\nIGRkZGDVqlVwc3ODWq3GkiVLoNFozIrj7e2NhIQEpKam4tVXXwUAbNu2DXfddZfZOd1555148803\n8dprr+G7774z+/1NSfldMGHCBCxfvhxDhw7FsmXLEBISgm+++Qb/9m//1un8/vCHP0CtVuO3335D\nfn4+SktLzXr/fffdh5iYGDQ2NkKpVGLTpk1QKpW4++67TY5hVUUjOTkZX375Jc6ePQutVgs3NzdM\nmDABkydPlnW/+/btQ319Pc6fP4/evXtDoVDgoYcewqxZs2Tdr8FgQH5+PoKDg43bTp8+bVbV74yy\nsjK8++67cHR0RElJCfz9/XHu3Dno9XpZ92upz1taWmqRz9sVlpoLHUlPT8eBAwegUCgAAAMHDkRO\nTg6ys7PNivPKK6/g8OHDcHR0NG4bMGAAIiMjO5WXs7MzEhISsHfv3i6dIZDyu2DZsmU4deoU8vLy\nMHDgQFRXV2PhwoV4/PHHzY719NNPN/vZ3d0dEydONDtOZmYmjhw5Ah8fH7i6uuK9996Di4sL0tPT\nTY7Bm/ssqKysDBkZGSgpKYEQAg4ODvDz80NsbKzx0FgOxcXFKCkpQVFREQIDAzF58mRERUUhOTkZ\n/v7+su23rKwMmZmZKCkpMfZX8vPzQ0xMDIYNGybbfm993uLiYgQEBBg/b1xcHB599FHZ9ktkj1g0\nqNt89dVXSE1NhZOTE2JiYjB9+nQAN1f3dOX8szn7feGFFzBt2rRu2S+RPbKq01M9TWRkJPR6fYvD\naYVCgd27d9vdfv/6179i3759EEJg9erV0Ol0LQ67u2O/9fX13bJfInvEomFBa9euRWJiItRqdbNz\nu/a631srbwBgy5YtWLRoEQYNGmS3+yWyRzw9ZWHbt2+Hl5cXQkND7X6/L730Evr3749Vq1ZBqVSi\nsrLSuPLm2LFjdrdfInvEokHdRq/X48CBAwgLC4OrqyuAmy0fsrOzZb1Xw1L7JbJHLBpERGQyq7oj\nnIiIrBuLBhERmYxFg4iITMaiQUREJvtf7gCAwDo5uUEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f738910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f174b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEbCAYAAADQ/Z9SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\np9jgiYh0ig2eiEin/j+qufqIfw5RiwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f41d890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "df[df.stint < 5].hist('hr', by='stint')\n",
    "\n",
    "plt.figure()\n",
    "df[df.stint < 5].boxplot('hr', by='stint')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scatter Matrix\n",
    "\n",
    "While doing this through each variable could be conveniently handled in a for loop or using a trellis plot, pandas includes a scatter_matrix which will provide both a histogram of each numeric field, but also a scatter plot for each field comparison. Use this wisely, as it's rather heavy plotting, and therefore, dependent on your data, memory intensive. You can, of course, subset to columns you are interested in comparing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x10fd81550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10fe96650>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x110036cd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11012b9d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x110210d10>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1102ff3d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1103f7710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x110705550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10f2e3990>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10f523dd0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x10f5dadd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10fb5a650>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10f45a0d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10edf1e50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x109144050>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x110ab8910>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10ec30a90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1102b0490>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10f178410>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10ef22650>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x10eb02750>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10eb42090>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11080e390>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11090c4d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x110653310>]], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtEAAALQCAYAAABWlSyiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fd81ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = ['ab', 'h', 'X2b', 'X3b', 'hr',]\n",
    "pd.scatter_matrix(last_year[cols], figsize=(12, 12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Groupbys and grouped plotting\n",
    "\n",
    "One final technique to consider (for today) is how we can use groupby and plotting alongside each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110a80150>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fc46b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWgAAAEQCAYAAACdlO55AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ee97390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110415cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEQCAYAAAC6Om+RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110a80e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "years_grouped = df.groupby('year')\n",
    "\n",
    "plt.figure()\n",
    "years_grouped.hr.agg(['mean']).plot()\n",
    "\n",
    "plt.figure()\n",
    "years_grouped.hr.agg(['sum']).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## On Your Own"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A very famous data set called Fisher's Iris data is included in sklearn. Import it with the following code into pandas (otherwise, it's a numpy ndarray)\n",
    "\n",
    "```python\n",
    "from sklearn import datasets\n",
    "iris = datasets.load_iris()\n",
    "iris_description = iris.DESCR\n",
    "irisdf = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "irisdf['target'] = iris.target\n",
    "print iris_description\n",
    "```\n",
    "\n",
    "1. describe, aggregate, and visualise the iris data set. what's interesting about it?\n",
    "    * be able to show both scatterplots and histograms using matplotlib, Trellis, and the scatter_matrix. Use the class target to group by.\n",
    "2. Which flowers are similar? Which seems to be in \"its own world?\"\n",
    "3. Write an .apply() function that would \"human learn\" the three flowers. It would be your best attempt to:\n",
    "    * split the flowers apart using the four columns\n",
    "    * assign either a 1, 2 or 3, similar to target\n",
    "    * test the performance of accuracy by measure how often your results match the original target vs the total count\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Review, Next Steps, Reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assignment 2: Due on Monday, Feb 9th\n",
    "* You'll be practicing this week's lectures in more detail with citibike data. Use the following [link](https://github.com/podopie/DAT18NYC/blob/master/assignments/02-data_manipulation_and_visualization.md) to get to the details.\n",
    "\n",
    "#### Additional Resources\n",
    "\n",
    "* Watch Andrew Montalenti's [\"Rapid Data Visualization\"](http://vimeo.com/79580138)\n",
    "   * Includes Pandas; D3.js; NVD3.js; Vega / Vincent; and PhantomJS. \n",
    "* View Wickham's [Grammar of Graphics Slides](http://www.slideshare.net/hadley/grammar-of-graphics-past-present-future)\n",
    "    * Or, read his [paper](http://vita.had.co.nz/papers/layered-grammar.html)\n",
    "* [another matplotlib tutorial](http://jakevdp.github.io/mpl_tutorial/)\n",
    "* [Principles of Analytic Graphics](https://github.com/DataScienceSpecialization/courses/blob/master/04_ExploratoryAnalysis/Principles/PrinciplesofAnalyticGraphics.pdf?raw=true)\n",
    "* [Matplotlib Docs](http://matplotlib.org/users/pyplot_tutorial.html)\n",
    "* [Pandas Docs](http://pandas.pydata.org/pandas-docs/stable/visualization.html)\n",
    "* [Choosing a Good Chart](http://extremepresentation.typepad.com/.shared/image.html?/photos/uncategorized/choosing_a_good_chart.jpg)\n",
    "* [Columbia's deck on visualisation](http://www2.research.att.com/~volinsky/DataMining/Columbia2011/Slides/Topic2-EDAViz.ppt)\n",
    "* [More practice with matplotlib and customisation](http://nbviewer.ipython.org/github/fonnesbeck/Bios366/blob/master/notebooks/Section2_4-Matplotlib.ipynb)\n",
    "\n",
    "#### Next Steps\n",
    "* We'll be learning and using statsmodels next week as a tool for further exploring and explaining data relationships. Consider going through [the documentation](http://statsmodels.sourceforge.net/stable/index.html) in your spare time to learn more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}