ch02_us_baby_names.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "names1880 = pd.read_csv('ch02/names/yob1880.txt', names=['name', 'sex', 'births'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "names1880"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 2000 entries, 0 to 1999\n",
        "Data columns (total 3 columns):\n",
        "name      2000  non-null values\n",
        "sex       2000  non-null values\n",
        "births    2000  non-null values\n",
        "dtypes: int64(1), object(2)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 2000 entries, 0 to 1999\n",
        "Data columns (total 3 columns):\n",
        "name      2000  non-null values\n",
        "sex       2000  non-null values\n",
        "births    2000  non-null values\n",
        "dtypes: int64(1), object(2)"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "names1880.groupby('sex').births.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "sex\n",
        "F       90993\n",
        "M      110493\n",
        "Name: births, dtype: int64"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "years = range(1880, 2011)\n",
      "pieces = []\n",
      "columns = ['name', 'sex', 'births']\n",
      "for year in years:\n",
      "    path = 'ch02/names/yob{0}.txt'.format(year)\n",
      "    frame = pd.read_csv(path, names=columns)\n",
      "    frame['year'] = year\n",
      "    pieces.append(frame)\n",
      "names = pd.concat(pieces, ignore_index=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "names"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 1690784 entries, 0 to 1690783\n",
        "Data columns (total 4 columns):\n",
        "name      1690784  non-null values\n",
        "sex       1690784  non-null values\n",
        "births    1690784  non-null values\n",
        "year      1690784  non-null values\n",
        "dtypes: int64(2), object(2)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 1690784 entries, 0 to 1690783\n",
        "Data columns (total 4 columns):\n",
        "name      1690784  non-null values\n",
        "sex       1690784  non-null values\n",
        "births    1690784  non-null values\n",
        "year      1690784  non-null values\n",
        "dtypes: int64(2), object(2)"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "names.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>name</th>\n",
        "      <th>sex</th>\n",
        "      <th>births</th>\n",
        "      <th>year</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>      Mary</td>\n",
        "      <td> F</td>\n",
        "      <td> 7065</td>\n",
        "      <td> 1880</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>      Anna</td>\n",
        "      <td> F</td>\n",
        "      <td> 2604</td>\n",
        "      <td> 1880</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>      Emma</td>\n",
        "      <td> F</td>\n",
        "      <td> 2003</td>\n",
        "      <td> 1880</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> Elizabeth</td>\n",
        "      <td> F</td>\n",
        "      <td> 1939</td>\n",
        "      <td> 1880</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>    Minnie</td>\n",
        "      <td> F</td>\n",
        "      <td> 1746</td>\n",
        "      <td> 1880</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "        name sex  births  year\n",
        "0       Mary   F    7065  1880\n",
        "1       Anna   F    2604  1880\n",
        "2       Emma   F    2003  1880\n",
        "3  Elizabeth   F    1939  1880\n",
        "4     Minnie   F    1746  1880"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_births = names.pivot_table('births', rows='year', cols='sex', aggfunc=sum)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_births.tail()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>sex</th>\n",
        "      <th>F</th>\n",
        "      <th>M</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2006</th>\n",
        "      <td> 1896468</td>\n",
        "      <td> 2050234</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
        "      <td> 1916888</td>\n",
        "      <td> 2069242</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 1883645</td>\n",
        "      <td> 2032310</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 1827643</td>\n",
        "      <td> 1973359</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td> 1759010</td>\n",
        "      <td> 1898382</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "sex         F        M\n",
        "year                  \n",
        "2006  1896468  2050234\n",
        "2007  1916888  2069242\n",
        "2008  1883645  2032310\n",
        "2009  1827643  1973359\n",
        "2010  1759010  1898382"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_births.plot(title='Total births by sex and year')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xcfe32d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lbio9cOAAxo8fL/c3xzFVF5UehcX/LMaNiTfQunlrAMC1a8BXXwHOzkBUFGBp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\n9fUlLi4ONzc38vLy8PHxsezv7e2t20WaISEhlhUIPvjgAy5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       "text": [
        "<matplotlib.figure.Figure at 0x36af5d0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# don't need this if py3\n",
      "# py2 doesn't integer division (floor) by default).\n",
      "from __future__ import division\n",
      "\n",
      "def add_prop(group):\n",
      "    # don't want integer division\n",
      "    #births = group.births.astype(float)\n",
      "    \n",
      "    group['prop'] = group.births / group.births.sum()\n",
      "    return group\n",
      "\n",
      "names = names.groupby(['year', 'sex']).apply(add_prop)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "names.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>name</th>\n",
        "      <th>sex</th>\n",
        "      <th>births</th>\n",
        "      <th>year</th>\n",
        "      <th>prop</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>      Mary</td>\n",
        "      <td> F</td>\n",
        "      <td> 7065</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.077643</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>      Anna</td>\n",
        "      <td> F</td>\n",
        "      <td> 2604</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.028618</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>      Emma</td>\n",
        "      <td> F</td>\n",
        "      <td> 2003</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.022013</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> Elizabeth</td>\n",
        "      <td> F</td>\n",
        "      <td> 1939</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.021309</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>    Minnie</td>\n",
        "      <td> F</td>\n",
        "      <td> 1746</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.019188</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "        name sex  births  year      prop\n",
        "0       Mary   F    7065  1880  0.077643\n",
        "1       Anna   F    2604  1880  0.028618\n",
        "2       Emma   F    2003  1880  0.022013\n",
        "3  Elizabeth   F    1939  1880  0.021309\n",
        "4     Minnie   F    1746  1880  0.019188"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "names"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 1690784 entries, 0 to 1690783\n",
        "Data columns (total 5 columns):\n",
        "name      1690784  non-null values\n",
        "sex       1690784  non-null values\n",
        "births    1690784  non-null values\n",
        "year      1690784  non-null values\n",
        "prop      1690784  non-null values\n",
        "dtypes: float64(1), int64(2), object(2)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 1690784 entries, 0 to 1690783\n",
        "Data columns (total 5 columns):\n",
        "name      1690784  non-null values\n",
        "sex       1690784  non-null values\n",
        "births    1690784  non-null values\n",
        "year      1690784  non-null values\n",
        "prop      1690784  non-null values\n",
        "dtypes: float64(1), int64(2), object(2)"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.allclose(names.groupby(['year', 'sex']).prop.sum(), 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_top1000(group):\n",
      "    return group.sort_index(by='births', ascending=False)[:1000]\n",
      "grouped = names.groupby(['year', 'sex'])\n",
      "top1000 = grouped.apply(get_top1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "top1000.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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></th>\n",
        "      <th>name</th>\n",
        "      <th>sex</th>\n",
        "      <th>births</th>\n",
        "      <th>year</th>\n",
        "      <th>prop</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th>sex</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=\"5\" valign=\"top\">1880</th>\n",
        "      <th rowspan=\"5\" valign=\"top\">F</th>\n",
        "      <th>0</th>\n",
        "      <td>      Mary</td>\n",
        "      <td> F</td>\n",
        "      <td> 7065</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.077643</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>      Anna</td>\n",
        "      <td> F</td>\n",
        "      <td> 2604</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.028618</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>      Emma</td>\n",
        "      <td> F</td>\n",
        "      <td> 2003</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.022013</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> Elizabeth</td>\n",
        "      <td> F</td>\n",
        "      <td> 1939</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.021309</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>    Minnie</td>\n",
        "      <td> F</td>\n",
        "      <td> 1746</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.019188</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "                 name sex  births  year      prop\n",
        "year sex                                         \n",
        "1880 F   0       Mary   F    7065  1880  0.077643\n",
        "         1       Anna   F    2604  1880  0.028618\n",
        "         2       Emma   F    2003  1880  0.022013\n",
        "         3  Elizabeth   F    1939  1880  0.021309\n",
        "         4     Minnie   F    1746  1880  0.019188"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "top1000"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "MultiIndex: 261877 entries, (1880, F, 0) to (2010, M, 1677643)\n",
        "Data columns (total 5 columns):\n",
        "name      261877  non-null values\n",
        "sex       261877  non-null values\n",
        "births    261877  non-null values\n",
        "year      261877  non-null values\n",
        "prop      261877  non-null values\n",
        "dtypes: float64(1), int64(2), object(2)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "MultiIndex: 261877 entries, (1880, F, 0) to (2010, M, 1677643)\n",
        "Data columns (total 5 columns):\n",
        "name      261877  non-null values\n",
        "sex       261877  non-null values\n",
        "births    261877  non-null values\n",
        "year      261877  non-null values\n",
        "prop      261877  non-null values\n",
        "dtypes: float64(1), int64(2), object(2)"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Analyzing Naming Trends\n",
      "========================="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "boys = top1000[top1000.sex == 'M']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "girls = top1000[top1000.sex == 'F']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_births = top1000.pivot_table('births', rows='year', cols='name', aggfunc=sum)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_births.ix[1990].dropna().head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "name\n",
        "Aaron    14545\n",
        "Abbey      507\n",
        "Abbie      311\n",
        "Abby      1293\n",
        "Abdul      122\n",
        "Name: 1990, dtype: float64"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subset = total_births[['John', 'Harry', 'Mary', 'Marilyn']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subset.plot(subplots=True, figsize=(12, 10), grid=False,\n",
      "            title='Number of births per year')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "array([<matplotlib.axes.AxesSubplot object at 0x373e310>,\n",
        "       <matplotlib.axes.AxesSubplot object at 0xb829550>,\n",
        "       <matplotlib.axes.AxesSubplot object at 0x6f48090>,\n",
        "       <matplotlib.axes.AxesSubplot object at 0x6f6e8d0>], dtype=object)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qFSdOnDB3GI2Gvb39JVdxk8RZCCFuAkVR8PPz44033iA8PNzc4YgbtG/fPoKC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AjA/PnziYiIwMLCgkWLFuHv7w/A3r17GT9+PHl5efTr14/FixcDUFBQwNixY9m/fz+2trZER0ff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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb800710>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Measuring the increase in naming diversity\n",
      "------------------------------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table = top1000.pivot_table('prop', rows='year', cols='sex', aggfunc=sum)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table.plot(title='Sum of table1000.prop by year and sex',\n",
      "           yticks=np.linspace(0, 1.2, 13),\n",
      "           xticks=range(1880, 2020, 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7a9c1d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LwfbB6GLaRaXtbytNz+eatwbr1AkICtBDUIAf3oKfwrraUxqLi5XfioqA5JpY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\nYxro/wDkZejLenaYsQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7a8ca90>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = boys[boys.year == 2010]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "MultiIndex: 1000 entries, (2010, M, 1676644) to (2010, M, 1677643)\n",
        "Data columns (total 5 columns):\n",
        "name      1000  non-null values\n",
        "sex       1000  non-null values\n",
        "births    1000  non-null values\n",
        "year      1000  non-null values\n",
        "prop      1000  non-null values\n",
        "dtypes: float64(1), int64(2), object(2)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "MultiIndex: 1000 entries, (2010, M, 1676644) to (2010, M, 1677643)\n",
        "Data columns (total 5 columns):\n",
        "name      1000  non-null values\n",
        "sex       1000  non-null values\n",
        "births    1000  non-null values\n",
        "year      1000  non-null values\n",
        "prop      1000  non-null values\n",
        "dtypes: float64(1), int64(2), object(2)"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prop_cumsum = df.sort_index(by='prop', ascending=False).prop.cumsum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prop_cumsum[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "year  sex         \n",
        "2010  M    1676644    0.011523\n",
        "           1676645    0.020934\n",
        "           1676646    0.029959\n",
        "           1676647    0.038930\n",
        "           1676648    0.047817\n",
        "           1676649    0.056579\n",
        "           1676650    0.065155\n",
        "           1676651    0.073414\n",
        "           1676652    0.081528\n",
        "           1676653    0.089621\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Alternate way of doing same thing.\n",
      "prop_cumsum = df.prop.order(ascending=False).cumsum()\n",
      "prop_cumsum[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "year  sex         \n",
        "2010  M    1676644    0.011523\n",
        "           1676645    0.020934\n",
        "           1676646    0.029959\n",
        "           1676647    0.038930\n",
        "           1676648    0.047817\n",
        "           1676649    0.056579\n",
        "           1676650    0.065155\n",
        "           1676651    0.073414\n",
        "           1676652    0.081528\n",
        "           1676653    0.089621\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prop_cumsum.searchsorted(0.5) + 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "117"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = boys[boys.year == 1900]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "in1900 = df.sort_index(by='prop', ascending=False).prop.cumsum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "in1900.searchsorted(0.5) + 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "25"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_quantile_count(group, q=0.5):\n",
      "    group = group.sort_index(by='prop', ascending=False)\n",
      "    return group.prop.cumsum().searchsorted(q) + 1\n",
      "\n",
      "diversity = top1000.groupby(['year', 'sex']).apply(get_quantile_count)\n",
      "diversity.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "year  sex\n",
        "1880  F      38\n",
        "      M      14\n",
        "1881  F      38\n",
        "      M      14\n",
        "1882  F      38\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diversity = diversity.unstack('sex')\n",
      "diversity.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>sex</th>\n",
        "      <th>F</th>\n",
        "      <th>M</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1880</th>\n",
        "      <td> 38</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1881</th>\n",
        "      <td> 38</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1882</th>\n",
        "      <td> 38</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1883</th>\n",
        "      <td> 39</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1884</th>\n",
        "      <td> 39</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "sex    F   M\n",
        "year        \n",
        "1880  38  14\n",
        "1881  38  14\n",
        "1882  38  15\n",
        "1883  39  15\n",
        "1884  39  16"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diversity.plot(title='Number of popular names in top 50%')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xc4c7350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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T4P/+T7sYibCe7yO+Z9Svo9g9dDe1ystYFTZxHL/JZKJTp044Ojry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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc490850>"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The \"Last letter\" Revolution\n",
      "------------------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# extract last letter from name column\n",
      "last_letters = names.name.str[-1]\n",
      "last_letters.name = 'last_letter'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table = names.pivot_table('births', rows=last_letters,\n",
      "                          cols=['sex', 'year'], aggfunc=sum)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table.ix['a']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "sex  year\n",
        "F    1880    31446\n",
        "     1881    31581\n",
        "     1882    36536\n",
        "     1883    38330\n",
        "     1884    43680\n",
        "     1885    45408\n",
        "     1886    49100\n",
        "     1887    48942\n",
        "     1888    59442\n",
        "     1889    58631\n",
        "     1890    62313\n",
        "     1891    60582\n",
        "     1892    68331\n",
        "     1893    67821\n",
        "     1894    70631\n",
        "...\n",
        "M    1996    42739\n",
        "     1997    41458\n",
        "     1998    41281\n",
        "     1999    40608\n",
        "     2000    40837\n",
        "     2001    39124\n",
        "     2002    38815\n",
        "     2003    37825\n",
        "     2004    38650\n",
        "     2005    36838\n",
        "     2006    36156\n",
        "     2007    34654\n",
        "     2008    32901\n",
        "     2009    31430\n",
        "     2010    28438\n",
        "Name: a, Length: 262, dtype: float64"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subtable = table.reindex(columns=[1910, 1960, 2010], level='year')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subtable.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th>sex</th>\n",
        "      <th colspan=\"3\" halign=\"left\">F</th>\n",
        "      <th colspan=\"3\" halign=\"left\">M</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th>1910</th>\n",
        "      <th>1960</th>\n",
        "      <th>2010</th>\n",
        "      <th>1910</th>\n",
        "      <th>1960</th>\n",
        "      <th>2010</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>last_letter</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>a</th>\n",
        "      <td> 108376</td>\n",
        "      <td> 691247</td>\n",
        "      <td> 670605</td>\n",
        "      <td>   977</td>\n",
        "      <td>   5204</td>\n",
        "      <td>  28438</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>b</th>\n",
        "      <td>    NaN</td>\n",
        "      <td>    694</td>\n",
        "      <td>    450</td>\n",
        "      <td>   411</td>\n",
        "      <td>   3912</td>\n",
        "      <td>  38859</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>c</th>\n",
        "      <td>      5</td>\n",
        "      <td>     49</td>\n",
        "      <td>    946</td>\n",
        "      <td>   482</td>\n",
        "      <td>  15476</td>\n",
        "      <td>  23125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>d</th>\n",
        "      <td>   6750</td>\n",
        "      <td>   3729</td>\n",
        "      <td>   2607</td>\n",
        "      <td> 22111</td>\n",
        "      <td> 262112</td>\n",
        "      <td>  44398</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>e</th>\n",
        "      <td> 133569</td>\n",
        "      <td> 435013</td>\n",
        "      <td> 313833</td>\n",
        "      <td> 28655</td>\n",
        "      <td> 178823</td>\n",
        "      <td> 129012</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "sex               F                      M                \n",
        "year           1910    1960    2010   1910    1960    2010\n",
        "last_letter                                               \n",
        "a            108376  691247  670605    977    5204   28438\n",
        "b               NaN     694     450    411    3912   38859\n",
        "c                 5      49     946    482   15476   23125\n",
        "d              6750    3729    2607  22111  262112   44398\n",
        "e            133569  435013  313833  28655  178823  129012"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subtable.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 70,
       "text": [
        "sex  year\n",
        "F    1910     396416\n",
        "     1960    2022062\n",
        "     2010    1759010\n",
        "M    1910     194198\n",
        "     1960    2132588\n",
        "     2010    1898382\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import division\n",
      "letter_prop = subtable / subtable.sum()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "letter_prop.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th>sex</th>\n",
        "      <th colspan=\"3\" halign=\"left\">F</th>\n",
        "      <th colspan=\"3\" halign=\"left\">M</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th>1910</th>\n",
        "      <th>1960</th>\n",
        "      <th>2010</th>\n",
        "      <th>1910</th>\n",
        "      <th>1960</th>\n",
        "      <th>2010</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>last_letter</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>a</th>\n",
        "      <td> 0.273390</td>\n",
        "      <td> 0.341853</td>\n",
        "      <td> 0.381240</td>\n",
        "      <td> 0.005031</td>\n",
        "      <td> 0.002440</td>\n",
        "      <td> 0.014980</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>b</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0.000343</td>\n",
        "      <td> 0.000256</td>\n",
        "      <td> 0.002116</td>\n",
        "      <td> 0.001834</td>\n",
        "      <td> 0.020470</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>c</th>\n",
        "      <td> 0.000013</td>\n",
        "      <td> 0.000024</td>\n",
        "      <td> 0.000538</td>\n",
        "      <td> 0.002482</td>\n",
        "      <td> 0.007257</td>\n",
        "      <td> 0.012181</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>d</th>\n",
        "      <td> 0.017028</td>\n",
        "      <td> 0.001844</td>\n",
        "      <td> 0.001482</td>\n",
        "      <td> 0.113858</td>\n",
        "      <td> 0.122908</td>\n",
        "      <td> 0.023387</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>e</th>\n",
        "      <td> 0.336941</td>\n",
        "      <td> 0.215133</td>\n",
        "      <td> 0.178415</td>\n",
        "      <td> 0.147556</td>\n",
        "      <td> 0.083853</td>\n",
        "      <td> 0.067959</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 83,
       "text": [
        "sex                 F                             M                    \n",
        "year             1910      1960      2010      1910      1960      2010\n",
        "last_letter                                                            \n",
        "a            0.273390  0.341853  0.381240  0.005031  0.002440  0.014980\n",
        "b                 NaN  0.000343  0.000256  0.002116  0.001834  0.020470\n",
        "c            0.000013  0.000024  0.000538  0.002482  0.007257  0.012181\n",
        "d            0.017028  0.001844  0.001482  0.113858  0.122908  0.023387\n",
        "e            0.336941  0.215133  0.178415  0.147556  0.083853  0.067959"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "fix, axes = plt.subplots(2, 1, figsize=(10, 8))\n",
      "letter_prop['M'].plot(kind='bar', rot=0, ax=axes[0], title='Male')\n",
      "letter_prop['F'].plot(kind='bar', rot=0, ax=axes[1], title='Female')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 77,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xd9eaad0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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d24Drb317RbyfRK8/AKQDiLp9351H0eR8pexP9dto66+/UvYnta//3dtA6etf\n93t+fj7Mkkw4cuSIFB0drZ9etWqVtHr16gbznTp1SurTp4+Uk5PT5GXNNMGotLS0Ji/TWrlKyRSV\nq+ZMUblqzhSVW5cJQJKM3Gzlc0qJ21SNmaJy1ZwpKrc5maY+D0zWVFVXV8PX1xepqano2bMnBg0a\nhMTERPj7++vn+fHHHzFs2DDs2LEDDzzwQJOWBVhTRUS2gzVVRGSOqX6LyeE/e3t7bNq0CdHR0dDp\ndJg1axb8/f2RkJAAAJg7dy5ee+01lJSUYN68eQAABwcHZGRkNLosERERUZvUvANm8mlOE9r6ocXW\nyBSVq+ZMUblqzhSVy+E/+XPVnCkqV82ZonLlHv6za90uHREREVHbwGv/ERHdxpoqIjKH1/4jIiIi\nEkyRnar6546w9VylZIrKVXOmqFw1Z4rKVXOmqFw1Z4rKVXOmqFy5MxXZqSIiIiKyNaypIiK6jTVV\nRGQOa6qIiIiIBFNkp0rN47WiMkXlqjlTVK6aM0XlqjlTVK6aM0XlqjlTVC5rqoiIiIhsEGuqiIhu\nY00VEZnDmioiIiIiwRTZqVLzeK2oTFG5as4UlavmTFG5as4UlavmTFG5as4UlcuaKiIiIiIbxJoq\nIqLbWFNFROawpoqIiIhIMEV2qtQ8XisqU1SumjNF5ao5U1SumjNF5ao5U1SuGjOdnJ2g0WgMbk7O\nTrLlt3hNVUpKCvz8/NC3b1+sWbOmwePnzp1DREQEOnbsiDfffNPgMU9PTwQHByMsLAyDBg2Sr9VE\nRETU5pVdKwPiUHubXvuz7FpZazbJJJM1VTqdDr6+vti/fz/c3NwwcOBAJCYmwt/fXz9PcXExLl26\nhN27d8PFxQUvvfSS/jEvLy+cPHkSrq6ujTeANVVEZCNYU0VkWzQaTW2Hqr641t0fm11TlZGRAR8f\nH3h6esLBwQFTp05FcnKywTz33nsvwsPD4eDgYDSDH0RERESkBiY7VYWFhfDw8NBPu7u7o7Cw0OJw\njUaDESNGIDw8HFu2bGl+K+/CMXD5M0XlqjlTVK6aM+XMNVarISc1blNmis1VcyYAIE/+SLnbam/q\nQWs/ZA4fPowePXqguLgYI0eOhJ+fHyIjIxvMFxMTA09PTwCAs7MzQkNDERUVBeDOCtefzsrKMvm4\nLU1nZWXJns/1l3/963D95V1/EdNyrX/ZtbLaGo06XgDigHQAUbfvSoeh1l5/Ee8nW1q/trI/qX39\n68idhyLpkUeOAAAgAElEQVTDyZZa/7rf8/PzYY7JmqqjR48iLi4OKSkpAIA33ngDdnZ2WLhwYYN5\n4+Pj4ejoaFBTZcnjrKkiotbQaK2GsXnBUgai1tCmaqrCw8ORk5OD/Px8VFVVYefOnRg3bpzRee9+\ngsrKSpSV1VboV1RUYN++fQgKCmpO+0kmTk6uDb+a6tT4lwiIiIjIciY7Vfb29ti0aROio6MREBCA\nKVOmwN/fHwkJCUhISAAAFBUVwcPDA//85z+xcuVK9OrVC+Xl5SgqKkJkZCRCQ0MxePBgPPbYYxg1\napQsjW5wSFAmInJtKbOsrAS1/4ffudXeZ12uKWrOFJWr5kxhuQqo1RCVKSpXzZmictWcCUAR+6nJ\nmioAGDNmDMaMGWNw39y5c/W/33fffSgoKGiwnKOjo378l4ioNTk5uRr8A0FEJAKv/acitV88uHtb\nc/tT29fYe581VUS2rU3VVBERERGRZRTZqeIYuPyZonLVnCkqV82ZwnIVUKshKlNUrpozReWqOROA\nIvZTRXaqiIiIiGwNa6pUhDVVpFasqSJSJtZUERERNQPPpUdKp8hOFcfA5c8UlavmTFG5as4UlquA\nWg1RmaJym5PZVs6lJypXzZkAFLGfKrJTRURERGRrWFOlIqypIrViTZUy8DOK7saaKhLGydmpYb2B\ns1NrN4uIiIig0E6VWsfAy66V1fbYp6P2Z9zt+2Ri6+uvtExRuWrOFJargFoNUZmictWcKSpXzZkA\nFLGfKrJTRURERGRrWFOlINaOLbNegdSKNVXKwM8ouhtrqoiIiIhUSJGdqrY+Bm7sBHi1/8HdJmBc\nGbCd9W8rmaJy1ZwpLFcBtRqiMkXlqjlTVK6aMwEoYj+1lzWNZHHnBHh30xi5j4iIiGyB2ZqqlJQU\nxMbGQqfTYfbs2Vi4cKHB4+fOncOMGTOQmZmJ119/HS+99JLFywKsqTLGeF0B0GgNCGuqiExiTZUy\n8DOK7tamaqp0Oh3mz5+PlJQUZGdnIzExEd9//73BPN26dcPGjRvx8ssvN3lZIiIiorbCZKcqIyMD\nPj4+8PT0hIODA6ZOnYrk5GSDee69916Eh4fDwcGhycs2l9rHwFlTpYxMUbm2nin6JLVK2ads/XUS\nnavmTFG5as4EoIj91GRNVWFhITw8PPTT7u7uOHbsmEXB1ixLRMqlP0ltHgCv2/fFyXeSWlIZOxh+\nUec2bVctSq+WtkKDiBpnslNl7I1sKWuWNScqKkoxuULa6iV/JKCc9VdKpqhcpWQq6X0qoq2KeZ0E\n5cqWWYOGNTWQr6Ouym2qwEwAithPTXaq3NzcUFBQoJ8uKCiAu7u7RcFNWTYmJgaenp4AAGdnZ4SG\nhupXtO7QnNqm76ibrn1cf/iz3psrPT3d4vwGeU1cntOctmRa767D9a3eHkv2pzuP6udu7farZbpW\nOhp9Be56vVq7vZxumWm9Vvo8qfs9Pz8fZkkm3Lp1S/L29pby8vKkmzdvSiEhIVJ2drbReZcvXy6t\nW7euycuaaYJRaWlpTV6mtXKbkwlAAiQjN0iIg4Tpt3/GoUnbz3juneVtZf3bSqaoXFvPtPZ9ao58\n+1S99k2v104jN1v5nGrr71OLXycZ31NtfZsqPbPR/VQmzX2fNsbkkSp7e3ts2rQJ0dHR0Ol0mDVr\nFvz9/ZGQkAAAmDt3LoqKijBw4ECUlpbCzs4O69evR3Z2NhwdHY0uS0RERNQW8dp/NojnqSIls9Xz\nyvA8VbbP4tcJaPX3FLUMW/08aez57Vq4LURERERtkiI7VQ2LT203V0hbeZ4qRWSKylVKppLep0o4\n/42oTFG5an6dROWqOROAIl5/RXaqiIhIOVydGp4Q1tVJvhPCEtkK1lTZINZUkZLZag0Ea6paj0aj\nMbb1G2xT1lTR3Wz184Q1VWScHYReUoSIiEgtFNmpUvsYuKzjynVnK44DML32Z9k1+S4popRtqvb3\nlM2/T+tRSlsV8zoJylXz6yQqV82ZABTx+iuyU0VERERka1hTZYNauqbK1sarSdlstQaCNVWthzVV\nZAknJ1eUlZU0fCCu4XRrf5409vwmz6hORERE1BJqO1TGut/KocjhP7WPgYuqVVHCeLWSMkXlKiWT\nNVXpisgUlWsrr5OTk2uDL+NoNBo4ObkC4DZVSiYAReynPFJFRERtlvGjH0BZmbKOgJAysKbKBrGm\nipTMWE2VfRxQfdd8Llotfi8tbbk2saaq1bRmTZWpz1O+prZFKX+jeJ4qahJ7NDx3Fc9+TNaoRu1H\nZf1bSZl8p+4gIrIFiuxUqX0MXHRNlZx/AJWyTdX+nmpOZmO1KnqsqVJEpqhc0a+T1f/8CT7xsVK2\nqVIyAShiP2VNFRE1S2O1Kkr7tg41j7Gvv2u1Ligt/b1Fnr/un7/6NE3556/uxMd5ALxq7yqL49FT\nsg5rqmyQTdRUNZyLrxMZaPL7tOFcLfaeYk2V/JpyLVFhNVXNzmwkN46vc2tiTRURERERAbCgU5WS\nkgI/Pz/07dsXa9asMTrPggUL0LdvX4SEhCAzM1N/v6enJ4KDgxEWFoZBgwbJ1mjWFcgfKSpXKdtU\n7e8pW3qfGqvVqjunEGBbbTVFMa+ToFylvE5Kqv1TcyYA/WtlrJ6uuV+oatGaKp1Oh/nz52P//v1w\nc3PDwIEDMW7cOPj7++vn2bt3Ly5cuICcnBwcO3YM8+bNw9GjRwHUrnR6ejpcXV0bewoiIgPGarV4\nTiEiqmOsng5oYk2dICZrqo4cOYL4+HikpKQAAFavXg0AWLRokX6e559/HkOHDsWUKVMAAH5+fvj6\n66/RvXt3eHl54cSJE+jWrVvjDWBNVQOsqSIlEFVT1ZRaHUuxpkp+rKkiuSllP212TVVhYSE8PDz0\n0+7u7igsLLR4Ho1GgxEjRiA8PBxbtmxp9goQtSRXJyeep4uIiJrM5PCfwTlnTGisx3bo0CH07NkT\nxcXFGDlyJPz8/BAZGdlgvpiYGHh6egIAnJ2dERoaiqioKAB3xjvrT2dlZSE2NrbRx5s7XX9sVY48\nAHjrrbfMro+x6TvqpmsfRx6AIgAR9eZIT7c4v0FeXaYXjNYWNGhNC61/S7/+dfelp6ejpKxM/19Q\n3aNDbx9Wbkvr7+TshLJrDQ+Xd+rSCZXllRblNXw/1U3D5PtJP7fF7/878zdn/RttQf02et2ZI+qu\nues/vyXPZ2ufJ015/zdleWOfJ3WfRw23vfnX/85cd+ffdgTAfTB4rQyf3fjnYaMtsPLztC28/qI/\nT5uyfK10mH3973rUYOkmvn6WrH/d7/n5+TBLMuHIkSNSdHS0fnrVqlXS6tWrDeaZO3eulJiYqJ/2\n9fWVioqKGmTFxcVJ69ata3C/mSYYlZaW1uRlWiu3OZkAJEAycoOEOEiYfvtnHJq0/Yzn1suaXi/z\nrltzXidJsp1t2pRMtay/0de+Ce+pJr9PLdymjb1P68i3T1n23m/u62/Lr70cuU15nSx9/S1+nZrw\nnhL1eWqOUl5/W8pUyn5q6nlM1lRVV1fD19cXqamp6NmzJwYNGoTExMQGheqbNm3C3r17cfToUcTG\nxuLo0aOorKyETqeDVqtFRUUFRo0aheXLl2PUqFEGz8GaqoZYUyU/YycqBIyfrNDS+g+lM3aNPgCy\nXFONNVVtH2uqSG5K2U9N9VtMDv/Z29tj06ZNiI6Ohk6nw6xZs+Dv74+EhAQAwNy5c/HII49g7969\n8PHxQZcuXbBt2zYAQFFRESZOnAigtnM2bdq0Bh0qopbCK9UTEZFoZs9TNWbMGPzwww+4cOECFi9e\nDKC2MzV37lz9PJs2bcKFCxdw6tQp3H///QAAb29vZGVlISsrC2fOnNEvKwdjY/W2miukrXnyR4rK\nVco25XtK/kglnf9H9Htfri8/8H0qf6SS3qdqzgSgiL9RvPYfEZFg9b/8UMcWzqlDRPLitf9sEGuq\n5NfoNrXT1F5Y9S5tbf2NYU1Vy9VqtMU6PdZUkdxaez+1VLNrqojavLor1dd393Qb0FihPhERyUeR\nF1RmXYH8kaJy1b5NbWX97xTq17/VY+u1KnYNr/Xl5CzjCVnV/N4XlKuY/bSN7/ttJROAIvZTHqki\nIttX/4hiHgAvoCyONUlEZFtYU2WDWFMlP1H1P0rR5FoVG6ypaq33vqm2Wko1NVWN1Cii4ZysqaIG\nWns/tRRrqoiISDxjNYpo5D6iNog1VYJzFVNXIChX7dtU1euvoPepqt/7onIV8jqpfd9XSiYAReyn\niuxUERHZo2HxenNPqklEJAfWVNmgptQA2McB1XfN5aLV4vfSUgtz1VxTBLTU+rs6OaHEyMkeG3ut\n5NZma6qMP3vza3WakNkUqqmpElL/1HI1VfZxln+ekvxaez+1lKl+C49UGWHskhK2+h9wNRp+Ud7Y\nH29qXXVn1OZrRWS7+HlK1lJkp0p0XYGcfwAVU6sgKFcx4/Uqr6uov/7GhtWa9Q+Fgt6nqn7vi8pV\nyOuk9n1fKZkAFLGfKrJTRUTi8L91yzg5uTY8IamTa2s3i6jZ5Lrwt5opvqbKWK2KtWPgxuofgJYd\nr7W1WpW2Wf8BtNT62+Z7qoXP/2NBZpPaaiSzaW0Vk2nNte+UpC3WVLXFz76maO33KWuqbICxoTpb\n/a+a/wUQtXFGLqdT+4eCiNRAkZ0qJdUV1M+UrQOooFoVxYzXq7yuQkm1Kjbd1rqTX8YBmF7vd5ko\n6bPPpl8n0ZlQzr6vlEwAivgbZbZTlZKSAj8/P/Tt2xdr1qwxOs+CBQvQt29fhISEIDMzs0nLNkdW\nVpZsWaJzhbS1SP5IUblyrb+T852jfEOHDpX/orqCtqmq31MKep8qJVNJn31K2aZq3Pcb+zyVS/12\nyjpCo4B9yuRlanQ6HebPn4/9+/fDzc0NAwcOxLhx4+Dv76+fZ+/evbhw4QJycnJw7NgxzJs3D0eP\nHrVo2ea6evWq1Rkic52cnVB27c4RqL/85S+y5OrdkDdOZK5c27TsWtmd//jTAAy9fb9cF9UVtE1F\nvFeFvP9FrL+C3qdKybT1zz4DCtmmbX3fd3JyRVlZScMH4m7/rPd5KtdR1frtrBuhqU/T3BIdBexT\nJo9UZWRkwMfHB56ennBwcMDUqVORnJxsMM/nn3+O6dOnAwAGDx6Mq1evoqioyKJlTVHyN2v0HYA4\nAA9D9iEAW1f/v6D4+Hh5jygRqVDdPlW3P3GfIkvVdqjuLjyxjrG/z0r6Gy2SyU5VYWEhPDw89NPu\n7u4oLCy0aJ6ffvrJ7LKmGHsjlFWUNPhgaQqjb4R2mgYdAGszDYj4x1LMP6uy5Rp0KkNqf9Y/cmdO\nW9qm+fn5ishU0jZVTFubkdnYHyv9PnV7f2rqPmWOqt9T3PebzHhHzfq/0WYJeK3qb1NZhiolE3bt\n2iXNnj1bP/3RRx9J8+fPN5jnsccekw4dOqSfHj58uHTixAmLlpUkSQoJCTF2nk3eeOONN9544403\nm7uFhIQ02m8yWVPl5uaGgoIC/XRBQQHc3d1NznP58mW4u7vj1q1bZpcFxBVeEhEREbUkk8N/4eHh\nyMnJQX5+PqqqqrBz506MGzfOYJ5x48Zh+/btAICjR4/C2dkZ3bt3t2hZIiIiorbC5JEqe3t7bNq0\nCdHR0dDpdJg1axb8/f2RkJAAAJg7dy4eeeQR7N27Fz4+PujSpQu2bdtmclkiIiKitqjVL1NDRERE\n1BYo8ozqcsrPz0dQUFBrN6NZ4uLi8Oabb7Z2Mxq1YcMGBAQE4Nlnn23tpjRK5Os/ZMgQIblyZotc\nf0dHRyG5RHK5du0a3nnnndZuBrUhqu9UKZmtX1PsnXfewf79+/HRRx+1dlNaxeHDhxWZLRdbf3+S\neJIk2fTFiEtKSrB58+bWbga1IYrrVE2YMAHh4eHo378/tmzZIktmdXU1nnnmGQQEBGDy5Mm4fv26\nLLnbt29HSEgIQkND8ac//UmWzNdffx2+vr6IjIzEDz/8IEvmjh07MHjwYISFheH5559HTU2N1ZnP\nP/88Ll68iNGjR+Ott96SoZW1VqxYAT8/P0RGRuLpp5+W5UidTqfDnDlz0L9/f0RHR+PGDXlO2yvy\nSI2I7IsXL+L+++/HyZMnZc+2Rn5+Pvz8/DBjxgz4+vpi2rRp2LdvH4YMGYJ+/frh+PHjVmX7+/vL\n/vr/4x//QFBQEIKCgrB+/Xqr8+q2gdyfU/U/T+Tan/Lz8+Hr64vp06cjKCgIly9ftjqzoqICjz76\nKEJDQxEUFIR///vfVmcCwKJFi5Cbm4uwsDAsXLjQ6ry7j/yuW7cO8fHxVmUuXrzYoONn7QjF2rVr\nsXHjRgC1V/sYPnw4AODAgQN45plnrGrr8ePHERISgps3b6KiogL9+/dHdna2VZnLly832IeWLFmC\nDRs2WJUJAAkJCQgLC0NYWBi8vLwwbNgwqzMBwOR5qmzR77//LkmSJFVWVkr9+/eXfvvtN6vy8vLy\nJI1GI/3vf/+TJEmSZs6cKa1bt87qdp45c0bq16+fvn117bbGiRMnpKCgIOn69etSaWmp5OPjI735\n5ptWZWZnZ0tjx46VqqurJUmSpHnz5knbt2+3uq2SJEmenp5Wvz71ZWRkSKGhodLNmzelsrIyqW/f\nvlavf15enmRvby+dOnVKkiRJevLJJ6UdO3bI0VzJ0dFRlhyR2Xl5eVL//v2lc+fOSWFhYdJ3330n\nS64kydtGe3t76cyZM1JNTY00YMAAaebMmZIkSVJycrI0fvx4q7PlfP3r9tPKykqpvLxcCgwMlDIz\nM63KFPE5JeLzpK6tdnZ20rFjx6zOqrNr1y7pueee009fu3ZNltz8/Hypf//+smRJ0p39qc66deuk\nuLg4qzIzMzOlhx9+WD8dEBAgXb58udl5R48elSZPnixJkiQ99NBD0uDBg6Vbt25JcXFx0nvvvWdV\nWyVJkpYuXSq9/PLL0p///Gdp9erVVufl5+dL999/vyRJkqTT6aQ+ffrI8ve0zq1bt6TIyEjpiy++\nkCVPcUeq1q9fj9DQUERERODy5cvIycmxOtPDwwMREREAgGeeeQaHDh2yOvPAgQN48skn4epae9p+\nFxcXqzMPHjyIiRMnomPHjtBqtRg3bpzVh9ZTU1Nx8uRJhIeHIywsDAcOHEBeXp7VbRXh8OHDGD9+\nPNq3bw9HR0eMHTtWlqEFLy8vBAcHAwAGDBgg5qzFNuzXX3/F+PHj8fHHH9tsfaGXlxcCAwOh0WgQ\nGBiIESNGAAD69+9v9esl9+t/6NAhTJw4EZ06dUKXLl0wceJEHDx40KpMQP7PKRGfJ3V69+6NQYMG\nyZIFAMHBwfjqq6+waNEiHDp0CE7NvSDvXeRaX5FCQ0Px66+/4ueff8apU6fg4uICNze3ZufVHY0u\nKytDx44dERERgRMnTuDQoUOIjIy0ur2vvvoq9u3bhxMnTuCVV16xOq93797o1q0bsrKysG/fPtx/\n//2y/D2ts2DBAgwfPhyPPvqoLHkmT6lga9LT05GamoqjR4+iY8eOGDp0KG7evGl1bv3aD0mSZKkF\n0Wg0su+wd2fKlT99+nSsWrVKliyRRK1/hw4d9L+3a9dOtuFfpXB2dkbv3r1x8OBB+Pn5tXZzjKr/\nGtnZ2aF9+/b636urq2XLluP1N/Y+leszRc5MUfsTAHTp0kW2LADo27cvMjMzsWfPHixduhTDhw/H\nsmXLZH0OOdjb2xuUT8j1WTJ58mTs2rULRUVFmDp1qlVZDg4O8PLywocffogHH3wQwcHBOHDgAC5c\nuCDL/n/lyhVUVFRAp9Ph+vXr6Ny5s9WZs2fPxrZt2/DLL79g5syZVufV+fDDD1FQUCBrXZ2ijlSV\nlpbCxcUFHTt2xLlz53D06FFZcn/88Ud91scffyxLb33YsGH4z3/+g99//x0A9D+t8cc//hG7d+/G\njRs3UFZWhi+++MLqD9bhw4dj165dKC4u1rfzxx9/tLqtIgwZMgT/7//9P9y8eRPl5eXYs2cPi6Fl\n0L59e3z66afYvn07EhMTW7s5ihcZGYndu3fj+vXrqKiowO7du2X5TJH7c0rE54koP//8Mzp27Ihp\n06bh5ZdfxrfffitLrlarRVmZfNdQ7N69O3799Vf8/vvvuHnzJr744gtZcqdMmYLExETs2rULkydP\ntjovMjIS69atw8MPP4zIyEi8++67uP/++2Voae35K1euXImnn35aljo1oLaWOiUlBSdOnEB0dLQs\nmSdPnsSbb74p+xepFHWkavTo0Xj33XcREBAAX19f/aFwa2g0Gvj6+uLtt9/GzJkzERgYiHnz5lmd\nGxAQgCVLluDhhx9Gu3btcP/992Pr1q1WZYaFhWHKlCkICQnBH/7wB1kOr/v7+2PlypUYNWoUampq\n4ODggM2bN6NXr15WZ8v9AR0eHo5x48YhODgY3bt3R1BQELp27Wp17t3tlKvdIv9AyZmt0WjQuXNn\nfPHFFxg5ciS0Wi0ee+wxWXLlYuo1kuOIjanppgoLC0NMTIx+/3zuuecQEhJiVSYA2T+n7v48GThw\noGxHq+R+758+fRp/+9vf9Ecp5ToNQrdu3TBkyBAEBQXhkUcewZo1a6zKc3BwwKuvvopBgwbBzc0N\nAQEBsmyLgIAAlJeXw93dHd27d7c6LzIyEqtWrUJERAQ6deqETp06ydLx3759Ozp06ICpU6eipqYG\nDz74INLT0xEVFWVVroODA4YNGwYXFxfZ3ltvv/02SkpKMHToUADAwIED8d5771mdy5N/kqJUVFSg\nS5cuqKysxMMPP4wtW7YgNDS0tZvVon777TdV1n6pWX5+PsaOHYvTp08Le474+Hg4OjripZdeEvYc\nRM1RU1ODAQMGYNeuXejTp09rN8ckRQ3/Ec2ZMwdhYWEYMGAAnnjiCdV1qH766Sc8+OCD+Nvf/tba\nTaEW1hJDc7Y6/EfqlZ2djb59+2LEiBE236ECeKSKiIiISBY8UkVEREQkA3aqiIiIiGTAThURERGR\nDNipIiIiIpIBO1VEREREMmCniohahaOjY7OWe+utt8xe/sPT09PsVQzqX5rp2rVrsp1QkojUi50q\nImoVzT0n0vr161FZWWl19htvvKH/vaSkpMnX/5IkSREX5CWilsNOFRG1qvLycowYMQIDBgxAcHAw\nPv/8cwC1Z89/9NFHERoaiqCgIPz73//Gxo0b8dNPP2Ho0KEYPny4Rfk7duzA4MGDERYWhueffx41\nNTVYtGgRrl+/jrCwMDzzzDNYvHgxcnNzERYWpr9e2dq1azFo0CCEhIQgLi4OQO2ZzX19fTF9+nQE\nBQXh8uXLQrYJESkTT/5JRK2i7mK2Op0OlZWV0Gq1uHLlCiIiIpCTk4OkpCR8+eWX+utxlZWVQavV\nwsvLCydPnoSrq2uj2XXz/PLLL1i4cCE+++wztGvXDi+88AIiIiLw7LPPGlxM99KlS3jsscf0l4HZ\nt28fkpKSkJCQgJqaGjz++ON45ZVX4OHhgT59+uDIkSOyXHuTiNoWRV1QmYjanpqaGixevBgHDx6E\nnZ0dfvrpJ/z6668IDg7Gyy+/jEWLFuGxxx7DQw891KRcSZKQmpqKkydPIjw8HABw/fp13HfffUbn\nrW/fvn3Yt28fwsLCANQeNbtw4QI8PDzQu3dvdqiIyCh2qoioVf3rX//ClStX8O2336Jdu3bw8vLC\njRs30LdvX2RmZmLPnj1YunQphg8fjmXLljU5f/r06QZF6ZZavHgx5syZY3Bffn4+unTp0uQsIlIH\n1lQRUasqLS3FH/7wB7Rr1w5paWm4dOkSAODnn39Gx44dMW3aNLz88svIzMwEUDtsWFpaajZXo9Fg\n+PDh2LVrF4qLiwEAv//+O3788UcAgIODA6qrq5Gfnw9vb2+DzOjoaGzduhUVFRUAgMLCQn0GEVFj\neKSKiFpF3Tf0pk2bBjc3N2zatAnt2rWDRqNBYGAgtmzZgjfeeAN2dnZwcHDAu+++CwCYM2cORo8e\nDTc3N6SmpprM9vf3x8qVKzFq1CjU1NTAwcEBmzdvRq9evTBnzhwEBwfD19cXADBkyBAEBQXhkUce\nwZo1a/D9998jIiICQG1HbseOHdBoNM3+1iIRtX0sVCeiVufl5YUPPvgAw4YNa/HnrjtSVV1dDTs7\nHrwnoubjJwgR2aRr165h1qxZ6NmzJ9zd3bFs2TLU1NQAAD788EMMGTIEf/3rX+Hi4gIfHx/873//\nw7Zt29CrVy90794d27dv12ft2bMHYWFh6Nq1K3r16oX4+PhmPS8RkSkc/iMim3D3QfOYmBjcd999\nyM3NRXl5OR577DF4eHjoi8czMjJQVFSE3r17o6ioCFFRUejatSu++uorFBcXY9KkSXjiiSfQuXNn\nODo6YseOHQgMDMTp06cxcuRIhIaG4vHHH2/QDnPPS0TUGA7/EVGr8/T0xG+//QZ7+9r/8yIiInDg\nwAFcvXoVHTt2BAAkJiZiy5YtOHDgAD788EOsWrUK58+fBwCcPn0aISEh+OWXX3DvvfcCAO655x4c\nOHAAwcHBDZ4vNjYWdnZ2+Mc//mEw/FdcXIzevXs3+rxERKbwSBURtTqNRoPk5GR9TdXx48fx5Zdf\nokePHvp5ampq0KtXL/109+7d9b936tQJAPQdqrr7ysvLAQDHjh3DokWLcPbsWVRVVeHmzZt48skn\nG7Tj0qVLuHXrlsnnJSJqDDtVRGRz3N3d0aFDB/z222+yFI8//fTTWLBgAb788ku0b98ef/nLX3Dl\nypUG83l4eMj6vESkLvzUICKb06NHD4waNQp//etfUVZWhpqaGuTm5uKbb75pVl55eTlcXFzQvn17\nZGRk4OOPPzZ6agS5n5eI1IWdKiKySdu3b0dVVRUCAgLg6uqKyZMno6ioCACMni/K1PmjNm/ejFdf\nfRVOTk5YsWIFpkyZ0uiypp6XiMgUs4XqKSkpiI2NhU6nw+zZs/VXcL/b8ePHERERgZ07d2LSpElN\nWjtYejIAACAASURBVJaIiIhI6Ux2qnQ6HXx9fbF//364ublh4MCBSExMhL+/f4P5Ro4cic6dO2PG\njBmYNGmSxcsSERERtQUmh/8yMjLg4+MDT09PODg4YOrUqUhOTm4w38aNG/HEE08YfPPG0mWJiIiI\n2gKTnarCwkJ4eHjop93d3VFYWNhgnuTkZMybNw/AndoES5YlIiIiaitMnlLBkguHxsbGYvXq1dBo\nNJAkSX9WZEsvOurj44Pc3FyL5iUiIiJqTSEhIcjKyjL6mMkjVW5ubigoKNBPFxQUwN3d3WCekydP\nYurUqfDy8kJSUhJeeOEFfP755xYtCwC5ubn6zpilt+XLlzd5mdbKVUqmktqqlEwltVUpmUpqq1Iy\nldRWpWQqqa1KybSltp46darRfpPJI1Xh4eHIyclBfn4+evbsiZ07dyIxMdFgnosXL+p/nzFjBsaO\nHYtx48ahurra7LLNlZ+fL0tOS+QqJVNUrpozReWqOVNUrpozReWqOVNUrpozReXKnWmyU2Vvb49N\nmzYhOjoaOp0Os2bNgr+/PxISEgAAc+fObfKyRERERG2S1Mqa04S0tDT5GyIoVymZonLVnCkqV82Z\nonLVnCkqV82ZonLVnCkqtzmZpvotZk/+KVpdgTsRERGRrTPVb7HZy9S4urrqL0XBW8Obq6ur7Ns8\nPT2dmQrIVXOmqFw1Z4rKVXOmqFw1Z4rKlTvTZE1VayopKeERLBMsPWUFERERtQybHf7jsKBp3D5E\nREQtT5HDf0RERERKwk4V6SlhvFpJmaJy1ZwpKlfNmaJy1ZwpKlfNmaJy5c5kp4qIiIhIBqypkllN\nTQ3s7MT3VZW6fYiIiJSMNVWNWL58OdavX6+fXrJkCTZs2IC1a9di0KBBCAkJQVxcnP7xCRMmIDw8\nHP3798eWLVv09zs6OuLll19GaGgojh492pKrQERERDZC1Z2qmTNnYvv27QBqjzDt3LkT9913Hy5c\nuICMjAxkZmbi5MmTOHjwIABg69atOHHiBI4fP44NGzagpKQEAFBZWYkHHngAWVlZePDBB1ttfayl\nhPFqJWWKylVzpqhcNWeKylVzpqhcNWeKylXNeapaQu/evdGtWzdkZWWhqKgIYWFhOH78OPbt24ew\nsDAAQEVFBS5cuIDIyEisX78eu3fvBgAUFBQgJycHgwYNQrt27TBp0qTWXBUiIiJqZaqvqfr3v/+N\nw4cP45dffsH06dORmpqKfv36Yc6cOQbzpaenY9myZfjqq6/QsWNHDB06FPHx8fjjH/8IrVaLsrIy\n4W2tjzVVRERELY81VSZMmDABKSkpOHHiBEaPHo3o6Ghs3boVFRUVAIDCwkIUFxejtLQULi4u6Nix\nI86dO8faKSIiIjKg+k6Vg4MDhg0bhieffBIajQYjR47E008/jYiICAQHB+PJJ59EeXk5Ro8ejerq\nagQEBGDx4sWIiIjQZ7SVS8YoYbxaSZmictWcKSpXzZmictWcKSpXzZmicllTJbOamhocPXoUu3bt\n0t+3YMECLFiwoMG8e/fuNZpRWloqrH1ERESkDKquqcrOzsbYsWMxceJErF27VuhzyY01VURERC3P\n1N9fs52qlJQUxMbGQqfTYfbs2Vi4cKHB48nJyXj11VdhZ2cHOzs7rF27FsOGDQMAeHp6wsnJCe3a\ntYODgwMyMjIsbhw7DaZx+xAREbW8Zheq63Q6zJ8/HykpKcjOzkZiYiK+//57g3lGjBiBU6dOITMz\nEx9++KHBt+Y0Gg3S09ORmZlptENFtkUJ49VKyhSVq+ZMUblqzhSVq+ZMUblqzhSVK3emyU5VRkYG\nfHx84OnpCQcHB0ydOhXJyckG83Tp0kX/e3l5Oe655x6Dx3k0hYiIiNTA5PDfrl278OWXX+ovybJj\nxw4cO3YMGzduNJhv9+7dWLx4MX7++Wfs27cPgwYNAgB4e3uja9euaNeuHebOnYvnnnuuYQM4/Ncs\n3D5EREQtz9TfX5Pf/rP0VAHjx4/H+PHjcfDgQTz77LP44YcfAACHDx9Gjx49UFxcjJEjR8LPzw+R\nkZENlo+JiYGnpycAwNnZGaGhoRY9L905dBkVFcVpTnOa05zmNKdlnq77PT8/H2ZJJhw5ckSKjo7W\nT69atUpavXq1qUUkb29v6cqVKw3uj4uLk9atW9fg/saaYKZpqidi+6SlpTFTAblqzhSVq+ZMUblq\nzhSVq+ZMUbnNyTT199dkTVV4eDhycnKQn5+Pqqoq7Ny5E+PGjTOYJzc3V38Y7NtvvwUAdOvWDZWV\nlfpLt1RUVGDfvn0ICgoy38sjIiIiUiJzPbK9e/dK/fr1k/r06SOtWrVKkiRJevfdd6V3331XkiRJ\nWrNmjRQYGCiFhoZKDz30kJSRkSFJkiTl5uZKISEhUkhIiBQYGKhf1tIen7H7tVoXCYCwm1brYm5z\nSJIkSRs3bpQGDBggdejQQYqJiTF4bMuWLZKPj4/k6OgojR49Wvrpp5/0jx04cECKioqSunbtKnl6\nejbIzcvLk6KioqTOnTtLfn5+0v79+xttgwUvHREREcnM1N9fRZ38s7bGS2RzLSv+/uyzz2BnZ4cv\nv/wS169fx7Zt2wDUjrlOmTIF6enp8PHxwYsvvojs7Gz9uOzx48dx/vx5VFZWYtWqVcjLyzPIjYiI\nwJAhQ/D6669jz549mDVrFnJychp8oxJgoToREVFr4AWVZTZhwgQ8/vjj6Natm8H9X3zxBSZPngx/\nf384ODhg2bJl+Oabb/Sdp4EDB2LatGnw8vJqkHn+/HlkZmYiPj4eHTp0wMSJExEcHIykpKQWWSdA\nGecAUVKmqFw1Z4rKVXOmqFw1Z4rKVXOmqFy5M9mpsoKxI2n176upqQEAnDlzxmzW2bNn4e3tbXDe\nr5CQEJw9e1am1hIREZFIHP4zfIYmDaktW7YMly9f1g//paam4qmnnkJqaip8fHwQGxuL999/Hx9/\n/DGmTJmiX27//v147rnnDIb/PvroI2zevBlHjhzR37d06VIUFhbq8w1a2ozhPycnV5SVlRjcp9W6\noLT09yblEBERqRWH/wS5e6MOHz4ccXFxmDRpEry8vODl5QWtVgt3d3ezWY6OjigtLTW47+rVq3By\ncpKtvbUdKsP6/Ls7WURERNQ87FRZwdjJUV944QWcP38eRUVFmDhxIqqrq9G/f3+zWYGBgbh48SLK\ny8v19506dQqBgYGyttkUJYxXKylTVK6aM0XlqjlTVK6aM0XlqjlTVC5rqmyATqfDjRs3UF1dDZ1O\nh5s3b+p/njlzBpIk4ccff8ScOXMQGxuLrl27Aqg9snXjxg3cunULkiTh5s2bqKqqAgD069cPoaGh\niI+Px40bN/Dpp5/izJkzmDRpUmuuKhEREVmINVWGz2BRnVJcXBxee+21Bve9+OKL+OMf/4jc3Fxo\ntVrMnDkTK1eu1B/RSk9Px7Bhw2qf6fb6RUVF4cCBAwCAS5cuISYmBseOHUPv3r3x9ttv6+dv0NJm\n1FQZ3348NQMREZGlTP39VVSnylihtZyUVLTNThUREVHLazOF6qWlv0OSJGE3pXSoRFHCeLWSMkXl\nqjlTVK6aM0XlqjlTVK6aM0XlsqaKiIiIyAYpaviP7uDwHxERUctrM8N/RERERLaKnSrSU8J4tZIy\nReWqOVNUrpozReWqOVNUrpozReWypoqIiIjIBrGmSqFYU0VERNTyWFNFRET/v737D4uqzvcA/h5g\nCoVBwFrbAAUDBeTHjKBcZFkxLDSVLTbTMq/mD8i9Pq37ZCmPugubVm62q+lW6N3qGkXuYmmrLbqa\n3NQbsipqiK6YTCBlaaj8EpTh3D+QkR/D8GPOF+Zw3q/nmWc4c8558z1n5gxfz/fjOUQkWKedqpyc\nHAQGBiIgIABr165tN3/nzp0IDw+HwWBARESE+ergXVmX7IsSxquVlCkqV82ZonLVnCkqV82ZonLV\nnCkqt1drqkwmExYvXoycnBwUFRUhKysLZ86cabXMxIkTcfLkSRQUFOC9995DcnJyl9ftLjd3N2g0\nGmEPN3e3LrVj06ZNiIyMhLOzM5555plW8/77v/8bAQEB0Ol0mDx5Mr777rtW848fP46f//zn0Ol0\nuO+++/DGG2+Y5xmNRkyYMAEuLi4ICgrC/v37bdpfRERE1Hus1lR9+eWXSE9PR05ODgDg1VdfBQAs\nX768w+V/85vfIC8vr8vrdvvef2ld3LKeSEOX6os++eQTODg4YM+ePbhx4wbeffddAE093hkzZiA3\nNxf+/v749a9/jaKiInNP+MqVKxg1ahTWr1+Pxx9/HDdv3kRZWRkCAwMBANHR0YiJicGaNWuwe/du\nzJ8/H8XFxbjnnnvatYE1VURERL2vxzVV5eXl8PHxMU97e3ujvLy83XI7duxAUFAQJk+ebD7z0tV1\nleixxx7DL37xCwwePLjV67t27cL06dMRFBQErVaLVatW4YsvvkBJSQkA4I9//CMmTZqEJ598Elqt\nFi4uLuYO1blz51BQUID09HTcfffdSEpKQlhYGLZv397r20dERETdZ7VT1XRmo3OPPvoozpw5g7//\n/e+YPXu2as58WDqT1vK1xsZGAEBhYSEA4MiRI/Dw8EBMTAyGDBmCxMRElJWVAQBOnz6N4cOHw8XF\nxbx+eHg4Tp8+LXozzJQwXq2kTFG5as4UlavmTFG5as4UlavmTFG5cmc6WZvp5eVl/qMPAGVlZfD2\n9u5w+djYWDQ0NKCiogLe3t5dXnfu3Lnw9fUFALi7u0Ov13dnG/pM205n81moZ599Fv7+/vj9738P\njUaD2tpaAE374Pjx49i3bx9CQkLw4osv4sknn8ShQ4dQXV2NQYMGtcpzc3Pr9Oxe8wciLi6uS9NA\n7u3n5ummZbq6fnenT5w4IWtebm4uTpw4IXt7m3H75d1+EdNq3n4Rn6eW+nr7+svxpPbtb9Zftr/5\nZ6PRiM5YralqaGjAyJEjsX//ftx///0YO3YssrKyEBQUZF7m66+/xvDhw6HRaHD8+HFMnz4dX3/9\ndZfWBZRZU9Vs5cqVKC8vN9dUAcCbb76J9evXo7KyEkuWLMGrr76K3bt3IyYmBnq9HhEREfjLX/4C\nAKioqMA999yD69evY9++fVi5cmWrM1OLFy+Go6MjNmzY0O53s6aKiIio9/W4psrJyQmbNm1CQkIC\ngoODMWPGDAQFBSEjIwMZGRkAgO3btyM0NBQGgwG//vWv8dFHH1ldtz+xNDz6q1/9CufOncOlS5eQ\nlJSEhoYGhISEAADCwsI6zBo1ahQuXLiA6upq82snT57EqFGj5G84ERERya7T61RNnjwZ//73v3H+\n/HmkpqYCAFJSUpCSkgIAePHFF1FYWIiCggIcPHgQY8aMsbpuf2AymVBXV4eGhgaYTCbU19ebnwsL\nCyFJEkpLS5GcnIwlS5aYh/WeeeYZfPLJJzh58iRu3bqFl156CbGxsdDpdBgxYgT0ej3S09NRV1eH\njz/+GIWFhfjlL3/Za9vV9tQtM+0zV82ZonLVnCkqV82ZonLVnCkqV+5MXlG9B1566SUMHDgQa9eu\nRWZmJgYMGIA1a9agrq4Os2bNgk6nQ1RUFGJiYvDSSy+Z15swYQJefvllTJkyBUOGDMGFCxfw4Ycf\nmud/9NFHOHr0KDw9PbFixQps37693f8wJCIiIvukqHv/ubm7oep6lbC26AbpUHmtUli+nFhTRURE\n1Pus/f1VVKeK7mCnioiIqPfxhsrUJUoYr1ZSpqhcNWeKylVzpqhcNWeKylVzpqhc1lQRERER2SEO\n/ykUh/+IiIh6H4f/iIiIiARjp4rMlDBeraRMUblqzhSVq+ZMUblqzhSVq+ZMUbmsqSIiIiKyQ6yp\nUijWVBEREfU+1lQRERERCaaoTpWnmxs0Go2wh6ebW5facfPmTcyfPx++vr5wc3ODwWBATk6Oef7+\n/fsRGBgIFxcXPPjggygtLTXPO3DgACZMmAB3d3f4+fm1yzYajZgwYQJcXFwQFBSE/fv3277jukgJ\n49VKyhSVq+ZMUblqzhSVq+ZMUblqzhSVq+qaqqtVVZAAYY+rVV27BU5DQwOGDh2KL774ApWVlVi9\nejWeeOIJlJaW4sqVK0hKSsKaNWtw9epVREZGYsaMGeZ1XV1dsWDBArz22msWs5988klERESgoqIC\na9asweOPP44rV650b0cRERFRr1NUTZVGo2lXESRrW4Ae1xeFh4fjd7/7Ha5cuYKtW7fi0KFDAIDa\n2lrcc889OHHiBEaMGGFeft++fVi4cCFKSkrMr507dw5hYWH48ccf4eLiAgAYP348nnrqKaSkpLRu\nK2uqiIiIeh1rqgT7/vvvce7cOYSEhOD06dMIDw83zxs4cCD8/f1RWFjYac7p06cxfPhwc4cKaOqs\nnT59Wki7iYiISD7sVNno1q1bmDVrFubOnYsRI0agpqYGbm1qs9zc3FBdXd1pVnV1NQYNGtRu3aou\nDkvaSgnj1UrKFJWr5kxRuWrOFJWr5kxRuWrOFJWr6poqe9PY2IjZs2fD2dkZmzZtAtBUM1VZWdlq\nuevXr0On03WaZ2nda9euteukERERkf3ptKYqJycHS5YsgclkwoIFC7Bs2bJW8z/44AP84Q9/gCRJ\n0Ol0eOuttxAWFgYA5v8d5+joCK1Wi/z8/PYNUGhNlSRJmDdvHkpLS/HZZ5/h7rvvBgBs2bIF//M/\n/2OuqaqpqcG9997b5Zqq8PBwXL58Ga6urgCA2NhYzJ49G8nJya3bypoqIiKiXtfjmiqTyYTFixcj\nJycHRUVFyMrKwpkzZ1otM3z4cHzxxRc4deoUVq1a1eqPv0ajQW5uLgoKCix2qJRs0aJFOHv2LD79\n9FNzhwoAHnvsMRQWFuLjjz9GXV0d0tPTodfrzR0qSZJQV1eHW7duQZIk1NfX4+bNmwCAESNGQK/X\nIz09HXV1dfj4449RWFiIX/7yl32yjURERNR1VjtV+fn58Pf3h6+vL7RaLWbOnImdO3e2WiY6Otpc\nBxQVFYWLFy+2mt8fz4J888032Lx5M06ePIn77rsPOp0OOp0OWVlZuOeee7B9+3asWLECnp6eOHr0\nKD766CPzuv/7v/+LgQMHYsqUKSgrK8OAAQMwadIk8/yPPvoIR48ehaenJ1asWIHt27dj8ODBvbJd\nShivVlKmqFw1Z4rKVXOmqFw1Z4rKVXOmqFy5M52szSwvL4ePj4952tvbG0eOHOlw+b/85S945JFH\nzNMajQYTJ06Eo6MjUlJSsHDhQpsa66HTQSOwaNujC3VPADBs2DA0NjZ2OD8+Pr7dGb1mcXFxVtcd\nNmwYDhw40KV2EBERkf2w2qlqqsHpmgMHDuCdd97B4cOHza8dPnwYP/3pT3H58mU89NBDCAwMRGxs\nbLt1586dC19fXwCAu7s79Hq9xd9R0aaIm+70suPi4ro0DeTefm6eblomLi4OcXFx3c7r/PfdyZcj\nr+2/KuRuL7dfvmkRnye1b3/za/awff1p+1tmc/vt4/Njabr5td7e/uafjUYjOmO1UD0vLw9paWnm\nW7C88sorcHBwaFesfurUKSQlJSEnJwf+/v4Ws9LT0+Hq6ornn3++dQN4Q+UeYaE6ERFR7+txoXpk\nZCSKi4thNBpx8+ZNbNu2DYmJia2WKS0tRVJSEjIzM1t1qGpra83XV6qpqcHevXsRGhpq67aQQG17\n7cy0z1w1Z4rKVXOmqFw1Z4rKVXOmqFy5M60O/zk5OWHTpk1ISEiAyWTC/PnzERQUhIyMDABASkoK\nfv/73+Pq1atYtGgRAJgvnXDp0iUkJSUBaLpX3qxZs/Dwww/L2ngiIiIie6Goe//RHRz+IyIi6n28\n9x8RERGRYOxUkZkSxquVlCkqV82ZonLVnCkqV82ZonLVnCkqt1drqvqSk5NTty7poDYeHh593QQi\nIiJqwa5rqizV/yCtzUtpFpdinZAFrKkiIiKyDWuqiIiIiARTZqeqREysEsZrRWWKylVzpqhcNWeK\nylVzpqhcNWeKylVzpqhcuTOV2akiIiIisjOsqVIR1lQRERHZhjVVRERERIIps1PFmirZM0XlqjlT\nVK6aM0XlqjlTVK6aM0XlqjlTVC5rqoiIiIjsEGuqVIQ1VURERLZhTRURERGRYMrsVLGmSr4wh6Ze\nd8uHm7ubbPF2v/0CM0XlqjlTVK6aM0XlqjlTVK6aM0Xlyp1pt/f+o17SiDtDqiUA/ICqtKq+aw8R\nEZFCsaZKRbq1T7n/iIiI2rGppionJweBgYEICAjA2rVr283/4IMPEB4ejrCwMMTExODUqVNdXpeI\niIiov7DaqTKZTFi8eDFycnJQVFSErKwsnDlzptUyw4cPxxdffIFTp05h1apVSE5O7vK6PcaaKtkz\nAQjZr0rZfjXXACgpU1SumjNF5ao5U1SumjNF5fbqdary8/Ph7+8PX19faLVazJw5Ezt37my1THR0\nNAYNGgQAiIqKwsWLF7u8LhEREVF/YbWmKjs7G3v27MGWLVsAAJmZmThy5Ag2btxocfl169bh3Llz\n2Lx5c5fXZU1V72FNFRERkW2s1VRZ/d9/TX+Eu+bAgQN45513cPjw4W6vO3fuXPj6+gIA3N3dodfr\nW8zNvf0c1/TUPETl13qJuDZLm+fdPrUXFxfH6aZXbj83T8P8v/7MP7fQ1+3lNKc5zWlOc7ovp5t/\nNhqN6JRkxZdffiklJCSYp19++WXp1VdfbbfcyZMnpQceeEAqLi7u9rodNQGABEhtHpCQBglzbj+n\noWn9No9ONqtDBw4c6NF6Ssm0uk9b7tce7j8529ofMkXlqjlTVK6aM0XlqjlTVK6aM0Xl9iTT2t9I\nB2sdrsjISBQXF8NoNOLmzZvYtm0bEhMTWy1TWlqKpKQkZGZmwt/fv1vrEhEREfUXnV6n6h//+AeW\nLFkCk8mE+fPnIzU1FRkZGQCAlJQULFiwAJ988gmGDh0KANBqtcjPz+9w3XYNYE1Vr2FNFRERkW2s\n1VTx4p8qwk4VERGRbfrfDZV5nSrZMwHwOlUCKKWtSskUlavmTFG5as4UlavmTFG5cmcqs1NFRERE\nZGc4/KciHP4jIiKyTf8b/iMiIiKyM8rsVLGmSvZMAKypEkApbVVKpqhcNWeKylVzpqhcNWeKymVN\nFREREZEdYk2VinR1nzqlAQ1tlvLQ6VBRWSmoZURERMrQ43v/kTo1wELXq6qqL5pCRESkGMoc/mNN\nleyZAFhTJYBS2qqUTFG5as4UlavmTFG5as4UlcuaKiIiIiI7xJoqFeE+JSIisg2vU0VEREQkmDI7\nVaypkj0TAGuqBFBKW5WSKSpXzZmictWcKSpXzZmicllTRURERGSHWFOlItynREREtmFNFREREZFg\nyuxUsaZK9kwArKkSQCltVUqmqFw1Z4rKVXOmqFw1Z4rK7fWaqpycHAQGBiIgIABr165tN//s2bOI\njo6Gs7MzXn/99VbzfH19ERYWBoPBgLFjx8rXaiIiIiI7Y7WmymQyYeTIkdi3bx+8vLwwZswYZGVl\nISgoyLzM5cuX8c0332DHjh3w8PDA888/b57n5+eHY8eOwdPTs+MGsKaq13CfEhER2abHNVX5+fnw\n9/eHr68vtFotZs6ciZ07d7Za5t5770VkZCS0Wq3FDP4hJiIiop5wc3eDRqNp9XBzd+vrZnXIaqeq\nvLwcPj4+5mlvb2+Ul5d3OVyj0WDixImIjIzEli1bet7KtlhTJXsmANZUCaCUtiolU1SumjNF5ao5\nU1SuGjOrrlc1jaakAZjT9Fx1vUq2fLm338nazKbhop47fPgwfvrTn+Ly5ct46KGHEBgYiNjY2HbL\nzZ07F76+vgAAd3d36PX6FnNzbz/HNT2VALgEwK/1EnFtljbPu73D4uLi+mT6xIkTsuefOHGix+u3\n259A0z71a/FzC7loM63w7e9ouqfbx+3v/Wk1b7+Iz1NLfb19/eV4Uvv2N5M7D5daT/bW9jf/bDQa\n0RmrNVV5eXlIS0tDTk4OAOCVV16Bg4MDli1b1m7Z9PR0uLq6tqqp6sp81lT1Hu5TIiJSEo2mg79R\nffj3qMc1VZGRkSguLobRaMTNmzexbds2JCYmWly27S+ora1FVVXTKbqamhrs3bsXoaGhPWk/ERER\nkd2z2qlycnLCpk2bkJCQgODgYMyYMQNBQUHIyMhARkYGAODSpUvw8fHBn/70J6xevRpDhw5FdXU1\nLl26hNjYWOj1ekRFRWHq1Kl4+OGH5Wk1a6pkzwTAmioBlNJWpWSKylVzpqhcNWeKylVzJgBF/I2y\nWlMFAJMnT8bkyZNbvZaSkmL++b777kNZWVm79VxdXc3jv0RERET9He/9pyLcp0TWebq54WpV+/9Z\n5KHToaKysg9aRKRuSqup6vRMFRGRWlytqmr3DwoA0FjoaBERtWW1pspusaZK9kwAihivVlKmqFw1\nZ4rKVXOmqFw1Z4rKVXMmAEX8jVJmp4qIiIjIzrCmSkW4T4ms02g0lof/wM8/UV9QWk0Vz1QRkSpZ\nuqcYEZEtlNmpYk2V7JkAFDFeraRMUblqzpQz19I9xeSkxn3KTLG5as4EoIi/UcrsVBERERHZGdZU\nqQj3KdEdHdZqWFoW/PwT9QXWVBERERGpkDI7Vaypkj0TgCLGq5WUKSpXzZnCclX82ReVq+ZMUblq\nzgSgiONUmZ0qIiIiIjvDmioV4T4luoM1VUT2z9Jx6pQGNFhYtrfu0cl7/xEREVG/0IAO/vFjB/fo\nVObwH2uqZM8EoIjxaiVlispVc6awXBV/9kXlqjlTVK6aMwEo4jhVZqeKiIiI+hU3N0/F3+Wg05qq\nnJwcLFmyBCaTCQsWLMCyZctazT979iyeeeYZFBQUYM2aNXj++ee7vC7AmqrexH1KdAdrqojsiy1/\no24v2SvHaY+vU2UymbB48WLk5OSgqKgIWVlZOHPmTKtlBg8ejI0bN2Lp0qXdXpeIiIiov7DaFII3\nugAAGFxJREFUqcrPz4e/vz98fX2h1Woxc+ZM7Ny5s9Uy9957LyIjI6HVaru9bo+xpkr2TACKGK9W\nUqaoXDVnCstV8WdfVK6aM0XlqjkTgCKOU6udqvLycvj4+Jinvb29UV5e3qVgW9YlIiIiUhqrl1Sw\npUisO+vOnTsXvr6+AAB3d3fo9foWc3NvP8c1PVnoqebemWte2jzvdi80Li6u0+m4uLhuLd+V6ebX\n5Mpr26vufnua17/TPpQA8Lv9aLN/W/825W9/b083v6bG7RdxPMm9/ebPu1+LTKjv+0Qp77+o7W+Z\nze3v2+9L60cgWv2Nap7bau0e7J+W63Y0Pzc3F0ajsX172rBaqJ6Xl4e0tDTk5OQAAF555RU4ODhY\nLDhPT0+Hq6uruVC9q+uyUL33cJ8S3cFCdSL70u8L1SMjI1FcXAyj0YibN29i27ZtSExMtLhs21/Q\nnXW7jTVVsmcCUMR4tZIyReWqOVNYroo/+6Jy1ZwpKlfNmQAUcZxaHf5zcnLCpk2bkJCQAJPJhPnz\n5yMoKAgZGRkAgJSUFFy6dAljxoxBZWUlHBwcsGHDBhQVFcHV1dXiukRERET9Ee/9pyLcp0R3cPiP\nyL70++E/IiIiIuoaZXaqWFMleyYARYxXKylTVK6aM4XlqvizLypXzZmictWcCUARx6kyO1VERERE\ndoY1VSrCfUp0B2uqiOwLa6qIiIiICIBSO1WsqZI9E4AixquVlCkqV82ZwnJV/NkXlavmTFG5as4E\noIjjVJmdKiKibnBz84RGo2n1ICKSG2uqVIT7tGs83dxwtaqq1WseOh0qKiv7qEVkK6XUahCpmVKO\nU2s1VVavqE6kRlerqtof1m06WURERG0pc/iPNVWyZwJQxHi1kjJF5ao5U1iuij/7onLVnCkqV82Z\nABRxnCqzU0VERERkZ1hTpSLcp12j0WhUvf39kVJqNYjUTCnHKa9TRURERCSYMjtVrKmSPROAIsar\nlZQpKlfNmcJyVfzZF5Wr5kxRuWrOBKCI41SZnSoiIiIiO8OaKhXhPu0a1lT1P0qp1SBSM6UcpzbV\nVOXk5CAwMBABAQFYu3atxWWee+45BAQEIDw8HAUFBebXfX19ERYWBoPBgLFjx/aw+URERET2z2qn\nymQyYfHixcjJyUFRURGysrJw5syZVst89tlnOH/+PIqLi7F582YsWrTIPE+j0SA3NxcFBQXIz8+X\nr9WsqZI9E4AixquVlCkqV82ZwnJV/NkXlavmTFG5as4EoIjj1GqnKj8/H/7+/vD19YVWq8XMmTOx\nc+fOVst8+umnmDNnDgAgKioK165dw/fff2+ez1PmREREpAZWa6qys7OxZ88ebNmyBQCQmZmJI0eO\nYOPGjeZlpk2bhtTUVIwbNw4AMHHiRPzhD3/A6NGjMXz4cAwaNAiOjo5ISUnBwoUL2zeANVW9hvu0\na1hT1f8opVaDSM2Ucpz2+N5/Xb2Te0fhhw4dwv3334/Lly/joYceQmBgIGJjY7uUSSQnNzdPVFVd\nbfe6TueBysqKPmgRERH1N1Y7VV5eXigrKzNPl5WVwdvb2+oyFy9ehJeXFwDg/vvvBwDce++9eOyx\nx5Cfn2+xUzV37lz4+voCANzd3aHX61vMzb39HNf0VALgEoDo1kvEtVnaPO/2eGlcXFyn0y3HVruy\nfFem169fD71eL1tebm4uTpw4gSVLlvRo/Xb7E2jap36wOF6d23Zaodvf1KGS0Hb7q6o0HX4G+tP2\ndzbd/JpceaKOp55u/x3N003zW33m/e4sEddmafPa/fD7pGUb7f39V8rxpPbtb7sPurp+k1x0eAR+\nCeC+1kuixdLNmXJvf/PPRqMRnZKsuHXrljR8+HCppKREqq+vl8LDw6WioqJWy+zevVuaPHmyJEmS\n9OWXX0pRUVGSJElSTU2NVFlZKUmSJFVXV0vjxo2T9uzZ0+53dNQEABIgtXlAQhokzLn9nIam9ds8\nOtmsDh04cKBH6ykl0+o+bblf++E+tbztdz5/LTP74/b3l8ye5try2e/p+9/f9ykzez+3v2cq5Ti1\n9ns6vU7VP/7xDyxZsgQmkwnz589HamoqMjIyAAApKSkAYP4fgi4uLnj33XcxevRoXLhwAUlJSQCA\nhoYGzJo1C6mpqe3yWVPVe9S8Ty1vOwC0//yxpqr/UUqtBpGaKeU4tVZTxYt/qoia9yk7VfJTUp2a\nUr6sidRMKcdp/7uhsoXaHzm0r79QTyYARVwDREmZonLtJfNOnVrrR3NHS0n7VM2ffVG5as4Ulavm\nTACKOE6tFqoTUfe5ubuh6npVq9d0g3SovFbZRy0iUgZLZz/t8cwnUUc4/Kciat6nvTn8p9F0sE8V\nvP8s6c4+7WtKGVZQu47eJ+57dVDKcdrj61QR9XsOXb8eGxERkTWsqWpBKWPLah6vlj2zEU3/Cppz\n+zlNvmgA6tyntzuqLR9u7m6yxbOmSn5KaatSMkXlqjkTgCKOU56pIuoFTrB8RsxDp0NFZT+rtWru\nqJbAfEHNqrSqjpcnIuonWFOlImrep9bqf+Te/g5rqiz/dsXu127v0z7cTqXUaqgda6rUTSnHaf+7\npAIREVEXuLl5thuO1mg0cHPz7OumUT+kzE4Va6pkzwSgiPFqUZmiPlPcp02ahz9bPjzdelZnpZT3\nXzHvk6Bce8nsT9dTU3MmAEUcp6ypIiLhGmDhpH4V66yIqH9hTZWKqHmfsqZKfr25T22llFoNtRNR\nU6Wk66mpnVKOU9ZUEREREQmmzE5VP6+p6qywUs3j1aIyWVMlf6aSjlNVv0+Ccltmerq5yVJTx32q\n3kwAijhOWVNlh+4UVrZ9nVf+JiLluVpVZX81dRbupsB7dJKtWFNlh0TVAHCfsqZKTqypUrfu3Py4\nq/fTtPg+OWiaLijbhhbArTavWbqYrpKup6Z2SjlOee8/IiKSlaUz6kLOpjdfob+NW2n8H6Vkf1hT\n1YJSxpbVPF4tKpM1VfJnKuk4VfX7JChXKe+Tkj6nas4EoIjjtNNOVU5ODgIDAxEQEIC1a9daXOa5\n555DQEAAwsPDUVBQ0K11e+SSfFEtnThxQrWZAITsV8Vsv6DPFPepPNzc7xQ6T5gwQfabNKv6fZIz\nt8XNtJvfJ0v3vOwxEcdpi0w5L1KrlPdfKZkAFHGcWh3+M5lMWLx4Mfbt2wcvLy+MGTMGiYmJCAoK\nMi/z2Wef4fz58yguLsaRI0ewaNEi5OXldWndHquzPcKSa9euqTYTgJD9qpjtF/SZ4j6VR9X1qjtD\nQAcATJD5Js1qfp/kzG05VHf7fQJgcfiuR0Qcpy0y5bxIrVLef6VkAlDEcWr1TFV+fj78/f3h6+sL\nrVaLmTNnYufOna2W+fTTTzFnzhwAQFRUFK5du4ZLly51aV0iIiKi/sJqp6q8vBw+Pj7maW9vb5SX\nl3dpmW+//bbTdXtMUCfYaDTad+btU+vp6enmU9N3WbieVU9PV4vYr3a/T5v1cNstXVNMjlxruE+b\nniwN1fT489+Dtlq8npzjnZ9FHKdC3idRuSK+p5WSCeUcpy0z5bqemKjPqVzHacubacveVsmK7Oxs\nacGCBebp999/X1q8eHGrZaZOnSodOnTIPB0fHy8dPXq0S+tKkiSFh4e3v9MlH3zwwQcffPDBhx0+\nwsPDO+w3Wa2p8vLyQllZmXm6rKwM3t7eVpe5ePEivL29cevWrU7XBQQWtBERERH1IqvDf5GRkSgu\nLobRaMTNmzexbds2JCYmtlomMTERW7duBQDk5eXB3d0dQ4YM6dK6RERERP2F1TNVTk5O2LRpExIS\nEmAymTB//nwEBQUhIyMDAJCSkoJHHnkEn332Gfz9/eHi4oJ3333X6rpERERE/VGf36aGiIiIqD9Q\n5hXVZWQ0GhEaGtrXzeiRtLQ0vP76633djA698cYbCA4OxuzZs/u6KR0S+f7HxMQIyZUzW+T2u7q6\nCsklksv169fx1ltv9XUzqB9RfadKyWS9UrEAb731Fvbt24f333+/r5vSJw4fPqzIbLnY++eTxJMk\nya5vUHz16lW8+eabfd0M6kcU16l67LHHEBkZiZCQEGzZskWWzIaGBjz99NMIDg7G9OnTcePGDVly\nt27divDwcOj1evznf/6nLJlr1qzByJEjERsbi3//+9+yZGZmZiIqKgoGgwHPPvssGhst3BK+m559\n9llcuHABkyZNwvr162VoZZOXXnoJgYGBiI2NxVNPPSXLmTqTyYTk5GSEhIQgISEBdXXyXLZX5Jka\nEdkXLlzA6NGjcezYMdmzbWE0GhEYGIhnnnkGI0eOxKxZs7B3717ExMRgxIgR+Ne//mVTdlBQkOzv\n/x//+EeEhoYiNDQUGzZssDmveR/I/T3V8vtEruPJaDRi5MiRmDNnDkJDQ3Hx4kWbM2tqajBlyhTo\n9XqEhobir3/9q82ZALB8+XJ8/fXXMBgMWLZsmc15bc/8rlu3Dunp6TZlpqamtur42TpC8dprr2Hj\nxo0AgN/85jeIj48HAHz++ed4+umnbWrrv/71L4SHh6O+vh41NTUICQlBUVGRTZm/+93vWh1DK1as\nwBtvvGFTJgBkZGTAYDDAYDDAz88PDz74oM2ZAGD1OlX2qKKiQpIkSaqtrZVCQkKkH3/80aa8kpIS\nSaPRSP/3f/8nSZIkzZs3T1q3bp3N7SwsLJRGjBhhbl9zu21x9OhRKTQ0VLpx44ZUWVkp+fv7S6+/\n/rpNmUVFRdK0adOkhoYGSZIkadGiRdLWrVttbqskSZKvr6/N709L+fn5kl6vl+rr66WqqiopICDA\n5u0vKSmRnJycpJMnT0qSJElPPPGElJmZKUdzJVdXV1lyRGaXlJRIISEh0tmzZyWDwSCdOnVKllxJ\nkreNTk5OUmFhodTY2ChFRERI8+bNkyRJknbu3Ck9+uijNmfL+f43H6e1tbVSdXW1NGrUKKmgoMCm\nTBHfUyK+T5rb6uDgIB05csTmrGbZ2dnSwoULzdPXr1+XJddoNEohISGyZEnSneOp2bp166S0tDSb\nMgsKCqTx48ebp4ODg6WLFy/2OC8vL0+aPn26JEmS9LOf/UyKioqSbt26JaWlpUmbN2+2qa2SJEkr\nV66Uli5dKv3Xf/2X9Oqrr9qcZzQapdGjR0uSJEkmk0l64IEHZPl72uzWrVtSbGystGvXLlnyFHem\nasOGDdDr9YiOjsbFixdRXFxsc6aPjw+io6MBAE8//TQOHTpkc+bnn3+OJ554Ap6eTVdu9fDwsDnz\n4MGDSEpKgrOzM3Q6HRITE20+tb5//34cO3YMkZGRMBgM+Pzzz1FSUmJzW0U4fPgwHn30Udx1111w\ndXXFtGnTZBla8PPzQ1hYGAAgIiJC3NWA7dQPP/yARx99FB9++KHd1hf6+flh1KhR0Gg0GDVqFCZO\nnAgACAkJsfn9kvv9P3ToEJKSkjBgwAC4uLggKSkJBw8etCkTkP97SsT3SbNhw4Zh7NixsmQBQFhY\nGP75z39i+fLlOHToENx6eteINuTaXpH0ej1++OEHfPfddzh58iQ8PDzg5eXV47zms9FVVVVwdnZG\ndHQ0jh49ikOHDiE2Ntbm9v72t7/F3r17cfToUbz44os25w0bNgyDBw/GiRMnsHfvXowePVqWv6fN\nnnvuOcTHx2PKlCmy5Fm9pIK9yc3Nxf79+5GXlwdnZ2dMmDAB9fX1Nue2rP2QJEmWWhCNRiP7Ads2\nU678OXPm4OWXX5YlSyRR23/33Xebf3Z0dJRt+Fcp3N3dMWzYMBw8eBCBgYF93RyLWr5HDg4OuOuu\nu8w/NzQ0yJYtx/tv6XMq13eKnJmijicAcHFxkS0LAAICAlBQUIDdu3dj5cqViI+Px6pVq2T9HXJw\ncnJqVT4h13fJ9OnTkZ2djUuXLmHmzJk2ZWm1Wvj5+eG9997DuHHjEBYWhs8//xznz5+X5fi/cuUK\nampqYDKZcOPGDQwcONDmzAULFuDdd9/F999/j3nz5tmc1+y9995DWVmZrHV1ijpTVVlZCQ8PDzg7\nO+Ps2bPIy8uTJbe0tNSc9eGHH8rSW3/wwQfxt7/9DRUVFQBgfrbFz3/+c+zYsQN1dXWoqqrCrl27\nbP5ijY+PR3Z2Ni5fvmxuZ2lpqc1tFSEmJgZ///vfUV9fj+rqauzevZvF0DK466678PHHH2Pr1q3I\nysrq6+YoXmxsLHbs2IEbN26gpqYGO3bskOU7Re7vKRHfJ6J89913cHZ2xqxZs7B06VIcP35cllyd\nToeqqipZsgBgyJAh+OGHH1BRUYH6+nrs2rVLltwZM2YgKysL2dnZmD59us15sbGxWLduHcaPH4/Y\n2Fi8/fbbGD16tAwtbbp+5erVq/HUU0/JUqcGNNVS5+Tk4OjRo0hISJAl89ixY3j99ddl/49UijpT\nNWnSJLz99tsIDg7GyJEjzafCbaHRaDBy5Ej8+c9/xrx58zBq1CgsWrTI5tzg4GCsWLEC48ePh6Oj\nI0aPHo133nnHpkyDwYAZM2YgPDwcP/nJT2Q5vR4UFITVq1fj4YcfRmNjI7RaLd58800MHTrU5my5\nv6AjIyORmJiIsLAwDBkyBKGhoRg0aJDNuW3bKVe7Rf6BkjNbo9Fg4MCB2LVrFx566CHodDpMnTpV\nlly5WHuP5DhjY226uwwGA+bOnWs+PhcuXIjw8HCbMgHI/j3V9vtkzJgxsp2tkvuz/9VXX+GFF14w\nn6WU6zIIgwcPRkxMDEJDQ/HII49g7dq1NuVptVr89re/xdixY+Hl5YXg4GBZ9kVwcDCqq6vh7e2N\nIUOG2JwXGxuLl19+GdHR0RgwYAAGDBggS8d/69atuPvuuzFz5kw0NjZi3LhxyM3NRVxcnE25Wq0W\nDz74IDw8PGT7bP35z3/G1atXMWHCBADAmDFjsHnzZptzefFPUpSamhq4uLigtrYW48ePx5YtW6DX\n6/u6Wb3qxx9/VGXtl5oZjUZMmzYNX331lbDfkZ6eDldXVzz//PPCfgdRTzQ2NiIiIgLZ2dl44IEH\n+ro5Vilq+I8oOTkZBoMBERERePzxx1XXofr2228xbtw4vPDCC33dFOplvTE0Z6/Df6ReRUVFCAgI\nwMSJE+2+QwXwTBURERGRLHimioiIiEgG7FQRERERyYCdKiIiIiIZsFNFREREJAN2qoiIiIhkwE4V\nEfUJV1fXHq23fv36Tm//4evr2+ldDFremun69euyXVCSiNSLnSoi6hM9vSbShg0bUFtba3P2K6+8\nYv756tWr3b7/lyRJirghLxH1HnaqiKhPVVdXY+LEiYiIiEBYWBg+/fRTAE1Xz58yZQr0ej1CQ0Px\n17/+FRs3bsS3336LCRMmID4+vkv5mZmZiIqKgsFgwLPPPovGxkYsX74cN27cgMFgwNNPP43U1FR8\n/fXXMBgM5vuVvfbaaxg7dizCw8ORlpYGoOnK5iNHjsScOXMQGhqKixcvCtknRKRMvPgnEfWJ5pvZ\nmkwm1NbWQqfT4cqVK4iOjkZxcTG2b9+OPXv2mO/HVVVVBZ1OBz8/Pxw7dgyenp4dZjcv8/3332PZ\nsmX45JNP4OjoiF/96leIjo7G7NmzW91M95tvvsHUqVPNt4HZu3cvtm/fjoyMDDQ2NuIXv/gFXnzx\nRfj4+OCBBx7Al19+Kcu9N4mof1HUDZWJqP9pbGxEamoqDh48CAcHB3z77bf44YcfEBYWhqVLl2L5\n8uWYOnUqfvazn3UrV5Ik7N+/H8eOHUNkZCQA4MaNG7jvvvssLtvS3r17sXfvXhgMBgBNZ83Onz8P\nHx8fDBs2jB0qIrKInSoi6lMffPABrly5guPHj8PR0RF+fn6oq6tDQEAACgoKsHv3bqxcuRLx8fFY\ntWpVt/PnzJnTqii9q1JTU5GcnNzqNaPRCBcXl25nEZE6sKaKiPpUZWUlfvKTn8DR0REHDhzAN998\nAwD47rvv4OzsjFmzZmHp0qUoKCgA0DRsWFlZ2WmuRqNBfHw8srOzcfnyZQBARUUFSktLAQBarRYN\nDQ3mzOahQABISEjAO++8g5qaGgBAeXm5OYOIqCM8U0VEfaL5f+jNmjUL06ZNQ1hYGCIjIxEUFAQA\n+Oqrr/DCCy/AwcEBWq0Wb7/9NgAgOTkZkyZNgpeXF/bv3281OygoCKtXr8bDDz+MxsZGaLVavPnm\nmxg6dCiSk5MRFhaGiIgIvP/++4iJiUFoaCgeeeQRrF27FmfOnEF0dDSApk5XZmYmNBpNj//XIhH1\nfyxUJyIiIpIBh/+IiIiIZMDhPyJSrP/4j/9AfX19q9cyMzMxatSoPmoREakZh/+IiIiIZMDhPyIi\nIiIZsFNFREREJAN2qoiIiIhkwE4VERERkQz+H+8ngcH/EFw+AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc9123d0>"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "letter_prop = table / table.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dny_ts = letter_prop.ix[['d', 'n', 'y'], 'M'].T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dny_ts.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>d</th>\n",
        "      <th>n</th>\n",
        "      <th>y</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1880</th>\n",
        "      <td> 0.083055</td>\n",
        "      <td> 0.153213</td>\n",
        "      <td> 0.075760</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1881</th>\n",
        "      <td> 0.083247</td>\n",
        "      <td> 0.153214</td>\n",
        "      <td> 0.077451</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1882</th>\n",
        "      <td> 0.085340</td>\n",
        "      <td> 0.149560</td>\n",
        "      <td> 0.077537</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1883</th>\n",
        "      <td> 0.084066</td>\n",
        "      <td> 0.151646</td>\n",
        "      <td> 0.079144</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1884</th>\n",
        "      <td> 0.086120</td>\n",
        "      <td> 0.149915</td>\n",
        "      <td> 0.080405</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 86,
       "text": [
        "             d         n         y\n",
        "year                              \n",
        "1880  0.083055  0.153213  0.075760\n",
        "1881  0.083247  0.153214  0.077451\n",
        "1882  0.085340  0.149560  0.077537\n",
        "1883  0.084066  0.151646  0.079144\n",
        "1884  0.086120  0.149915  0.080405"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dny_ts.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x2a094690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PqhmaGYMKTt9VUm4bQUxMDOzt7WFrawsACAwMRFRUFJydnbXbtGnTBm3atMGO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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6a2da10>"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Boy names that became girl names (and vice versa)\n",
      "----------------------------------------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_names = top1000.name.unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mask = np.array(['lesl' in x.lower() for x in all_names])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.Series(mask).value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 92,
       "text": [
        "False    6860\n",
        "True        5\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lesley_like = all_names[mask]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lesley_like"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 94,
       "text": [
        "array(['Leslie', 'Lesley', 'Leslee', 'Lesli', 'Lesly'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# alternate way of obtaining lesley_like\n",
      "lesley_like = top1000.name[top1000.name.str.lower().str.contains('lesl')].unique()\n",
      "lesley_like"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 97,
       "text": [
        "array(['Leslie', 'Lesley', 'Leslee', 'Lesli', 'Lesly'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filtered = top1000[top1000.name.isin(lesley_like)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filtered.groupby('name').births.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 99,
       "text": [
        "name\n",
        "Leslee      1082\n",
        "Lesley     35022\n",
        "Lesli        929\n",
        "Leslie    370429\n",
        "Lesly      10067\n",
        "Name: births, dtype: int64"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table = filtered.pivot_table('births', rows='year', cols='sex', aggfunc=sum)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>sex</th>\n",
        "      <th>F</th>\n",
        "      <th>M</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1880</th>\n",
        "      <td>  8</td>\n",
        "      <td>  79</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1881</th>\n",
        "      <td> 11</td>\n",
        "      <td>  92</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1882</th>\n",
        "      <td>  9</td>\n",
        "      <td> 128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1883</th>\n",
        "      <td>  7</td>\n",
        "      <td> 125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1884</th>\n",
        "      <td> 15</td>\n",
        "      <td> 125</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 101,
       "text": [
        "sex    F    M\n",
        "year         \n",
        "1880   8   79\n",
        "1881  11   92\n",
        "1882   9  128\n",
        "1883   7  125\n",
        "1884  15  125"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table = table.div(table.sum(axis=1), axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 106
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table.tail()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>sex</th>\n",
        "      <th>F</th>\n",
        "      <th>M</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2006</th>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 107,
       "text": [
        "sex   F   M\n",
        "year       \n",
        "2006  1 NaN\n",
        "2007  1 NaN\n",
        "2008  1 NaN\n",
        "2009  1 NaN\n",
        "2010  1 NaN"
       ]
      }
     ],
     "prompt_number": 107
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table.plot(style={'M': 'k-', 'F': 'k--'})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 108,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x2a095a90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vvvsOixcvlvopqIV79kxvrFmzBomJifjxxx8rNP8ff/yBDRs24NixY1pOxiqL\nPtUVSa+Nw5hING3j3Lt3j3+YZXqLi72aRO9bipy/qnr2PXr0wCeffFLh+Usi8rYHxM/PCnHPnukN\ne3t7JCQkVHj+1q1bazENY7qFe/ZMbxAR6tSpg7S0NP6RtZrQp7rCPXvG1GRgYKD2ETn5+fm4e/du\nFaRiTDdwsVeT6H1LkfOXJ7s6ffv79+/D29sby5Yt0zCZekTe9oD4+VkhLvZMr5S2Z09EWLVqFTp1\n6oRhw4YhMDCwitMxJh3u2TO9snTpUmRnZxe71/79999j//79CA4O1u6lY5lk9KmucM+esXIoqY0j\nk8mwd+9ehIaGcqFnlc7BwQF16tRB3bp1UbduXdSrVw+PHz+WNBMXezWJ3rcUOX9FevaZmZm4f/++\n8nEbGxvcvHkTVlZWlZCwdCJve0D8/FIwMDDAH3/8gczMTGRmZiIjIwONGjWSNBMXe6ZXHB0d8eDB\nA4SGhmLOnDkq42rUqCFRKsakxz17pleICNbW1vDx8YG7uzvmz58vdSRWiXS1rjg6OmLr1q3o0aOH\n2vNwz56xcjAwMEDXrl1x6tQpdO3aVeo4TGKLFy+GgYFBkb+SrlRZ3PQVuaolEeHDDz+EqakpTE1N\nMWjQIM2eiDZo5eaGaqjCVVUKXb4PpzpEzl/e7CtXriQjIyPKy8urnEDlJPK2J9Lt/LpaVxwcHOjk\nyZPlmqek56Kt58h79kzv1KpVC0ZGRqhZs6bUURjTGVzs1dStWzepI2hE5Pzlze7o6IiaNWtqdFE0\nbRJ52wPi52eFuNgzvdO3b1/4+Pjg9OnTUkdhTGdwsVeT6Mcai5y/Itm7du2qM8Ve5G0PiJ9fCvHx\n8ejevbvUMVRwsWd66XWxLygowNKlS+Hn5yfUzaEZ0zY+zp7pJYVCAXNzczg6OqJBgwYACs+u3bJl\nCwwMDCROx7RFn+oKH2fPWAUYGhpi6tSp8PPzQ2hoKA4ePIibN2/i22+/lToaY5LgYq8m0fuWIucv\nT/Zjx47h9u3bAIDvvvsO06dPh6GhId5++20cPXoUBw8exODBg5GSklJJaYsSedsD4udnhbjYM72y\ndetWlQugvalhw4a4evUqXF1d4enpiQMHDlRxOsakwz17ple8vLywceNGtG3bttTprl27hv79+2Ph\nwoWYNm1aFaVj2qZPdaWye/Zc7JlesbCwwJ07d2BpaVnmtPHx8fD19UWfPn2wYMECyS9By8pPn+oK\n/0CrI0Q/y2D3AAAgAElEQVTvW4qcX93sWVlZePnyJSwsLNSa3tHREefPn0dGRgZcXV0xYMAAPHv2\nTIOkxRN52wPi52eFuNgzvZGQkAA7OzsYGqr/srawsMC2bduQmJiIzMxMnDhxohITMiYdLvZqEv36\nICLnVzf722+/XeHr17/99tvo0KEDYmJiKjR/aUTe9oD4+aXg4OCA//znP3j69KnK461bt4ahoaEk\n123iYs/0hp2dHSZMmFDh+V1dXXHv3j0tJmLVlYGBAZo0aYKgoCDlY1FRUcjOzpbspD4u9moSvW8p\ncv6qyu7i4lIpe/Yib3tA/PxSGT16NAIDA5XDv/zyC8aMGSPZD8pc7Bn7f1xcXBAbGwuFQiF1FKYH\n2rdvj4yMDNy7dw9yuRz79u3D6NGjJcvDh14y9gZ7e3ucPn0ajo6OUkdhaiirrmirZVLe2vX6HrSX\nLl1CVlYWunTpgh9++AEhISGoWbMmHj58CHt7+yJZJT30MjQ0FC4uLnBycoK/v3+J00VGRsLIyAgH\nDx7UOBRjUqmsVg6TBhFp5a8iDAwM8PHHH2P37t2St3CAMoq9XC7HtGnTEBoaiujoaAQFBRX7RpDL\n5ViwYAF69eqlt3vvovctRc6vTvaCggLMnz9f49dfZfxIK/K2B8TPLyV7e3s0adIEx44dk/ym46UW\n+ytXrqBZs2ZwcHBAzZo1MWLECAQHBxeZ7n//+x+GDBmi9sksjGmbTCbDnj17NP7aznv2TNu2bduG\nU6dOoXbt2pLmKLXYy2Qy2NnZKYdtbW0hk8mKTBMcHIwpU6YA0F6PTNeIfqyxyPnVyf7o0SM0btxY\n43W5urpqvdiLvO0B8fNLrUmTJnjnnXeUw1LVSKPSRqoTatasWVixYoXyRwR9beMw3abNYs/H2jNN\nxcfHF/u4kZER5HJ5Faf5f+subaSNjQ0SExOVw4mJibC1tVWZ5tq1axgxYgQAIC0tDceOHUPNmjXR\nv3//Isvz8/ODg4MDAMDExAStWrVS7jW87gvq6vCPP/4oVF59yv9mz7ik6X/99VfUq1dPOV1F19e1\na1fI5XIcPnwYJiYmVZZfl4d1Ob8+ioiIwI4dOwBAWS+1gkqRn59PTZo0ofj4eMrNzSVPT0+Kjo4u\ncXo/Pz86cOBAsePKWJXOCw8PlzqCRkTOX1b2o0ePkpOTEz1+/Fgr6+vQoQOdPn1aK8siEnvbE+l2\nftHryptKei7aeo6l9uyNjIywfv16+Pr6ws3NDcOHD4erqys2b96MzZs3a+8TRwCv9yhEJXL+srL7\n+vri3Llzal3WWB3abuWIvO0B8fOzQnxSFWP/smrVKshkMvz4449SR2Fl0Ke6IvlJVayQ6D1CkfNX\ndXZvb28EBwdr7Yc0kbc9IH5+VoiLPWP/4uXlBQsLC4SGhkodhTGt4TYOY8XYsWMH9u/fj5CQEKmj\nsFKYmZnh+fPnUsfQClNT02LvlMb3oGXVWkZGBqZPn44dO3ZUykkq2dnZsLe3x6VLl9C0aVOtL58x\ndXHPvoqJ3rcUOX9x2U+fPg2ZTFZpZyPWrl0bfn5+WjnqTORtD3B+fcHFngkpLCwMPj4+lbqOyZMn\nY/v27cjKyqrU9TBWFbiNw4Tk5uaGnTt3ok2bNpW6niFDhqBDhw74/PPPK3U9jJWEe/as2pLJZPDw\n8MA///yDGjVqVOq6bt++DV9fXzx48AB16tSp1HUxVhzu2Vcx0ft+Iuf/d/bLly+jY8eOlV7oAcDD\nwwMdO3bUqHcv8rYHOL++4GLPhOPj44MNGzZU2fq++eYbrFy5Eq9evaqydTKmbdzGYUwNgwcPho2N\nDdatW6e392xguol79oxVoefPn6Nnz57o1q0bVq9ezQWfVRnu2Vcx0ft+IufXheympqY4ceIEIiIi\nMGvWrHJdN0cX8muC8+sHLvaMqel1wY+KikLfvn315jR9Vj1wG4exciooKMC8efNw5MgRXL58GQ0a\nNJA6EtNj3MZh1VJkZCQ+/PBDSTMYGRnhhx9+QNOmTXH+/HlJszCmLi72ahK97ydy/jezx8XF4a23\n3pIuzBtatmyJ6OjoMqcTedsDnF9fcLFnQomPj9fuTZg14ObmplaxZ0wXcM+eCWXixIl45513MGXK\nFKmj4NKlS5g2bRquXr0qdRSmx7hnz6qlhw8fwtHRUeoYAP7/G5MrFAqpozBWJi72ahK97ydy/jez\nx8fH60yxr1+/PurXr4/ExMRSpxN52wOcX19wsWdCuXbtGpo1ayZ1DCXu2zNRcM+eMQ3MnDkT9vb2\nfL17Vmm4Z8+YDuA9eyYKLvZqEr3vJ3J+Xc6uTrHX5fzq4Pz6gYs9Yxpwc3NDTEwMtyiZzuOePROG\nQqGAoaHu7Z9YWlrixo0bsLa2ljoK00Pcs2fVjq+vL8LDw6WOUYSrqyv37ZnO42KvJtH7fiLnf51d\nJpOhYcOG0oYpRll9e5G3PcD59QUXeyaM5ORkWFlZSR2jiHfffRc///wzLl68KHUUxkrEPXsmhFev\nXsHMzAzZ2dk6d0tAIkJQUBDmzZuHnj17YsKECejYsSPS0tIQEBCAjIwMLF++XOqYTFDcs2fVSkpK\nCqysrHSu0AOFb8aRI0ciJiYGTk5OmDZtGqytreHi4oIHDx5g165dvNfPJMfFXk2i9/1Ezh8REYF/\n/vkHNjY2UkcpVb169bBo0SLcunULly5dQnx8PLZu3YoRI0bgyy+/FPabrcivHUD8/NrCxZ4JoUOH\nDjhz5ozUMdTm6OgIExMTAECvXr2QnJyMEydOSJyKVWfcs2esCuzfvx8rV67E5s2bQURo3rw56tat\nK3UsJgBt1U4u9oxVAYVCgdGjR+PevXvIzc2FiYkJzp49q5MniTHdUqU/0IaGhsLFxQVOTk7w9/cv\nMn737t3w9PSEh4cHOnXqhNu3b2scTNeI3vcTOb/I2YHC/IaGhtizZw+uX7+OqKgoGBgYYOPGjVJH\nU4s+bH+mRrGXy+WYNm0aQkNDER0djaCgIMTExKhM06RJE5w5cwa3b9/G119/jU8//bTSAjMmOkND\nQ2zduhVLlixBQkKCyji5XI709HSJkjF9VmYb5+LFi1iyZAlCQ0MBACtWrAAAfPHFF8VO//z5c7Rs\n2RJJSUmqK+I2DtNAVlYW6tSpo5OHXlbU999/j/DwcOzbtw8NGjTA06dPMWLECCQkJCA6Oho1atSQ\nOiLTAVXWxpHJZLCzs1MO29raQiaTlTj9tm3b0Lt3b42DMfYmGxsbPHv2TOoYWjV//nw4OzujefPm\n+PLLL9G2bVu0atUKpqamOHTokNTxmJ4xKmuC8uxJhYeHIyAgAOfPny92vJ+fHxwcHAAAJiYmaNWq\nFbp16wbg/++r6erwjz/+KFRefcofGhqKV69e4fbt2/D29pY8T3mH3+wZ/3v8xo0bMWfOHMyZMwcj\nR47E0qVLERwcjPnz56NBgwY68XxLyy/CsGj5IyIisGPHDgBQ1kutoDJcvHiRfH19lcPLli2jFStW\nFJnu1q1b1LRpU4qNjS12OWqsSqeFh4dLHUEjIufftWsXOTg4SB2jwsq77eVyObm5uVFYWFjlBCon\nkV87ROLn11btLHMp+fn51KRJE4qPj6fc3Fzy9PSk6OholWkePXpETZs2pYsXL5a8IsGLPZPO2bNn\nqUOHDlLHqFK//PIL9ejRQ+oYTAdoq3aW2bM3MjLC+vXr4evrCzc3NwwfPhyurq7YvHkzNm/eDAD4\n73//i+fPn2PKlClo3bo12rZtq72vHqzaS05OrnY3Bvnoo4/w119/4ebNm1JHYXqCT6pSU0REhLK/\nJiKR88+bNw9EhNWrV0sdpUIquu2XLl2K5ORk/Pzzz9oPVQ4iv3YA8fPzVS9ZtdGnTx9hC70mxo8f\nj7179yIzM1PqKEwP8J49Yzps0KBB6NWrF5+oWI3xnj1j1cCkSZOwadMm3lFiGuNir6Y3j9UVkcj5\nRc4OaJbfx8cH6enpiIyMLPe8crkcDx48qPC6X6vO21+fcLFnTIcZGhpi4cKF6NevH9asWYOcnBy1\n5iMiTJ48Ge+++y7kcnklp2Qi4J4902m5ublIT0+HpaWl1FEkdefOHSxatAhRUVEICwtDkyZNVMbn\n5+cjLCwMzZs3R5MmTTB//nycPXsWqampOHDgAFq1aiVRcqYp7tmzauHEiRMYOnSo1DEk5+7ujsOH\nD2POnDno3r07/v77bwCFH4a//PILXFxcsHjxYnTu3BnW1tYIDQ1FSEgIfHx8uI3BAOhZsb948SIO\nHDhQKcsW/Q0jav7Dhw/D3d1d6hga0ea2/+yzzzB//nx4e3vj/fffh7m5OQIDAxEQEIArV65AJpMh\nPDwc586dg5mZWZFrw1SEqK+d10TPry1lXghNJAcOHICpqWmx41JTU7F69epib77CdJNcLsfvv/+O\ntWvXSh1Fp0ydOhWNGzdGfn4+9u/fr7zXLVD4ld/FxUU53LVrV0ydOhUKhYLvilXN6VXPvn379vD3\n90fXrl1VHiciDB06FAcOHMCTJ0/QsGHDIvPGxMTAxcVFkuulP3r0CHl5eXBycqrydeuy8+fPY8qU\nKXp557Oq1Lx5c+zfvx+enp5SR2EVUO179rm5udi2bZtyI2RnZyMqKgrvvvtukWn37t2L6Oho7Nq1\nCzVr1iwy/unTp/Dw8FD78LaMjAyEhoZq7cMrODgYK1eu1Mqy9MmhQ4fw4YcfSh1DeNpo5TDxCVvs\n8/LyMGHCBFy/fh0AEBkZCXd3d9SpU0dluuTkZMycOROBgYEYNWpUsW2eAwcOQC6XK5dVnIiICKSk\npGD06NGwt7fH4MGDtbbH2bNnT4SFhVXqNx9de7Pn5+dj0aJFGDJkCCZPnoxFixYV2Z6mpqYYOnSo\nzmUvL6nzd+3aVZkhKSkJ0dHR5Zpf6vyaEj2/tghb7OvWrYtvvvkGu3btAlD4lb9Tp04gIiQmJioL\nZ05ODtavXw8vL68Sl7Vnzx789ttvmDx5cqnrXLt2LQwMDBAXFwc/Pz+cPXu23LlfvnyJtWvXqhR2\nV1dX5OfnK4+w0Hepqanw8fHB9evXMWzYMHh6euI///kPFAqFynRfffUVWrZsKVFK/dG1a1ecOXMG\nQUFB8PT0xMiRI1XGv3jxArm5uRKlY1VGKxdKVkNlrOqvv/4iS0tLys/Pp6ioKIqJiSGFQkEWFhaU\nkJCg1jJkMhmZmppSTk5OmdP+9ttvFBcXR0REr169IoVCUeK0Z86cofz8/CKP5+XlkaenJ+3cuZOe\nPXumXMaYMWNo06ZNamUWXWpqKvn7+1NBQYHUUaoNJycncnZ2pitXrpC9vT3duHFDOa5Hjx40d+5c\nCdOx0mirdgpd7ImI2rVrR8eOHVN5rG/fvvTrr7+qNb9CoSCZTKb1XB06dKCDBw8WOy4yMpIsLS3J\ny8uLjhw5QkREgYGBNHjwYK3nYIyocMfo5cuXRES0aNEimjVrFhERXbt2jczNzcnCwkKtHR5W9bRV\nO6u0jZOeno5ff/212HFEhBUrVpT71O7Ro0cjKChI5bF27drh8uXLas1vYGCg1o0xyur7/f777zh4\n8KByeMaMGVi3bh2o8ANVZVovLy+MHDkSf/31F3r27AmgsG//zjvvqJW5IkTuW4qcHdCN/E5OTjA2\nNgYAjBkzBnv27EF+fj7WrFmDBQsWoGXLljh8+HCx8+pCfk2Inl9rtPKRoQYANHToUKpVqxZlZ2cX\nGf/w4UMCUGzr47VXr17R9u3bVR7LzMykjIwMlcfCwsKoc+fOxS5jz549FWqXlHYfy7y8PGrWrBmd\nOHFC5TEbGxsaPXo0rVu3rsg8WVlZpd7GUdtEvg+nyNmJdDN/x44daf369WRmZkbp6em0d+9e6t69\ne7HT6mL+8hA9v7bKdJUW+5YtW1KfPn3o7t27RcYfPHiQevfuXeoyzp07R15eXmWuKz09vcQPjv37\n99OAAQNKnPfChQuUnp5e5jretHnz5mLvF7pixQpycnKix48fl2t5onndHmDi2LJlCxkZGdHnn39O\nREQ5OTlkYWFBsbGxEidj/6atYl+lbZy9e/fijz/+gJubW5Fx165dQ5s2bUqd/9KlS2jfvn2Z66lf\nvz46deqE5OTkIuPatGmDq1evljjvd999h+3bt+PFixfIzMzEunXr8Ndff5U4/fXr1zF79mwsW7as\nyLh58+bh9u3ben0Rr7i4OJibmxd7jkJBQQFat26NgQMHIiwsrEJHL7HKMWzYMNjb22PmzJkAgP/8\n5z8YM2YMNmzYUOSoqIp6/PhxieP4SpxVr0qLfXFF/jVtFnsAOHfuHOzt7Ys87ujoiFevXmHy5MnF\nHtc+ffp07N69G9bW1mjUqBEuXLgAAwODEvt+w4YNQ7169Yq9ybqhoSFq1aqlVt7KVlL+yMhIPH36\ntMLL3bRpEzw8PPDZZ58VKRJBQUEwNjZGz549MWnSJGzcuLFC6xC956qL+evXr4+4uDjY2dkpH5s6\ndSpCQkLQsGFDDBo0CDdu3ABQsfzHjx+Hra0t7t69W2RcSEgILCwssGTJEpXXjLY+ZP5NF7e/JLTy\n/UANpa1K3cMlbW1tlYc+auKDDz4o81CzgoICevHihXK4pL7fkydP6Pnz5xpnqmzF5U9OTqb69euT\nm5sbJSUlFTvf7t276cmTJyUuNz8/n169ekUdO3akLVu2qIz7+uuv6eTJk0RUuD3z8vK0ll0kouVP\nSkqijRs3koWFBfn7+6v8FqWOvLw8cnFxof79+9P777+vPLxYoVDQ2rVrycrKig4fPkydO3em3r17\n05YtW6hbt25kYWFRKUfGibb9/01bZVonin1BQQHt2rVL+aLIyMgoUhgSExPJ3Ny81GPb1fXixQuS\ny+UaL6cyJCQk0EcffVQl6xo9ejQtWLCAVq5cSY6OjsX23hctWkQ2NjZ06tSpUpd148YNcnJy4mPn\n9cjDhw+pc+fO5ODgQCNHjqRNmzap9b5Zu3Yt+fr6Kov+kSNHKDc3l8aPH08eHh708OFDIir8UFiw\nYAENHTqUDh06RAsXLqQ+ffpo5T2uT/Sq2P9bjx496NChQyqPyWSyIkfi6KOCggKytLSs9B/KUlNT\nydPTkzIzM4mIKCoqqsRpjx8/Tg0bNqTAwMBSl8k/1OofuVxOd+7coYCAAGrfvj35+flRQUEBKRQK\n2rRpE7m6upKrqyt5enrSpEmTaO/evWRubk4xMTFERBQSEkJOTk7UpUsXGjBggPL1Vpzc3Fxq1aoV\nBQQEVNXTE4LQxf7Vq1e0c+fOEqfdu3cveXl56dQnfFV+FZw6dSp9//33ZU6Xm5tb5LDTkhSXvzzf\nbu7evUv29va0cuVKtefRFtG/hutL/pcvX5K3tzeNHj2ahg0bRh4eHnT+/Hm6c+cOXblyhVavXk3d\nu3enxYsXq8w/cuRIWrhwoVqvt1u3bpG5uTnNnj2bxowZQ4MGDaJBgwbRqFGj6MGDBxrlF5W2ir0k\n18apWbMmpk+fjidPnhQ7fujQoZDL5SonKVUnw4cPx549e5CXl1fiNM+ePUPHjh1ha2uLuXPnIiEh\nocg0RIT8/PwSl1Ge65u7ubnh3LlzCAsLQ1ZWltrzMf1hbGyMP/74Ay9evIC5uTkuX76Mjh07okWL\nFnj33Xfx+eef4+TJk/j2229V5tu9ezeWLVum1uvNw8MDO3bsgLW1Nby9vfHRRx9h5MiRcHV1RceO\nHXH69OnKenr6TysfGWr496qmTZtGAwYMKLHHGxoaSs2bNy/1JCt9JZfLaeDAgdS6dWuKj48vMj4t\nLY1at25Nc+bMoYSEBJo5cyaNHDmyyHRr1qxRnhYfEhKi8oMzY6IJCwujhg0b0oEDB6SOUqW0VaYl\nK/a5ubnUtWvXEo+KUSgU1K1bN9q6dWtVxNM5CoWCduzYUWyBvnXrFi1evLjMNldaWhpZW1vT9u3b\nqUGDBiUeccOYKCIjI8nCwqJavZaFL/ZEhcUIAPXt27fYeW7dukXXr1+v7GhqEbXvd/jwYQJAEydO\nlDpKhYm67V/j/Nr1zTff0AcffKD2b3q6lr+8tFXsJb0HbYMGDfDw4UOkpaUVO97Dw6OKE+mfAQMG\n4PDhw0Vu6sKYqL766iu0a9cOAQEBGD9+vNRxhKFX96BljFUPUVFR6N69Oz7//HPMmzcPNWrUkDpS\npan296BljFVfLVu2xNWrV/Hnn3+iS5cuePTokdSRdB4XezWJfn0NkfOLnB3g/JWlcePGOHHiBEaO\nHFnqNah0NX9Vk7RnzxhjmjA0NMRnn30mdQwhcM+eMcZ0GPfsGWOMqa3MYh8aGgoXFxc4OTnB39+/\n2GlmzJgBJycneHp6Kq+BrW9E7/uJnF/k7ADnl5ro+bWl1GIvl8sxbdo0hIaGIjo6GkFBQYiJiVGZ\nJiQkBHFxcYiNjcWWLVswZcqUSg0slZs3b0odQSMi5xc5O8D5pSZ6fm0ptdhfuXIFzZo1g4ODA2rW\nrIkRI0YgODhYZZrff/8dn3zyCQCgXbt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       "text": [
        "<matplotlib.figure.Figure at 0x1605a550>"
       ]
      }
     ],
     "prompt_number": 108
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}