{
 "metadata": {
  "name": "",
  "signature": "sha256:b51e49669fa904230ceace9528cc741e8260ae7d77a018fa6ed7879b2dbf2dc8"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "import pandas as pd\n",
      "from IPython import display"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/walrus/.virtualenvs/py/local/lib/python2.7/site-packages/pandas/io/excel.py:626: UserWarning: Installed openpyxl is not supported at this time. Use >=1.6.1 and <2.0.0.\n",
        "  .format(openpyxl_compat.start_ver, openpyxl_compat.stop_ver))\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dados_usp = pd.read_csv('tabela-nomes_data.csv', sep=';', decimal=',')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Valor em bilh\u00f5es por ano por Categoria"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dc = dados_usp.groupby(dados_usp.Categoria)\n",
      "anual = dc['Salario Mensal'].sum()*12\n",
      "anual.plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x40c1790>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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W+eyzz2A0GmEymTBixAil/dy5cwEAe/bswV/+8hcYjUZERUXh8uXLAIp7yNOmTUNwcDB8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\nkerYISEiIiLVsUNCREREqvv/8Mou+BH2IB8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x40c13d0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Histograma do sal\u00e1rio mensal"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dados_usp['Salario Mensal'].hist(bins=20, range=(0,35000))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x420e050>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+PFasWMHmQETkUq57JzUREbmDaz6Lya1voPP7/Zg3bx4qKysRCoUAAP39/QiH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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x421fe90>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Quantos recebem mais de R$35.000?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "display.HTML('<H3> {:3d} recebem mais de R$35.000,00 </H3>'.format(len(dados_usp[dados_usp['Salario Mensal'] > 35000])))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<H3>  19 recebem mais de R$35.000,00 </H3>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<IPython.core.display.HTML at 0x53e8310>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Quantos recebem mais de R$24.000 e n\u00e3o s\u00e3o aposentados?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mais_24 = dados_usp[(dados_usp['Categoria'] != 'Docente Aposentado') & \\\n",
      "                    (dados_usp['Categoria'] != 'Funcion\u00e1rio Aposentado') & \\\n",
      "                    (dados_usp['Salario Mensal'] > 24000)]\n",
      "\n",
      "display.HTML('<H3>{} recebem mais de R$24.000 e n\u00e3o s\u00e3o aposentados</H3>'.format(len(mais_24)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<H3>453 recebem mais de R$24.000 e n\u00e3o s\u00e3o aposentados</H3>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<IPython.core.display.HTML at 0x5b15390>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mais_24['Fun\u00e7\u00e3o'].value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "Professor Titular                      400\n",
        "Professor Associado                     25\n",
        "Procurador                              15\n",
        "Analista de Sistemas                     3\n",
        "T\u00e9cnico em Assuntos Administrativos      2\n",
        "Professor Doutor                         2\n",
        "Analista Assuntos Administrativos        1\n",
        "Analista Acad\u00eamico                       1\n",
        "Analista de Recursos Humanos             1\n",
        "Analista de Comunicacao Visual           1\n",
        "F\u00edsico                                   1\n",
        "T\u00e9cnico Acad\u00eamico                        1\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Quem recebe mais de 35.000?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dados_usp[dados_usp['Salario Mensal'] > 35000][['Nome', 'Unidade da USP', 'Fun\u00e7\u00e3o', 'Categoria', 'Salario Mensal']]"
     ],
     "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>Nome</th>\n",
        "      <th>Unidade da USP</th>\n",
        "      <th>Fun\u00e7\u00e3o</th>\n",
        "      <th>Categoria</th>\n",
        "      <th>Salario Mensal</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>       Arrigo Leonardo Angelini</td>\n",
        "      <td>                           Instituto de Psicologia</td>\n",
        "      <td> Professor Catedratico</td>\n",
        "      <td>     Docente Aposentado</td>\n",
        "      <td> 60248.38</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>            Sergio de Iudicibus</td>\n",
        "      <td> Faculdade de Economia, Administra\u00e7\u00e3o e Contabi...</td>\n",
        "      <td>     Professor Titular</td>\n",
        "      <td>     Docente Aposentado</td>\n",
        "      <td> 47422.56</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>                   Boris Fausto</td>\n",
        "      <td>             Reitoria da Universidade de S\u00e3o Paulo</td>\n",
        "      <td>            Procurador</td>\n",
        "      <td> Funcion\u00e1rio Aposentado</td>\n",
        "      <td> 45973.65</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>        Berta Lange de Morretes</td>\n",
        "      <td>                          Instituto de Bioci\u00eancias</td>\n",
        "      <td>     Professor Titular</td>\n",
        "      <td>     Docente Aposentado</td>\n",
        "      <td> 45942.21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>                    Fabio Prado</td>\n",
        "      <td>             Reitoria da Universidade de S\u00e3o Paulo</td>\n",
        "      <td>            Procurador</td>\n",
        "      <td> Funcion\u00e1rio Aposentado</td>\n",
        "      <td> 45136.16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>           Affonso Renato Meira</td>\n",
        "      <td>                             Faculdade de Medicina</td>\n",
        "      <td>     Professor Titular</td>\n",
        "      <td>     Docente Aposentado</td>\n",
        "      <td> 42129.90</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>    Jose Moacyr Vianna Coutinho</td>\n",
        "      <td>             Reitoria da Universidade de S\u00e3o Paulo</td>\n",
        "      <td>     Professor Titular</td>\n",
        "      <td>     Docente Aposentado</td>\n",
        "      <td> 42129.90</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>   Maria Sylvia Carvalho Franco</td>\n",
        "      <td> Faculdade de Filosofia, Letras e Ci\u00eancias Humanas</td>\n",
        "      <td>     Professor Titular</td>\n",
        "      <td>     Docente Aposentado</td>\n",
        "      <td> 42129.90</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>        Erwin Theodor Rosenthal</td>\n",
        "      <td> Faculdade de Filosofia, Letras e Ci\u00eancias Humanas</td>\n",
        "      <td> Professor Catedratico</td>\n",
        "      <td>     Docente Aposentado</td>\n",
        "      <td> 41606.39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>                Antonio Morales</td>\n",
        "      <td> Faculdade de Economia, Administra\u00e7\u00e3o e Contabi...</td>\n",
        "      <td> Professor Catedratico</td>\n",
        "      <td>     Docente Aposentado</td>\n",
        "      <td> 40223.76</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>              Manuel Nunes Dias</td>\n",
        "      <td> Faculdade de Filosofia, Letras e Ci\u00eancias Humanas</td>\n",
        "      <td>     Professor Titular</td>\n",
        "      <td>     Docente Aposentado</td>\n",
        "      <td> 40223.76</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>  Maria Thereza Schorer Petrone</td>\n",
        "      <td> Faculdade de Filosofia, Letras e Ci\u00eancias Humanas</td>\n",
        "      <td>     Professor Titular</td>\n",
        "      <td>     Docente Aposentado</td>\n",
        "      <td> 40223.76</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>   Sonia Apparecida de Siqueira</td>\n",
        "      <td> Faculdade de Filosofia, Letras e Ci\u00eancias Humanas</td>\n",
        "      <td>     Professor Titular</td>\n",
        "      <td>     Docente Aposentado</td>\n",
        "      <td> 40223.76</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>                Giorgio Moscati</td>\n",
        "      <td>                               Instituto de F\u00edsica</td>\n",
        "      <td>     Professor Titular</td>\n",
        "      <td>     Docente Aposentado</td>\n",
        "      <td> 39260.04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>                   Jose Barbosa</td>\n",
        "      <td>                          Faculdade de Odontologia</td>\n",
        "      <td>     Professor Titular</td>\n",
        "      <td>     Docente Aposentado</td>\n",
        "      <td> 38317.60</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>              Mario Demar Perez</td>\n",
        "      <td>                  Instituto de Ci\u00eancias Biom\u00e9dicas</td>\n",
        "      <td>     Professor Titular</td>\n",
        "      <td>     Docente Aposentado</td>\n",
        "      <td> 37211.63</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>                 Massaud Moises</td>\n",
        "      <td> Faculdade de Filosofia, Letras e Ci\u00eancias Humanas</td>\n",
        "      <td>     Professor Titular</td>\n",
        "      <td>     Docente Aposentado</td>\n",
        "      <td> 37211.63</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>                Lea Goldenstein</td>\n",
        "      <td> Faculdade de Filosofia, Letras e Ci\u00eancias Humanas</td>\n",
        "      <td>     Professor Titular</td>\n",
        "      <td>     Docente Aposentado</td>\n",
        "      <td> 35549.21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> Guilherme da Costa Pinto Filho</td>\n",
        "      <td>             Reitoria da Universidade de S\u00e3o Paulo</td>\n",
        "      <td>            Procurador</td>\n",
        "      <td> Funcion\u00e1rio Aposentado</td>\n",
        "      <td> 35211.18</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "                              Nome  \\\n",
        "0         Arrigo Leonardo Angelini   \n",
        "1              Sergio de Iudicibus   \n",
        "2                     Boris Fausto   \n",
        "3          Berta Lange de Morretes   \n",
        "4                      Fabio Prado   \n",
        "5             Affonso Renato Meira   \n",
        "6      Jose Moacyr Vianna Coutinho   \n",
        "7     Maria Sylvia Carvalho Franco   \n",
        "8          Erwin Theodor Rosenthal   \n",
        "9                  Antonio Morales   \n",
        "10               Manuel Nunes Dias   \n",
        "11   Maria Thereza Schorer Petrone   \n",
        "12    Sonia Apparecida de Siqueira   \n",
        "13                 Giorgio Moscati   \n",
        "14                    Jose Barbosa   \n",
        "15               Mario Demar Perez   \n",
        "16                  Massaud Moises   \n",
        "17                 Lea Goldenstein   \n",
        "18  Guilherme da Costa Pinto Filho   \n",
        "\n",
        "                                       Unidade da USP                 Fun\u00e7\u00e3o  \\\n",
        "0                             Instituto de Psicologia  Professor Catedratico   \n",
        "1   Faculdade de Economia, Administra\u00e7\u00e3o e Contabi...      Professor Titular   \n",
        "2               Reitoria da Universidade de S\u00e3o Paulo             Procurador   \n",
        "3                            Instituto de Bioci\u00eancias      Professor Titular   \n",
        "4               Reitoria da Universidade de S\u00e3o Paulo             Procurador   \n",
        "5                               Faculdade de Medicina      Professor Titular   \n",
        "6               Reitoria da Universidade de S\u00e3o Paulo      Professor Titular   \n",
        "7   Faculdade de Filosofia, Letras e Ci\u00eancias Humanas      Professor Titular   \n",
        "8   Faculdade de Filosofia, Letras e Ci\u00eancias Humanas  Professor Catedratico   \n",
        "9   Faculdade de Economia, Administra\u00e7\u00e3o e Contabi...  Professor Catedratico   \n",
        "10  Faculdade de Filosofia, Letras e Ci\u00eancias Humanas      Professor Titular   \n",
        "11  Faculdade de Filosofia, Letras e Ci\u00eancias Humanas      Professor Titular   \n",
        "12  Faculdade de Filosofia, Letras e Ci\u00eancias Humanas      Professor Titular   \n",
        "13                                Instituto de F\u00edsica      Professor Titular   \n",
        "14                           Faculdade de Odontologia      Professor Titular   \n",
        "15                   Instituto de Ci\u00eancias Biom\u00e9dicas      Professor Titular   \n",
        "16  Faculdade de Filosofia, Letras e Ci\u00eancias Humanas      Professor Titular   \n",
        "17  Faculdade de Filosofia, Letras e Ci\u00eancias Humanas      Professor Titular   \n",
        "18              Reitoria da Universidade de S\u00e3o Paulo             Procurador   \n",
        "\n",
        "                 Categoria  Salario Mensal  \n",
        "0       Docente Aposentado        60248.38  \n",
        "1       Docente Aposentado        47422.56  \n",
        "2   Funcion\u00e1rio Aposentado        45973.65  \n",
        "3       Docente Aposentado        45942.21  \n",
        "4   Funcion\u00e1rio Aposentado        45136.16  \n",
        "5       Docente Aposentado        42129.90  \n",
        "6       Docente Aposentado        42129.90  \n",
        "7       Docente Aposentado        42129.90  \n",
        "8       Docente Aposentado        41606.39  \n",
        "9       Docente Aposentado        40223.76  \n",
        "10      Docente Aposentado        40223.76  \n",
        "11      Docente Aposentado        40223.76  \n",
        "12      Docente Aposentado        40223.76  \n",
        "13      Docente Aposentado        39260.04  \n",
        "14      Docente Aposentado        38317.60  \n",
        "15      Docente Aposentado        37211.63  \n",
        "16      Docente Aposentado        37211.63  \n",
        "17      Docente Aposentado        35549.21  \n",
        "18  Funcion\u00e1rio Aposentado        35211.18  "
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Custo anual dos aposentados"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "aposentados = dados_usp[(dados_usp['Categoria'] == 'Docente Aposentado') | \\\n",
      "                    (dados_usp['Categoria'] == 'Funcion\u00e1rio Aposentado')]\n",
      "\n",
      "display.HTML(\"<h3>R${:,.2f}</h3>\".format(aposentados['Salario Mensal'].sum()*12))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<h3>R$771,632,985.12</h3>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<IPython.core.display.HTML at 0x4ea4550>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Histograma do valor mensal por unidade dos que recebem mais de 35000"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dados_usp[dados_usp['Salario Mensal'] > 35000].groupby(dados_usp['Unidade da USP'])['Salario Mensal'].sum().plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 225,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x9de8590>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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KSgrOnj0LW1tb/Pvf/26ybGRkJCwtLds9poYGDRqE3bt3t3p/w5PS0QG0K0Of18b56S9D\nzk1XuHHJfvdSUlKgUqng6+sLS0tLzJgxQ9i2dOlSWFtbQyqVIjg4GMePH0dCQgKCg4OhUCiQlZUF\ntVqNuLg4REREIDc3F66urhg7diwAzd7F2NhY+Pv7a9Qhl8uRlZWF9PR0ODg4QCqVwsfHB4WFhVpx\nZmdnw9HRERKJBMuXL9fYtmbNGtjZ2UEqlSI0NLTZnJ2dnXH16lWUl5dj/PjxkMlkEIvFQuNOpVIh\nNTUVAPDDDz9AqVRCJpPB3d0dAJCfn4+JEydCKpXC0dER58+f1zpGTk4O3NzcIJVK4e7ujps3bwIA\nrl27BgcHByGPHj16COXFYrHw3sXFBUqlEkqlEsePH282J8YYY50EMfY7ZWpqSkREycnJ1KtXL7p1\n6xbV1taSo6MjHTlyhO7fv08WFhZC+aKiIiIiUqvVFBcXJ6xvuGxubk4PHjzQOgYRUWxsLKnV6kbr\nEIvFdOjQISIiWrFiBS1atEgrXm9vb9q6dSsREW3cuFGo+8cff6TAwEAiIqqpqSEvLy+hrobMzc3p\n/v37RET0t7/9jZYuXUpxcXE0d+5crRxVKhWlpqbS3bt3aciQIZSTk0NERAUFBUREFBQURP/617+I\niCgpKYlkMhkREUVFRVFQUBAREXl5edGXX35JRERbtmyhiRMnEhHR+PHjaceOHURE9MUXXwh5ZGdn\nk42NDRERlZeXU2VlJRERZWZmkq2trVY+AAggfgkv/jpnjDVPF98V3HPJGAA7OzsMGjQIIpEIMpkM\n169fh5mZGUxMTDB79mzEx8ejW7duQnlqg8nQ9XUUFRWhqKgIzs7OAIBZs2bh0KFDWuWPHTuGN998\nEwA0elcTExORmJgIuVwOpVKJK1eu4OrVq40e09XVFXK5HKWlpVi2bBnEYjH++9//YunSpThy5Ah6\n9uypEd+JEyfg4uKCYcOGAQDMzMwAAEePHsXMmTOFOh88eICSkhKNY504cQLTpk0T4j1y5Iiwvn5u\nZX0+T3r06BHmzJkDiUQCPz+/3zyFgDHGmO4Yd3QAjHUGzz//vPDeyMgIVVVVMDIywsmTJ3HgwAHE\nxsbi//7v/3DgwAEAzz63sGG5Jy/0aaqO1jRcly1bhsDAwGbLpaSkoE+fPsJyz549kZaWhu+++w7L\nly/H2LFj8cEHHzQbY2NxNla2tY3w9evXY+DAgdi6dStqampgYmLSREk1APPH780AyACoHi+nPP5X\nX5fD0LJ8/jfFo/49gE67HBYWBplM1mni4fw4v/rlhnMuO0M8bZFPdHQ0gLoLO3Wi3ftGGeukGg6L\ne3l5CeuDgoIoOjqaSktLKS8vj4iICgsLqW/fvkREtGDBAoqKihLKNxziFovFlJ2dLWwbMWIEXbp0\niWpqasjHx4f8/f0brUMqldLhw4eJiCgkJIT+/ve/a8U7YcIE2rZtGxERffbZZ0L8iYmJZG9vT6Wl\npURE9Ouvv9Ldu3e19n9yyJ6IKDc3lyoqKoiIKCEhgSZNmkRE/xsWv3fvHg0ZMkTIqX7/d955hz78\n8EPh/CkUCiLSHBafMGGCMIwfFRVFPj4+RFQ3LL5z504iItq0aVOjw+KLFy+mdevWEVHdkLpIJNLK\nBwY/LJ5s0MPiycnJHR1Cu+L89Jch50akm+8K7rlkv1sNe9qe7HUTiUQoKSnBG2+8gcrKShAR1q9f\nDwCYOnUq5s6di4iICK2rmwMDA+Hp6YnBgwfjwIED+Pjjj+Hl5YV+/frB1tYWZWVljdYRExODefPm\noby8HMOHD0dUVJRWvOHh4Zg2bRpWr16NN954Q4jZw8MDly5dgqOjIwCgR48e2LZtG/r169dkvvXO\nnz+P4OBgdOnSBV27dsUXX3yhsf3FF1/E5s2b4ePjg9raWgwYMAA//vgjQkNDERAQAKlUiu7duyMm\nJkY4Rv1xIiIi4O/vjzVr1qB///5CTmFhYZgxYwb+/e9/Y9y4cejVq5dWjPPnz8df/vIXfPnll/D0\n9Gz1baP0m6qjA2hX9T0shorz01+GnJuu8E3UGWM6VVFRIcxf3bFjB3bu3In4+PgW11PXEOWvr/9p\n/xsjM8b0H99EnTFmcFJTUyGTySCVSvHFF19g3bp1HR1SJ5XS0QG0q4bz2gwR56e/DDk3XeFhccaY\nTo0ZMwbp6ekdHQZjjLF2wsPijDG9xM8W18TPFmeMPQtdDItzzyVjTG/xb2PGGOt8eM4lY4x1QoY+\n74vz02+GnJ8h56Yr3LhkjDHGGGNthudcMsb0ki7mDTHGmKHhWxExxhhjjDG9wo1LxhjrhAx93hfn\np98MOT9Dzk1XuHHJGGOMMcbaDM+5ZIzpJZ5zyRhjLcdzLhljjDHGmF7hxiVjjHVChj7vi/PTb4ac\nnyHnpivcuGSMMcYYY22G51wyxtpFYWEhduzYgXnz5rVL/fr2bHF+9jdjrDPgOZeM6SFTU9NW7bdn\nzx5cunRJWA4JCUFSUhIAICwsDBUVFS2uozVaGr+5uTkkEgnkcjnkcjlOnDgBAFi4cCHEYnGT+zk5\nOf2mOOuQ3rxKSgraIF/GGOv8uHHJWBtrbY9afHw8MjIyhOWVK1fCzc0NABAeHo7y8vIW19EaLY1f\nJBIhJSUFaWlpSEtLg4ODA+7evYvJkyc/tQF59OjR3xSnoTP0eV+cn34z5PwMOTdd4cYlY+0kJSUF\nKpUKvr6+sLS0xIwZM4RtS5cuhbW1NaRSKYKDg3H8+HEkJCQgODgYCoUCWVlZUKvViIuLQ0REBHJz\nc+Hq6oqxY8cC0OxdjI2Nhb+/v0YdcrkcWVlZSE9Ph4ODA6RSKXx8fFBYWKgVZ3Z2NhwdHSGRSLB8\n+XKNbWvWrIGdnR2kUilCQ0ObzPXJIZby8nL885//BABcvHgR9vb2kMvlkEqluHbtmlYOq1evhkQi\ngUwmE/aLjIyEnZ0dZDIZJk+e/Ew9t4wxxjoBYoy1KVNTUyIiSk5Opl69etGtW7eotraWHB0d6ciR\nI3T//n2ysLAQyhcVFRERkVqtpri4OGF9w2Vzc3N68OCB1jGIiGJjY0mtVjdah1gspkOHDhER0YoV\nK2jRokVa8Xp7e9PWrVuJiGjjxo1C3T/++CMFBgYSEVFNTQ15eXkJdTU0bNgwEovFJJPJyMHBgYiI\nsrOzycbGhoiIgoKCaPv27UREVFVVRRUVFRo57N+/n0aPHi2sz8/PJyLSyHf58uUUERGhcVwABJAe\nvfjrljHW8XTxXcQ9l4y1Izs7OwwaNAgikQgymQzXr1+HmZkZTExMMHv2bMTHx6Nbt25CeWqDSdb1\ndRQVFaGoqAjOzs4AgFmzZuHQoUNa5Y8dO4Y333wTADR6VxMTE5GYmAi5XA6lUokrV67g6tWrWvs3\nHBY/fvy41vbRo0fj3//+Nz755BPk5OTAxMREY/tPP/2EgIAAYX3v3r0BAOfPn4ezszMkEgm2b9+O\nixcvtuZ0MMYY0zFuXDLWjp5//nnhvZGREaqqqmBkZISTJ09i8uTJ2LdvHzw9PYUyzzrfsWG5J4eL\nm6qjNQ3XZcuWCXMpMzMz4e/v3+I63nzzTSQkJKBbt254/fXXkZycrBVvY7Gp1Wp89tlnOHfuHEJC\nQlBZWdlI7WoAoY9fYQBSGmxL6WTLmnO5UlJSnrocFhbWovL6tsz56feyIedX/76zxNMW+ajVaqjV\n6qdOb2pT7d43ytjvTMNhcS8vL2F9UFAQRUdHU2lpKeXl5RERUWFhIfXt25eIiBYsWEBRUVFC+YZD\n3GKxmLKzs4VtI0aMoEuXLlFNTQ35+PiQv79/o3VIpVI6fPgwERGFhITQ3//+d614J0yYQNu2bSMi\nos8++0yIPzExkezt7am0tJSIiH799Ve6e/eu1v5PDtkTaQ6LX7t2TVj/7rvvUnh4uMZ5+uGHH2j0\n6NFUXl5ORP8bFn/xxRfp7t279OjRI3J3dxeG/uvBwIfFk5OTW1Re33B++s2Q8zPk3Ih4WJwxvdSw\n5/DJXkSRSISSkhJ4e3tDKpXC2dkZ69evBwBMnToVa9asgVKpRFZWlsZ+gYGB8PT0FC7o+fjjj+Hl\n5QUnJycMGjRIKPdkHTExMQgODoZUKsW5c+ewYsUKrXjDw8OxceNGSCQS5ObmCjF7eHhg2rRpwsU+\nfn5+KC0tfWq+ja3ftWsXbGxsIJfLcfHiRbz11lsa28eNG4cJEybA1tYWcrkc69atAwB8+OGHsLe3\nx5gxY2Bpaal397X8rVQqVUeH0K44P/1myPkZcm66wjdRZ4zppbrGpj59fbX/jYsZY6w5fBN1xhj7\nnWo4f8oQcX76zZDzM+TcdIUbl4wxxhhjrM3wsDhjTC/p2xxMfrY4Y6wz0MWwuHG71s4YY+2Ifxsz\nxljnw8PijDHWCRn6vC/OT78Zcn6GnJuucOOSMcYYY4y1GZ5zyRjTS7qYN8QYY4aGb0XEGGOMMcb0\nCjcuGWOsEzL0eV+cn34z5PwMOTdd4cYlY4wxxhhrMzznkjGml3jOJWOMtRzPuWSMMcYYY3qFG5eM\nMdYJGfq8L85Pvxlyfoacm65w45IxxhhjjLUZnnPJGNNL+vJscX6mOGOsM+nQOZcVFRVYv349/va3\nv2HTpk2orq5u10AY01ffffcdkpKSOjoMndu8eTMKCws7OArq9K+SkoL2S58xxjqhJhuXs2bNQmpq\nKsRiMfbv348lS5boMi7WyZiamrZqvz179uDSpUvCckhIiNAQCwsLQ0VFRYvraI2Wxl9aWoq//vWv\nGDFiBGxtbeHq6oqTJ08CAJycnIRyp06dwsmTJ/H999+3Osbc3Fz4+vq2at+GVCoVRo4cCblcDisr\nK0RGRgrbxo8fj+Li4t98jHoffvgh+vTpAzMzsyZjOXPmTLsc+/fC0Od9cX76zZDzM+TcdMW4qQ2X\nLl3C+fPnAQBz5szBqFGjdBYU63xaOwQZHx8Pb29vWFpaAgBWrlwpbAsPD8fMmTPRrVu3FtXRGi2N\nf86cORg+fDiuXr0KAMjJyUFGRgYA4OjRo0K5UaNG/eb/NgYNGoTdu3f/pjqAuhy/+uorKBQKFBQU\nYPjw4fD394exsTG+++6731x/Qx988EGzsdRr62Mzxhjr3JrsuTQ2Nm70Pft9S0lJgUqlgq+vLywt\nLTFjxgxh29KlS2FtbQ2pVIrg4GAcP34cCQkJCA4OhkKhQFZWFtRqNeLi4hAREYHc3Fy4urpi7Nix\nADR7F2NjY+Hv769Rh1wuR1ZWFtLT0+Hg4ACpVAofH59Gh2azs7Ph6OgIiUSC5cuXa2xbs2YN7Ozs\nIJVKERoaqrXvtWvXcPLkSXz00UfCOnNzc7z++utacTZWV05ODiwtLREYGAgbGxuMGzcOlZWVAICr\nV6/C3d0dMpkMSqUS2dnZyMnJgVgsFvZ1cXGBUqmEUqnE8ePHAQC3b9+Gi4sL5HI5xGIxjhw50ujn\nUz+Ppri4GKampjAyMhLiz8+vm/f36aefQiwWQywWIzw8XDjuyJEj4e/vDwsLC0yfPh2JiYlwcnLC\nK6+8glOnTgEAysrKEBAQADs7O8jlcuzZswdA3TSaqVOnwsrKCj4+Pho90g2P/eWXX0IqlUImk2HW\nrFkAgISEBDg4OEChUMDDwwN3794FABw8eBByuRxyuRwKhQKlpaWN5myoVCpVR4fQrjg//WbI+Rly\nbjpDTejSpQuZmpoKr4bLPXr0aGo3ZqBMTU2JiCg5OZl69epFt27dotraWnJ0dKQjR47Q/fv3ycLC\nQihfVFRERERqtZri4uKE9Q2Xzc3N6cGDB1rHICKKjY0ltVrdaB1isZgOHTpEREQrVqygRYsWacXr\n7e1NW7duJSKijRs3CnX/+OOPFBgYSERENTU15OXlJdRVb8+ePTRp0qRmz0VTdWVnZ5OxsTGdPXuW\niIj8/Pxo27ZtRERkZ2dH3377LRERPXz4kMrLyyk7O5tsbGyIiKi8vJwqKyuJiCgzM5NsbW2JiGjt\n2rW0atUqIiKqra2lkpISrbheffVVsrCwIIlEQt26daPNmzcL2+rP9enTp0ksFlN5eTmVlpaStbU1\npaWlCTFfuHCBamtrSalUUkBAgHA+Jk6cSEREy5YtE85rfn4+vfzyy1RWVkbr1q2j2bNnExHRuXPn\nyNjYmFINOFxHAAAgAElEQVRTUzWOfeHCBXrllVeEzzw/P5+IiAoKCoQ4IyMjacmSJcJneOzYMSIi\nKisro+rqao18ARBAevBq8muWMcZ0ThffSU12SdbU1Oisgcv0i52dHQYNGgQAkMlkuH79OhwcHGBi\nYoLZs2fDy8sLXl5eQnlqg6vS6usoKipCUVERnJ2dAdTNDW5svuKxY8cQHx8PAJgxYwbee+89AEBi\nYiISExMhl8sB1PXEXb16VagPePYh9KbqGjJkCF566SVIJBIAgFKpRE5ODkpLS5Gbm4s33ngDAPDc\nc89p1fno0SMEBQXh7NmzMDIywi+//AKg7pwHBASgqqoKEydOhFQq1dq34bD4/fv3MXr0aHh6emLI\nkCHCOTxy5Ah8fHyEqQg+Pj44fPgwJkyYgJdeegnW1tYAAGtra7i7uwMAbGxskJOTI+R86NAh/Oc/\n/wFQN6px8+ZNHD58GAsXLgQAiMViIfd6RISkpCT4+fmhT58+AIDevXsDAG7evAk/Pz/cuXMHjx49\nwp/+9CcAdXNbFy9ejOnTp8PHxweDBw9u5FNQAzB//N4MgAyA6vFyyuN/O3r58dLjeVz1vSLNLYeF\nhUEmkz1zeX1b5vz0e9mQ82s457IzxNMW+URHRwOoG0nSiaZanWVlZfTw4UNh+fLly7Ru3TqNHiT2\n+9Gw59LLy0tYHxQURNHR0URU1xO3f/9+CggIIDc3NyJqWc9lwx7xrVu3NtpzWVhYSEOHDhXKXb16\nlRQKhVa8ffv2FXq6ioqKhPiXLFlCmzZtemquV69epT/96U9UU1Pz1HPRVF0NeyKJ6nodV65cSSUl\nJfTHP/7xqeVDQkIoODiYiIiqq6vJ2NhYKHf79m2KjIwkmUxGX375pVY9KpVK6C0kIpoyZQrt3r2b\niOrO9f379yk8PJxWrFghlFm+fDlFRERQTk6ORsxqtZpiY2O14lMqlXTlyhWtY0+cOJGSkpKEZYVC\nodFzef/+fYqIiKD3339fa99XX32VEhISiIgoJSWFVCqVsO3ChQu0evVqGjZsGF2+fFljPxh4z2Vy\ncnKr9tMXnJ9+M+T8DDk3It30XDY553LcuHG4fv06gLp5Yg4ODsjOzsbGjRuxdOlS3bR8md4oKytD\nYWEhXnvtNXz66ac4e/YsAKBHjx5NXin85LYBAwbg8uXLqK2tRXx8vNCD2LBcr1690Lt3b2HO4dat\nW4Vfag05OTlhx44dAIDt27cL68eNG4ctW7agrKwMAHDr1i3cu3dPY9/hw4fD1tYWISEhwrqcnBzs\n379fo9yz1FWPiGBqaoo//vGPwjzFhw8fal0tX1xcjD/84Q8A6uYn1o8g3LhxA/369cOcOXMwZ84c\npKWlNXkcACgvL0daWhqGDx8ubBOJRHB2dsa3336LiooKlJWV4dtvv4Wzs/Mz9y6PGzcOERERwnJq\naioAwMXFBV999RUA4MKFCzh37pzGfiKRCG5ubti9e7cw/7KgoEDIub4nvP7XNVA399Xa2hr/+Mc/\nMGrUKFy5cuWZYjQUjf1dGxLOT78Zcn6GnJuuNNm4LCwsxMsvvwwAiImJwbRp0xAREYHvv/8e+/bt\n01mArHNoOFT85LCxSCRCSUkJvL29IZVK4ezsjPXr1wMApk6dijVr1kCpVCIrK0tjv8DAQHh6egoX\n9Hz88cfw8vKCk5OT0NhorI6YmBgEBwdDKpXi3LlzWLFihVa84eHh2LhxIyQSCXJzc4WYPTw8MG3a\nNOFiHz8/v0YvFPl//+//IS8vDyNGjIBYLIa/vz8GDBigkf/T6mrsHAF1jeENGzZAKpXCyckJeXl5\nGtvnz5+PmJgYyGQyXLlyRbh4KDk5GTKZDAqFArt27RKGoJ80ffp0yOVy2Nrawt/fXxiyr69fLpdD\nrVbDzs4ODg4OmDt3rjDE3lTMDd9/8MEHqKqqgkQigY2NjXD1/9tvv43S0lJYWVkhJCQEtra2WrFZ\nWVnh/fffx6uvvgqZTCbc3iw0NBS+vr6wtbVFv379hGOFh4dDLBZDKpXiueeew2uvvdZozowxxjqX\nJp/QI5FIhN6H0aNHIzg4GJMmTdLaxhhjHaGuEfrb5/O2v9Y9DSPl8Z0ZDBXnp98MOT9Dzg3QzRN6\nmrygRywW491338WgQYNw7do1/PnPfwZQN5SlL49dY4wxxhhjutVkz2V5eTnCw8Nx584dBAQECENn\nx44dw7Vr1zBz5kydBsoYYw3py49cfrY4Y6wz0UXPZZONS8YY68x08QXJGGOGRhffnU1e0FP/BI/6\nl0QigZubGz766CPhaSOMMcbaR8N77Rkizk+/GXJ+hpybrjQ55zIhIUFrXX5+PmJiYrBgwQJERka2\na2CMMcYYY0z/tGpYXCaTIT09vT3iYYyxZ8LD4owx1nIdOiz+NPyFzhhjjDHGGtNk4zI1NRVnzpzR\neP30009Qq9VwcXHRZYyMMfa7Y+jzvjg//WbI+RlybrrS5JzLJUuWaD2ho2/fvlCpVAgMDNRJcIwx\nxhhjTL/wrYgYY3qJ51wyxljLddo5l4wxxhhjjDWGG5eMMdYJGfq8L85Pvxlyfoacm65w45Ixxhhj\njLWZZ5pzef78eWRkZKCyslK4yOett95q9+AYY6wp+vJs8WfFzyBnjOlCp3i2eGhoKA4ePIiLFy9i\n/Pjx+P777zFmzBjExsa2a2CMsc7p1q1b2L17NxYtWtShcdQ1Lg3pgh6+QIkx1v46xQU9sbGx+Omn\nnzBw4EBERUXh7NmzKCwsbNegGGsrpqamrdpvz549uHTpkrAcEhKCpKQkAEBYWBgqKipaXEdrtDR+\nc3NzSCQSyOVySCQS7N27V9jm5OTUqhhCQkJw4MABAEBVVRU++OADDBs2DJ988kmr6gOAuXPn/uZz\nY+gMfd4X56ffDDk/Q85NV5q8z2W9bt26wcjICMbGxigqKkL//v1x8+ZNXcTG2G/W2qHT+Ph4eHt7\nw9LSEgCwcuVKYVt4eDhmzpyJbt26taiO1mhp/CKRCCkpKejTpw8yMzPx5z//GRMmTAAAHD16tFUx\nNMy9a9eu2LJlS6vqaSgyMvI318EYY6xzarbnctSoUSgoKMDcuXNha2sLuVyO0aNH6yI2xtpMSkoK\nVCoVfH19YWlpiRkzZgjbli5dCmtra0ilUgQHB+P48eNISEhAcHAwFAoFsrKyoFarERcXh4iICOTm\n5sLV1RVjx44FoNm7GBsbC39/f4065HI5srKykJ6eDgcHB0ilUvj4+DQ6ApCdnQ1HR0dIJBIsX75c\nY9uaNWtgZ2cHqVSK0NDQJnOtH+4oKipCnz59hPX1cRIRgoODIRaLIZFIsGvXLqHM6tWrIZFIIJPJ\n8M9//hMAhNyBuid3qVQq2NrawtPTE3fu3AEAqFQqLF26FPb29rCwsMCRI0cAADU1NXj33XchFosh\nlUqxceNGofyZM2cAAPPnz8eoUaNgY2OjkdeTn8vvjUql6ugQ2hXnp98MOT9Dzk1nqAWysrIoPT29\nJbsw1qFMTU2JiCg5OZl69epFt27dotraWnJ0dKQjR47Q/fv3ycLCQihfVFRERERqtZri4uKE9Q2X\nzc3N6cGDB1rHICKKjY0ltVrdaB1isZgOHTpEREQrVqygRYsWacXr7e1NW7duJSKijRs3CnX/+OOP\nFBgYSERENTU15OXlJdTV0LBhw0gsFpONjQ298MILtG/fPq04Y2NjycPDg2praykvL4+GDh1Kt2/f\npv3799Po0aOpoqKCiIgKCgo08nj06BE5OjrS/fv3iYhox44dFBAQQEREKpWK3n33XSIi2r9/P7m7\nuxMR0WeffUa+vr5UU1NDRET5+flC+dTUVI111dXVpFKp6Ny5c01+Lg0BIIAM6NWir2PGGGsVXXzX\nNDksnpqa2uSQ3JkzZ6BQKNqntctYO7Gzs8OgQYMAADKZDNevX4eDgwNMTEwwe/ZseHl5wcvLSyhP\nbTDhub6OoqIiFBUVwdnZGQAwa9Ys+Pr6apU/duwY4uPjAQAzZszAe++9BwBITExEYmIi5HI5AKCs\nrAxXr14V6qvXcFg8KysLY8eOhaurK1544QWhzJEjRzBt2jSIRCL0798fr776Kk6dOoWDBw8iICAA\nJiYmAAAzMzONPK5cuYKLFy/C3d0dQF2vZP35BAAfHx8AgEKhQE5ODgDgwIEDePvtt9GlS90gSe/e\nvbVy3rlzJyIjI1FdXY3bt2/j0qVLsLKyavJz0aQGYP74vRkAGQDV4+WUx//qy/L/etiBurm9MplM\nWK6fB2Yoy5yffi8bcn4N51x2hnjaIp/o6GgAdfPydaKpVuerr75KKpWK7O3tydjYmBQKBSkUCjI2\nNiYHB4d2b/Uy1hYa9lx6eXkJ64OCgig6OpqIiB4+fEj79++ngIAAcnNzI6KW9Vz26NFDeL9169ZG\ney4LCwtp6NChQrmrV6+SQqHQirdv375UXV1NRHW9dfXxL1myhDZt2tRsvk/GZm9vT6dOndI4F4sX\nL6YtW7YIZWbOnEl79+6lJUuWUGRkpFadarWaYmNj6fz58+To6NjocVUNeiLv3btH5ubmRET0l7/8\nhf773/82WT4rK4tGjBhBhYWFwrGe9rk0BAPvuUxOTm78QzYQnJ9+M+T8DDk3It30XDY55zIlJQXJ\nyckYNGgQzpw5g9TUVKSmpiItLU2jt4IxfVZWVobCwkK89tpr+PTTT3H27FkAQI8ePVBcXNzoPk9u\nGzBgAC5fvoza2lrEx8cLPf4Ny/Xq1Qu9e/cW5iJu3bpV+IXZkJOTE3bs2AEA2L59u7B+3Lhx2LJl\nC8rKygDU3Q7o3r17jcZHj3tL7969i+zsbAwbNkxju7OzM3bu3Ina2lrcu3cPhw4dgr29PTw8PBAV\nFSVcCV9QUCDsIxKJYGFhgXv37uHEiRMA6q4cz8jIaDSGeh4eHti0aRNqamq06gSA4uJidO/eHT17\n9kReXh6+//57iESiJj+X35PG/j4MCeen3ww5P0POTVeavVr88uXLEIvFwrKNjQ3fQoTpjYZTO56c\n5iESiVBSUoI33ngDlZWVICKsX78eADB16lTMnTsXERER2L17t8Z+gYGB8PT0xODBg3HgwAF8/PHH\n8PLyQr9+/WBrays0AJ+sIyYmBvPmzUN5eTmGDx+OqKgorXjDw8Mxbdo0rF69Gm+88YYQs4eHBy5d\nugRHR0cAdQ3Xbdu2oV+/flp1uLq6wsjICFVVVVi9erVQpr6uSZMm4fjx45BKpRCJRFizZg369++P\ncePGIT09Hba2tnjuuecwfvx4fPTRR0K9Xbt2RWxsLN555x0UFRWhuroaixcvhpWVVZPnfc6cOcjM\nzIREIkHXrl0RGBiI+fPnC+WkUinkcjlGjhyJIUOGYMyYMQDQ5OfCGGOs82v2JupTp06FqakpZsyY\nASLCV199hdLSUnz99de6ipExxrQY+k3UUxrMvzREnJ9+M+T8DDk3QDc3UW+25zIqKgqff/45wsPD\nAQAuLi54++232zUoxhhjjDGmn57p2eKMMdbZ8LPFGWOs5TpFzyVjjHVW/NuYMcY6n2af0MMYY0z3\nGt5rzxBxfvrNkPMz5Nx05Zkbl+Xl5e0ZB2OMMcYYMwDNzrk8duwY5syZg5KSEty8eRPp6enYvHkz\nPvvsM13FyBhjWnQxb4gxxgyNLr47m+25XLRoEX744Qe8+OKLAOoem3fw4MF2DYoxxhhjjOmnZxoW\nHzp0qMaysTFfB8QYY+3J0Od9cX76zZDzM+TcdKXZVuLQoUNx9OhRAMCjR4+wYcMGWFpatntgjDHG\nGGNM/zQ75/LevXtYuHAhfvrpJxAR/vznP2PDhg3o27evrmJkjDEtPOeSMcZaThffnXwTdcaYXuLG\nJWOMtVyH3kR9wYIFWoE0fCLGhg0b2jUwxhj7PTP05xtzfvrNkPMz5Nx0pckLepRKJZRKJR4+fIgz\nZ87glVdewYgRI5CWloZHjx7pMkbGGGOMMaYnmh0Wt7e3x5EjR9C1a1cAQFVVFcaMGYOff/5ZJwEy\nxlhj9OnZ4vzccMZYZ9Ep7nNZWFiI4uJiYbmkpASFhYXtGhRjjD0b0otXSUlBu50BxhjrbJptXC5d\nuhQKhQKzZs3CrFmzoFAosGzZMl3ExpgGIyMjyOVy4XXjxo02qTcnJwdisbjRbSqVCqmpqc9cV0pK\nCry9vVsdi1qtRlxcXIv22bx5MywtLWFpaQl7e3vh1mFtGVtRURE+//zzZss97Vw+i/Hjx2v8mP09\nM/R77XF++s2Q8zPk3HSl2ftc+vv7w9PTEz///DNEIhFWr16NP/zhD7qIjTENL7zwAtLS0nR6TJFI\npNPh15Yeb9++fdi8eTOOHj2KPn36IC0tDRMnTsTJkycxYMCANouroKAAn332Gd5+++02q7Mx3333\nXbvWzxhjrP090xN6TExMMHDgQJiZmSEzMxOHDh1q77gYa1ZZWRnc3d2hVCohkUiwd+9eYduXX34J\nqVQKmUyGWbNmAdDuFTQ1NdWqs6KiAlOnToWVlRV8fHxQUVEhbJs/fz5GjRoFGxsbhIaGCut/+OEH\nWFpaQqlUIj4+XiO+gIAA2NvbQ6FQaMTXUFBQEEaOHAkPDw/cvXtXmAuTmpoKlUoFW1tbeHp64s6d\nO1r7rl69GmvXrkWfPn0AAHK5HLNmzcLGjRufGlt+fj4mTpwIqVQKR0dHnD9/HgAQGhqKgIAAuLq6\nYvjw4YiIiABQN4Jx7do1yOVyvPfeewCA4OBgiMViSCQS7Nq1Syu2yspK+Pv7QyKRQKFQCL0B5eXl\n8PPzg7W1NXx8fODg4IAzZ84AAMzNzZGfXzc3cdKkSbC1tYWNjQ0iIyMbPXeGzNCvVuX89Jsh52fI\nuekMNWPz5s1kY2NDZmZmpFKpyMTEhFxdXZvbjbE2Z2RkRDKZjGQyGfn4+FB1dTUVFxcTEdG9e/do\nxIgRRER04cIFeuWVV+jBgwdERFRQUEBERGq1mmJjY4X6TE1NiYgoOzubbGxsiIho3bp1NHv2bCIi\nOnfuHBkbG1NqaioREeXn5xMRUXV1NalUKjp37hxVVFTQkCFD6OrVq0RE5OfnR97e3kREtGzZMtq2\nbZsQwyuvvEJlZWUaOcXFxZGHhwfV1tZSbm4umZmZUVxcHD169IgcHR3p/v37RES0Y8cOCggI0Don\nffr0Ec5BvT179pCPjw9VVlY2GVtQUBD961//IiKipKQkkslkREQUEhJCTk5O9OjRI7p//z717duX\nqqurKScnRzhHRESxsbFC3Hl5eTR06FC6c+eOxrlcu3atcC4vX75MQ4cOpcrKSlqzZg3NmzdP+Kwa\nnmNzc3Phc6s/3+Xl5WRjYyOsrweAANKTV7NftYwxphO6+D5qdlg8PDwcp06dgqOjI5KTk3H58mWe\nc8k6RLdu3TSGxauqqrBs2TIcPnwYXbp0QW5uLvLy8pCUlAQ/Pz+hN8/MzOyZj3H48GEsXLgQAIRe\nuXo7d+5EZGQkqqurcfv2bWRkZKCmpgYvvfQShg8fDgCYMWMGNm/eDABITExEQkIC1q5dCwB4+PAh\nbt68CQsLC43jTZs2DSKRCAMHDoSbmxsA4MqVK7h48SLc3d0BADU1NRg0aNAz5UCPez4vX77cZGxH\njx7FN998AwBwdXXFgwcPUFJSApFIhPHjx6Nr167o27cv+vfvj7y8PK0rC48ePSrE3b9/f7z66qs4\nefKkxnzLo0eP4p133gEAWFhYYNiwYcjMzMTRo0exaNEiAIC1tbXGOW4oPDwc3377LQDg119/xS+/\n/AJ7e/snSqkBmD9+bwZABkD1eDnl8b+dY7m+57a+V6S55bCwMMhksmcur2/LnJ9+Lxtyfg3nXHaG\neNoin+joaAB1o0M60VzrU6lUEhGRVCqliooKIiKytLRszwYvY42q72msFxUVRVOmTKHq6moiquv1\nysnJoYiICHr//fe19p8zZw7t2rWLiIhqamroueeeIyLNnsuJEydSUlKSsI9CoaDU1FTKysqiESNG\nUGFhIRHV9YJGR0dTeno6ubi4COX37NlDXl5eRFT3305mZuZTc1q0aBFt2bJFWPbx8aG4uDg6f/48\nOTo6NntOxowZoxEvEdEHH3xAK1aseGpscrmcsrKyhG1Dhgyh4uJiCg0NpbVr1wrrbWxs6Pr16xrn\niIho8eLFGnHPnDmTEhISNMpNmjRJIzZnZ2c6d+4cTZw4kZKTk4X19eeY6H89l8nJyTRmzBjhO0el\nUtHBgwc18oSB91w2PEeGiPPTb4acnyHnRqSbnstm51wOGTIEBQUFmDhxIjw8PDBhwgTdtXwZe4ri\n4mL0798fRkZGSE5OxvXr1yESieDm5obdu3cLc/cKCupuA2Nubi5c+b13715UVVVp1eni4oKvvvoK\nAHDhwgWcO3dOOFb37t3Rs2dP5OXl4fvvv4dIJMLIkSORk5ODrKwsAMDXX38t1DVu3DiNJ1k1djGS\ni4sLdu7cidraWty+fRvJyckA6nr67t2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xcissvAsxEMXIZevWrZGdnW3Rc0okEouOmtT1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\nY4wxxli9+X/g5IAOW0G6YAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9db5c10>"
       ]
      }
     ],
     "prompt_number": 225
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Histograma do n\u00famero de pessoas por unidade que recebem mais de 35000"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dados_usp[dados_usp['Salario Mensal'] > 35000].groupby('Unidade da USP')['Salario Mensal'].count()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 243,
       "text": [
        "Unidade da USP\n",
        "Faculdade de Economia, Administra\u00e7\u00e3o e Contabilidade    2\n",
        "Faculdade de Filosofia, Letras e Ci\u00eancias Humanas       7\n",
        "Faculdade de Medicina                                   1\n",
        "Faculdade de Odontologia                                1\n",
        "Instituto de Bioci\u00eancias                                1\n",
        "Instituto de Ci\u00eancias Biom\u00e9dicas                        1\n",
        "Instituto de F\u00edsica                                     1\n",
        "Instituto de Psicologia                                 1\n",
        "Reitoria da Universidade de S\u00e3o Paulo                   4\n",
        "Name: Salario Mensal, dtype: int64"
       ]
      }
     ],
     "prompt_number": 243
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Porcentagem de quantos recebem mais que 24.000"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "valor_separador = 24000\n",
      "\n",
      "total_maior = dados_usp[dados_usp['Salario Mensal'] >= valor_separador]['Salario Mensal'].sum()\n",
      "num_total_maior = float(len(dados_usp[dados_usp['Salario Mensal'] > valor_separador]))\n",
      "\n",
      "total_menor = dados_usp[dados_usp['Salario Mensal'] < valor_separador]['Salario Mensal'].sum()\n",
      "num_total_menor = float(len(dados_usp[dados_usp['Salario Mensal'] < valor_separador]))\n",
      "\n",
      "total_mes = dados_usp['Salario Mensal'].sum()\n",
      "num_total_mes = float(len(dados_usp))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 332
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "display.HTML('<h2>{:.2%} ({:.0f} de {:.0f}) recebem menos de R${:.2f}, sendo que eles representam {:.2%} do custo da USP em sal\u00e1rios.</h2><p> \\\n",
      "              <h2>{:.2%} ({:.0f} de {:.0f}) recebem mais de R${:.2f}, sendo que eles representam {:.2%} do custo da USP em sal\u00e1rios.</h2></h2>'\\\n",
      "             .format(num_total_menor/num_total_mes,\n",
      "                     num_total_menor, num_total_mes, valor_separador,\\\n",
      "                     total_menor/total_mes, \\\n",
      "                     num_total_maior/num_total_mes, num_total_maior, num_total_mes, valor_separador, \n",
      "                     total_maior/total_mes))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<h2>96.74% (28272 de 29225) recebem menos de R$24000.00, sendo que eles representam 90.66% do custo da USP em sal\u00e1rios.</h2><p>               <h2>3.26% (953 de 29225) recebem mais de R$24000.00, sendo que eles representam 9.34% do custo da USP em sal\u00e1rios.</h2></h2>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 335,
       "text": [
        "<IPython.core.display.HTML at 0x8d60c10>"
       ]
      }
     ],
     "prompt_number": 335
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}